International Journal of Intelligent Systems Technologies and Applications (25 papers in press)
Detection of Glaucoma based on Cup-to-Disc Ratio using Fundus Images
by Imran Qureshi, Muhammad Attique, Muhammad Sharif, Tanzila Saba
Abstract: Glaucoma is a permanent damage of optic nerves which cause of partial or complete visual loss. This work presents a glaucoma detection scheme by measuring CDR from fundus photographs. The proposed system consists of image acquisition, feature extraction and glaucoma assessment steps. Image acquisition discusses the transformation of a RGB fundus image into grey form and enhancing the contrast of fundus features. While, boundary of optic disc and cup were segmented in feature extraction step. Finally, a cup-to-disc ratio of an exploited image will compute to assess glaucoma in the image. The proposed system is tested on 398 fundus images from four publicly available datasets, obtaining an average value of sensitivity 90.6%, specificity 97% and accuracy 96.1% in glaucoma diagnosis. The achieved results show the suitability of proposed art for glaucoma detection.
Keywords: Cup-to-disc ratio (CDR); fundus images; glaucoma; image processing; optic disc; segmentation.
DESIGN OF GENERALIZED PREDICTIVE CONTROLLER FOR DYNAMIC POSITIONING SYSTEM OF SURFACE SHIPS
by Werneld Ngongi, Jialu Du, Rui Wang
Abstract: This paper presents a generalized predictive control algorithm (GPCA) for ship dynamic positioning (DP) controller using Controlled Autoregressive Integral Moving Average (CARIMA) model to describe the controlled object. The proposed control system is capable of making position and heading of the ship converge to the desired values by choosing the error correction coefficient, parameter adaptation and feedback correction techniques. Firstly, the basic principle of the generalized predictive control algorithm is introduced. Secondly, the generalized predictive control algorithm is used to design the ship dynamic positioning controller. Finally, the simulation of the designed controller is given. Simulation results prove the effectiveness and robustness of the controller.
Keywords: Dynamic positioning; Generalized Predictive Controller; feedback correction; Rolling Optimization; performance index; surface ships.
Meta-Heuristic Techniques for Path Planning: Recent Trends and Advancements
by Monica Sood, Vinod Kumar Panchal
Abstract: Path planning is a propitious research domain with extensive application areas. It is the procedure to construct a collision-free path from specified source to destination point. Earlier, classical techniques were widely implemented to solve path planning problems. Classical techniques are very easy to implement but they are time-consuming and are not effective in case of uncertainties. But meta-heuristic techniques have the ability even to perform in an approximate and uncertain environment. This makes the use of meta-heuristic techniques in a more focused manner for the optimal path planning research. This paper presents the Overview, recent trends and advancement from year 2001 to 2017 in the field of optimal path planning using meta-heuristic techniques. During the study, different meta-heuristic algorithms are analyzed and classified into three categories: swarm based meta-heuristic techniques, other than swarm based techniques and combinational meta-heuristic techniques. In addition, basic understanding and applicability of specific algorithms for path planning are also discussed along with its strengths and downsides.
Keywords: Path Planning; Meta-Heuristic Techniques; Optimization; Swarm Intelligence; Artificial Intelligence; Machine Learning; Computational Intelligence.
A Novel and Improved Developer Rank Algorithm for Bug Assignment
by Asmita Yadav, Sandeep Kumar Singh
Abstract: Analytical studies on automatic bug triaging approach have the main objective to recommend appropriate developer for bug report with reduced bug tossing length, time and effort in bug resolution. In bug triaging process, if the first recommended developer cannot fix a bug, it is tossed to another developer and the tossing process is continued till the bug gets assigned and resolved. Existing approaches to the best of our knowledge have not considered developers contributions and performance assessment metrics for bug triaging process. In this paper, we proposed a novel and improved two phase Bug Triager that involves a developer profile creation and assignment phases. In this, developer profile is built by using individual contributions (IC) and performance assessment (PA) metrics. Contribution and performance of a developer in pre-fixed bug reports are analyzed to calculate a developers weighted score. This score indicates the level of expertise to fix and resolve a newly reported bug. This approach is tested on two open source projects- Eclipse and Mozilla. Empirical results show that proposed approach has achieved a significantly higher F-score up to 90% for both projects and has effectively reduced bug tossing length up to 11.8% as compared to existing approaches.
