International Journal of Wireless and Mobile Computing (93 papers in press)
Multicast stable path routing protocol for wireless ad-hoc networks
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
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
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 field 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
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
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
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
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
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
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
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.
Performance evaluation of AODV, DSDV and DSR protocols in wireless sensor networks
by Rohin Rakheja, Sonam Khera, Neelam Turk
Abstract: In wireless sensor networks, the routing protocol and path selection are two of the most important factors when designing the network. Owing to the severe limitations on the resources available, the selected protocol should provide high energy efficiency without compromising in terms of data delivery rate, security, integrity. Hence the analysis of the characteristics of these protocols is the major step before selecting them for real-world applications. In this paper, AODV (Ad-hoc On-demand Distance Vector), DSDV (Destination Sequenced Distance Vector) and DSR (Dynamic Source Routing) protocols have been simulated using Network Simulator 2 (NS2.35) software package. Performance parameters, such as instantaneous throughput, total energy consumption of the network, residual energy of each node, packet delivery rate and average throughput, have been calculated over multiple networks of 100, 150 and 200 nodes. The simulation runs for 65 seconds and each network has two static client and sink nodes. The data traffic starts at t = 1.0 and stops at t = 61.0, where (t) is time in seconds. Graphical representation has been done with the help of data extraction and manipulation based on trace files and drawing software. Experiments reveal the characteristics and behaviour of these protocols to substantiate the conclusions.
Keywords: wireless sensor networks; OSPF; shortest paths; energy consumption.
PeneVector Marine multifunctional geological sampling/testing integrated equipment
by Wei Zhang, Qi Chen, Linqi Xia, Tao Li
Abstract: An integrated seabed geological sampling/testing system is developed for marine geological survey. The system has the functions of static pressure penetration sampling, vibration penetration sampling and in-situ test. The static pressure penetration sampling function adopts the fuzzy control algorithm to control the speed and torque of the friction wheel to ensure the synchronism of the two friction wheels. The double friction wheel holds the sampling pipe to penetrate into the stratum for sampling, which has high fidelity. In the sand layer, the sand can be liquefied and sampled by the function of vibration penetration sampling. For in situ testing, multi-functional static cone penetration is used to obtain the mechanical properties, resistivity and geothermal gradient of the seabed directly.
Keywords: geological survey; seabed type; static pressure sampling; vibration sampling; static cone penetration.
Network lifetime maximisation with low power consumption by the use of an ANFIS-based technique in wireless sensor networks
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
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
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.
A method of walking trajectory for biped robot based on Newton's interpolation
by Yingli Shu, Quande Yuan, Huazhong Li, Wende Ke
Abstract: Trajectory design of a biped robot is the premise of effective walking. By analysing the kinematics characteristics of a biped robot, Newton's interpolation is used to design the trajectory of the insertion point when planning periodic motion and realising ZMP (zero moment point) constraint. With the increase of interpolation points, the curve fitting effect is improved, and it will not lead to more expensive calculation cost, which can effectively meet the real-time requirements. Simulation results show the effectiveness of the method.
Keywords: biped robot; interpolation; trajectory; walking.
Narrowband internet of things: performance analysis of coverage enhancement in uplink transmission
by Rasveen Singh, Shilpy Agrawal, Khyati Chopra
Abstract: Narrowband Internet of Things (NB-IoT) is a wireless standard and a novel technology for the IoT devices and applications. The extended coverage, low cost, and long battery life make NB-IoT an excellent candidate for IoT applications. One of the primary goals of NB-IoT is to enhance coverage beyond the existing cellular technologies like general packet radio service and long-term evolution (GPRS and LTE). To accomplish this, the NB-IoT system utilizes a repetition technique in which the same signal is repeated several times with different subcarrier spacing in the uplink. We propose the repetition model with the optimization algorithm, i.e., moth flame optimisation (MFO). The optimisation reduces the block error rate (BLER) even in the worst channel condition, which increases the performance evaluation in a single-tone and multi-tone transmission with sufficient transmission time, eventually increasing the radio coverage. The conducted evaluation showed that signal could be recovered even in low S/N, thereby providing better coverage.
Keywords: repetition; resource unit; single-tone; multi-tone; coverage enhancement.
Performance evaluation of FBMC vs OFDM in tapped delay line doubly selective channels
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
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
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
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
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.
