Forthcoming and Online First Articles

International Journal of Wireless and Mobile Computing

International Journal of Wireless and Mobile Computing (IJWMC)

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International Journal of Wireless and Mobile Computing (98 papers in press)

Regular Issues

  • Multicast stable path routing protocol for wireless ad-hoc networks   Order a copy of this article
    by K.S. Saravanan, N. Rajendran 
    Abstract: Wireless Ad-Hoc Networks (WANETs) enable steady communication between moving nodes through multi-hop wireless routing path. The problem identified is how to improve the lifetime of the route and reduce the need for route maintenance. This helps to save bandwidth and reduce the congestion control available in the network. This paper aims to focus on redesign and development of multicast stable path routing protocol with special features that determine long-living routes in these networks. An extensive ns-2 simulation based performance has been analysed of three widely recognised stability oriented wireless ad-hoc network routing protocols, namely are Associativity Based Routing (ABR) protocol, Flow Oriented Routing Protocol (FORP) and Lifetime Route Assessment Based Routing Protocol (LRABP). The order of ranking of the protocols in terms of packet delivery ratio, average hop count per route, end-to end delay per packet and the number of route transitions is presented.
    Keywords: wireless ad-hoc networks; multicast routing protocol; wireless communication; routing protocol.

  • Research of small fabric defects detection method based on deep learning network   Order a copy of this article
    by Siqing You, Kexin Fu, Peiran Peng, Ying Wang 
    Abstract: For quality improvement of textile products, fabric defects detection is significant. In this paper, the detection capacity of SSD for small defects was studied. The loss of feature information was reduced through the reduction of layers of SSD network; then the size of the default box was adjusted based on the K-means clustering algorithm, and the adaptive histogram equalisation algorithm was applied to enhance the defect features and effectively improve the detection accuracy. The improved SSD network model was tested to verify the fabric defects dataset, which further improved the accuracy of detection. In addition, the two-stage algorithm was compared to find the optimal algorithm for small object detection. According to the test results, the subsequent improvement method for small object detection with SSD was proposed.
    Keywords: fabric defects detection; default box; feature enhancement; SSD; faster RCNN.

  • Field theory trusted measurement model for IoT transactions   Order a copy of this article
    by Meng Xu, Bei Gong, Wei Wang 
    Abstract: The Internet of Things (IoT) allows the concept of connecting billions of tiny devices to retrieve and share information regarding numerous applications, such as healthcare, environment, and industries. Trusted measurement technology is crucial for the security of the sensing layer of the IoT, especially the trusted measurement technology oriented to transaction IoT nodes. In the traditional trust management system, historical behaviour data are considered to predict the trust value of the network entity, while the nodes' trust between network entities is rarely considered. This paper proposes a novel fi eld theory trusted measurement model of the sensing layer network, which can well adapt to the transaction scenarios of the IoT.
    Keywords: field theory; internet of things; trust measurement; transaction scenario.

  • Dynamic time warping-based evolutionary robotic vision for gesture recognition in physical exercises   Order a copy of this article
    by Quan Wei, Kubota Naoyuki, Ahmad Lotfi 
    Abstract: In this paper, we propose a three-dimensional posture evaluating system from two-dimensional images, which can be implemented in physical exercises for elderly people. In this system, two-dimensional coordinates of human joints are first captured and calculated, then our proposed Dynamic Time Warping Steady State Genetic algorithm (DTW-based SSGA) is used for the evaluation of three-dimensional rotational variables from RGB images for the human arm. Finally, these predicted rotational variables would be compared with the template of sample posture by Dynamic Time Warping (DTW) to check the complement of physical exercises. The experimental result shows that our proposed DTW-based SSGA performs with higher accuracy than other evolutionary algorithms, such as standard Steady State Genetic Algorithm (SSGA) and Particle Swarm Optimisation (PSO) when evaluating human joint variables with templates, especially in the physical exercises for rehabilitation.
    Keywords: gesture recognition; forward kinematics; evolutionary computing; dynamic time warping.

  • Research on trusted SDN network construction technology   Order a copy of this article
    by Fazhi Qi, Zhihui Sun, Yongli Yang 
    Abstract: In this paper, we combine trusted computing with SDN. By active measurement of the SDN controller when it is starting and running, we can guarantee the trust of the SDN controller. By actively measuring the behaviour of the SDN data transponder in the domain, we can guarantee trust of the SDN data transponder. When the cross-domain data interaction is involved, by trusted network connection mechanism, we can guarantee the trust of the transmission of data in different domains so as to build a trusted SDN network as a whole.
    Keywords: trusted computing; SDN; active measurement.

  • A method of spatial place representation based on visual place cell firing   Order a copy of this article
    by Naigong Yu, Hui Feng 
    Abstract: Constructing a model of visual place cells (VPCs), which produce sensitive firing to visual information, is of great significance for studying bionic positioning and bionic navigation. Based on the physiological research of place cells and the analysis of existing VPC generation models, a firing model of VPCs based on the distance perception of landmarks by the agent is proposed in the paper. Based on the firing activity of VPCs, a spatial place representation method is proposed. The method mainly includes exploring the environment and detecting landmarks, calculating the firing rate of VPCs, adding VPCs and constructing the map of VPCs. Through simulation experiments, the reliability of the positioning performance of the proposed method is verified, and the influence of various parameters in the model on the accuracy of spatial representation of the VPCs map is analysed.
    Keywords: visual place cell; spatial representation; bionic positioning; bionic navigation.

  • Research on leak detection and location of urban gas pipeline network based on RSSI algorithm   Order a copy of this article
    by Liming Wei 
    Abstract: To solve the leakage problem of urban gas pipelines, this paper presents a method of detecting and locating leakages based on the RSSI algorithm. This technique can analyse and calculate the signal strength received between ZigBee nodes when a pipeline leaks and ultimately obtain the location of the leak. Firstly, the algorithm model is established by using the RSSI signal strength values between the leak target point and each receiving point. Secondly, the distance between the leak point and each receiving point is obtained by the model. Lastly, the approximate coordinates of the leak point are obtained by the least squares method. The simulation results show that the proposed algorithm has high positioning accuracy and wide application prospects.
    Keywords: gas pipeline network; fire early warning; least squares method; RSSI algorithm; ZigBee technology.

  • Multi-objective workflow scheduling in the cloud environment based on NSGA-II   Order a copy of this article
    by Tingting Dong, Chuangbai Xiao 
    Abstract: The emergence of cloud computing offers a novel perspective to solve large-scale computing problems. Workflow scheduling is a major problem in the cloud environment, and parallelism and dependency are two important characteristics of tasks in a workflow, which increases the complexity of problem. Workflow scheduling is also a multi-objective scheduling problem, and task execution time and cost are the two extremely significant goals for users and providers in the cloud environment. Existing heuristic algorithms are popular, but they lack of robustness and need to be revised when the problem statement changes. Evolutionary algorithms have a complete algorithm system, which is widely used in the multi-objective scheduling problem. In this paper, Nondominated Sorting Genetic Algorithm-II (NSGA-II) is utilized to solve the workflow scheduling problem aiming at minimizing the task execution time and cost. Some real-world workflows are used to make simulation expriments, and comparative simulations with genetic algorithm are given. Results show that NSGA-II is effective for the workflow scheduling.
    Keywords: cloud computing; workflow scheduling; non-dominated sorting genetic algorithm-II; multi-objective scheduling.

  • Application of improved deep reinforcement learning algorithm in traffic signal control   Order a copy of this article
    by Wang Qiang, Song Shuaidi, Zhang Tengyun, Wang Zelin 
    Abstract: For a regional road network, the signal control system lights at multiple intersections belong to the core technology of intelligent innovation. The relevant personnel need to integrate and analyse the relevant status information of the intersection area based on the DQN method and strategy, and strengthen the control of the signal light system effect, to achieve fast and effective detection. In this paper, we propose a reinforcement learning DQN+ algorithm by using the improved DQN reward and punishment function. Experiments show that DQN+ has obvious advantages in terms of average queue length (AQL), average speed (AS) and average waiting time (AWT) at four intersections.
    Keywords: intelligent transportation; traffic signal control; reinforcement learning; deep reinforcement learning.

  • Enhancing artificial bee colony algorithm with depth-first search and direction information   Order a copy of this article
    by Xinyu Zhou, Hao Tang, Shuixiu Wu, Mingwen Wang 
    Abstract: In recent years, artificial bee colony (ABC) algorithm has been criticized for its solution search equation, which makes the search capability bias towards exploration at the expense of exploitation. To solve the defect, many improved ABC variants have been proposed aiming to use the elite individuals. Although these related works have shown effectiveness, they rarely take the factor of search direction into account. In fact, the search direction has an important role in determining the performance of ABC. Thus, in this work, we are motivated to investigate how to combine the idea of using the elite individuals with the search direction, and a new ABC variant, called DDABC, is designed. In the DDABC, the depth-first search (DFS) mechanism and direction information learning (DIL) mechanism are introduced, and the former mechanism is to allocate more computation resources to the elite individuals, while the latter mechanism aims to adapt the search to the promising directions. To verify the effectiveness of the DDABC, experiments are carried out on 22 classic test functions, and three relative ABC variants are included as the competitors. The comparison results show the competitive performance of our approach.
    Keywords: artificial bee colony; exploration and exploitation; depth-first search; direction information learning.

  • Network lifetime maximisation with low power consumption by the use of an ANFIS-based technique in wireless sensor networks   Order a copy of this article
    by Nune SrinivasRao, K.V.S.N. Rama Rao 
    Abstract: The routing in a wireless sensor network (WSN) is important for improving the network's functioning, because inappropriate routing methods and routing degrade the sensor network energy, impacting the network lifetime. Clustering strategies for reducing the energy consumption and extending the network life have been employed widely. The clustering mechanism can extend the network's service life and network failure. In this study, the IoT has contributed in improving network performance with a new energy efficient ANFIS-based routing approach for WSN. A new distributed cluster creation methodology that enables the self-organisation of local nodes, a novel method for the adjustment of clusters and the turning of the cluster head centre location to distribute the energy burden equally through all sensing nodes incorporates the suggested ANFIS-based routing. The simulation result shows that the proposed scheme outperforms conventional methods with an improvement of 80% in network lifetime and 27% in throughput.
    Keywords: energy-efficient routing protocol; base station; cluster head; network lifetime; wireless sensor network.

  • A blind receiver for OFDM communications   Order a copy of this article
    by Min Lu, Min Zhang, Guangxue Yue, Bolin Ma, Wei Li 
    Abstract: Owing to channel fading and noise interference in different environments, how to accurately restore the transmitted bit stream at the receiving end has become a key issue of the orthogonal frequency division multiplexing (OFDM) systems. We propose a dual-path mixed deep learning (DMDL) framework for the blind OFDM receiver, which combines the densely connected convolutional networks (DenseNets) and the residual networks (ResNets). The DMDL receiver can solve the problem of gradient explosion and feature disappearance in the network training, and it does not require pilots for the channel estimation. The experimental results show that on the additive white Gaussian noise (AWGN) channel, the performance of the DMDL receiver can be improved by 1.62 dB over the traditional receiver. On the Rayleigh fading channel, the performance improvement of the DMDL receiver can reach 1.94 dB. The DMDL model also has excellent performance in cyclic prefix-free and Doppler frequency shift environment.
    Keywords: blind receiver; deep learning; DenseNet; OFDM; ResNet; signal detection; wireless communication.

  • Machine learning-based approach for the detection of phishing websites   Order a copy of this article
    by Yaqin Wang, Jingsha He, Nafei Zhu 
    Abstract: Compared with traditional forms of crime, cyber-attacks and cyber-crimes have removed the limitation on distance and speed. With very low cost, phishing is a very effective way of launching network attacks with the purpose of obtaining sensitive information about users, such as username, password and payment voucher, through counterfeiting regular websites so as to steal users private information and personal property using the obtained information. Both the trust that internet users have and the development of the internet itself can be affected by this kind of attack, making it imperative to detect this type of attack. Many methods have been proposed for the detection of phishing websites in the literature in recent years based on techniques ranging from conventional classifiers to complex hybrid classifiers. Meanwhile, although convolutional neural networks (CNNs) can achieve very high accuracy in classification tasks, not much research has been done on the use of CNNs for the detection of phishing websites. This paper proposes a CNN-based scheme for the detection of phishing websites in which four dimensions of the features of phishing websites are defined and CNN is used to extract local features. The proposed CNN-based scheme is compared with several machine learning-based methods on the effectiveness of detecting phishing websites, which shows that the proposed scheme can achieve the accuracy rate of 97.39% and is better than the other classification methods in terms of accuracy, recall and F1-score.
    Keywords: convolutional neural network; classification; machine learning; phishing website detection.

