International Journal of Computational Systems Engineering (45 papers in press)
Cross Layer based Modified Virtual Backoff Algorithm for Wireless Sensor Networks
by Ramesh Babu Palamakula, P. Venkata Krishna
Abstract: Since decade and more, there is a tremendous improvement in the usage of wireless sensor networks in various applications. The network operation is guaranteed to be effective and fruitful with efficient MAC protocol. Hence, this paper proposes the Cross Layer based Virtual Backoff Algorithm (CLM-VBA). The cross layer architecture is designed in this paper which involves three layers: physical layer, MAC layer and Network layer. The priority is given to the neighbouring nodes which are determined using the cross layer architecture. Two different counters are maintained to keep track of number of accesses and number of attempts along with sequence number. The delay sensitive applications are given preference when compared to the delay insensitive applications. Sleep mode is used for each node in order to conserve energy at each node. A buffer is maintained at each node in order to improve the performance of the system. The proposed algorithm CLM-VBA is compared with VBA, S-MAC and M-VBA in terms of delay, packet delivery ratio, energy consumption and number of collisions and proved to be better.
Keywords: backoff; channel access; cross layer; MAC; collision.
Novel Approximation based Dynamical Modelling and Nonlinear Control of Electromagnetic Levitation System
by Ravi Gandhi, Dipak Adhyaru
Abstract: This paper presents the novel design of dynamical model for Electromagnetic Levitation System (EMLS) as a function of electromagnetic coil inductance in electrical and mechanical subsystems using Novel Approximation (NA). Proposed inductance model satisfies the requirement of electromagnetic force with the higher order polynomials (i.e. 2nd and 3rd order) in the denominator to best fit the experimental data. To stabilize the nonlinear EMLS in the larger operating range, the Input-Output Feedback Linearization (IOFL) based nonlinear controller is designed and implemented. Theorem 1 to guarantee the stability for the EMLS in the large region on the basis of quadratic Lyapunov function is proposed. The investigations reveal that the proposed controller provides smooth and fast stabilizing and tracking control response for different kind of trajectories. Under bounded vertical disturbances (i.e. sinusoidal and random types), proposed controller maintains the robustness.
Keywords: magnetic levitation; novel approximation; feedback linearizing nonlinear control; Lyapunov function; stability.
Soft computing based on a selection index method with risk preferences under uncertainty: applications to construction industry
by H. Gitinavard, N. Foroozesh, S.Meysam Mousavi
Abstract: Decision making in construction industry is a dynamic procedure which is concerned with choosing a reasonable strategy to accomplish the stated objectives. It is the way towards choosing a feasible alternative from an arrangement of options. When the option has just a single criterion, its nature is unsurprising which settles on less complicated decision making. Uncertainty happens when there is no information to take care of the issue. In this paper, a new developed hesitant fuzzy preference selection index (HFPSI) method in view of another soft computing approach with risk preferences of decision makers (DMs) is proposed to manage multi-criteria decision making (MCDM) issues in construction industry while applying hesitant fuzzy sets (HFSs) to represent the uncertain information under hesitant uncertainty. The proposed technique notwithstanding considering subjective surveying criteria, respects the DMs' troubles in deciding the membership of an element into a set. Furthermore, its best option decision depends on finding an option that at the same time considers the ideas of preference relation and hesitant fuzzy. The proposed HFPSI approach is actualized by utilizing two real case studies in the construction industry and the results are examined. The studied cases for the best construction project and the best contractor also demonstrate the effectiveness of applying the proposed method while considering a group of the DMs ideas and hesitancy. It was also shown through the application cases that the method can help DMs in reaching a reliable decision under uncertain environments through using HFSs.
Keywords: Group decision making; hesitant fuzzy sets (HFSs); construction project selection problem; contractor selection problem.
An Automatic Transition from the Design to the Implementation of a Spatial Data Warehouse
by Sana EZZEDINE, Sami Yassin Turki, Sami Faiz
Abstract: Existing approaches for the design and implementation of Spatial Data Warehouses have not considered spatial and non-spatial decision makers requirements. In this paper, we propose an approach based on the Model Driven Architecture to transit automatically from the design of the Spatial Data Warehouse to its implementation. The implementation takes into account spatial and non spatial requirements.