A self-management mobile application system for patients with mild cognitive impairment and mild dementia
by Fadi Thabtah, Arun Padmavathy, Thanh Trung Thai, Daymond Goulder-Horobin
Abstract: The projected increase in the number of dementia cases has prompted the development of an automated solution to assist patients with self-management in everyday life. This paper proposes a new mobile application (app) called DDoMate to help users with mild cognitive impairment (MC) or mild dementia, and their caregivers, to organise tasks to improve the patients quality of life. DDoMate has been developed using the latest in research design for medical mobile apps for elderly people to ensure a carefree experience without hassle. DDoMate offers easy to navigate interfaces with large fonts and a dynamic environment that enables users to record their own voices for reminders to manage tasks. DDoMate is implemented in the Android environment and is accessible through the Google App Store. Data within the proposed app is anonymous and can be further analysed using machine learning to improve the self-management characteristics of mild dementia patients.
Keywords: cognitive computation; data management; early dementia; digital informatics; mobile computing.
Energy-efficient cooperative spectrum sensing for detection of licensed users in a cognitive radio network using eigenvalue detector
by Samson Ojo, Zacheaus Adeyemo, Festus Ojo
Abstract: Spectrum Hole Detection (SHD) in a Cognitive Radio Network (CRN) is of great importance to prevent licensed users from harmful interference. However, channel impairment affects the SHD resulting in interference. The existing Cooperative Spectrum Sensing (CSS) used to solve this problem suffers from large reporting overhead, resulting in energy inefficiency. Hence, this paper proposes an Energy-Efficient CSS (EECSS) for SHD in a CRN using different Secondary Users (SUs) to carry out local sensing with eigenvalue detector. The received signals from the primary user form a square matrix to determine the ratio of maximum to minimum eigenvalue. The SUs form clusters to reduce the reporting overheads, which are combined at the Cluster Head (CH) using the majority fusion rule. The proposed technique is simulated using MATLAB software and evaluated using Probability of Detection (PD) and Sensing Time (ST). The results obtained show that EECSS gives better performance than CSS with higher PD and lower ST values.
Keywords: eigenvalue detector; probability of detection; sensing time; secondary user; primary user; cluster.
Real-time detection system of bird nests on power transmission lines based on lightweight network
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.
Targeted sentiment classification with multi-attention network
by Xiao Tian, Peiyu Liu, Zhenfang Zhu
Abstract: Targeted sentiment classification aims to recognise the sentiment polarity of specific targets. However, existing methods mainly depend on a crude attention mechanism, while neglecting the mutual effects between target and context. In order to solve this problem, this paper introduces a multi-attention network (MAN) for aspect level sentiment classification. We jointly modelled intra-level and inter-level attentional components to capture the interaction between target and context. The former attention mechanism pays attention to the context relation, whereas the latter attention mechanism considers important parts in a sentence. The experiments conducted on Laptop, Restaurant and Twitter datasets indicate that our model surpasses the baseline model.
Keywords: attention mechanism; self-attention; targeted sentiment analysis; emotion analysis; neural network.
Research on heavy truck recognition algorithm based on deep learning
by Huan Wang, Dun Zhang, Zhikai Huang
Abstract: The Chinese economy has developed rapidly by taking advantage of convenient and rapid freight transport. Heavy trucks have always attracted much attention as the leading force in freight transportation. Although heavy trucks have significant advantages in freight transportation, their high emissions cause air pollution and high accident rates, which have always been criticised. Monitoring of heavy trucks has been improving in China, and this paper adopts deep learning to identify heavy trucks to strengthen their supervision. Given the multi-target recognition problem in the actual scene, this paper uses a one-stage algorithm for target detection. The representative network SSD (Single Shot Multi-Box Detector) and YOLO (You Only Live Once) are compared. YOLO adopts the YOLOv5s structure, and the SSD network is subdivided into two networks by replacing the backbone structure. One backbone structure is VGG, and the other is Mobilenetv2. The final experimental results show that the SSD network with VGG as the backbone structure achieves the best map value of 93.24%, which is 6.02% and 8.14% higher than the SSD and YOLOv5s training models with Mobilenetv2 backbone structure, respectively.
Keywords: convolutional neural network; deep learning; target detection; heavy truck recognition.