  • Performance evaluation of FBMC vs OFDM in tapped delay line doubly selective channels   Order a copy of this article
    by Ritesh Baranwal, B.B. Tiwari 
    Abstract: As the technology grows in wireless communication, we move towards 5G. In mobile communication, requires a greater number of users in limited bandwidth, and also the technology used is easily accessible to all. For these requirements, several types of research have been conducted to fulfill these requirements. One of the researches is to find a suitable waveform for 5G. But requirements for 5G waveform are very low out-of-band (OOB) radiation, low Peak to Average Power Ratio (PAPR), low reliability, and low latency communication, enhanced mobile broadband. This paper investigates filter Bank multicarrier (FBMC) waveform in time and frequency selective channel used Gabor Theory. Performance evaluation for the parameters like Bit Error Rate (BER), PAPR, Power Spectral Densities for FBMC and OFDM in various real-time doubly selective TDL-A, TDL-B, TDL-C, pedestrian channel, vehicular channel as suggested by the Third Generation Partnership Project (3GPP) has been done.
    Keywords: 5G; Python; FBMC; OFDM; PHYDYAS; vehicular communication; TDL channel.

  • Cold-start recommendation algorithm based on user preference estimation   Order a copy of this article
    by Biao Cai, Jiahui Xin, Xu Ou 
    Abstract: In order to improve the dilemma of collaborative filtering in the face of cold start and achieve a better balance between accuracy and diversity, this paper considers the influence of user characteristics on recommendation results and proposes a Preference Estimation Network (PEN) based on maximum likelihood. PEN uses the user's characteristic information to estimate the user's preference information, and represents the user's preference vector with the item's label system. On this basis, PEN-Rec, an improved version of the traditional recommendation algorithm based on preference vector estimation and particle swarm optimisation, is proposed. Finally, the PEN-Rec algorithm is compared with the benchmark algorithm on six public evaluation indicators using open datasets, and the experimental results show that the accuracy, diversity and novelty of the PEN-Rec algorithm are all improved.
    Keywords: recommendation; feature impact; preference estimation; label vector.

  • Link prediction with Fusion of DeepWalk and node structural information   Order a copy of this article
    by Xinhui Xiang, Biao Cai, Yunfen Luo 
    Abstract: The existing link prediction algorithms are mainly based on structural information or network embedding, but minimal research has been conducted on the fusion of these algorithms. It is found that the structure-based algorithms have high accuracy, but the complexity is higher owing to the introduction of high-order structural information while the network embedding algorithms have low complexity, but because the structural information of the node is not fully used, the accuracy is not as good as some structure-based algorithms. Therefore, by combining the structural attributes of nodes and the degree of convergence between node pairs, this paper proposes two new improved similarity algorithms the similarity algorithm based on edge-degree DeepWalk cosine (EDDWC) and the similarity algorithm based on preferential attachment mechanism DeepWalk cosine (PADWC). Experiments show that the performances of the proposed algorithms are greatly improved over that of the DeepWalk algorithm, and they are also better than other link prediction algorithms.
    Keywords: link prediction; DeepWalk; edge-degree; preferential attachment mechanism; cosine similarity.

  • Hop count, ETX and energy selection based objective function for image data transmission over 6LoWPAN in IoT   Order a copy of this article
    by Archana Bhat, Geetha V 
    Abstract: Internet of things (IoT) is technology that connects millions of things to the internet for collecting data and controlling things. 6LoWPAN looks promising for future IoT networks as it works with IPv6, which is essential to address millions of things. However, as the 6LoWPAN devices are resource constrained with payload constraint at the data link layer, it needs efficient mechanisms to send packets over IEEE 802.15.4 MAC layer. The challenge increases when the sensors used in the devices are camera or audio recordings. Multimedia data transmission over 6LoWPAN is great challenge, and this paper addresses the same with respect to selection of Objective Function (OF) for multimedia data traffic. A new hop count, ETX and energy selection based OF is proposed in this work. The proposed technique is compared with existing OF, and the simulation results shows that the proposed technique provides better performance.
    Keywords: 6LoWPAN; objective function; IPv6; multimedia; RPL; IEEE 802.15.4.

  • Risk assessment for vehicle injury accidents in non-coal mines based on Bow-tie model   Order a copy of this article
    by Bo Wei, Yuan Li, Guixian Liu, Yi Zhao 
    Abstract: In order to reduce the incidence of vehicle injury accidents in non-coal mines, a quantitative risk assessment method based on the fuzzy bow-tie model is proposed. First, the bow-tie model of vehicle injury accidents in non-coal mines is established. Then, the fuzzy failure probability of vehicle injury accidents is calculated by using fuzzy set theory and expert evaluation method. Finally, the risk value of non-coal mines vehicle injury accident consequence is obtained based on fuzzy analytic hierarchy process. Taking a mine vehicle injury accident as an example, the results show that the probability of vehicle injury accident is 2.104
    Keywords: bow-tie model; vehicular injury accident in non-coal mines; risk assessment; fuzzy set theory; fuzzy analytic hierarchy process.

  • Real-time detection system of bird nests on power transmission lines based on lightweight network   Order a copy of this article
    by Haopeng Yang, Enrang Zheng, Yichen Wang, Junge Shen 
    Abstract: In response to real-time detection requirements for bird nests and other hidden danger on power grid transmission lines, this paper proposes a lightweight real-time detection system of bird nests. In terms of bird nests on transmission towers, there are many small targets, which may lead to possible loss of data. Thereby, the algorithm detects small targets of bird nests through three scales: low, middle and high scales. At the same time, the DIoU-NMS calculation method is used to make the prediction box closer to the real box. The average accuracy of the improved algorithm is 90.05%, which is 7.38% higher than the original one. The detection speed of the detection system of bird nests in NVIDIA Xavier NX, an embedded device, is 26.3 FPS. With higher detection accuracy and real-time detection speed, the requirements of high-precision and real-time inspection of the state grid in line inspection can be met.
    Keywords: bird nests detection; lightweight network; multi-scale fusion; attention mechanism; non-maximum suppression; object detection.

  • Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection   Order a copy of this article
    by Annu Paul, Varghese Paul 
    Abstract: This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95, and 0.94, respectively.
    Keywords: credit card; deep learning; fraud detection; data augmentation; data transformation.

  • A dynamic displacement map based on deep Q network to assist the rendering of stylised 3D models   Order a copy of this article
    by Hao Zheng, Houqun Yang, Mengshi Huang, Yizhen Wang 
    Abstract: In the process of rendering with NPR, there will often be a problem of reduced perception of the NPR effect caused by the fixed spatial structure of the static mesh. Therefore, this research first establishes a convolutional neural network to conduct supervised training on numerous hand-drawn target stylised images, and perform continuous angle recognition through stylized images then output its angle vector. Secondly, generating the displacement map in real time by inputting the observation angle through the fully connected neural network model. After that, sampling the real-time displacement map to dynamically generate the deformation of the model. In the end, the goal of breaking the model's sense of space and enhancing the NPR rendering effect can be achieved. Besides, the experimental results of this study verify the effectiveness of the method.
    Keywords: non-photorealistic rendering; displacement map; stylised rendering; deep reinforcement learning.

  • A study on dual-sense broadband circularly polarised monopole antenna for UWB applications   Order a copy of this article
    by Umesh Singh, Kalyan Mondal, Rajesh Mishra 
    Abstract: The proposed work is designed with the embedded of stubs and Parasitic Strips (PSs) under the radiator. An FR4 substrate is used to design the antenna (?_r= 4.4, h = 1.6 mm). The overall size of the antenna is 0.8?_0
    Keywords: monopole; CP; dual-sense; stubs; ARBW; satellite;.

  • Fuzzy Borda combined model in small town sewage treatment process Alternative Selection   Order a copy of this article
    by Fang He, Bo Wu 
    Abstract: With the increasing pressure of sewage treatment in small towns, it is necessary to establish a set of systematic and objective evaluation system to select the most suitable sewage treatment technology in small towns. In this paper, according to sewage characteristics, sewage disposal difficulties, treatment requirements and development of small towns in our country, seven processes were selected as follows: A/O, oxidation ditch, CASS, SBR, biological contact oxidation, BAF and artificial wetland. The evaluation index system was set up through literature research and consulting experts. Comprehensive index method, analytic hierarchy process and weighted arithmetic average method were used to evaluate the priority of each process. The consistency was tested by Spearman rank correlation coefficient. Based on three single evaluation results, fuzzy Borda combination evaluation model was established to evaluate the priority of sewage treatment process. Finally, an example was introduced to prove the feasibility of the combination evaluation model.
    Keywords: small towns; sewage treatment process; combination evaluation; priority; fuzzy Borda method.

  • Fast retrieval of similar images of pulmonary nodules based on deep multi-index hashing   Order a copy of this article
    by Rui Hao, Yaxue Qin, Yan Qiang 
    Abstract: CT image retrieval of pulmonary nodules mainly uses deep neural network embedded in hash layer to extract hash codes, which are directly as the index address for linear search. With the increasing number of clinical lung CT images and the complexity of image expression, the traditional retrieval methods are inefficient. We propose a multi-index hash retrieval algorithm based on deep hash features. First, a hash layer is added to the convolutional neural network (CNN) which can simultaneously learn the high-level semantic features of images and the corresponding hash function expression. Secondly, the hash codes extracted are effectively divided and multi-index tables are constructed. The query algorithm is designed based on the drawer principle. Finally, the complexity analysis of the whole index algorithm is carried out and experimental results show that the proposed algorithm can effectively reduce the retrieval cost while maintaining the accuracy.
    Keywords: pulmonary nodule; deep learning; hash feature; image retrieval; multi-index hashing.

  • Sub-optimal antenna selection technique over Weibull-Gamma fading channel for MIMO communication systems   Order a copy of this article
    by Selvam Paranche Damodaran, Vijayakumar Perumal, Ganesan Verappan 
    Abstract: In this paper, the orthogonal space-time block code (OSTBC) of the Multi-Input Multi-Output (MIMO) system is considered. The advantage of the MIMO system is higher multiplexing gain and diversity gain. Owing to the increased complexity of the MIMO systems, it is difficult to use the MIMO system for practical applications. In this paper, we considered the antenna selection technique to reduce the cost and complexity to achieve the desired gain. The multipath and shadowing degrades the system performance in wireless communication. To model the multipath and shadowing effects, the composite WeibullGamma fading (WGF) channel is considered in this paper. The performance of a MIMO communication system is assessed in this paper using antenna selection techniques (AST) over the WGF channel. The channel state information on the transmitter side (CSIT) can be used to improve the system capacity and error rate at the same time with reduced complexity of the hardware. The CSIT in AST is used for orthogonal space-time block code (OSTBC) of the MIMO system over the WGF channel to improve the bit-error rate (BER) of the system. The SNR performance improvement is discussed over Weibull and gamma fading parameters. In this paper, both optimal and suboptimal AST analysis is derived for various numbers of antennas and capacity improvement of the channel is analysed. The simulations are done for the proposed sub-optimal algorithm to show the performance in high SNR region with fewer antennas selected for transmitting. The suboptimal AST shows the desired capacity improvement with less complexity.
    Keywords: MIMO communication; antenna selection techniques; Weibull—Gamma fading; channel state information on the transmitter side.

  • Split and rotated microstrip patch antenna with improved performance   Order a copy of this article
    by Josephin Pon Gloria Jeyaraj, Anand Swaminathan 
    Abstract: A novel low profile split and rotated microstrip patch antenna (SRMPA) with improved antenna performance is presented. The proposed antenna overcomes the limitations of the conventional microstrip patch antenna (CMPA) such as low gain, less front-to-back ratio (FBR), and more spurious radiation by the ground plane by a simple alteration in the antenna geometry. Its performance is mainly based on the opening angle (D) between the two arms of the antenna. Because the near-field interactions between the two arms are stronger at smaller opening angles, the current and scattering field amplitude are expected to be maximum. Therefore, for D<90
    Keywords: SRMPA; surface current distribution; opening angles; antenna parameters.

  • A novel trust-based approach for intrusion detection architecture in wireless sensor networks   Order a copy of this article
    by Mr. Jeelani, Kishan Pal Singh, Aasim Zafar 
    Abstract: Wireless sensor networks (WSNs) is a new technology that can be used to monitor the environment. Because sensor nodes in wireless sensor networks are installed in an open environment, they are more vulnerable to attacks. The sensor network lifetime improvement is dependent on minimum energy use. Protection is also a major concern when it comes to designing protocols for multi-hop secure routing. The results based on trust have proven to be more effective in addressing malicious node attacks. In this article, we propose a novel trust-based approach for intrusion detection architecture (IDA) in a wireless sensor network that is called the trust-based approach for varying nodes with energy (TBNE) model. TBNE finds the misbehaving nodes in the network. The structure is based on the trust model for secure communication in WSN and improves the performance of nodes. The simulation has been done with QualNet 5.0 simulator.
    Keywords: wireless sensor network; throughput; packet delivery ratio.