We use the formalism of the Platform Independent Model to design the Spatial Data Warehouse and the Platform Specific Model for the implementation.
An experimentation of the different steps of our approach is presented. The case study is about five users in urban governance field. The experimentation allowed to generate a personalized Spatial Data Warehouse for a group of users sharing similar spatial requirements. The experimentation is evaluated by the use of the diffusion theory.
Keywords: Spatial Data Warehouse; decision maker’s requirements; Model Driven Architecture; automatic generation.
KNN based Ensemble Selection for Imbalance Learning
by Guirong Zheng, Huaping Guo
Abstract: Classification of imbalance datasets is one of the crucial issues in the field of machine learning. Since the distribution of imbalance dataset is extremely skew, the traditional classifications always come up with a disappointed performance. Different with the traditional methods, this paper reconsiders class imbalance problem from the viewpoint of ensemble learning. However, many ensembles tend to build defective base classifiers which are helpless to improve the generalized ability of ensemble would be produced. To solve this problem, a novel ensemble algorithm named NNES (k-Nearest Neighbor based Ensemble Section) is proposed in this paper. To evaluate the local properties of an unlabeled instance, NNES tends to pay more attention to minority and improve its performance on imbalance datasets. Experimental results show that NNES can improve the classification performance of the imbalance datasets effectively. Moreover, the improvement would be strengthened when some sampling techniques are introduced in.
Keywords: imbalance datasets; ensemble; k-Nearest Neighbor; classification; sampling techniques.
Design of Type-2 Fuzzy Logic Power System Stabilizer Using Limit cycle approach
by Naidu I. E. S, Sudha K. R
Abstract: Power system is subjected to wide range of operating conditions. Heavily loaded power systems are subjected to Hopf bifurcations resulting in oscillatory instability. A power system shifts to the dynamic instability region encountered by unstable limit cycles, this result in increase of oscillatory behavior of power system. Many of the authors suggested the theories like Poincar
Keywords: Dynamic analysis; Dynamic stability limit; Hopf bifurcation; Limit cycles; Eigen value analysis; Genetic algorithm;.
Optimal Path Planning of UAV using Grey Wolf Optimizer
by Soundarya M S, Anusha Danashaker, Rohith P, Kavitha Panneerselvam, Seshadari Srinivasan
Abstract: This paper aims at proposing a novel approach for path planning of Unmanned Aerial Vehicle (UAV) along with obstacle avoidance. The path planning is achieved through swarm intelligence algorithm inspired by the behaviour of grey wolves known as Grey Wolf Optimizer (GWO). The optimal path planning of UAV using GWO is obtained by proper choice of objective function for targets and obstacle avoidance condition. The algorithm has three search agents namely alpha, beta and gamma which help in proper convergence of solution to the target while avoiding obstacle. The proposed approach is tested with different test cases of target and obstacles conditions and the simulated results have been reported. Simulation is carried out in MATLAB environment.
Keywords: GWO; Path Planning; UAV.
A review on gamma interconnection network
by Shilpa Gupta, G.L. Pahuja
Abstract: In past decades considerable development has been made in big data communication and computation in super computer systems. Multistage interconnection networks are widely used in these super computer systems for reliable communication, because of their cost effectiveness, fault tolerance property and low transmission delay. Multistage interconnection networks (MIN) are classified according to their switch element size, number of stages, connection pattern between stages etc. Lot of researchers have compared various MIN on basis of different aspects of reliability and fault tolerance but selection of different MIN for comparison was not precise to one or more class of interconnection networks (IN) but was random. In this paper an attempt has been made to discuss all Gamma networks proposed till date to the best of our knowledge, and compared on various reliability issues so as to achieve a comprehensive statement about all Gamma MIN which has been broadly used in high speed packet switching super computers. It has been observed that other researchers have taken same reliability value for different types of switches, wherein here in this paper different reliability values have been applied to different type of switches to compute all reliability parameters of all Gamma Networks. also terminal reliability (TR)of all Gamma Networks have been computed and compared for all possible Tag Values for Network size of 8
Keywords: Fault-Tolerance; Gamma Interconnection Network (GIN); Multistage Interconnection Network (MIN); Reliability; Switch Element (SE).