Weld defect detection of power battery pack based on image segmentation
by Bo Tao, Fuqiang He, Quan Tang, Zhinan Guo, Hansen Long, Shidong Li, Yongcheng Cao, Guijian Ruan
Abstract: The safety and production efficiency are an important part of the power batteries production process and need to be considered seriously. Aiming at the welding quality of a power battery, a three-dimensional detection method based on the line laser sensor was proposed. Firstly, the depth data of the weld surface of the battery top cover is obtained by using a line laser sensor, and the defect area was segmented by using a multi thresholds segmentation method based on contour lines. Through the connected domain algorithm, the centres of defective areas are located. And the defect type is determined according to distance between the centres of the defect areas. Experimental results show that the detection rate reaches 97%, which indicates that the scheme has high detection accuracy and strong stability, and verifies the effectiveness of the method.
Keywords: power battery; line laser sensor; threshold segmentation; connected domain; defect classification.
Target detection and recognition method based on embedded vision
by Xiao Zhao, Qi Zou, Zhenjia Chen
Abstract: Vehicles have become an essential means of transportation in peoples daily lives. A large number of vehicles will need scientific and effective detection and management. The practical application of vehicle detection and recognition technology is imperative. The existing vehicle recognition technology is only for computer training and operation, and the application in the actual environment on the embedded platform may not achieve good results. If bad vehicle perspective or licence plate information is not detected, the effect is general. We propose a vehicle detection and recognition model for embedded platform and apply it to the actual environment. Support vector machine (SVM) uses histogram of oriented gradient (HOG) feature combined with window sliding detection to complete vehicle detection. On this basis, convolutional neural network (CNN) is used to realise licence plate recognition. Furthermore, oriented fast and rotated brief (ORB) feature extraction method is used to extract vehicle key information quickly and accurately. Licence plate information and vehicle ORB features are stored on the embedded device as the unique features of the vehicle. Moreover, ORB can be used to match the extracted information with the feature database, so as to identify the recorded vehicles. We have deployed to the embedded platform, with good timeliness, high accuracy and practical value, which can be applied to parking lot or high-speed detection port and other scenes.
Keywords: embedded vision; vehicle recognition; feature extraction.
Remaining useful life prediction for lithium-ion battery using a data-driven method
by Zhiyang Jin, Chao Fang, Jingjin Wu, Jinsong Li, Wenqian Zeng, Xiaokang Zhao
Abstract: Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is one of the key technologies in the battery management system (BMS). To boost the prediction accuracy of Li-ion battery RUL, a data-driven approach is developed, through the combination of long and short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). First and foremost, the battery capacity extracted from the National Aeronautics and Space Administration (NASA) battery data set is used as original data and the CEEMDAN is used to divide the original data into components of dissimilar frequencies. Then, the LSTM model is used to predict components of different frequencies. Finally, the CEEMDAN-LSTM prediction results are integrated to acquire the final prediction of the Li-ion battery RUL. The results show that the proposed method is superior for Li-ion battery RUL prediction.
Keywords: Li-ion battery; RUL; LSTM; CEEMDAN.
Research on remote sensing image classification method using two-stream convolutional neural network
by Kai Peng, Juan Hu, Siyu Liu, Fang Qu, Houqun Yang, Jing Chen
Abstract: Owing to the lack of remote sensing image dataset and no regional pertinence in terms of characteristics for classification, we have published the remote sensing image of some areas in Haikou City, Hainan Province, and made a HN-7 dataset, which has the regional characteristics specific of Hainan Province. The HN-7 dataset consists of seven classes, of which the construction site and dirt road categories appear in the public remote sensing dataset for the first time. Owing to the limited quantity of the HN-7 dataset, we decided to train a small convolutional neural network from scratch for the classification task, by using a three-layer two-stream network for improving the accuracy of the neural network model; our model achieved 98.57% accuracy on the test set. We compared the accuracies of four common networks trained on HN-7, and the results showed that our model achieves the best performance.
Keywords: classification method; remote sensing image; two-stream convolutional.
Apple's internetwork operating system and Google's Android in sub-Saharan Africa: the mobile internet services dimension
by Francis Osang
Abstract: We extended the technology acceptance model (TAM) to enable us carry out a comparative study of iOS and Android in the sub-Saharan Africa on mobile internet services for educational purpose in a natural end-user environment. To test the model, we conducted a survey of 180 students from a private university in Nigeria. We tested the exploratory factor analysis (EFA) on the latent variables to analyse test items measuring the constructs and the ordinary least squares multiple regression to analyse the results. We found a 75% and 36% predictive power of the model for iOS and Android, respectively. Additionally, perceived ease of use significantly influenced adoption decisions for both iOS and Android. Perceived cost, social norms and all other constructs except perceived enjoyment were supported for iOS and rejected for Android. Strategies that factors in reduced cost and ease of use must be considered if full penetration is to be achieved.