  • Data sharing with privacy protection based on blockchain and federated learning in edge computing enabled IoT   Order a copy of this article
    by Shiqiang Zhang, Zhenhu Ning 
    Abstract: Data sharing of Internet of things devices is a powerful means and technology to break the data island in the era of big data. However, frequent privacy leaks indicate that privacy protection has become one of the most urgent problems in data sharing. The existing data sharing schemes usually provide data to the data demanders through access control authorisation through a third-party organization. This way can protect the privacy of data to a certain extent. But the biggest problem is that the data owner will lose control of the data, which increases the risk of privacy disclosure. In this paper, we proposed a new data sharing scheme based on blockchain and federated learning. The data sharing problem is transformed into a machine learning problem. The IoT devices train the model locally and use differential privacy technology to avoid privacy leakage, and ensures its security through the blockchain network aggregation model.
    Keywords: data sharing; blockchain; federated learning; differential privacy; edge computing; IoT.

  • High-performance mobility management using KGMO in heterogeneous networks   Order a copy of this article
    by Kiran Mannem, Pasumarthy Nageswara Rao, .S.Chandra Mohan Reddy 
    Abstract: A heterogeneous network plays an important role in communication systems, because parallel connections with multiple devices can be done. The complexity of network management is increased, owing to more device connections. Managing mobility is the major challenge in a network that is connected to mobile devices. The performance of the network must be balanced while the mobility process is in progress to get a stable operation to the other users. In this work, optimisation-based mobility management (MM) techniques are proposed to improve network performance. The Long Term Evolution (LTE) standard is modelled on a network architecture to test the developed MM. Decision-making efficiency has been improved through the use of Kinetic Gas Molecular Optimisation (KGMO), which can be performed with much less repetition compared with Particle Swarm Optimisation (PSO) methods. The proposed method improves the performance of MM in terms of throughput by 27% when compared with the PSO method.
    Keywords: mobility management; throughput; handover; kinetic gas molecular optimisation; heterogeneous network.

  • Multi-band polarisation-sensitive metamaterial absorber using ant colony optimisation algorithm   Order a copy of this article
    by Raed Ashraf Kamil Albadri, Bilal A. Tuama, Shihab A. Shawkat, Khalid Saeed Lateef Al-badri 
    Abstract: A multi-band metamaterial absorber (MBMMA) comprising a single-ring-multi-cuts (SRMC)-shaped resonator printed on FR4 dielectric layer backed by copper ground plane is simulated for six-band absorption applications. The proposed absorber presented multiple absorption peaks at 9.23, 10.33, 11.12, 12.69, 15.75, 17.38 and 18.55 GHz with high absorption rate. The physical mechanism of the seven-bands absorption is analysed by electric field distribution and current distributions. Furthermore, the geometric parameters of the MBMMA are optimised by using the ant colony optimisation (ACO) algorithm. The proposed optimisation of this metamaterial absorber is different from most previous works prepared empirically by parameter sweep, i.e. parameter sweeps that are time-consuming and unoptimised. In addition, the proposed absorber is affected by the change in polarisation angles at normal incidence. The proposed structure can be easily manufactured because it adopts a single patterned square ring, and can also be extended to other frequencies in applications such as biosensors, monitoring and imaging. This perfect absorber is presently drawing high interest throughout the research of microwave band, especially in energy harvesting, radar cross-section reduction, and sensors. X band and Ku band have been the key focus of the proposed design so that the structure can be used in multi-band applications.
    Keywords: metamaterial; multi-band; perfect absorber; ant colony optimisation algorithm.

  • A survey of lung nodule computer-aided diagnostic system based on deep learning   Order a copy of this article
    by Tongyuan Huang, Yuling Yang 
    Abstract: With the development of machine learning, especially deep learning, the research of pulmonary nodules based on deep learning has made great progress, which has important theoretical research significance and practical application value. Therefore, it is necessary to summarise the latest research in order to provide some reference for researchers in this field. In this paper, the related research, typical methods and processes in the field of pulmonary nodules are analysed and summarised in detail. Firstly, the background knowledge in the field of pulmonary nodules is introduced. Secondly, the commonly used data sets and evaluation indexes are summarised and analysed. Then, the computer-aided diagnostic system related processes and key sub problems are summarised and analysed. Finally, the development trend and conclusion of pulmonary nodule computer-aided diagnostic system are prospected.
    Keywords: machine learning; deep learning; pulmonary nodule; CAD system.

  • Technology adoption of enablers of 5G networks for m-learning: an analysis with interpretive structural modelling and MICMAC   Order a copy of this article
    by L. Kala, Hameed T. A. Shahul, V.R. Pramod 
    Abstract: Mobile learning (m-learning) is one of the real-time applications of 5G technology with an impulsive future. COVID-19 pandemic enhanced the adoption of m-learning over wireless networks by facilitating continued formal education or work from home. This research aims to analyse enablers of 5G networks that enhance real-time m-learning by applying Interpretive Structural Modelling (ISM), a set-theory-based structural modelling method widely employed in many engineering and technology related research fields. Data was collected through questionnaire-based information gathering and from one-to-one discussions with experts. Modelling was performed to identify the correlations among system parameters through a hierarchically structured model. Further, the enablers were classified into different clusters based on their driving powers and dependency with MICMAC analysis, by which the results were validated. The study shows that enablers of 5G will undoubtedly support and uphold the system performance for future real-time scenarios of m-learning by eliminating all the inhibiting parameters of former 4G wireless networks.
    Keywords: 5G; wireless networks; enablers; mobile learning; ISM; MICMAC; driving power dependence.

  • An enhanced genetic algorithm for computation task offloading in MEC scenario   Order a copy of this article
    by Zhao Jiacheng, Li Wenzao, Liu Hantao, Yu Peizhen, Li Hanyun, Wen Zhan 
    Abstract: The explosive growth of the Internet of Things (IoT) and 5G communication technologies has driven increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, owing to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared with the conventional genetic algorithm.
    Keywords: task offloading; genetic algorithm; bandwidth constraint; NP-hard; dense terminal distribution; offloading strategy; mobile edge computing; 5G.

  • Maximum ladle shell temperature prediction based on GABP neural network   Order a copy of this article
    by Ying Sun, Peng Huang, Bo Tao, Juntong Yun, Guojun Zhao, Xin Liu 
    Abstract: Intelligent manufacturing is the main development trend of today's manufacturing industry, and talents are the first resource. Through the analysis of the current situation of cultivating talents in mechanical engineering in colleges and universities, it is found that most students of this major have difficulty in involving in knowledge of other fields outside their specialties, and their knowledge structure is relatively single. In response to the above problems, this paper proposes the training mode of multidisciplinary cross-fertilization of talents in mechanical engineering, which is analysed through the study of maximum temperature prediction of steel ladle shell. The BP neural network based on the improved genetic algorithm is trained on the experimental data samples to achieve the maximum temperature prediction of ladle shell under different thickness combinations of insulation layer, safety layer and working layer. By learning the knowledge of target prediction, students' overall development is promoted.
    Keywords: intelligent manufacturing; multidisciplinary cross-fertilization; goal prediction; genetic algorithm; BP neural network.

  • Performance comparison of TOA-based indoor positioning algorithms using ultrawideband technology in 3D   Order a copy of this article
    by B. Venkata Krishnaveni, K. Suresh Reddy, P. Ramana Reddy Reddy 
    Abstract: In internet of things, localisation of devices is very important. The key point of location-based service is how to calculate the information of position in indoor environments. Distinctive imaginative methodologies and improvements have been proposed; however, definite solid indoor localisation is still a challenging work. Ultra wideband innovation has arisen as a feasible contender for exact indoor situating. For a noise-free environment, the mathematical technique of trilateration is best for position assessment yet genuine conditions which turns out to be uproarious offer ascent to various point convergence position issue and the circumstance turns out to be far more detestable with the expansion in number of reference points. We acknowledge that the study provided in this paper gives a coordinated audit and connection of the positioning techniques, algorithms using the ultrawideband will be valuable for professionals to keep up to date with the continuous upgrades in the field.
    Keywords: internet of things; localisation; TOA positioning; ultrawideband.

  • Indoor object segmentation based on YOLACT++   Order a copy of this article
    by Ying Sun, Zichen Zhao, Bo Tao, Xin Liu, Juntong Yun, Ying Liu 
    Abstract: Intelligent manufacturing originates from the research of artificial intelligence, which can not only reduce operating costs, but also improve product quality, and has become the way to cultivate new advantages in forging international competition in manufacturing. With the transformation of traditional manufacturing to intelligent manufacturing, the course of intelligent manufacturing should also be different from the traditional teaching mode, but through the survey, it has been found that the course of intelligent manufacturing still has a single teaching mode, insufficient innovative guidance for students and insufficient combination of theory and practice. In order to solve the issues mentioned above, the PAE (Project-Analysis-Evaluation) structure is proposed in this paper, and it is combined with constructivist theory and analysed through the study of indoor object instance segmentation detection. The YOLACT++ algorithm is pre-trained on SUNRGBD dataset and applied to indoor environment detection in this paper.
    Keywords: intelligent manufacturing; instance segmentation; YOLACT++; PAE; teaching under constructivism.

  • Data security-web login authentication process using password-generating tile array token interval timed coloured Petri nets   Order a copy of this article
    by M.I. Metilda, D. Lalitha, S. Vaithyasubramanian 
    Abstract: In this paper, password-generating tile array token interval timed coloured Petri nets is proposed to create a secure password. The proposed method provides a new process of authentication for individuals to secure their password. In spite of using only alphabets and characters as a password, web users can use tiles as their password, which has alphabets or characters in it. It provides a new security to protect the information and gives a big challenge for the hackers to identify the password. The main objective of this paper is to provide high security with low computational cost. The proposed method provides low computational cost (45.6%), low latency (65.3%), and high security level (95.6%) better performance when compared with the existing methods, such as MEAS and OTPT.
    Keywords: Petri nets; timed Petri net; tile pasting; password generation; tile password.

  • Review of distributed denial of service attack detection in software defined networks   Order a copy of this article
    by P. Karthika, A. Karmel 
    Abstract: This paper discusses the enhancement of networks, and the ability to withstand DDoS attacks. This survey reviews 65 papers that concern on DDoS attack detection in SDN. Therefore, the systematic analyses on the proposed method are applied to each reviewed paper. In addition, the performance metrics and their best achievements in detecting the attack in each research paper are also analysed, and the mitigation technique used in each paper is examined. The chronological assessment and various tools used for implementing DDoS attack detection in SDN are also considered and reviewed. Finally, the survey depicts numerous research gaps and challenges that are more supportive for researchers to develop novel methods for detecting DDoS attacks in SDN.
    Keywords: DDoS attack detection; software defined networks; mitigation techniques; systematic analysis; chronological assessments.

  • A Q-learning approach for adjusting CWS and TxOP in LAA for Wi-Fi and LAA coexisting networks   Order a copy of this article
    by Tzu-Teng Pan, I-Sung Lai, Shang-Juh Kao, Fu-Min Chang 
    Abstract: Listen-Before-Talk (LBT) protocol is an essential mechanism for unlicensed band allocation in Wi-Fi and Licensed Assisted Access (LAA) coexistence networks. To enhance LBT performance, most researchers adjust the contention window size (CWS) or transmission opportunity (TxOP) for reducing collision and determining better channel occupancy time. However, when two parameters are considered simultaneously, the calculation time of the algorithm will be greatly increased. This paper proposes a new approach to adjust both values of CWS and TxOP simultaneously by Q-learning algorithm. We aim to optimise the adjustment of the two-parameters combination to maximise network throughput and achieve differentiated service. To use the Q-learning algorithm to adjust the CWS and TxOP parameters dynamically, we define the agent, environment, state, and action. We also develop a reward function to help the agent find better combinations of CWS and TxOP. The simulation results reveal that the system throughput of the proposed approach is 12%, 13%, and 7.4% better than Fair Downlink Traffic Management (FDTM), Multi-Agent Reinforcement Learning (MARL), and Maglogiannis Q-learning scheme (QLS), respectively. Compared with fixed values of CWS and TxOP, the throughput of the Wi-Fi network increases by 20.7%. When the network environment changes from uniform scenarios to uneven scenarios, the adjusting time of our approach is 97.5% and 67% less than those of FDTM and MARL.
    Keywords: Q-learning; listen-before-talk; coexistence networks.