Elicitation of Software Testing Requirements from the Selected Set of Softwares Requirements in GOREP
by Mohd Sadiq, Sanjida Nazneen
Abstract: Requirements engineering (RE) is employed to elicit, model, and analyse the requirements of software. Software requirements elicitation is the first sub-process of RE; and it is used to identify the requirements of software according to the need of the stakeholders. In literature, we identify that goal oriented requirements elicitation process (GOREP) do not support the identification of testing requirements from the functional requirements (FR) of software in early phase of RE. Therefore, to tackle this research issue, we proposed a method for the identification of the testing requirements from the FR in GOREP. In real life applications, only those requirements are implemented and tested, which are selected by the stakeholders. So in the proposed method we used fuzzy based technique for FR selection on the basis of non-functional requirements (NFR). The canonical representation of multiplication operation (CRMO) associated with L-1 R-1 inverse arithmetic principle and graded mean integration representation (GMIR) based on triangular fuzzy numbers (TFNs) have been applied for the selection of the FR. In our work, an Institute Examination System (IES) is used as a case study to explain the steps of the proposed method.
Keywords: Functional Requirements; Non-Functional Requirements; Testing Requirements; Test Cases; Fuzzy Based Approach; and Triangular Fuzzy Numbers.
Portfolio Optimization Based On Minimum Total Risk Acceptance Level and Its Components Using Improved Genetic Algorithm
by Sahar Mojaver
Abstract: Risk and return are two factors that have always been considered in investment field. Along with the emergence of portfolio optimization models, with the Markowitz model as the most important one, the need of understanding the methods of solving these models became important as well. One of the most important meta-heuristic methods for portfolio optimization a model is improved Genetic Algorithm (GA). One goal of this research is to study the efficacy of this algorithm in portfolio optimization. For this purpose, we once compute the efficient frontier and compare it with efficient frontiers obtained from exact method. To do so, 25 companies from Tehran Stock Exchange companies are selected for this purpose. Calculations are done in Matlab7.6 software. Result show that efficient frontier obtained by GA is the same as that of exact method's, which indicates the high efficiency of GA in portfolio optimization. Also this research shows that in comparison between optimal portfolios obtained by solutions having systematic and unsystematic risk functions, stock diversification in portfolios with unsystematic risk function is much more than that with systematic risk function. Proposed algorithm can solve the portfolio optimization based on minimum total risk acceptance level and its components by using genetic algorithm. An improved GA is proposed for this purpose.
Keywords: Portfolio Optimization; Risk Acceptance Level; Markowitz Model; Genetic Algorithm; Systematic Risk.
Grenade-Cauchy Operator Integrated Artificial Bee Colony Optimization for Reliable QoS based Web Service Composition
by Udhaya Shree S., Amuthan A.
Abstract: Web service composition is considered as the potential method of integrating diversified number of applications independent of the characteristic features of service providers. Majority of the dynamic web service composition techniques in the literature addresses the problem of web service composition through the perspectives of Quality of Service (QoS) or transactional characteristics. GCO-ABC algorithm is proposed as an attempt of developing a dynamic service composition methodology that depends on the integration of transactional and QoS-based properties of web service. The transactional characteristics of web services are analyzed and the problem of dynamic web service composition is modeled using a directed acyclic graph with constraints. GCO-ABC algorithm is applied on the work flow sequence of web service composition developed as a constrained directed acyclic graph for determining the near optimal solution with efficacy and it is also predominant in comparison to the traditional Ant Colony Optimization Algorithm (ACOM) and its variant Improved Ant Colony Optimization Algorithm (IACOM). The performance of GCO-ABC is investigated through empirical and simulation means and the determined results infer that the proposed scheme is not only efficient than IACOM, ACOM and Brute force scheme but also capable of approximating the searching solutions into a much more optimal candidate solution.
Keywords: Grenade Explosion; Cauchy operator; Reliable QoS; Web Service Composition; Ant Colony Optimization; Artificial Bee Colony Algorithm.