Keywords: Bug Repository; Bug Triaging; Developer’ Expertise; Bug Assignment; Bug Reports; Bug Tossing; Developer Contribution Assessment.
Biased Face Patching Approach for Age Invariant Face Recognition using Convolutional Neural Network
by Mrudula Nimbarte, Kishor K. Bhoyar
Abstract: In recent years, a lot of interest is observed among researchers, in the domain of age invariant face recognition. The growing research interest is due to its commercial applications in many real-world scenarios. Many researchers have proposed innovative approaches to solve this problem, but still there is a significant gap. In this paper, we propose a novel technique to fill in the gap, where instead of using a whole face of a person, we use horizontal and vertical face patches. Two different feature vectors are obtained from these patches using Convolutional Neural Networks (CNN). Then fusion of these two feature vectors is done using weighted average of features of both patches. Lastly, SVM is used as a classifier on the fused vector. Two publicly available datasets, FGNET and MORPH (Album 2) are used for testing the performance of the system. This novel approach outperforms the other contemporary approaches with very good Rank-1 recognition rate, on both datasets.
Keywords: Face Recognition; AIFR; Aging Model; Deep Learning; CNN; Weighted Average.
Automatic Sizing of CMOS based Analog Circuits Using Cuckoo Search Algorithm
by Pankaj Prajapati
Abstract: The increasing complexity of physical models ofMOSFETand process
variations with downscaling of CMOS technology have made the manual design
of analog circuits challenging and time-consuming. Therefore, development of
efficient automatic analog circuit design techniques looks very attractive. In this
work, the Cuckoo Search (CS) algorithm has been tested for the optimum design
of CMOS based analog circuits with high optimization fitness. The CS algorithm
has been implemented using C language and interfaced with Ng-spice circuit
simulator. In this work, the CS algorithm has been used as a searching tool
for transistor sizing and Ng-spice has been used as a fitness creator. Various
analog circuits like CMOS common-source amplifier, CMOS cascode amplifier
and CMOS differential amplifier using a current mirror load have been optimized
using this automatic optimization tool with BSIM3v3MOSFETmodels using 180
nm CMOS technology. This technique gives more accurate results and consumes
less time as compared to manual circuit design.
Keywords: Cuckoo Search algorithm; Optimization; Fitness; Simulator ;
Product Service Model Constructing Method for intelligent home based on Positive Creative Design Thinking
by Weiwei Wang, Ting Wei
Abstract: With the booming market economy, companies need to maintain competitive advantage through positive and innovative design thinking. Building a service model is an indispensable part of this approach. The design aims to improve the competitiveness of the enterprise by extracting effective user value, establishing a product service system, satisfying the user's needs, and analyzing the method of extracting the shape. In this paper, the researcher first selects the target user, draws the user journey map, analyzes the user's psychological activities, and uses the innovative design thinking to extract the user value. Secondly, according to the positive value element, the human-object three-dimensional ecological circle is created, at the same time, using AHP Hierarchical Analysis software to analyze product modeling and build the product service model. Finally, the reliability of the model construction method was verified by the intelligent air-housekeeper product service system, and the requirements to meet the needs of users were met. At the same time, it can also provide certain reference for other product service design, reflecting the market competitive advantage of the product.
Keywords: Product Service Model; User Value; Positive Creative Design Thinking; Intelligent Air-housekeeper Product Service.