Keywords: Android; iOS; decision to adopt; mobile internet; perceived enjoyment; perceived cost; social norms; i-Phone.
Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection
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
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.
Robust zero-watermarking algorithm for medical images based on K-means and DCT
by Wenxing Zhang, Jingbing Li, Uzair Aslam Bhatti, Mengxing Huang, Jixin Ma, Cheng Zeng
Abstract: To better protect patient information in medical images and improve the security of medical image transmission, this paper studies a robust watermarking algorithm for medical images based on K-means and discrete cosine transform (DCT). Firstly, the watermark is preprocessed by chaotic encryption to make it more secure. Then use the K-means clustering algorithm to classify the grey values of the pixels in the medical image to obtain the feature image after the cluster segmentation; then use the DCT to extract the feature coefficient matrix and transform it into the feature hash sequence of the image. Finally, the zero-watermark technology is used to combine the feature hash sequence with the encrypted watermark to realize the embedding and extraction of the watermark. Experiments show that the algorithm not only can resist conventional attacks, but also has good robustness against geometric attacks.
Keywords: medical image; K-means clustering; feature vector; DCT; zero watermark.
Study of the monitoring system for double row steel sheet pile cofferdam engineering
by Jianjun Wang, Guiqin Liu
Abstract: The construction monitoring and control standard of the steel sheet pile cofferdam is still the standard of the foundation pit of civil structures. In this paper, by referring to the literature related to double-wall steel sheet pile cofferdam, the detection content, structure calculation method and error analysis of double-wall steel sheet pile cofferdam project are summarised systematically. To ensure the safety of the cofferdam structure, the automatic monitoring system of steel cofferdam is established to realise the organic combination of real-time monitoring and control of steel cofferdam, so as to take timely measures to ensure the smooth progress of the project and analyse the error which will be used to improve the model and algorithm. Finally, the calculation error of between the model and algorithm will shrink and be smaller, making the results more reliable.
Keywords: cofferdam; monitoring; safety.
Energy efficient sink relocation using whale optimisation technique in virtual grid based wireless sensor network
by A. Keerthika, Victor Berlin Hency
Abstract: Wireless Sensor Network (WSN) is an efficient network for monitoring and recording the physical environment and transfers the monitored data into the central location using widely distributed sensor nodes. One of the main problems in WSN is the issue of developing an energy-efficient routing protocol that achieves less energy consumption and enhances the lifetime of the network. During the past decades, researcher used the mobile sink to reduce the energy problem and hotspot problems. In this work, Virtual Grid-Based Energy Efficient Sink Relocation (VGESR) is proposed to solve these issues. The grid clustering is achieved by employing K-means clustering. After clustering, the Leader Node (LN) selection is done by calculating the Acceptability Factor (AF). Acceptability factor is calculated based on the nodes residual energy, available bandwidth and Received Signal Strength (RSS). Whale Optimisation (WOA) technique is employed for the optimal sink relocation based on the fitness value of the nodes. The results obtained from the simulation prove that the proposed VGESR performs well in terms of life span and energy use. The proposed VGESR simulation is performed with the omnet++ tool.
Keywords: acceptability factor; clustering; network lifetime; sink relocation; whale optimisation; wireless sensor networks.
Direction-of-arrival estimation for partially polarised signals with switch-based multi-polarised uniform linear array
by Yujian Pan, Jingke Zhang, Zongfeng Qi
Abstract: In this paper, a new switch-based multi-polarised receiver architecture and two compatible direction-of-arrival (DOA) estimation algorithms are proposed for the partially polarised signals. In the receiver, each polarised element in an antenna is connected to a common radio frequency (RF) chain via a switch, which reduces the number of RF chains. For DOA estimation, an ESPRIT-based algorithm and a joint annihilation-based algorithm are proposed. The ESPRIT-based algorithm is based on summing the covariance matrices of different polarized outputs, and the joint annihilation-based algorithm is based on annihilating different polarized outputs by a common filter. Compared with other algorithms, the ESPRIT-based algorithm, which only takes about 91 us to perform one estimation, is more efficient, and the joint annihilation-based algorithm, which can approach the Cramer-Rao lower bound (CRLB), is more accurate. It is also concluded that the tri-polarised uniform linear array (ULA) can offer more accurate estimation than the dual-polarised ULA.