Performance Comparison of Multiagent Cooperative Reinforcement Learning Algorithms for Dynamic Decision Making in Retail Shop Application
by Deepak A. Vidhate
Abstract: A novel approach by Expertise based on Multiagent Cooperative Reinforcement Learning Algorithms (EMCRLA) for dynamic decision making in the retail application is proposed in this paper. Performance comparison between Cooperative Reinforcement Learning Algorithms and Expertise based Multiagent Cooperative Reinforcement Learning Algorithms (EMCRLA) is demonstrated. Different cooperation schemes for multi-agent cooperative reinforcement learning i.e. EGroup scheme, EDynamic scheme is proposed here. Implementation outcome includes demonstration of recommended cooperation schemes that are competent enough to speed up the collection of agents that achieves excellent action policies. This approach is developed for a three retailer stores in the retail marketplace. Retailers will be able to help each other and obtain profit from cooperation knowledge through learning their own strategies that exactly stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train helpfully in the circumstances. Assuming significant hypothesis on the vendors stock policy, restock period, arrival process of the consumers, the approach is modelled as Markov decision process model that make it possible to design learning algorithms. Dynamic consumer performance is noticeably learned using the proposed algorithms. The paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for one year sale duration. The results obtained by the proposed expertise based cooperation approach show that such methods will be able to put a quick convergence of agents in the dynamic environment.
Keywords: Cooperation schemes; Multiagent learning; Reinforcement learning.
Towards Recent Developments in the Methods, Metrics and Datasets of Software Fault Prediction
by Deepak Sharma, Pravin Chandra
Abstract: The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modeling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modeling techniques based on traditional and computational intelligence based methods are also reviewed. This paper is an endeavor to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios.
Keywords: Software Fault Prediction; Fault Tolerance; Computational Intelligence; Software Metrics; Evaluation Metrics.
Performance analysis of WOA optimized PID controllers for LFC of interconnected thermal power systems
by Rajesh Bhatt, Girish Parmar, Rajeev Gupta
Abstract: Nature inspired Whale optimization algorithm (WOA) has been implemented for load frequency control (LFC) of two areas interconnected non reheat thermal power systems in order to maintain the system frequency at a prescheduled value in the presence of disturbances. Two similar PID controllers have been used in each area and control optimization problem for proper tuning of controllers parameters is defined by considering the integral of time multiplied absolute error (ITAE) as an objective function. The superiority of WOA optimized PID controllers has been established by comparing the results with other existing approaches such as; BFOA/PI, DE/PI, NSGA-II/PI, GSA/PI and GWO/PI for the same system in the presence of load changes of different magnitude and location. For performance analysis, the percentage reduction in ITAE, settling times of frequency & tie line power deviations and minimum damping ratio (MDR), etc. have also been calculated. The simulation results show that the WOA/PID approach gives far better results in terms of system dynamic responses, ITAE, MDR, settling times and overshoots of 〖∆f〗_1,〖∆f〗_2,and 〖∆P〗_Tie for the same system under study when compared with some existing approaches.
Keywords: Load frequency control (LFC); two area interconnected non reheat thermal power system; PID controller; Whale optimization algorithm (WOA).
Special Issue on: Biomedical Signal and Imaging Trends and Artificial Intelligence Developments
Comparative studies of Discrete Cosine transform (DCT) and Lifting Wavelet Transform (LWT) techniques for compression of Blood Pressure Signal in Salt Sensitive Dahl Rat
by Vibha Aggarwal, Manjeet Singh Patterh, Virinder Kumar Singla
Abstract: This paper introduces a study based on quality controlled Discrete Cosine transform (DCT) and Lifting Wavelet Transform (LWT) based compression method for Blood Pressure Signal Compression in Salt Sensitive Dahl Rat. The transformed coefficients are thresholded using the bisection algorithm to match the predefined user specified percentage root mean square difference (PRD) within the tolerance. Then, the binary lookup table is made to store the position map for zero and non-zero coefficients (NZC). The NZC are quantized by Max-Lloyd quantizer followed by Arithmetic coding. Lookup table is encoded by Huffman coding. The results are presented on different Blood Pressure signals of varying characteristic. There is no significant difference in before quantization PRD (BPRD) and after quantization PRD (QPRD) in various signals in both transforms. Mean compression ratio increases with an increase in user define PRD (UPRD).