  • Aggregation techniques in wireless communication using federated learning: a survey   Order a copy of this article
    by Gaganbir Kaur, Surender K. Grewal 
    Abstract: With the recent explosive rise in mobiles, IoT devices and smart gadgets, the data generated by these devices has grown exponentially. Given that the data generated by these devices is private, transmitting large amount of private data is not practical. So a new learning paradigm has been introduced known as federated learning, which is a machine learning technique. In this technique, user data is not transmitted to the base server as in centralized approach but only the locally updated model is transmitted. These model updates generated by the devices are aggregated at the server which updates its global model according to the local models and transmits back to the devices for next round. This technique reduces the privacy risk and also decreases the communication overhead. Various aggregation schemes are proposed in literature for increasing the performance and accuracy of the system while also increasing the security and reliability. This paper presents a survey of the latest advances in research of such aggregation techniques.
    Keywords: federated learning; machine learning; stochastic gradient descent; aggregation techniques; federated averaging.

  • An optimised multi-channel neural network model based on CLDNN for automatic modulation recognition   Order a copy of this article
    by Yan Gao, Shengyu Ma, Jian Shi, Xiangbai Liao, Guangxue Yue 
    Abstract: To achieve high accuracy blind modulation identification of wireless communication, a novel multi-channel deep learning framework based on the Convolutional Long Short-Term Memory Fully Connected Deep Neural Network (MC-CLDNN) is proposed. To make network training more efficient, we use the gated recurrent unit (GRU) sequence model as the substructure. Furthermore, the skip connection is added to alleviate the problem of gradient disappearance in the network training and reduce the negative effect of pooling layer processes time series data on the subsequent sequence model. We test the feasibility of the model based on two open-source datasets RadioML2016.10a and RadioML2016.10b. The simulation results show that the proposed model can identify most modulation modes efficiently under the influence of various factors such as Additive White Gaussian Noise (AWGN), multipath fading, frequency offset. In the signal-to-noise ratio (SNR) range of 0-18dB, the overall recognition accuracy of the MC-CLDNN can reach 93%, and the area under the receiver operating characteristic (ROC) curve accounts for more than 99%. Therefore, the model has the characteristics of high recognition accuracy and strong generalization ability. Its comprehensive performance is better than most of the existing deep learning models.
    Keywords: automatic modulation recognition; convolutional long short-term memory fully connected deep neural network; gated recurrent unit; convolution neural network.

  • Distracted driving behaviour recognition based on transfer learning and model fusion   Order a copy of this article
    by Guantai Luo, Wanghui Xiao, Xinwei Chen, Jin Tao, Chentao Zhang 
    Abstract: To recognise distracted driving behaviour, traditional manual feature extraction is subjective and complex; single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by a stacking method, using a five-fold cross-validation method to reduce over-fitting. Experiment results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition.
    Keywords: deep learning; transfer learning; model fusion; pattern recognition; distracted driving behaviour.

  • Performance analysis of Rayleigh fading wireless networks with multiple propagation paths and spatial diversity   Order a copy of this article
    by Ridhima Mehta 
    Abstract: Electromagnetic signals propagating through the wireless medium undergo scattering, short-term fading and random attenuations coupled with the power loss due to user mobile devices. This results in multipath components of the transmitted information arriving with different phase, frequency, power and delay at the receiver with fluctuating signal quality. The generalised model for multipath signal estimation is developed in this work with distinct optimal gain and receive diversity operation deployed in the wireless system. The presented modelling technique determines the channel impulse response and complex fading factor for different multipath transmission elements. This study investigates the performance of radio signal propagation in Rayleigh distributed fading channels with antenna diversity at mobile receiver. For this purpose, several network attributes including the bit error rate (BER), signal-to-noise ratio (SNR), average delay and root mean square (RMS) delay spread characterising the efficiency of wireless communications are estimated for varying number of multipath scattered components. These quantitative parameters can be exploited to predict the channel conditions for successful data transmission in typical multipath scenarios. In contrast to the previous related works, our proposed work effectively combines the influence of multiple propagation paths affected by fading and scattering, and spatial receiver diversity principles for extensive information modelling and analysis in the specific wireless communication environment.
    Keywords: MFSK; multipath propagation; Rayleigh fading; spatial diversity.

  • Robust Min-Norm algorithms for coherent sources DOA estimation basing on Toeplitz matrix reconstruction methods   Order a copy of this article
    by Naceur Aounallah 
    Abstract: Most of the classical high resolution algorithms such as ESPRIT, MUSIC or Min-Norm, demonstrate their ability to estimate the directions by which non-coherent signals are arrived on a sensor array. However, the need to enhance this kind of algorithms is becoming increasingly important in order to obtain good estimation also in coherent environments. In this paper, two different algorithms for direction-of-arrival (DOA) estimation are devised. These two new algorithms improve the performance of the Min-Norm algorithm by incorporating decorrelation techniques as a tool to overcome coherent source estimation problems. Simulation examples are conducted to validate the robustness and the effectiveness of the new proposed algorithms compared to the conventional Min-Norm high resolution algorithm.
    Keywords: array signal processing; DOA estimation; coherent sources; Toeplitz matrix; decorrelation method.

  • Compatibility issues of wireless sensor network routing in internet of things applications   Order a copy of this article
    by Sarvesh Kumar Sharma, Mridul Chawla 
    Abstract: Wireless Sensor Networks (WSN) possess several applications with variety of data processing techniques. Many data collection approaches are proposed and keep updating with the new application requirements. WSN is an integrated part of Internet of Things (IoT) added scalability, heterogeneity and complicated processing requirement to the WSN challenges. The accuracy of data is important as it is being used by medical fields, industries and other scientific areas. Data routing approaches are categorised according to the requirement of the application specific network. The paper describes the challenges and issues faced by the designing of data routing approaches for future WSN applications. Our literature review reveals that the extensive research in routing protocols targets energy efficiency with trade-offs between other WSN features. The compatibility issues of existing routing approaches are provided with respect to the design goals of IoT.
    Keywords: wireless sensor networks; data routing; energy efficiency; fault tolerance; routing issues; security attack.

  • Research on power distribution control of parallel microgrid based on adaptive capacitor algorithm   Order a copy of this article
    by Zhanying Tong, Liutong Xu 
    Abstract: In order to improve the stability and flexibility of microgrid operation, a droop control strategy for parallel microgrid power distribution based on an adaptive virtual capacitor algorithm is proposed. The adaptive virtual capacitor is connected in parallel at the output of microgrid inverter to achieve accurate reactive power sharing of microgrid. The experimental results show that the average power sharing error of distributed generation under the control of adaptive virtual capacitor algorithm is 1.05%, which can effectively realise the accurate power distribution of the microgrid and effectively avoid the problem of voltage drop. When adding additional load to the system, the distributed generation tends to be stable within 0.1 s after a short transient process. The adaptive virtual capacitor algorithm has good adaptability to load changes, can effectively realise the accurate power distribution of parallel micro grid, and provides a new research idea for the operation optimisation of the smart grid.
    Keywords: microgrid; virtual capacitance; droop control; reactive power; power sharing.

  • Two-phase approach for the detection and isolation of black hole attack in mobile ad hoc network   Order a copy of this article
    by Pankaj Khuresha, Sonal Sood, Mandeep Sandhu, Anurag Dixit 
    Abstract: A mobile ad hoc network (MANET) is an infrastructure-less network in which no central controller is present and nodes can communicate with each other independently. Owing to unique nature of the network, malicious nodes can enter the network which triggers various types of attack. The black hole is the attack in which the malicious node does not forward any packets and all the packets will be dropped in the network. In this research work, an approach is proposed for the detection and isolation of black hole attack in MANET. The proposed approach works in two phases: in the first phase the malicious node will be detected and in the second phase the malicious node will be isolated from the network. The proposed methodology is implemented in network simulator version 2 and results are analysed in terms of throughput, delay and packet loss.
    Keywords: MANET; black hole; malicious nodes; clustering; trust.

  • Research on distortion quality evaluation of computer network shared image based on visual sensitivity   Order a copy of this article
    by Junru Li 
    Abstract: Shared image distortion will affect the user's experience, and then damage people's life and entertainment experience. In view of this, this research starts with the evaluation and classification of network shared image distortion quality, improves the shared image distortion quality evaluation algorithm combined with the sensitive characteristics of human vision, and verifies its performance superiority through comparative experiments. The results show that the performance of some improved reference quality evaluation algorithms reaches the highest values, which are 0.7923, 0.3224, 0.7931 and 0.8213, respectively. The improved non-reference quality evaluation algorithm achieves the highest value of positive indicators in the comparison of performance values, which are 0.487 and 0.287, respectively, while the lowest value of negative indicators is 0.902. It can be seen that the improved shared image quality evaluation algorithm conforms to the sensitive characteristics of human eyes, has high computational efficiency and has broad application prospects.
    Keywords: image quality evaluation; visual sensitivity; partial reference evaluation; non-reference evaluation.

  • A novel approach to control the sidelobe levels in OFDM radar waveform design using a hybrid of subcarrier weighting and time domain windowing   Order a copy of this article
    by C.G. Raghavendra, D. Ashish, Chitirala V. S. S. P. K. Chaitanya 
    Abstract: Taking the advantage of orthogonal frequency division multiplexing (OFDM), a novel waveform is developed, which performs well for radar communications. A major bottleneck of using OFDM is out-of-band (OOB) radiations, which weaken the ability of radar systems. To successfully design an OFDM system, it is necessary to curtail the sidelobe levels of OFDM signals. We propose a technology to deal with such issues. In this paper, a novel technique for reducing the sidelobes in OFDM radar signals is projected and examined. Subcarrier weighting technique is the method used to scale down the sidelobe peaks by multiplying real valued weighting coefficients with the used subcarriers. In order to obtain optimal subcarrier, further it is subjected to windowing. The proposed scheme is the hybrid of subcarrier weighting associated with pattern based schemes and time domain windowing which enhances the performance of OFDM radar signal. To validate the merit of the proposed radar waveform, we have obtained numerous simulation results to correlate with existing radar waveform. The results demonstrate that proposed OFDM waveform shows the superiority in functioning with reduction in sidelobe levels.
    Keywords: OFDM; MCPC; sidelobe suppression; radar communication; subcarrier weighting; windowing.

  • Optimised recurrent neural network based localisation in wireless sensor networks: a composite approach   Order a copy of this article
    by Shivakumar Kagi, Basavaraj S. Mathapati 
    Abstract: Localisation is one of the key techniques in the wireless sensor network. The location estimation methods can be classified into target/source localisation and node self-localisation. There are several challenges in some special scenarios. Therefore, the anchor node-based distance estimation scheme is used in this research work. In the anchor-based localisation technique, the unknown node uses the position of the anchor node to estimate its location. The trained Recurrent Neural Network (RNN) with the extracted Angle Of Arrival (AoA) and RSSI features of the anchor node and the estimated nodes makes the localisation of the unknown node more precise. Further, to lessen the localisation errors in RNN, its weights are fine-tuned by an Improved Whale optimisation Algorithm (IWOA).
    Keywords: WSN; node localisation; AoA and RSSI based feature computation; RNN; IWOA.

  • Research on employment quality evaluation system of skilled talents   Order a copy of this article
    by Guojun Zheng 
    Abstract: In the new development stage of China, skilled talents shoulder the important mission of in-depth implementation of innovation-driven development strategy, which is an important basis for enterprises to enhance competitiveness and improve economic benefits, and also the key to stabilise and expand employment and achieve common prosperity. The employment quality of skilled talents should actively adapt to the needs of economic restructuring and industrial upgrading to achieve higher quality and fuller employment. This paper constructs an indicator system of employment quality and skilled talents supply from the macro level, evaluates the employment quality and skilled talents supply in the two years before and after the outbreak of COVID-19 by using the entropy method, and calculates the coupling coordination and correlation degree between the two systems. The research shows that the level of economic development is an important dimension affecting the employment quality, and the education level has the least influence on the employment quality of skilled talents. After the outbreak of the epidemic, employment training and employment opportunities have a greater impact on the quality of employment, and lead to a more serious shortage of skilled talents. The antagonistic coupling between the quality of employment and the supply of skilled talents has become more serious due to the impact of the epidemic.
    Keywords: employment quality evaluation; skilled talents; COVID-19; economic development.