Keywords: direction-of-arrival estimation; ESPRIT; joint annihilation; multi-polarised array; partially polarised signal.
Computation offloading using K nearest neighbour time critical optimisation algorithm in fog computing
by Ashwini Kumar Jha, Minal Patel, Tanmay Pawar
Abstract: The wide range of IoT devices and wireless devices used in health care, hospitals, and the enterprise generates a large volume of digital data that must be processed, analysed, and stored. Owing to the small processing capacity of these devices, the data generated cannot be processed on-board. Therefore, we suggest offloading this data to an efficient server. Time-critical applications cannot rely on the availability of cloud servers since they are in a remote location. The paper examines algorithms such as Deep Reinforcement Learning for Online Computation Offloading (DROO), Coordinate Descent, Adaptive boosting, and then implements the K nearest neighbour time critical optimization algorithm as a fog offloading network topology. The offloading decision is based on the cost function, which includes latency, memory consumption, and model accuracy. The topology implementing KNN can be trained quickly and offers almost 99% accuracy when it comes to data offloading. Based on the comparative analysis, it excels over other machine learning approaches.
Keywords: fog computing; edge computing; computation offloading; cloud computing; K-nearest neighbour.
A study on dual-sense broadband circularly polarised monopole antenna for UWB applications
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;.
Pattern recognition of surface electromyography based on multi-scale convolutional neural network with attention mechanism
by Beibei Wang, Hui Zheng, Jing Jie, Miao Zhang, Yintao Ke, Yang Liu
Abstract: Natural control methods based on surface electromyography (sEMG) pattern recognition have been widely applied in the field of hand prostheses. However, the control robustness and accuracy are difficult to meet many real-life applications. This paper proposes a multi-scale convolutional neural network (MSCNN) model based on the attention mechanism, which can automatically learn gesture features through convolution. The model generates features through convolution kernels of different sizes to achieve the fusion of features of different degrees firstly. After that, the attention mechanism is used to calculate the weights of different scales, and then the fused comprehensive features are obtained. The proposed model has been verified on the SIA_delsys_16_movement and NinaPro datasets. The experimental results showed that the proposed model has better classification accuracy, and the attention mechanism can validly improve the classification performance of the convolutional neural network.
Keywords: surface electromyography; convolutional neural network; gesture recognition; machine learning; attention mechanism.
Hardware implementation of approximate multipliers for signal processing applications
by Elango Konguvel, I. Hariharan, R. Sujatha, M. Kannan
Abstract: Multiplication is a complex and substantial arithmetic task involved in signal processing applications. The hardware complexity of the multiplier is always high when compared with any other arithmetic operation. Approximate multiplication is a common operation used in many signal processing applications for improved performance and low power computation. The proposed approximate multiplier design is based on the approximate 4-2 compressor and self-error recovery technique. A small modification of the truth table entries in the approximate 42 compressor shows performance improvement at a cost of small accuracy. The designed multiplier promises to have improved performance when compared with the earlier approximate designs. The computational errors arising because of this multiplication approximation can be considered as trade-off for the significant gains in power and area.
Keywords: approximate computing; adders; multipliers; hardware; error analysis; VLSI design.
Fuzzy Borda combined model in small town sewage treatment process Alternative Selection
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.
Segmentation of lung parenchyma based on a new U-NET network
by Cheng Liying, Jiang Longtao, Wang Xiaowei, Liu Zuchen, Zhao Shuai
Abstract: In this paper, the U-NET network was selected as the basic segmentation model. It was found in the experiment that the segmentation accuracy of U-NET for upper lung and lower lung parenchyma was low. In view of this phenomenon, a new network model, New U-NET, was proposed. It adds input images of the same depth and corresponding input images of different depths as additional information and directly adds them to the result of deconvolution, so that the network can obtain more feature information in the decoding process, and the original information will be retained completely. Experimental data show that the proposed New U-NET network model solves the problem of low segmentation accuracy of the original U-NET network segmentation model at both ends of lung, improves the segmentation accuracy of lung parenchyma on the whole, and verifies that the New U-NET network model is more suitable for parenchyma segmentation.