Local and Global Features Fusion to estimate Expression Invariant Human Age
by Subhash Chand Agrawal, Anand Singh Jalal, Rajesh Kumar Tripathi
Abstract: Human beings can easily estimate the age or age group of a person from a facial image where as this capability is not prominent in machines. This problem becomes more complex in the presence of facial expressions and due to age progression. In this paper, we introduced a novel method for age prediction using combination of local and global features. After detecting the face from image, we partition the facial image in 16*16 non-overlapping blocks and apply Grey-Level Co-Occurrence matrix (GLCM) on these blocks. After calculating four facial parts (Eyes, forehead, left cheek and right cheek) from facial image, features from second local feature Gabor filter are obtained. Global feature, Histogram of Oriented Gradients (HOG) is used to extract features from complete face image. Experimental results show that fusion of local and global features perform better than existing approaches and reported 6.31years mean absolute error (MAE) on PAL dataset.
Keywords: GLCM; Local feature; Global feature; Facial Expression.
Improving English-Arabic statistical machine translation with morpho-syntactic and semantic word class
by Ines Turki
Abstract: In this paper, we present a new method for the extraction and integrating of morpho-syntactic and semantic word classes in a Statistical Machine Translation (SMT) context to improve the quality of English-Arabic translation. It can be applied across different statistical machine translations and with languages that have complicated morphological paradigms. In our method, we first identify morpho-syntactic word classes to build up our statistical language model. Then, we apply a semantic word clustering algorithm for English. The obtained semantic word classes are projected from the English side to the featured Arabic side. This projection is based on
available word alignment provided by the alignment step using GIZA++ tool. Finally, we apply a new process to incorporate semantic classes in order to improve the SMT quality. We show its efficacy on small and larger English to Arabic translation tasks. The experimental results show that introducing morpho-syntactic and semantic word classes achieves 7.7 % of relative improvement on the BLEU score.
Keywords: Morpho-syntactic word classes; semantic word classes; alignment; Statistical machine translation.
A QoS-aware virtual resource pricing service based on game theory in federated clouds
by Tienan Zhang
Abstract: Recently, federated cloud platform has become a promising paradigm to provide cloud services for various kinds of users in a distributed manner. To compete for cloud users, it is critically important for each cloud provider to select an optimal price that best corresponds to their service qualities as well as remains attractive to cloud users. In this paper, we first formulate the pricing strategy of individual cloud provider as a constrained optimization programming problem to analyze the behaviours of both cloud users and cloud providers. Then, we present game-based model which introduces a set of virtual resource agents to help providers adjusting their prices with aiming at achieving a global optimal solution. Theoretical analysis is present to prove the validity and effectiveness of the proposed game model, and extensive experiments are conducted in a real-world cloud platform to evaluate its performance. The experimental results show that the proposed pricing model can significantly improve the resource revenue for cloud providers and provide desirable quality-of-service (QoS) for user tasks in terms of various performance metrics.
Keywords: cloud computing; pricing strategy; virtual machine; quality-of-service.
On Collaborative Filtering Model Optimized with Multi-Item Attribute Information Space for Enhanced Recommendation Accuracy
by Folasade Isinkaye, Yetunde Folajimi, Adesesan Adeyemo
Abstract: Recommender system is a type of information filtering system that is designed to curtail the difficulties of information overload by automatically suggesting relevant items to users tailored to their preferences. Bayesian Personalized Smart Linear Methods (BPRSLIM) is a variant of item-based collaborative filtering technique used in information filtering system. Although, this algorithm has shown outstanding performance in a range of applications, nevertheless it suffers serious limitation of inability to provide accurate and reliable recommendations when the user-item matrix contains insufficient rating information, this always reduces its accuracy. In this paper, we propose a framework that integrates multi-item attribute information besides the classic information of users and items into BPRSLIM model in order to ease the sparsity problem associated with it and hence improves its performance accuracy. The enhanced model is expected to outperform the original BPRSLIM model
Keywords: BPRSLIM; Sparsity Problem; Recommender System; Collaborative Filtering; Item Attribute Information; Optimization.