  • LTE 2100 MHz band half-wave two element rectifier array for wireless electromagnetic energy harvesting   Order a copy of this article
    by Pradeep Chandrakant Dhanawade, Shivajirao M. Sangale 
    Abstract: In this manuscript, a two-element half-wave rectenna array for wireless energy harvesting from LTE2100 MHz band is presented. The 2100 MHz band is chosen based on the spectrum survey in the locality. An outdoor peak power of -15.8 dBm is sensed using a 1.5 dBi gain wideband antenna and spectrum analyser. A half-wave rectifier circuit using two different Schottky diodes and a capacitor filter is developed and connected in mirror image form. The proposed structure combines the direct current power of individual elements using two series capacitors improving the rectenna efficiency. The reported full-wave rectifier array has 19.95% and 63.01% radio-frequency to direct current efficiency for conventional and high-performance Schottky diodes respectively. A detailed analysis of major design parameters have been performed and presented in the manuscript which will help researchers to choose a suitable operating band and design components for rectenna design. The presented half-wave-rectifier rectenna has a comparable conversion efficiency with the full-wave-rectifier rectennas resulting in improved throughput wireless energy harvesting systems.
    Keywords: rectifier; rectenna; Schottky diode; wireless energy harvesting; rectenna array.

  • Research on crop diseases classification model based on MobileNet   Order a copy of this article
    by Zejun Wang, Fangfang Zhang, Fengying Ma, Peng Ji, Lei Kou, Michaël Wyk, Maoyong Cao 
    Abstract: To address the problems of large size and low recognition accuracy of the convolutional neural networks (CNNs) for crop disease recognition, this paper proposes an improved model of MobileNet called MobileNet-LR-SE. The model firstly uses the LR structure to reverse the feature information and get the new feature information. These together with the original feature information from the input to the feature fusion layer. The LR structure introduces residual connection and uses the Leaky ReLU activation function. Secondly, it embeds the SE module to complete the final image classification. The LR structure solves the problem of ignoring negative feature information in the training process of ordinary neural networks. The SE module improves the attention of the network model to the useful channels. Experiments show that the MobileNet-LR-SE model has a high accuracy rate when evaluated on the two crop disease data sets, with the number of network parameters of only 2.01M.
    Keywords: deep learning; convolutional neural network; lightweight neural network; crop diseases; image classification.

  • Sensor cloud virtualisation systems for improving performance of IoT based wireless sensor networks   Order a copy of this article
    by S. Senthil Kumaran, S.P. Balakannan 
    Abstract: A cloud is a new paradigm for IoT-based wireless sensor networks (WSN) that overcomes several limitations of traditional WSN and decouples the owners of the physical sensors from the network users. This paper proposes a cloud-based Internet of Medical Devices (IoMD) novel architecture for the healthcare system to validate the efficiency of sensor-cloud virtualisation technique. A novel architecture for validation case study. IoT, cloud computing, and fog are the three key technologies that make up the framework outlined in this paper. IoT and medical devices are integrated into our cloud-based architecture, and deep learning algorithms are used to process the collected data. A deep learning neural network method called Generative Adversarial Network (GAN) model that runs in both fog and cloud platform and capable of processing massive data in a fast and efficient manner. The suggested GAN is trained on a real dataset from the UCI Machine Learning Repository. Even yet, the results show that the GAN classifier can correctly categorise the medical data activities with a 99.16% accuracy rate. The proposed architecture for validation case study will ensure to benefit the sensor-cloud virtualisation paradigm for developing innovative applications in different sectors of the IoT system.
    Keywords: cloud-based internet of medical devices; cloud computing; wireless sensor network; sensor data; fog computing.

  • Signal strength and energy based efficient AODV routing algorithm in MANET   Order a copy of this article
    by Priyanks Pandey, Raghuraj Singh 
    Abstract: In recent years, Mobile Ad Hoc Network (MANET) has become one of the most popular research areas in the wireless networking domain. However, one of the major challenges remains to develop an efficient routing algorithm which depicts par excellence performance on all performance parameters even under highly dynamic network. Ad Hoc On-Demand Distance Vector (AODV) is a generalized routing protocol which establishes routes to destinations on demand in MANET environment and supports unicast as well as multicast routing. Many enhancements have also been proposed in AODV from time to time. These enhancements are based on various features which define a specific environment. But, these enhancements do not perform well on all considered performance metrics such as packet delivery ratio, delay, normalized routing load and throughput in highly dynamic network environment. In this paper, we have proposed an Enhanced version of AODV, namely (ENAODV) algorithm considering two important and additional stability parameters i.e. energy and signal strength along with hop count and sequence number in route selection process. Algorithm has been simulated using NS2 simulator and evaluated under different network conditions with varying maximum speed. Performance of the algorithm has been evaluated to be better on all parameters like throughput, normalized routing load, packet delivery ratio, control overhead and end to end delay than the AODV algorithm.
    Keywords: MANET; signal strength; RWP; AODV.

  • Pilot-based channel estimation in spatial modulated OFDM systems for wobile Wireless applications   Order a copy of this article
    by Anetha Mary Soman, Nakkeeran R, Shinu Mathew John 
    Abstract: Spatial modulation (SM), a novel and favourable digital modulation technology, provides spectral and energy efficiency. An integration of SM with Orthogonal Frequency Division Multiplexing (SM OFDM) is a recently evolved transmission technique that has been suggested as a replacement for multiple input multiple output (MIMO) OFDM transmission. In practical scenarios, channel estimation is significant for detecting transmitted data coherently. This paper investigates estimate power, Mean Square Error (MSE) and Bit Error Rate (BER) parameter metrics of the SM OFDM communication system with channel estimation algorithm using comb type pilots for Additive White Gaussian Noise (AWGN) channel and Rayleigh fading channel employing International Telecommunication Union (ITU) specified standard model. Simulation output shows that, for an AWGN channel, there results an improvement of approximately 1 dB power with MMSE estimate using DFT and an improvement of approximately 0.3 dB power with the LS-linear/spline using DFT. Also, MSE reduces as the signal-to-noise ratio increases for different interpolations used for LS/MMSE estimation with DFT, and there results a performance improvement in BER when compared with conventional LS/MMSE estimators. For Rayleigh fading channel there arises an enhanced performance in estimate power with MMSE channel estimation for ITU channel model, and also an enhanced performance with channel estimation based on DFT for ITU model. DFT-based estimation results in low MSE for the different interpolations used in ITU model and also the results show a performance improvement in BER for MMSE and DFT based estimation when compared with conventional LS/MMSE estimators. In summary, the simulation output shows that incorporating the DFT algorithm can provide better estimate power, BER and MSE by eliminating the effect of noise externally the extreme channel delay length.
    Keywords: MIMO; multicarrier modulation; spatial modulation; channel estimation; interpolation.

  • DC-PHD: multitarget counting and tracking using binary proximity sensors   Order a copy of this article
    by Nourhan Abdelnaiem, Hossam Fahmy, Anar A. Hady 
    Abstract: Efficient multiple target tracking and counting has become an essential requirement for many wireless binary sensor networks (WSN) applications. WSNs are inexpensive, such that sensor nodes could be easily deployed in any area of interest (AOI). Sensor nodes are simple, cheap and could sense the presence of a target that lies within its range. The simplest type of WSNs is the wireless binary sensor networks (WBSN), in which the deployed sensor nodes are binary. This paper investigates the problem of tracking and counting multiple individual targets that are present in a binary sensor network. An enhanced probability hypothesis density-based filter is proposed by introducing the spatial and temporal dependencies to improve the targets localization accuracy. The implementation of dynamic counting techniques is considered to improve the efficiency of the estimations of targets trajectories. These enhancements were motivated by the lack to differentiate between multiple targets when using the PHD filtering techniques. Simulations compare the performance of the proposed algorithm with the previously mentioned target tracking approaches, to verify the efficiency and accuracy of the proposed target counting and tracking technique in binary sensor networks.
    Keywords: dynamic counting; multi-target counting and tracking; particle filter; probability hypothesis density-based filter; wireless binary sensor networks.

  • Load balancing routing in RPL for the internet of things networks: a survey   Order a copy of this article
    by Kala Venugopal, T.G. Basavaraju 
    Abstract: Presently, when the Internet of Things (IoT) makes virtually everything smart by improving every aspect of our life, continuous development in this area is imperative. As the IoT deals with the Low power Lossy Networks (LLNs) with constrained resources, routing in such constrained networks is considered an acute problem. The Internet Engineering Task Force (IETF) has come out with a de facto routing protocol for IoT networks called the IPv6 Routing Protocol over Low power lossy networks (RPL). Unfortunately, though RPL is formulated with numerous salient characteristics, load balancing is a principal concern that is left unaddressed. Load balancing in IoT guarantees fair dissemination of traffic load amid the nodes in the network and affects the connectivity, stability, reliability, and lifetime of LLNs. This paper elucidates the state-of-the-art load balancing routing protocols and issues of load balancing, aiding researchers to design efficient and reliable load balancing routing protocols for IoT networks.
    Keywords: internet of things; load balancing; low power lossy networks; RPL.

    by S. Shibu, V. Saminadan 
    Abstract: The performance of the Long Term Evolution-Advanced (LTE-A) user equipment (UE) is severely degraded when it operates under high mobility conditions. In LTE-A, Orthogonal Frequency Division Multiple Access (OFDMA) receivers are used in the user equipment which operates in a frequently changing radio environment and produces high Doppler and delay spread. The orthogonality between the subcarriers is affected due to the doubly selective channel because it makes sudden variations in the OFDM block, induces power leakage between subcarriers, and creates inter-carrier interference (ICI) at the LTE-A receiver. To achieve higher downlink system performance and throughput at high mobility conditions, ordering parallel Successive interference cancellation (OPSIC) technique for LTE-A Heterogeneous Networks (HetNet) is proposed. The proposed scheme consists of several approximation steps. A parallel interference cancellation scheme is introduced in the existing ordering successive interference cancellation (OSIC) to remove the interference at every approximation stage which is to reduce the complexity of the successive interference cancellation and improve the data rate at UE. The experiment results show that the proposed OPSIC provides a better data rate at UE compared to traditional detection schemes.
    Keywords: high mobility; Doppler spread; ICI; SIC; LTE-A.

  • Intelligent layout design of building damping structure based on ramp model   Order a copy of this article
    by Xinjun Wang 
    Abstract: With the increasing frequency of earthquakes, it has had a significant impact on citizens' property and life safety. In order to reduce the losses caused by earthquakes, the research is conducted from the perspective of structural layout of building shock absorption. This paper realises the synchronous optimisation of the layout position and damping coefficient of viscous fluid dampers under the actual ground motion. The ramp model in the density method of structural topology optimisation is used to continuously process the discrete design variables in the objective function of the optimisation problem, and then the moving asymptote method is used to solve the optimisation problem. The results show, different damper groups will lead to great differences in project cost; when a single type of damper is selected to participate in the layout optimisation, the required total damping coefficient is 23020 k Nm-1s. When two types of dampers are used to participate in the layout optimization, the required total damping coefficient is 20550.8 k Nm-1s. The cost of a single group of dampers is significantly higher than that of two groups of dampers.
    Keywords: building shock absorption; damper layout; damper coefficient; structural cost; ramp model; synchronous optimisation.

  • Construction of mental health monitoring system based on model transfer learning algorithm   Order a copy of this article
    by Panpan Li, Feng Liang 
    Abstract: In order to monitor people's mental health in real-time and effectively, this topic has conducted in-depth research on the model transfer learning algorithm, including its learning process, classification criteria, network structure optimisation, etc. The research takes model transfer learning algorithm as the main research method, and innovatively adopts residual learning and gradient descent algorithm to optimize the performance of model transfer learning algorithm, and then compares and analyses the application effects of model transfer learning algorithm and traditional machine learning algorithm in various data sets of mental health monitoring, so as to ensure the accuracy of monitoring results. The results show that the model transfer learning algorithm is significantly better than the traditional machine learning algorithm in accuracy, recall and F1 score, and it requires less network training time. This shows that the mental health monitoring system based on model transfer learning algorithm has good performance and can monitor mental health accurately and efficiently.
    Keywords: model transfer learning; transfer learning; mental health; monitoring system.

  • Application of BIM application benefit evaluation model based on fuzzy AHP in the whole life cycle of tunnel engineering   Order a copy of this article
    by Xiaohong Wu, Haifeng Wu, Chenwen Zhan 
    Abstract: Tunnel engineering plays an important role in traffic planning, but it faces many problems in the process of periodic construction because of its large construction scale, long investment and construction time and prominent geological disaster risk. This paper introduces the life cycle theory and establishes the benefit evaluation model of BIM Technology Application under the guidance of Fuzzy AHP. The comprehensive operation rate of the model has been improved by 59.9% under both the construction level and the operation level of the project. And the weight score of some engineering indicators is also more than 90 points, which greatly improves the project management level and coordination efficiency. This model can effectively provide new application ideas for engineering construction, and establish the benefit evaluation system of BIM application in the whole life cycle.
    Keywords: fuzzy AHP technology; BIM technology; applying benefit evaluation model; tunnel engineering; life cycle.