Keywords: New U-NET; lung parenchymal segmentation; CT images of lung; deep learning.
DstNet: deep spatial-temporal network for real-time action recognition and localisation in untrimmed video
by Zhi Liu, Junting Li, Xian Wang
Abstract: Action recognition is a hot research direction of computer vision. How to deal with human action in untrimmed video in real time is a very significant challenge. It can be widely used in fields such as real-time monitoring. In this paper, we propose an end-to-end Deep Spatial-Temporal Network (DstNet) for action recognition and localization. First of all, the untrimmed video is clipped into segments with fixed length. Then the Convolutional 3 Dimension (C3D) network is used to extract highly dimensional features for each segment. Finally, the extracted feature sequences of several continual segments are input into Long Short-Term Memory (LSTM) network to find the intrinsic relationship among clipped segments to take action recognition and localization simultaneously in the untrimmed video. While maintaining good accuracy, our network has the function of real-time video processing, and has achieved good results in the standard evaluation performance of THUMOS14.
Keywords: action recognition; action localisation; LSTM; C3D; untrimmed video.
Robust zero-watermarking algorithm for medical images based on Hadamard-DWT-DCT
by Mingshuai Sheng
Abstract: As the IT industry grows rapidly, information security is particularly important. Digital development is a double-edged sword for the medical field, which not only brings convenience to patients and doctors, but also has hidden dangers for information security. We propose a Hadamard-DWT-DCT-based zero watermark algorithm for medical images for the problem of privacy information leakage when medical images are transmitted on the Internet. First, the raw medical image is chunked using the Hadamard transformation and produces a coefficient matrix, which is then transformed into a wavelet coefficient matrix, which can be efficiently compressed and stored. The wavelet coefficient matrix is then DCT-transformed to finally obtain the eigenvector. Experimental results show that the proposed algorithm is not only highly robust, but the watermark can still be effectively extracted against different degrees of geometric and conventional attack interference, but also can encrypt the patient privacy information contained in the medical image.
Keywords: Hadamard-DWT-DCT; invisibility; medical images; robustness; zero watermark.
Fast retrieval of similar images of pulmonary nodules based on deep multi-index hashing
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
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
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
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
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
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
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
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.
Research on a safety evaluation method based on ANP-fuzzy decision for coal mine ventilation system
by Hongjuan Cai, Hengqiang Gao
Abstract: Aiming at the ambiguity and randomness of the indicators in the comprehensive evaluation of the safety of the mine ventilation system, a two-level fuzzy comprehensive evaluation model of safety is established. The network analytic method (ANP) is used to calculate the indicators which have enhanced the objectivity and scientificity of the safety evaluation of the mine ventilation system. According to the model, the results showed that the safety level of the mine was only 'general safety'. Through single-factor evaluation and analysis of the weights of various indicators, the system can be based on the employees' physical fatigue (A3), employees' attendance (A7), volume fraction of gas and toxic gas (C3), dust mass concentration (C5), air volume supply-demand ratio (C7), ventilation system hole volume ratio (C8), and regular inspection (B1) to improve the safety of the mine ventilation system.
Keywords: ANP; fuzzy comprehensive decision; coal mine ventilation system; safety evaluation.
Technology adoption of enablers of 5G networks for m-learning: an analysis with interpretive structural modelling and MICMAC
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.
A novel primary user detection using OFDM internal structures on Raspberry Pi
by Mobin Alizadeh, Javad Kazemitabar
Abstract: Using autocorrelation-based techniques for detecting OFDM signals in cognitive radio systems is well studied. Correlating the cyclic prefix with its replica provides a means to distinguish an OFDM signal from noise as shown in previous research. A subtle yet crucial shortcoming of this autocorrelation based method is that it may mistake a sinusoidal for an OFDM signal. All for that to happen is for the sinusoid to have the proper period; the algorithm would then find a repeating pattern and declare OFDM signal detection. In this paper, we modify the conventional autocorrelation based method to avoid generating false-alarms in the presence of sinusoidal signals. We test our algorithm on a custom-built Raspberry Pi.
Keywords: cognitive radio; primary user detection; OFDM; spectrum sensing; Raspberry Pi.
An enhanced genetic algorithm for computation task offloading in MEC scenario
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.