Image Matching Technique Based on SURF Descriptors for Offline Handwritten Arabic Word Segmentation
by Maamar Kef, Leila Chergui
Abstract: Image matching is an important task with many applications in computer vision and robotics. Recently, several scale-invariant features have been proposed in the literature and one of them is the local descriptors namely Speeded-Up Robust Features (SURF). Those features are scale and rotation-invariant descriptor, and have the advantage to being calculated quickly and efficiently. In this paper we presents a new segmentation system of handwritten Arabic words based on SURF descriptors. Firstly, a set of Arabic characters images were used to build 106 characters' patterns, which are used by a segmentation process based on an image matching technique. Tests were performed on our new databese of handwritten Arabic words. A high correct segmentation rate was reported.
Keywords: Image Matching; SURF Descriptors; Arabic Handwriting Recognition; Keypoints; Segmentation.
Improved motorized spindle control using a biogeography-based optimization algorithm
by Yuhou Wu, Zhen Ning Pan, LiXiu Zhang
Abstract: The control performance of motorized spindle affects the development of equipment manufacturing technology. The control theory, represented by direct torque control (DTC), is applied to the control of motorized spindle, which makes more intelligent algorithms applied to the control of motorized spindle. The biogeography-based optimization (BBO) algorithm is introduced to estimate the equivalent stator resistance in the classical DTC method. The random disturbance operator is added to the algorithm because it can enhance the exploitation ability by maintaining the original exploration ability and can effectively identify the equivalent stator resistance to obtain an accurate estimation of the stator flux and improve the control performance of the DTC. The paper designs a new BBO-DTC algorithm according to the characteristics of the BBO algorithm. Using the classical DTC, BBO-DTC and ANN-CBR (artificial neural networks and case based reasoning) algorithms to simulate the online identification of stator resistance, the results show that the BBO-DTC algorithm has higher accuracy in identifying the equivalent stator resistance, which verifies the effectiveness of the algorithm. BBO-DTC algorithm achieves the purpose of suppressing current harmonics and reducing torque by improving the online identification accuracy of the stator resistance of the motorized spindle, thus improving the performance of the motorized spindle control system. The DSP (digital signal processing) and IGBT (insulated gate bipolar transistor) modules are used to establish the hardware of the experimental circuit. The results show that the vibrational speed of the motorized spindle is significantly reduced at each operating frequency after using the BBO-DTC algorithm. The torque ripple of the motorized spindle is obviously reduced, especially at 150 Hz and from 450~600 Hz. Additionally, the fluctuation in the no-load relative input torque is also reduced, and the torque takes only 0.03 s to reach a relatively stable state. Analyses of the electromagnetic vibration speed of the motorized spindle and the experimental input torque data indicate that the control performance of the BBO-DTC algorithm is improved. Not only is the dynamic performance of the system improved, but variations in the electromagnetic flux and the electromagnetic torque are also reduced. The control performance of motorized spindle is effectively improved.
Keywords: DTC; BBO; BBO-DTC; Migration operator; Perturbation operator; Stator resistance; Stator flux.
Deploying NSBA algorithm for Bi-Objective Manufacturing Cells Considering Percentage Utilization of Machines
by Tamal Ghosh, Kristian Martinsen
Abstract: Percentage Utilization of Machines is considered as an important production factor for manufacturing Cell Formation Problem (CFP) in Cellular Manufacturing (CM). This recently developed concept correctly emphasize ration data in context of CM. In this paper, a utilization based bi-objective mathematical model is developed, which minimizes the total machine utilization induced by bottleneck machines and number of voids. Thereafter, a new data-generating algorithm is introduced. The abovementioned bi-objective CFP is solved using a Non-Dominated Sorting Bat Algorithm (NSBA), which is compared with published Multi-Objective Bat Algorithm (MOBA) successfully. Statistical tests are conducted and data consistency is confirmed on obtained results. The computational experiments depict that the Pareto solutions of NSBA are 35.7% improved. The contribution of this research is threefold. First, an accurate bi-objective mathematical expression is developed for utilization based CFPs. Second, a novel data-generating algorithm is stated. Third, NSBA technique is successfully tested.