  • Research on the construction of enterprise human resource allocation model based on multi-objective particle swarm optimisation algorithm   Order a copy of this article
    by Lidan Wang, Qiuyan Guo 
    Abstract: The irrationality of human resource allocation and the unfitness of talent positions make it difficult for the original human resource management model of the enterprise to give full play to its actual effect to a certain extent, which has a negative impact on the overall economic benefits of the enterprise. Therefore, the research combines the perspective of multi-objective problems and the particle algorithm with the characteristics of fast convergence, simplicity and parallel search, makes a systematic study of multi-objective optimization, and introduces matrix criteria to the configuration model for testing. The results show that, The improved multi-objective particle swarm optimization algorithm has the highest accuracy of 98.54% on the data set, and the classification performance and combination mode of the algorithm have good application results. At the same time, the human resource model under the algorithm makes the maximum enrolment rate reach 9% and the maximum decline of turnover intention reach 10%. The optimization of enterprise human resource allocation model can realize the high efficiency of the overall system of the enterprise and promote its long-term benign development.
    Keywords: multi-objective particle swarm optimisation algorithm; enterprise development; human resource allocation model; employee satisfaction evaluation.

  • Competitive crow search algorithm-based hierarchical attention network for dysarthric speech recognition   Order a copy of this article
    by Bhuvaneshwari Jolad, Rajashri Khanai 
    Abstract: The common difficulty of speech recognition is articulation deficiency produced by an athetoid, a kind of cerebral palsy. In this paper, the effectual dysarthria speech recognition approach is introduced using the developed Competitive Crow Search Algorithm-based Hierarchical Attention Network (CCSA-based HAN). Here, the spectral subtraction method is used for removing unwanted noises. Then, the specific features are extracted, and then to improve the performance the data augmentation is done. The data augmentation process is performed by adding various noises, like street noise, train noise, and party crowd noise to the input signal. In addition, the HAN classifier is employed for recognising dysarthric speech. Here, CCSA is devised for obtaining effective recognition output, which is designed by incorporating Competitive Swarm Optimiser (CSO) and Crow Search Algorithm (CSA). The developed dysarthria speech recognition approach outperforms other existing methods with accuracy of 0.9141, sensitivity of 0.9208, and specificity of 0.9172.
    Keywords: hierarchical attention network; dysarthric speech recognition; competitive swarm optimiser; crow search algorithm.

  • HetNet security solution using femtocell network architecture and UMTS technology in the millimetre range   Order a copy of this article
    by Devidas Chikhale, Mahesh Munde, Shankar Deosarkar 
    Abstract: Heterogeneous network security is a prime concern in the era where billions of human beings and physical devices will get connected through next-generation technologies like millimetre-wave wireless, the internet of things, artificial intelligence, and machine learning. Higher bandwidth in the millimetre range will help to achieve seamless connectivity and tens of gigabit per second data rate. In femtocells, handoff security will play a vital role to avoid call drops. This article focuses on how new handoff security algorithm deployed in the femtocells network by a method that uses Call Admission Control protocol and UMTS technology. Call admission control protocol gives variable quality of services by efficient use of network resources and available bandwidth. The number of handoffs and probability of call drops is less when the user moves from femtocells to macrocells. Dynamic threshold time reduces number of handoffs and improves handoff security. The handover mechanism is an important aspect in deciding the quality of communication. Novel handover policy reduces the outage probability and increases the signal to interference plus noise ratio. It directly affects the revenue of the operator and call quality.
    Keywords: femtocells; handoff; call admission control; HetNet security; millimetre wave; UMTS.

  • Zero bias error compensation method of laser gyro based on neural network   Order a copy of this article
    by Juan Cui, Cong Zhong 
    Abstract: Aiming at the problem that the accuracy of the current compensation model for laser gyro bias error is low, an improved RBFNN bias error compensation model of laser gyro is proposed. The standardization constant and data centre of the original data are obtained through the self-organizing feature mapping network. The sample centre of the new sample data is obtained by the fastest decline of the expected variance of OLS algorithm. The results show, the improved RBF neural network algorithm has the best performance. under normal temperature, temperature change rate of 1 / min and temperature change rate of 3 / min, the zero bias range of laser gyro is 3.491-3.508 / h, 3.992-4.021 / h and 4.092-4.123 / h, respectively. The research results provide new reference suggestions for the zero bias temperature compensation scheme of laser gyro at different temperatures.
    Keywords: laser gyroscope; radial basis function neural network; self-organising feature mapping network; least squares method; temperature compensation.

  • Tracking control of steering collision avoidance trajectory based on simulated annealing algorithms   Order a copy of this article
    by Minglu Han, Huipeng Chen, Xu Zhang, Chenyang Kuang, Junlin Chen, Jian Gao 
    Abstract: In response to the traditional MPC algorithm's difficult parameter adjustment leading to the inability to steer in a timely and accurate manner to avoid collisions, this paper designs an optimisation algorithm based on the simulated annealing algorithm with automatic parameter adjustment MPC. A vehicle dynamics model and a prediction model are established, and the simulated annealing algorithm is used to solve the objective function of the predetermined trajectory and obtain the weight matrix applicable to the prediction model. MPC The optimized controller is used to achieve the steering and collision avoidance trajectory tracking control of the vehicle. The simulation results under two operating conditions of medium and high speed show that the controller can achieve fast response of vehicle steering and collision avoidance at different speeds and can keep the tracking error within 5%. The controller has the characteristics of timeliness, accuracy and stability.
    Keywords: steering collision avoidance; polynomial trajectories; simulated annealing algorithms; MPC; trajectory tracking.

  • QoS-based handover approach for 5G mobile communication system   Order a copy of this article
    by Amina Gharsallah, Nouri Omheni, Faouzi Zarai, Mahmoud Neji 
    Abstract: 5G mobile communication systems are in-depth fusions of multi-radio access technologies characterised by frequent handover between cells. Handover management is a particularly challenging issue for 5G networks development. In this article, a novel optimised handover framework is proposed to find the optimal network to connect with a good quality of service in accordance with the users preferences. This framework is based on an extension of IEEE 802.21 standard with new components and new service primitives for seamless handover. Moreover, the proposed vertical handover process is based on an adaptive heuristic model aimed at achieving an optimised network during the decision-making stage. Simulation results demonstrate that, compared to other existing works, the proposed framework is capable of selecting the best network candidate accurately based on the quality of service requirements of the application, network conditions, mobile terminal conditions and user preferences. It significantly reduces the handover delay, handover blocking probability and packet loss rate.
    Keywords: 5G mobile network; ultra-dense network; media independent handover; vertical handover optimisation; fast handover.

  • Research on enterprise financial crisis early warning management based on PLS-BP   Order a copy of this article
    by Ling Zhu 
    Abstract: Finding an efficient and accurate early warning method is of great significance to enterprises, investors and the development of China's national economy. The partial least squares (PLS) method is used to screen the variables, the BP neural network is used to establish the model, and the PLS-BP early warning model is constructed. In the simulation experiment, the prediction accuracy ofPLS-BP early warning model in T-2 period is 100%, 93.75% in T-3 period and 87.5% in T-4 period, which are significantly higher than the traditional financial early warning methods. The above results prove that PLS-BP early warning model can accurately predict the financial crisis of enterprises and has certain practical value.
    Keywords: partial least squares; early warning of financial crisis; variable screening; BP neural network.

  • Vocational education policy analysis based on word frequency analysis technology   Order a copy of this article
    by Juyan Che, Weinan Liu, Wang Jie, Zaiyang Xie 
    Abstract: With the continuous development of economy and progress of society, vocational education policies are also constantly changing. Taking the release of "The 20 Articles of Vocational Education Reform" as the dividing point, select 20 vocational education policy documents from official government websites, use ROST software to analyse word frequency in detail, so as to extract and integrate high-frequency words. According to the six dimensions involved basic concepts, subjects, mechanism support, implementation effects, service provision, and development planning, understand the inherent characteristics of vocational education policies, such as focusing on teaching reform to improve quality of talents training. Therefore, the vocational education-related subjects can adapt to vocational education policies changes, strengthen the implementation, cultivate high-quality vocational and technical personnel urgently needed by the society, so as to promote the vigorous development of the economy and society.
    Keywords: vocational education; word frequency analysis; policy interpretation.

  • A feature fusion pedestrian detection algorithm   Order a copy of this article
    by Nan Xiang, Lu Wang, Xiaoxia Ma, Chongliu Jia, Yuemou Jian, Lifang Zhu 
    Abstract: When pedestrians are in different angles and positions, The feature extraction and fusion capabilities are often limited of YOLO series model. Aimed at this problem, we propose an improved feature fusion pedestrian detection algorithm YOLO-SCr. To enhance the ability of cross-scale feature extraction and detection speed, we reconstruct the network structure of the YOLO algorithm in the backbone part and convolution layer part, respectively. Then, to strengthen the feature fusion ability of pedestrians at different scales ,we introduce the spatial pyramid pooling module and shuffle & CBAM(Convolutional Block Attention Module) attention mechanisms in different positions before YOLO layers. The experimental results show that compared with the detection algorithm such as YOLOv3, YOLO-SCr can performance effectively improve the detection accuracy , Recall and speed.
    Keywords: YOLO series ; feature extraction ; feature fusion ;spatial pyramid pooling; pedestrian detection ; shuffle & CBAM attention;.

  • Handover decision algorithm for heterogeneous wireless network with approaches multi-attribute decision-making technique   Order a copy of this article
    by Sepide Memar Montazerin, Mohammadreza Soltanaghaei 
    Abstract: Vertical handover is the most critical component of the new generation of heterogeneous wireless networks (4G and 5G networks). Accordingly, extensive researches have been introduced to improve this critical issue; most of them have been developed based on multi-metric techniques. These methods are designed to provide the required quality of service (QoS) for a wide range of applications. Furthermore, they provide roaming across different network technologies. These methods provide continuity of user connectivity with high quality, but they increase energy consumption. Accordingly, energy awareness in vertical handover techniques in today's age of heterogeneous networks is very important. In this paper, based on the development of the TOPSIS technique, an energy-efficient multi-metric decision algorithm is proposed for vertical handover in heterogeneous networks. This algorithm is the interface of three Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). The proposed method is designed in such a way that in addition to improving the quality of handover, it is also effective in optimising energy consumption. Simulation results using NS-2 show the superiority of the proposed method in improving delay metrics, unnecessary handover, failures and energy consumptions compared with similar methods.
    Keywords: heterogeneous wireless networks; vertical handover; energy consumption.

  • Anomaly detection system in 5G networks via deep learning model   Order a copy of this article
    by Vikram Sadashiv Gawali, Nihar M. Ranjan 
    Abstract: Fifth Generation (5G) networks are susceptible to a number of attacks that target the 5G platform's major components, including radio communication, user equipment, core and edge networks. To defend the system against external and internal assaults, a dependable and precise security technique is needed. The sophisticated communication features of 5G technologies are introducing new problems to cyber security defensive systems. Despite the innovative approaches that have emerged in recent years, 5G would make Intrusion Detection Systems (IDS) and protection events obsolete when they're not updated. Consequently, the aim of this work is to provide a unique feature extraction and detection system for 5G networks. The input data goes through a preparation phase first. The extracted characteristics include statistical and higher order statistical features, technical indicators, raw features, information gain, & improved entropy. This procedure is then applied to the preprocessed data. Finally, the detection phase receives the retrieved characteristics, here Hybrid Classifier (HC), including Deep Belief Network (DBN) and Bidirectional Long Short Term Memory (Bi-LSTM) is used. To convert detection stage accurate and precise, the weights of both Bi-LSTM and DBN is optimised using a novel Deer Hunting updated Sun Flower Optimisation (DHSFO) model that hybrids the concept of Sun Flower Optimisation (SFO) and Deer Hunting Optimisation (DHO) algorithm. Lastly, the outcomes of the suggested system are computed using a variety of metrics, comprising False Positive Rate (FPR), accuracy, precision, False Negative Rate (FNR), sensitivity, specificity, F-measure, Net Predictive Value (NPV), and Matthews Correlation Coefficient (MCC), in contrast with existing methods.
    Keywords: 5G network; anomaly detection; feature extraction; hybrid classifiers; optimisation.