Keywords: Blood Pressure signal in Salt Sensitive Dahl Rat; Compression; Nonlinear transform; Linear transform.
B-mode breast ultrasound image segmentation techniques: an investigation and comparative analysis
by Madan Lal, Lakhwinder Kaur, Savita Gupta
Abstract: Breast cancer is the second leading reason for death among women. A commonly used method for detection of breast cancer is ultrasound imaging. Ultrasonic imaging is a low cost, easy to use, non-invasive and portable process, but it suffers from acoustic interferences (speckle noise) and other artifacts, As a result, it becomes difficult for the experts to directly identify the exact shapes of abnormalities in these images. Numerous techniques have been proposed by different researchers for visual enhancement and for segmentation of lesion regions in Breast Ultrasound images. In this work, different automatic and semi-automatic Breast Ultrasound image segmentation techniques have been reviewed with a brief explanation of their different technological aspects. Performance of selected methods has been evaluated on a database of 45 B-mode Breast Ultrasound images containing benign and malignant tumors (25 benign and 20 malignant). For performance analysis of the segmentation methods, manually delineated images by an expert radiologist are used as ground truth images whereas boundary and area error metrics are used for comparison of quantitative results.
Keywords: B-Mode Breast Ultrasound (BUS) Image; Speckle Noise; Thresholding; Region Growing; Fuzzy Clustering; Watershed; Active Contour; Level Set.
An improved unsupervised mapping technique using AMSOM for neurodegenerative disease detection
by ISHA SUWALKA, Navneet Agrawal
Abstract: The most challenging aspect in medical imaging is the accuracy of detection of neurodegenerative diseases .The advent of new imaging techniques has yet limited manual evaluations, manual reorientation and other time consuming limitations with reduced resolution . Therefore, there is a need to develop efficient algorithm for proper detection with quantitative information of significance for the clinicians. The proposed algorithm includes improved adaptive moving self organizing mapping (AMSOM) which trains the extracted features along with Mini-Mental State Examination (MMSE) factor and volumetric parameter using Volume based method (VBM) for computing feature data set which in total improves time iteration rate ,mean square error ,sensitivity and accuracy. The algorithm is improved version of moving mapping method which on one hand tackles drawback of SOM of fixed grid mapping and improves neighbourhood function of neuron which provides better detection and classification yielding promising results. It further improves performance of AMSOM by better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons. This paper uses real MRI dataset taken from OASIS having a cross-sectional collection of 416 subjects aged 18 to 96. The analysis includes different comparison of mapping approaches that reveals features associated to the Alzheimer Disease.
Keywords: self organizing mapping for MRI image; hierarchical mapping with GHSOM; e- database using OASIS ;moving neuron concept using AMSOM;clustering for detection of Alzheimer Disease.
Active Contours using Global Models for Medical Image Segmentation
by Ramgopal Kashyap, Vivek Tiwari
Abstract: Accurate segmentation with denoising are subject of research in the field of medical imaging and computer vision. This paper presents an enhanced energy based active contour model with a level set detailing. Local energy fitting term impacts neighborhood drive to pull the shape and restrict it to protest limits. Thus, the global intensity fitting term drives the movement of contour at distant from the object boundaries. The global energy term depends on worldwide division calculation, which can better catch energy data of picture than Chan-Vese (CV) model. Both neighborhood and worldwide terms are commonly absorbed to build a vitality work in light of a level set plan to portion images with force inhomogeneity, Experiments demonstrate that the proposed model has the upside of commotion resistance and is better than conventional image segmentation. Results demonstrate that the proposed method performs better both subjectively and quantitatively contrasted with other best in class methods.
Keywords: Denoising;Energy based active contour;Image segmentation; Intensity inhomogeneity; Local binary fitting; Local region based active contour.