Keywords: Machine Utilization Percentage; Cellular Manufacturing; NSBA; Bi-Objective Mathematical Model.
Application of Artificial Intelligence to Wind Power Generation: Modeling, Control and Fault Detection
by Hadjira BOUAZZA
Abstract: Power converters play a key-role in the grid-integration of wind power generation and as any physical device, they are prone to mal function and failure. There is, therefore, a need for converter health monitoring and fault detection to ensure a reliable and sustainable operation of the wind turbine. This paper presents different artificial intelligence-based fault detection using fuzzy and neuro-fuzzy techniques. The proposed methods are designed for the detection of one or two open-circuit fault in the power switches of the rotor side converter (RSC) of a doubly-fed induction generation (DFIG) wind energy conversion system (WECS). In the proposed detection method only the average values of the three-phase rotor current are used to identify the faulty switch. Alongside these condition monitoring strategies, the paper also present two fuzzy logic-based controllers for the regulation of the real and reactive power flow between the grid and the converter. The performances of the controllers are evaluated under different operating conditions of the power system and the reliability, feasibility and the effectiveness of the proposed fault detection have been verified under various open-switch fault conditions.
Keywords: Wind energy; DFIG; MPPT; fault detection; open-switch fault; type 2 fuzzy logic; ANFIS.
GEOSS: An Intelligent Methodology for Identifying Site Suitability of Air Sample Collection
by Kamonasish Mistry, Biplab Biswas, Siwen Zhang, Tao Wu, Liang Zhou, Abdelfettah Benchrif, Srimanta Gupta
Abstract: Epistemology of Air Pollution (AP) has well known through numerous researches and a few literature has proved that AP level changes with changes in land use land cover (LULC) types. However, there is no such attempt to develop any common methodology or model for optimum sampling which can be correlate between LULC types and changes with the AP level and changes. A pre-planned, well-calculated Geospatial method is an ultimate need to evaluate the ambient AP level, type and its variation over different LULC types. GEOSS (Geospatial Estimation of Optimum Sample Site) has been innovated to identify the optimum AP sampling sites so that it can represent the wide spatial coverage over varied LULC types. Image processing using geospatial techniques and statistical tools have used to select the optimum location of sampling. While few studies have collected AP samples from the field mostly used random sampling method, which always do not reflect all the LULC types, and most often, they are clustered in distribution, GEOSS has tried to overcome the major issues of random sampling with giving emphasis on geospatial techniques to select the optimum location of sites for sample collection. Validation approach based on Nearest Neighbour Analysis has justified that GEOSS employed sampling points are distributed that is more systematic and are fulfilled all the basic assumptions of the present sampling procedure.
Keywords: Geospatial Modelling; Optimum location; Land use land cover; Kolkata Metropolitan Area; Air pollution level and change; Sampling techniques.
Study of the Fractal Nature of Evapotranspiration Time Series from Agricultural Regions of Northern Karnataka
by Uttam Patil, Nandini Sidnal
Abstract: Multifractal detrended fluctuation analysis (MFDFA) renders valuablerninsights pertaining to the randomness, inner regularity and long range correlations in the time series data. We applied this technique to assess the fractal behavior and inherent correlations of the potential and reference crop evapotranspiration data collected from two regions of Karnataka, viz., Belgaum and Raichur. The annual periodic nature of thernevapotranspiration data series was removed using seasonal trend decomposition method and it was seen that all the decomposed series consisted of long-term persistence. The multifractal behvaior in the evapotranspiration series at the two stations were seen owing to the strong dependency of generalized Hurst exponent H(q) on the values of q. We also tested for the reduction in the dependence of h(q) on q by shuffling thernevapotranspiration time series and it is indicative of the fact that the multifractality is responsible for the correlation characteristics and also the probability density function of the evapotranspiration data series. The two regions, Belgaum and Raichur vary considerably in climatic and geomorphic conditions and this difference is evident in their corresponding evapotranspiration data as fractal properties. This is depicted by the values of the generalized Hurst exponent H(q) obtained using MFDFA technique.