  • Analytical review and study on various course recommendation systems   Order a copy of this article
    by V. Anupama, M. Sudheep Elayidom 
    Abstract: In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based, and data mining-based techniques. Besides, the classification of techniques, tools used, implemented software tools, and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation systems are discussed. The machine learning-based approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use JAVA as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy, and RMS have been commonly used as performance metrics.
    Keywords: recommendation system; root mean square; collaborative filtering; F-measure; data-mining.

  • Research on a laser cutting path planning method based on improved ant colony optimisation   Order a copy of this article
    by Naigong Yu, Qiao Xu, Zhen Zhang 
    Abstract: Laser cutting path planning for fabric patterns is critical to cutting efficiency. The ant colony optimisation algorithm commonly used in this field is constrained by the complete cutting and cannot plan a true global optimal path, resulting in large empty strokes. To solve this problem, this paper proposes an ant colony optimisation method based on virtual segmentation of multiple feature points for path planning of laser cutting. The method first changes the feature point selection strategy of traditional ant colony optimisation and increases the number of feature points in a single pattern. Then the single closed pattern is virtually divided into multiple open contours. Finally, the optimal cutting path is planned based on the solution of the travelling salesman problem. Experiments show that the cutting planning path obtained by the proposed method has a higher degree of compression on the idle stroke and significantly improves the laser cutting efficiency.
    Keywords: laser cutting; path planning; ant colony optimisation; virtual segmentation.

  • Two novel blind CFO estimation techniques for CP-OFDM   Order a copy of this article
    by Mohammadreza Janbazi Roudsari, Javad Kazemitabar, Hossein Miar-Naeimi 
    Abstract: In this paper, two new cyclic prefix (CP) based blind carrier frequency offset (CFO) estimation methods for orthogonal frequency division multiplexing (OFDM) transmission over multipath channels are proposed. In doing so, we first estimate the maximum delay of the fading channel. We borrow the concept of remodulation introduced in earlier works and use the repetitive structure of CP to calculate a maximum-likelihood based measure. In the first proposed method we use particle swarm optimisation aided search on all possible samples to find the optimal set. This technique provides performance improvement at the expense of more complexity. Then, in a second proposed method, we average over the optimal set of samples to estimate CFO. The second technique provides a major improvement over previous works while offering less complexity. Simulation results corroborate that both our proposed methods significantly decrease the mean square error.
    Keywords: orthogonal frequency division multiplexing; carrier frequency offset; cyclic prefix.

  • Review of sensors for detecting insulating gas ozone   Order a copy of this article
    by Xiaoyu Li, Pengfei Jia, Min Xu, Lin Zhang 
    Abstract: Air quality monitoring and analysis have been highlighted as a significant issue at the moment. So people began to pay more attention to the research direction of pollution gas detection equipment. Ozone (O3) is one of the primary indoor as well as environmental air pollutants. It can cause several health problems. Therefore, the research of pollution gas detection equipment has also become a direction of interest to experts. Existing gas sensors can not only measure a range of common gases (O2, CO2, etc.), but also monitor harmful gases (SO2, NO2, CO, etc.). The existing literature mainly optimises the sensor performance by changing the sensor materials and improving the material synthesis technology, so that the sensor detects ozone more accurately and quickly. Based on the diverse types of gas sensors, this paper reviews a variety of sensors to detect ozone gas.
    Keywords: insulating gas; ozone; gas sensor; gas detection.

  • Application of fluorescence spectrometry combined with second-order correction algorithm in food pigment detection   Order a copy of this article
    by Qing Yang, Yinghui Gu, Jinshuai Lu 
    Abstract: Food pigment is a common food additive, but the abuse of pigment will produce serious food safety problems, which is not conducive to people's health and safety. With the development of modern science and technology, there are more and more kinds of synthetic pigment. The traditional pigment detection methods have the disadvantages of high cost, low efficiency and low accuracy. In this study, fluorescence spectrometry is proposed to effectively detect and classify food pigments. By introducing the second-order correction algorithm to build the classification model of food pigments, we can quickly and non-destructively detect the pigments in food without any separation. Through the simulation analysis of three different drinks, it can be seen that the proposed method can quickly identify the types of pigment and judge the safety and compliance of pigment use.
    Keywords: fluorescence spectrometry; second order correction algorithm; food pigments.

  • Research on computer vision capture technology based on deep convolution neural network algorithm   Order a copy of this article
    by Bo Pan 
    Abstract: The current vehicle detection methods have some problems, such as poor recognition rate, easy to be affected by illumination and occlusion, so they need to be further improved and optimised. This paper studies the construction of a target detection model based on deep convolution neural network, improves and optimises the convolution neural network by using linear discriminant analysis, and constructs the lda-cnn target detection model based on the optimised convolution neural network for vehicle detection and recognition. The results show that the detection accuracy of the lda-cnn model in complex situations is 95.67, and the lowest loss value in the training process is 0.17. The above results show that the target detection model based on the improved convolutional neural network can minimise the influence of illumination and occlusion and improve the detection accuracy and efficiency, and has high practicability.
    Keywords: convolutional neural network; computer vision; target detection; linear discriminant analysis.

  • Application of gesture recognition in graphic design and control of information interaction system   Order a copy of this article
    by Bingjie Zhou 
    Abstract: The current human-computer interaction system does not easily recognise complex actions. In order to realise the graphic control of human-computer information interaction in a complex environment, an information interaction system based on gesture recognition is proposed. Firstly, motion detection and skin colour detection are used to optimise the effect of gesture recognition, so as to establish an information interaction system. In the design of the information interaction system, gesture recognition is taken as the basic information of graphics generation, a graphics generation module is constructed, and the effects of gesture recognition and graphics design are tested and analysed. The results show that the gesture recognition accuracy after processing can reach 100%, and the graphic design display effect of the information interaction system interface meets the basic requirements of graphic design. Graphic design based on information interaction technology improves the efficiency of people controlling graphics through gestures, and realises natural human-computer interaction.
    Keywords: human-computer information interaction; gesture recognition; motion detection; skin colour detection; graphic control.

  • RepVgg-Tree: a multi-branch tree-based parametric reconstruction network for tiny steel surface-defect detection   Order a copy of this article
    by LingYun Zhu, YueMou Jian, Lu Wang, ChongLiu JIa, XIaoXia Ma 
    Abstract: The surface defect detection of steel plates is an essential quality control process in digital manufacturing factories. Traditional inspection technologies based on image processing are challenging to give a complete solution of tiny defect scale and are time-consuming. We propose a novel convolutional neural network with multi-branch tree architecture, of which RepVgg-Tree can be used to realise the decoupling of training-time and inference-time architecture through structure reparameterisation. Each branch has a Vgg-like inference-time body, while the training-time model has a multi-branch topology. Furthermore, the Double-Tree attention mechanism is used to locate feature position, improve the model's expression ability, and reduce information loss. Also, the generalised focal loss is introduced to deal with the imbalance problem of positive samples and negative ones. Meanwhile, experimental results show that our model achieves satisfactory AP and average accuracy performance, respectively 95.85% and 95.96%, effectively detecting tiny steel surface defects on the NEU dataset.
    Keywords: defect detection; RepVgg-Tree; Double-Tree; attention mechanism; tiny defect.

  • Performance analysis of various shortest-path routing algorithms using the RYU controller in SDN   Order a copy of this article
    by Deepak Kumar, Jawahar Thakur 
    Abstract: In this paper, our objective is to establish efficient routing in SDN with low latency and high throughput. To implement this research work, we have used the mininet emulator, Abilene topology, RYU controller, and various shortest path algorithms such as Dijkstra (Dij), Extended Dijkstra (EDij), and Modified Extended Dijkstra (MEDij) and Round Robin (RR). Each algorithm runs separately, one by one, for 120 seconds. These algorithms are analysed in terms of throughput and latency as performance parameters by evaluating their average mean, geometric mean, and harmonic mean value to find which algorithm is the optimal choice among the above-discussed algorithms. The research work is novel because previous studies have implemented the shortest path algorithms using Floodlight or POX controller, but this study use Python supported RYU controller and performs better compared to previous studies This research can be extended to improve load balancing and security across nodes, and routing links using meta-heuristic algorithms such as particle swarm optimisation. There is also scope for exploring and implementing deep learning-supported QoS-based approaches for effective routing optimisation.
    Keywords: Dijkstra; latency; OpenFlow; performance; throughput.

  • Towards energy-efficient 5G heterogeneous networks through dynamic small cell zoom and sleep control algorithm   Order a copy of this article
    by Janani Natarajan, Rebekka B 
    Abstract: The tremendously escalating mobile traffic and bandwidth hungry applications is challenging the network operators to provide guaranteed quality of service (QoS) over wider coverage and effective network resource usage. One of the effective solutions is heterogeneous network (HetNet) comprising an overlay of small cells (SCs) within a macrocell coverage. For enhancement in network energy efficiency (EE), we propose a joint small cell zoom and sleep strategy. The small cell zoom technique involves load-aware adaptive power control of the SCs for optimum network power consumption through lower SBS use together with appropriate user load balance. The small cell sleep method switches the SBSs with higher interference to sleep mode, thereby improving the network capacity as well as power saving. Simulation results show an EE improvement of the proposed sleep and zoom scheme by 25%, 26% and 28%, respectively, compared with three similar benchmark schemes in the literature.
    Keywords: heterogeneous networks; small cells; energy efficiency; small cell zoom; small cell sleep; adaptive power control.

  • Application of hybrid genetic algorithm in large traffic scheduling in SDN architecture   Order a copy of this article
    by Yuhan Feng 
    Abstract: The scale of data centres and network traffic at the core of modern information service infrastructure is increasing. At present, there are many problems in network traffic management under the new architecture, such as too many large flow conflicts and low flexibility. A large flow scheduling mechanism for SDN architecture based on hybrid genetic algorithm is proposed. Based on the analysis of data centre network topology and architecture module, this paper focuses on hgsafs traffic scheduling algorithm, which uses GA algorithm to make up for the problems of SA algorithm in global search ability and local solution. The four traffic scheduling algorithms have the highest average network delay when the network load is 90%, and the four traffic scheduling algorithms have the highest average network throughput. The highest average network delay algorithm is GFE algorithm, with a value of 119.3 ms, while the highest average network throughput is hgsafs algorithm, with a value of 746.3 mbps. The proposed SDN architecture scheduling algorithm has feasibility in large traffic scheduling.
    Keywords: simulated annealing algorithm; genetic algorithm; SDN architecture; traffic scheduling.

  • Research on automatic hole-making technology of industrial robot based on Hough circle detection algorithm   Order a copy of this article
    by Wenfeng Hou 
    Abstract: With the improvement of hole-making quality requirements in the modern mechanical manufacturing industry, the traditional manual detection method is no longer applicable. The mechanical system of real-time quality control and automatic hole-making is necessary. Given this, this research combines Hough circle detection with an industrial robot automatic hole-making detection system. In the research process, by analysing the overall architecture of the automatic hole-making detection system, confirm the important position and requirements of the detection link, then apply Hough circle detection, and test its application effect through experiments. The average inaccuracy of the detection results of four types of defect is 98.0%. It shows that the application of Hough circle detection to industrial robot automatic hole-making system has high effectiveness and feasibility, and provides a feasible idea for related fields.
    Keywords: automatic drilling; Hough transform; circle detection; real-time detection.

  • Research on power system stability evaluation based on grey correlation support   Order a copy of this article
    by Kai Zhang, Yongyan Xu 
    Abstract: The promotion of wind power generation can effectively optimize China's energy structure and promote the sustainable development of economy and society. However, the application and promotion of wind power will bring a new problem, that is, it increases the complexity of power system structure and poses a certain hidden danger to the security of power grid. Therefore, the Kuramoto model is used to analyse the transient stability of power system, and a comprehensive evaluation system of power system stability is constructed from the two directions of static security and dynamic security. Finally, based on grey correlation analysis, a comprehensive evaluation model (GRA) of power system stability is constructed. The results show that the accuracy of the evaluation model reaches 97.5%. Therefore, the evaluation model can evaluate the stability of power system efficiently and accurately, and avoid large-scale blackouts caused by power system collapse.
    Keywords: grey correlation analysis; wind power; power system; stability; Kuramoto model.