Application of Ensemble Artificial Neural Network for the Classification of White Blood Cells using Microscopic Blood Images
by Jyoti Rawat, Annapurna Singh, Harvendra Singh Bhadauria, Jitendra Virmani, Jagrtar Singh Devgun
Abstract: Introduction: This work exhibits an application of ensemble artificial neural network for the white blood cell classification. The incentive for experimenting with the Artificial neural network (ANN) based computer aided classification (CAC) design is that these designs based on ensemble methods are expected to yield a better outcome in comparison to the outcome achieved by CAC system designs based on single multiclass classifier. In recent times, digital image processing technique has been widely utilized as a part of health diagnosis. In order to overcome the problems of manual diagnosis in recognizing the morphology of blood cells, the automated analysis is frequently utilized by a pathologist. In the pathology lab, white blood cells are analyzed by an expert that is a suspicious and subjective task. Remembering the end goal to enhance the precision, an automatic white blood cell classification framework is crucial for helping the pathologists in diagnosing various haematological disorders like leukemia or lymphoma. This work gives a semi-automated technique to identify and classify white blood cell.
Method: In this work, a k-means clustering algorithm is used to segment the nucleus by upgrading the district of the white blood cell nucleus and stifling the other components of the blood smear images. From each cell image, different features, such as shape, chromatic and texture, are extracted. This feature set was used to train the classifier in order to determine different classes of white cell.
Results: Performing this evaluation, classification models allowed us to establish that CAC system design based on the ensemble artificial neural network is the most suitable model for the four class white cell classification, with an accuracy of 95 %.
Conclusions: The proposed method analyzes the blood cells automatically via image processing techniques, and it represents a medicinal method to avoid the plentiful drawbacks associated with the labour-intensive examination of white cells.
Keywords: White blood cell; Segmentation; k-means clustering; Texture features; Shape features; Chromatic features; Artificial neural network classifier.
A Computerized Framework for prediction of fatty and Dense Breast Tissue Using Principal Component Analysis and Multi-resolution Texture Descriptors
by Indrajeet Kumar, Harvendra Singh Bhadauria, Jitendra Virmani
Abstract: The present work proposes a computerized framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors. For this study 480 MLO view digitized screen film mammograms have been taken from the DDSM dataset. A fixed ROIs size of 128
Keywords: Mammography; Breast density classification; Multi-resolution texture descriptors; principal component analysis; Support vector machine (SVM) classifier.
GPU-based Focus-Driven Multi-coordinates Viewing System for Large Volume Data Visualization
by PIYUSH KUMAR, ANUPAM AGRAWAL
Abstract: The advancements in biomedical scanning modalities such as Computed Tomography (CT), Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are improving in their resolution day by day. The newly physicians may face some problem rely on exploring 2D slices and diagnosing with 3D full humans anatomy structure at the same time. In this paper, we are presenting a generalized contactless interactive Graphics Processing Unit (GPU) accelerated Compute Unified Device Architecture (CUDA) based focus and context visualization approach with displaying the inner anatomy of the large scale visible human male dataset in Multi-Coordinate Viewing System (MCVS). The focusing area has been achieved by 3D Cartesian Region of Interest (ROI). The large dataset has been structured by using Octree method. The volume rendering part has been done by using an improved ray intersection cube method for voxels with the ray casting algorithm. The final results would allow the doctors to diagnose and analyze the atlas of 8-bit CT-scan data using three dimensional visualization with the efficient frame rate rendering speed in multi-operations like zooming, rotating, dragging. The system is tested for multiple types of 3D medical datasets ranging from 10 MB to 3.15 GB. Medical practitioners and physicians are able to peer inside of the dataset to use the features of the inner information. This system is further tested with three NVIDIA CUDA enabled GPU cards for the performance analysis. The scope of this system is to explore of the human body for surgery purpose.
Keywords: Volume Visualization; Focus-driven; MCVS; Focus and Context; MRI dataset; Medical dataset;.