Keywords: Agriculture; Evapotranspiration; MFDFA.
Special Issue on: Recent Advancements in Autonomous Devices for Real-World Applications
Improving Network Lifetime and Speed for 6LoWPAN Networks Using Machine Learning
by Shubhangi Kharche, Sanjay Pawar
Abstract: Wireless communication networks have an inherent optimization issue of effectively routing data between nodes. This issue is multi-objective in nature, and covers optimization of routing speed, the network lifetime, packet delivery ratio and overall network throughput. In this paper, a machine learning (ML) based algorithm is proposed for minimizing the network delay and increasing network lifetime for 6LoWPAN networks based on RPL routing. The ML based approach is compared with normal RPL routing in order to check the performance of the system when compared to recent routing protocols. It is observed that the proposed machine learning based approach reduces the network delay by more than 20% and improves the network lifetime by more than 25% when compared to RPL based 6LoWPAN networks. The machine learning approach also takes into account the link quality between the nodes, thereby improving the overall QoS of the communication system by selecting paths with minimal delay, minimal energy consumption and maximum link quality.
Keywords: Machine learning; 6LoWPAN; RPL; Feedback mechanism; Artificial Intelligence;.
SMART AIRPORT MANAGEMENT AND FLIGHT SERVICE DELAY PREDICTION USING LINEAR REGRESSION TECHNIQUE
by D. Haripriya, S. Ramyasree
Abstract: In airports, there is a possibility of individual entry using fake tickets to involve in crime activities. The baggage mislaid and delay of flight service may lead to dissatisfaction among the passengers. The fake ticket identification, avoiding mislaid baggage and flight service delay notification are focused in this paper as existing systems are not attending all the issues together. An automated system is designed to increase the customer facilities in terms of flight delay notification, baggage mislaid alert and ticket bookings. The Baggage are handled using RFID tags whereas fake tickets are identified through QR scanning. The flight delays are predicted using machine learning based linear regression technique with 97% accuracy. Mobile app is developed for ticket booking to improve the passenger facilitation to a greater extent and web app is developed for airport management to verify the fake ticket. The proposed airport management system makes the air travel more customer friendly with high security.
Keywords: Linear regression technique; RFID; QR code; Machine learning; Airport management.
Design of BTI Sensor Based Improved SRAM for Mobile Computing Applications
by Kumar Neeraj, J.K. Das, Hari Shanker Srivastava
Abstract: Reliability of electronic components is the major concern as the CMOS technology is scaled down especially in mobile computing applications of MPEG video processor design. Scaling CMOS technology leads to increase in power density per unit area in an exponentially manner. BTI is one of the serious problems in SRAM cell design at low technology level. In this paper a detection technique is proposed which detects the BTI effect on SRAM using SNM calculation. The proposed prototype is used to detect faults during read and write cycle of aged SRAM, which affects the reliability of the circuit. The diagnostics of fault is done by detection of BTI effect on SRAM using static noise margin (SNM) calculation. The circuit design on CMOS technology is carried out using HSPICE simulator in cadence.
Keywords: Static RAM cell; CMOS technology; Bias Temperature Instability (BTI); Technology Scaling; Static Noise Margin (SNM).