  • Measurement and traceability of transmission error of gear hobbing machine tool based on gear harmonic frequency   Order a copy of this article
    by Dongya Li 
    Abstract: At present, the machining accuracy, transmission error chain error measurement and optimization design of gear hobbing machine transmission control system are facing the bottleneck of development. Based on this, this research takes the rotary device of the gear hobbing machine tool as the research object, and creatively analyses the measurement method of the transmission error of the gear hobbing machine tool. This method can obtain the variation law of the transmission error of the gear hobbing machine, and identify the transmission error type of the gear hobbing machine. The transmission error detection platform of gear hobbing machine is built, and the transmission error measurement of gear hobbing machine is completed. The maximum variation amplitude of the transmission error curve of the 1st, 6th, 15th and 156th harmonic frequencies is 11.36 angular seconds, 6.78 angular seconds, 15.63 angular seconds and 15.63 angular seconds. The transmission error of gear hobbing machine tool under different harmonic frequencies is caused by the installation eccentricity of different gears and the accumulated error of tooth pitch, but the gear B and C in the transmission chain of the hob workbench have the greatest impact on the transmission error of the hob. Compared with other methods, the proposed method has better detection accuracy. B. The transmission error accuracy of C, D, E, G, H, I, J gears exceeds 90%, while the detection accuracy of other detection methods is lower than 88%. The research results provide a new development idea and research direction for improving the machining accuracy of gear hobbing machine tools, and effectively solve the problem of transmission error traceability of gear hobbing machine tools.
    Keywords: gear hobbing machine tool; error measurement; harmonic frequency; fault traceability; gear; mathematical model.

  • Application of deepLabV3 + network model in garbage detection and classification   Order a copy of this article
    by Lixiang Shi, Guofang Liu 
    Abstract: In order to optimise the cleaning performance of the intelligent sweeping robot, the problem of garbage area segmentation and garbage detection and classification of the sweeping robot is studied and proposed by using the deepLabV3+ network and DBN model. The results show that the semantic segmentation accuracy of the deepLabV3+ model is 91.7%, and the semantic segmentation effect of the model image is good. The average detection and recognition accuracy of DBN deep LabV3+ model for various types of indoor garbage is 92.48%. The detection and recognition effect of garbage with regular shape is better. The detection and recognition rates of plastic bottles and battery garbage are 96.21% and 94.79% respectively. The proposal of the deepLabV3+ model provides a new research idea for the improvement of the intelligent level of the sweeping robot, and has certain reference value for the construction of the garbage detection, identification and classification system.
    Keywords: computer vision; deepLabV3+; refuse classification; sweeping robot; area detection.

  • Research on private desktop cloud platform based on random scheduling algorithm and load-balancing scheduling algorithm   Order a copy of this article
    by Dunkai Zhou, Jinsong Zhou 
    Abstract: With the development of computer and network technology, cloud computing, which is separated from local computing, has gradually become the first choice for users when local computing power is insufficient. In the cloud computing environment, the dynamic node changes and large-scale task requirements in the computer computing process make cloud computing resource scheduling a major problem in this field. To solve this problem, this research starts from the design requirements of the private desktop cloud, and realizes the dynamic reception of computing services, the query of computing status, and the task stealing based on the real-time load status by improving and optimizing the scheduling module. Scientific scheduling and load balancing are achieved by effectively allocating computing tasks within the limited computing power. After that, the advantages of its performance are analysed by comparing the experimental results with the actual application results. The results show that the improved scheduling algorithm performs better than other algorithms under two different processor parallel conditions of 10 and 40, and achieves small floating differences in CPU utilization, memory utilization, sending traffic, and receiving traffic in practical application, which can more balance tasks and perform calculations more efficiently. It can be seen that the improved scheduling algorithm has better performance, can meet the design requirements of the overall framework of the private cloud desktop, and has the advantages of scientific scheduling, efficient scheduling, and load balancing in task scheduling.
    Keywords: random algorithm; load balancing; cloud platform; desktop cloud.

  • An information transmission scheme based on secure QR code in IoT   Order a copy of this article
    by TingDa Shen, Feng Guo, Chuan-Kun Wu, ChangQiang Jing, Lijiao Ding 
    Abstract: At present, Quick Response (QR) codes are widely used in products, as they can provide valuable services such as product traceability and data anti-counterfeiting. However, the source code of QR code is usually publicly available, and the data in the QR code is stored in clear text. It cannot be used directly in some specific fields and applications that require confidentiality, so QR code with data encryption is preferred. At present, QR code can be encrypted by Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES) algorithms. However, those algorithms may not meet the national requirements. In addition, to avoid QR code being copied, dynamic QR code is also indispensable. This paper designs a secure QR code, which uses embedded technology and encryption algorithm SM4 (issued by the State Password Administration of China) to encrypt the information, so it can be embedded in the internet of things equipment to realise convenient secure data transmission.
    Keywords: QR Code; SM4; IoT secure; embedded system.

  • Deep reinforcement learning multi-robot cooperative scheduling based on service entity network   Order a copy of this article
    by Xueguang Jin, Chengrui Wu, Yan Yan, Yingli Liu 
    Abstract: Multi-robots are increasingly deployed with the development of automation in agriculture, industry, and warehousing logistics. With the help of CPS virtualisation technology, services or tasks can be decomposed into a network with capability or function entity nodes and edges connecting nodes. In this paper, the service entity network is extended with human, robot, and IT resources as a task-decomposed network with public entities, private entities, and links. Based on the service entity network virtualisation architecture, it is possible to form a global service entity network corresponding to the correlated tasks. Meanwhile, deep reinforcement learning multi-robot cooperative scheduling based on a service entity network framework is studied, which makes it possible to jointly optimise the deployment of multi-robot tasks with multi-service entity networks. The results show that the model based on the artificial intelligence virtualisation architecture achieves a better performance.
    Keywords: service entity network; virtualisation technology; multi-robot cooperative scheduling.

  • Identifying rice leaf diseases using an improved AlexNet model   Order a copy of this article
    by Le Yang, Xiaoyun Yu, Mingfu Liao, Shaoping Zhang, Huibin Long, Huanhuan Zhang, Yuanjun Liao 
    Abstract: In order to facilitate farmers to accurately identify rice leaf diseases, this study improves the original AlexNet model to identify eight kinds of rice leaf diseases. The original AlexNet model was improved by changing the original Relu activation function to Leakyrelu activation function, as well as removing the last convolution layer and changing the number of convolutional kernels of each original convolutional layer to 1/2 of the original. Finally, the output nodes of the full connection layer were reduced. The results show that the recognition rate of the improved model reaches 99.23%, significantly higher than that of the original, and so, a new idea for the identification of rice leaf diseases was put forward. In addition, this paper verifies the generalisation of the model by designing five comparative experiments with different activation functions, different dataset sizes, different optimizers, different batch sizes, and different learning rates.
    Keywords: rice leaves disease identification; AlexNet; deep learning.

  • Research on intelligent recognition technology of gymnastics posture based on KNN fusion DTW algorithm based on sensor technology   Order a copy of this article
    by Dong Fu 
    Abstract: In order to realise the scientific and intelligent training of gymnasts, a research on the intelligent recognition algorithm of human movement and posture in the process of gymnastics using Kinect sensor technology is proposed. Firstly, a Kinect sensor is used to obtain the basic data of human posture, and the relative distance and angle sequence of human joint points are taken as the basic characteristic parameters of posture recognition. After completing the training of the sample set, the KNN algorithm is used to recognise the gymnastics posture, match the standard target of the best angle curve, and realise the evaluation of the movement. The DTW difference is used as the experimental parameter to obtain the final action score. This time, 10 gymnasts were selected for simulation. The results of simulation analysis show that the proposed method can well realise the recognition and quantitative evaluation of gymnastic movements.
    Keywords: gymnastics; body posture; catch gymnastics; Kinect sensor; evaluation.

  • SBER: Stable and Balance Energy Routing Protocol to Enhance the Stability and Energy for WBANs   Order a copy of this article
    by Sara Raed, Salah Abdulghani Alabady 
    Abstract: Stability and reduced energy consumption are essential in the design requirements of Wireless Body Area Network (WBAN) routing protocols. For instance, many energy-efficient routing protocol solutions have been suggested for WBANs; however, the significant feature of stability in these existing solutions has not been effectively addressed. In this paper, we propose a Stable and Balance Energy Routing (SBER) protocol to improve the stability period and manage the limited power of the WBAN network efficiently. SBER consists of two solutions, namely, the next-hop node selection and adding awareness to the transmission of control packets techniques. For analysis of the performance of the SBER protocol, MATLAB has been used. The average improvements rate of the SBER in terms of network residual energy over ERRS, M-ATTEMPT, and SIMPL protocols are 35%, 52%, and 100% respectively, which proves SBER to be a more efficient and reliable approach for WBANs.
    Keywords: WBANs; stability period; routing protocol; SBER; ERRS; M-ATTEMPT; SIMPL.

  • Optimisation driven generative adversarial network for course recommendation in e-learning   Order a copy of this article
    by Jobin P. Varghese, R. Vijayakumar 
    Abstract: This research created a mechanism for course recommendation based on collaborative filtering to categorise the attitudes. Here, the positively reviewed courses are identified using sentiment categorisation using the recommended Shuffled Shepherd Bat Optimisation-based Generative Adversarial network (SSBO-based GAN). The input data is fed into a matrix creation process where the data-driven matrix form is created. For course grouping, the Enhanced Fuzzy C-means method (FCM) is used. The course matching is then completed using Canberra distance and holoentropy. The GAN classifier then does the sentiment categorisation. The Bat Algorithm (BA) and the Shuffled Shepherd Optimisation Algorithm (SSOA) are combined to create the Shuffled Shepherd Bat Optimization (SSBO), which is used to train the GAN. Positive course reviews are gleaned from categorised attitudes in this case, aiding in course selection. The suggested SSBO-based GAN displayed improved performance with an F-measure of 96.6%, a recall of 97.1%, and a precision of 96.1%.
    Keywords: collaborative filtering; enhanced fuzzy C-means algorithm; course recommendation; Canberra distance; generative adversarial network.

  • Research on fire alarm system of the intelligent building based on information fusion   Order a copy of this article
    by Sun Xuejing 
    Abstract: In order to effectively reduce the hazards caused by fire and improve the accuracy of fire alarm systems, this paper proposes to use STM32 microcontroller as the control core, use the communication method of Zigbee wireless communication technology combined with CAN bus technology, apply the QPSO-BP neural network algorithm based on multi-sensor information fusion method to fire alarm judgment, and use the fire protection partition in the building as the basis for the distributed intelligent building fire alarm system. The results show that the distributed intelligent building fire alarm system designed in this paper meets the design requirements of the system while fully considering the economic benefits and makes up for the shortcomings of the traditional fire alarm system. The algorithm output results are accurate and reliable, providing a reference for the design of building fire alarm systems.
    Keywords: intelligent building fire alarm; information fusion; QPSO-BP neural network algorithm; Zigbee technology.

  • Hand target detection based on improved YOLOv5   Order a copy of this article
    by Zhu Xu, Jinbao Meng, Juanyan Fang 
    Abstract: With the growing maturity of deep learning-based target detection algorithms, their deployment in intelligent service robots for target detection has become popular nowadays. In order to improve the precision of real-time hand detection and recognition by intelligent service robots, enabling them to detect hands accurately in a variety of environments. This paper proposes a hand detection method based on improved YOLOv5 deep convolutional neural network. YOLOv5s is selected as the base target detection model, the SE attention module is added to the network neck detection layer to guide the model to pay more attention to the channel features of small target to improve the detection performance, and the detection layer is added to enhance the feature learning ability of the network for target regions. The loss function of the detection model is optimized according to the hand image features to improve the confidence of the prediction frame. The experimental results show that the proposed hand detection method based on the improved YOLOv5 deep convolutional neural network can achieve a precision of 99.02%, which is 6.54% better than the original YOLOv5.
    Keywords: target detection; YOLOv5; convolutional neural network; hand detection.

  • A hybrid meta-heuristic algorithm to detect malicious activity based on dynamic ON VANET environmental information   Order a copy of this article
    by Gagan Preet Kour Marwah, Anuj Jain 
    Abstract: VANET has the characteristics of self-organisation, rapid topology changes, and frequent link disconnection that perhaps led to challenging issues. In order to mitigate these issues, a highly effective technology is required; therefore, this work has adopted a Hybrid Firefly Optimisation Algorithm (FOA) and a Whale Optimisation Algorithm (WOA) named as HFWOA-VANET. The HFWOA-VANET has the features of both meta-heuristic algorithms and is implemented to enhance the performance of VANET. This process is mainly based on consideration of Quality of Service (QoS) parameters of each vehicle. Therefore, the performance of vehicle can be determined and the better service in VANET platform is enabled. The implementation of this work is done on NS2 platform and the obtained results are analysed for ensuring the performance of the proposed model. Moreover, the performance of the model is compared with the existing technology; therefore, the proposed model can be ensured as a more effective technique than the existing technique in terms of performance metrics.
    Keywords: VANET; firefly optimisation algorithm; whale optimisation algorithm; QOS; QMM-VANET; HFWOA-VANET.