Volumetric Tumor Detection Using Improved Region Grow Algorithm
by Shitala Prasad, Shikha Gupta
Abstract: This paper works on segmentation of brain pathological tissues (Tumor, Edema an Narcotic core) and visualize it in 3D for their better physiological understanding. We propose a novel approach which combines threshold and region grow algorithm for tumor detection. In this proposed system, FLAIR and T2 modalities of MRI are used due to their unique ability to detect the high and low contrast lesions with great accuracy. In this approach, first the tumor is segmented from an image which is a combination of FLAIR and T2 image using a threshold value, selected automatically based on the intensity variance of tumor and normal tissues in 3D MR images. Then the tumor part is extracted from the actual 3D MRI of brain by selecting the largest connected volume. To correctly detect tumor 26 connected neighbors are used. The method is evaluated using a publically available BRAT dataset of 80 different patients having Gliomas tumors. The accuracy in terms of detection is reached to 97.5\% which is best compared to other state-of-the-art in given time frame. The algorithm takes 4-5 minutes for generating the 3D visualization for final output.
Keywords: 3D Volumetric; Brain Tumor; Region Growing Algorithm; Thresholding; Volexl Seeding.
Multimodality Medical Image Fusion using Nonsubsampled Rotated Wavelet Transform for Cancer Treatment
by Satishkumar Chavan, Abhijeet Pawar, Sanjay Talbar
Abstract: This paper presents nonsubsampled rotated wavelet transform (NSRWT) based feature extraction approach to multimodality medical image fusion (MMIF). The nonsubsampled rotated wavelet filters are designed to extract textural and edge features. These filters are applied on axial brain images of two modalities namely Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to extract spectral features. These extracted features are selected using entropy-based fusion rule to form new composite spectral feature plane. Entropy-based fusion rule preserves dominant spectral features and imparts all relevant information from both the modalities to the fused image. The inverse nonsubsampled rotated wavelet transform is applied to reconstruct fused image from the composite spectral slice. The proposed algorithm is evaluated using 39 pilot image slices of 23 patients subjectively and objectively for efficient fusion. Three expert radiologists have verified the subjective quality of fused image to ascertain anatomical structures from source images. Subjective score by radiologists reveals that the fused image using proposed algorithm is superior in terms of visualization of abnormalities over other wavelet based techniques. The objective evaluation of fused images involves estimation of fusion parameters like image quality index (IQI), edge quality measure (EQa,b), mean structural similarity index measure (mSSIM), etc. The proposed algorithm presents better performance metrics over the state of the art wavelet based algorithms.
Keywords: Multimodality Medical Image Fusion; Discrete Wavelet Transform; Rotated Wavelet Filters; Nonsubsampled Rotated Wavelet Transform; Cancer Treatment; Radiotherapy.
Comparison of feature extraction techniques for classification of hardwood species
by Arvind R. Yadav, R.S. Anand, M.L. Dewal, Sangeeta Gupta, Jayendra Kumar
Abstract: The texture of an image plays an important role in identification and classification of images. The hardwood species of an image contains four key elements namely, vessels (popularly known as pores in cross-section view), fibers, parenchymas and rays, useful in its identification and classification. Further, the arrangements of all these elements posses texture rich features. Thus, in this work investigation of existing texture feature extraction techniques for the classification of hardwood species have been done. The texture features are extracted from grayscale images of hardwood species to reduce the computational complexity. Further, linear support vector machine (SVM), radial basis function (RBF) kernel SVM, Random Forest (RF) and Linear discriminant analysis (LDA) have been employed as classifiers to investigate the efficacy of the texture feature extraction techniques. The classification accuracy of the existing texture descriptors has been compared. Further, Principal component analysis (PCA) and minimal-redundancy-maximal-relevance (mRMR) feature selection method is employed to select the best subset of feature vector data. The PCA reduced feature vector data of co-occurrence of adjacent local binary pattern (CoALBP24) texture feature extraction technique has attained maximum classification accuracy of 96.33
Keywords: Texture features; support vector machine; feature selection; hardwood species.