A review of wireless channel estimation techniques: challenges and solutions
by M.N. Drakshayini, Manjunath R. Kounte
Abstract: In wireless communication, the transmitted signal is subject to distortion, noise, frequency shift, non-linear attenuation, fading, and so on, owing to the inherent nature of the physical characteristics of the channel. To compensate for these impairments, efficient and accurate channel estimation is an imperative requirement. In this review, channel estimation techniques available in the literature are selectively identified, analysed, and evaluated. Channel estimation methods can be broadly classified into two major divisions, model-based and deep learning based. Model-based methods strive for block-wise optimisation. On the contrary, deep learning based methods provide end-to-end optimisation irrespective of variations in the channel characteristics. The main objective is to reduce the computational overhead while improving the accuracy of the channel estimation under a diverse transmission and propagation environment. In this paper, we review the contributions of various authors in dealing with channel estimation for the application of deep learning techniques in channel estimation.
Keywords: deep learning; convolutional neural network; channel state information; channel estimation.
Construction of comprehensive evaluation model of the CPA multi-criteria decision-making for location of charging pile
by Zixia Chen, Wang Meng, Fanlong Zeng, Hanmin Zhu
Abstract: Based on the advantages of subjective and objective weighting methods, this paper constructs a combination optimisation weighting model of G1-CRITIC to solve the multi-criteria decision-making problem of electric vehicle charging pile. The solution process is a comprehensive evaluation process based on the combination of the G1 algorithm of probability language, the Entropy Weight algorithm and the CPA algorithm. In order to validate the applicability of the proposed charging pile location decision-making model, which is based on the construction of the location index system, this paper uses the MATLAB software to generate the simulation data for four alternative location problems. Relevant experts are invited to adopt the comprehensive evaluation method designed in this paper is used to make a decision on the location of electric vehicle charging pile. The results of this study show that the decision-making method can obviously improve the decision-making level of multi-criteria decision-making problems.
Keywords: location problem of charging pile; multi-criteria decision-making; comprehensive evaluation; model and algorithm.
Maximum ladle shell temperature prediction based on GABP neural network
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
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++
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
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
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 statistical model-checking approach to analyse the random access protocol
by Ahmed Roumane, Bouabdellah Kechar
Abstract: Mobile cellular networks are becoming the most important technology in the telecom industry, this made them a preferred subject for research and development of new hardware and software systems. In order to check the validity of these systems, one can use either a simulation or formal methods. Recently, new emerging methods have been proposed as alternative solutions, such as Statistical Model Checking (SMC). In this paper, we present a comprehensive framework based on SMC that could be used to analyse the cellular network protocol RAP (Random-Access Procedure), by using UPPAAL. We model the system using a simplified network of timed automata, we check the validity of our model by running some concrete simulations and after that we perform a formal verification of some properties of the protocol. Finally, the statistical approach, SMC, is used to study the performance of the system.
Keywords: mobile network; cellular network; formal verification; model checking; statistical model checking; random access procedure.
A Q-learning approach for adjusting CWS and TxOP in LAA for Wi-Fi and LAA coexisting networks
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
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
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
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
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
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
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.
A survey on trends on mobile app development and applications
by Mazen Lahham, Hussein Hazimeh, Mohammad Malli
Abstract: The widespread use of smartphones and the increasing demand for mobile users to have similar desktop functionality and performance, have resulted in better and faster mobile innovation technology. We present, in this survey, an overview of the body of the literature that deals with the latest emerging mobile technologies and examines its impact on the current mobile app ecosystem. To the best of our knowledge, this is the first work that combines the most significant mobile advancements while showing the futuristic potentials and challenges faced by the mobile app industry. Therefore, the paper defines the criteria for selecting these latest mobile developments since it cannot incorporate all of them owing to the magnitude of the subject.
Keywords: mobile app development; smartphone applications.
Research on power distribution control of parallel microgrid based on adaptive capacitor algorithm
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
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
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
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
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
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
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
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
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
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
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
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
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.
ORDERING PARALLEL SUCCESSIVE INTERFERENCE CANCELLATION MECHANISM TO MITIGATE ICI INTERFERENCE AT DOWNLINK OF THE LTE-A HETNET
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
Keywords: high mobility; Doppler spread; ICI; SIC; LTE-A.
Intelligent layout design of building damping structure based on ramp model
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
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
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
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
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.