Hybrid Genetic Algorithm in Partial Transmit Sequence to Improve OFDM
by Ravikumar Polukonda
Abstract: The work takes into consideration use of a technique known as the Partial Transmit Sequence (PTS) for the diminution of a Peak-to-Average-Power-Ratio (PAPR) of the Orthogonal Frequency Division Multiplexing (OFDM) signal in that of the wireless communication systems. The conventional scheme of the PTS uses extensive random search to explore the combinations of the phase vectors to improve PAPR, but this elevates the complexity of the search and also exponentially increase the number of phase vectors that demands high computational cost and compromise on accuracy. Here in this work, there is a suboptimal algorithm used for the phase optimization which is based on an enhanced version of the Genetic Algorithm (GA) which is applied for exploring optimal combination of the phase vectors providing an enhanced performance which is compared with the currently active algorithms like the Particle Swarm Optimization (PSO) algorithm or the Bacterial Foraging Optimization (BFO) algorithm. This hybrid GA will enhance the accuracy and the rate of convergence of all conventional algorithms with only a few parameters that required adjustment. The results of simulation proved that the hybrid GA-PSO and the GA-BFO based PTS algorithm was able to attain a reasonable reduction in the PAPR by employing a simple network structure on being compared to the other conventional algorithms.
Keywords: Orthogonal Frequency Division Multiplexing (OFDM); rnPartial Transmit Sequence (PTS); rnPeak to Average Power Ratio (PAPR); rnGenetic Algorithm (GA); rnParticle Swarm Optimization (PSO) and rnBacterial Foraging Optimization (BFO).
ADAPTIVE BEAM FORMING OF MIMO SYSTEM USING OPTIMAL STEERING VECTOR WITH HYBRID BACTERIAL FORAGING OPTIMIZATION ALGORITHM FOR CHANNEL SELECTION
by Sekhar Babu, P.V. Naganjaneyulu, K. Satya Prasad
Abstract: The modern communication systemsface several issues that are quite challenging, more so in those involving smart antennas such as the antenna array beam. The antennas help the array in improving reception of that of a signal thereby improving the Signal-to-Interference Ratio (SIR). In the case of some of these coherent Multiple Input Multiple Output (MIMO) radar an optimal target signal and its processing can be achieved for any of the waveforms that are transmitted or the pattern of the radiation beam, thus making a transmit beam to be forming through a waveform design without bringing down the performance of target detection. There are several other techniques of that of Adaptive Beam Forming (ABF) which were proposed until now for optimizing the ability of steering of the array with regard to that of the main load and nulls, thus improving the Signal-to-Interference-Plus-Noise Ratio (SINR). In this work, an optimized neural network based on Bacterial Foraging Optimization Algorithm (BFOA) along with a Greedy Algorithm and a Tabu Search (TS) algorithm for the channel selection having an adaptive beam which is formed by using a steering vector is proposed. In the proposed hybrid, adaptive seed dispersion will make the BFOA-TS be able to converge faster when compared to a BFOA-Greedy. This type of behaviour has been duly verified by means of an application of the BFOA-TS and the BFOA-Greedy on the test functions which are well-known. The results of this experiment proved that the method proposed was able to achieve a better performance compared to that of the others.
Keywords: Beam Forming; Smart Antenna; Multiple Input Multiple Output (MIMO) System; Channel Selection; Bacterial Foraging Optimization Algorithm (BFOA); Greedy algorithm and Tabu Search (TS).
Multiple data cost based stereo matching method to generate dense disparity maps from images under radiometric variations.
by Akhil Appu Shetty, V.I. George, C. Gurudas Nayak, Raviraj Shetty
Abstract: Stereo matching algorithms are capable of providing dense 3D information of the environment, through two images taken simultaneously from a pair of cameras placed horizontally and parallel to each other. This 3D information is generated in the form of disparity maps. The depth of the objects in the images can be extracted from the disparity map through the equation (b*f/d), where (b) and (f) indicate the baseline and focal length of the cameras, while (d) indicates the disparity obtained through the stereo matching method. Obtaining an accurate disparity map from a stereo image pair is not only a challenging task but also computationally expensive as we have to search for similar pixels in the reference and target images. In addition to this, if we take into consideration the environmental effect like difference in illumination and exposure conditions, then the difficulty of the task in hand increases drastically. The authors, in this research work, try to overcome this problem by combining multiple stereo cost functions in the form of a linear equation. Moreover, to reduce the computation time, a segmentation based cost aggregation method is followed in an attempt to produce an accurate disparity map even in the presence of radiometric variations in the images. The radiometric condition in the target image (left image) is fixed to (11), which indicates the illumination and exposure condition for the image. The radiometric conditions (exposure and illumination) for the target image (right image) are varied from (10) to (22) indicating a large variation in both illumination and exposure. The performance of the proposed algorithm is observed while varying the relationship parameter ? between the cost functions and the number of segments the images are broken into. Different image pairs with varying radiometric conditions used in this research work were obtained from the Middlebury stereo dataset.
Keywords: Stereo matching;Middlebury stereo dataset;SLIC segmentation;disparity maps.
Neural Network Decoder for (7, 4) Hamming Code
by Aldrin Vaz, C. Gurudas Nayak, Dayananda Nayak
Abstract: To ensure the accuracy, integrity and fault-tolerance in the data to be transmitted, Error Correcting Codes (ECC) are used. To decode the received data and correct the errors, different techniques have been developed. In this paper, Artificial Neural Networks (ANN) have been used instead of traditional error correcting techniques, because of their real-time operation, self-organization and adaptive learning and to project what will most likely happen on the analogy of human brain. A decoding approach based on Back propagation Algorithm for feed forward ANN has been simulated using MATLAB for (7, 4) Hamming Code. The designed ANN is trained on all possible combination of code words such that it can detect and correct up to 1-bit error. The synaptic weights are updated during each training cycle of the network. The simulation results show that the proposed technique is correctly able to detect and correct 1-bit error in the received data.
Keywords: Artificial Neural Network; Back Propagation Algorithm; Error Correcting Code; Hamming Code.
Implementation and Evaluation of a Trust Model with Data Integrity Based Scheduling in Cloud
by A.V.H. Sai Prasad, G.V.S. Raj Kumar
Abstract: Cloud computing is a model which provide services to the users as per their demand using the infrastructure belonging to various systems on a cloud that can be accessed using internet. By means of a simple and easy to understand Graphical User Interface (GUI) or Applications Programming Interface (API), the cloud computer can hide the inherent complexity of the infrastructure and associated fine details. Jobs in cloud computing are created based on priorities. By assigning the job to a suitable resource until a valid or optimal schedule is reached, the job that has the highest associated priority is first executed. This has introduced several challenges and also risks, from a security point of view and this also decreases the efficacy of the conventional protective approaches. To address the cloud challenges, data integrity plays a vital role and provides accuracy and consistence of the stored data without modified. This work suggests a trust based Min-Min and Max-Min algorithm as several unknown parties or enterprises provide various services. Max-Min schedules the longer task first when the instance is being scheduled and the Min-Min allows the task having the shortest computation time to take precedence. The Min-Min and Max-Min is based on the completion time and execution time only. Hence the trust factor is added to the conventional Min-Min and Max-Min algorithm along with the completion and execution time to support security and integrity in cloud. The specific characteristics of security within the cloud environment are assured using Trusted Third Party (TTP). For making the confidentiality, integrity and the authentication of the data and communication that are involved, cryptography has to be used. A horizontal level of service is presented by the solution which is made available to all the entities that are involved, this solution makes use of security mesh inside which the required trust is maintained.
Keywords: Cloud Computing; \r\nScheduling; \r\nSecurity; \r\nMin-Min Algorithm; \r\nMax-Min Algorithm and \r\nTrust Model.