Myoelectric Control of Upper Limb Prostheses using Linear Discriminant Analysis and Multilayer Perceptron Neural Network with Back Propagation Algorithm
by Sachin Negi, Yatindra Kumar, V.M. Mishra
Abstract: Electromyogram (EMG) signals or myoelectric signals (MES) have two prominent areas in the field of biomedical instrumentation. EMG signals are primarily used to analyse the neuromuscular diseases such as myopathy and neuropathy. In addition the EMG signal can be utilized in myoelectric control systems- where the external devices like upper limb prostheses, intelligent wheelchairs, and assistive robots can be controlled by acquiring surface EMG signals. The aim of present work is to obtain classification accuracy first by using linear discriminant analysis (LDA) classifier where principal component analysis (PCA) and uncorrelated linear discriminant analysis (ULDA) feature reduction techniques are used for upper limb prostheses control application. Next the multilayer perceptron (MLP) neural network with back propagation algorithm is used to calculate the classification accuracy for upper limb prostheses control.
Keywords: EMG; MCS; LDA; PCA; ULDA; MLP; Back propagation.
Comparative Study of LVQ and BPN ECG Classifier
by Ashish Nainwal, Yatindra Kumar, Bhola Jha
Abstract: ECG is the electrical waveform og heart activity.It contains much information
on Heart disease. It is very important to diagnosis the heart disease as soon as possible
otherwise it can be harmful to patient. This paper presents to classify ECG signal using
learning vector quantization and Beck propagation neural network and feature of ECG
(morphology and frequency Domain) features.In this paper the 45 ECG signals from MIT-
BIH arrhythmia database are used to clssify in to two classes,one is normal and another
one is abnormal using above mentioned classifier. Out of 45 signals 25 are normal and 20
are abnormal according to MIT-BIH. 28 morphological features and 4 frequency domain
features are set as an input to the classifier. The performance of classifier measures in
the terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP). The system
performance is achieved with 82.35% accuracy using LVQ and 94.11% using BPN.
Keywords: Back Propagation Neural Network; Learning Vector Quantization; ECG;rnMIT-BIH.
Automatic feature extraction of ECG signal based on adaptive window dependent Differential histogram approach and validation with CSE database
by Sucharita Mitra, Madhuchhanda Mitra, Basudev Halder
Abstract: A very simple and novel idea based on Adaptive window dependent Differential histogram approach has been proposed for automatic detection and identification of ECG waves with its characteristic features. To facilitate the estimation of the waves, the normalized signal has been divided into a few small windows by an Adaptive window selection technique. By counting the number of changes between successive samples as frequency, the Differential histogram has been plotted. Some of the zones having an area more than a pre-defined threshold are depicted as QRS zones. The local maxima of these zones are referred as the R-peaks. T and P peaks are also detected. Baseline point and clinically significant time plane features have been computed and validated with reference values of the CSE database. Proposed technique achieved better performance in comparison with CSE groups. Its accuracy is achieved in Sensitivity (99.86%), Positive Productivity (99.76%) and Detection accuracy (99.8%).
Keywords: Adaptive window; Differential histogram; CSE database; baseline; Sensitivity; ECG signal; QRS zones; R-peaks; Distinctive point’s; Sample values.
A Comparative study on Kapurs and Tsallis entropy for multilevel thresholding of MR Images via Particle Swarm Optimization Technique
by Taranjit Kaur, Barjinder Singh Saini, Savita Gupta
Abstract: The present paper explores both the Kapurs and Tsallis entropy for a three level thresholding of Brain MR images. The optimal thresholds are obtained by the maximization of these entropies using a population-based search technique called as Particle swarm optimization (PSO). The algorithm is implemented for the segregation of various tissue constituents, i.e., cerebral spinal fluid (CSF), white matter (WM) and, gray matter (GM) region from the simulated images obtained from the brain web database. The efficacy of the thresholding methods was evaluated by the measure of the spatial overlap i.e. the Dice coefficient (Dice). The experimental results show that 1) For both the WM and CSF the Tsallis entropy outperforms the Kapurs entropy by achieving an average value of 0.967279 and 0.878031 respectively. 2) For the GM, the Kapurs entropy is more beneficial which is duly justified by the mean value of Dice which was 0.851025 for this case.
Keywords: Kapur’s; Tsallis; multilevel thresholding; PSO.
Special Issue on: Advances in Computational Systems
Analysis of THD In Various Power Electronic Converters to Regulate the Voltage from Renewable Energy Sources
by Jency Joseph, Josh F. T., Ronaldo Lamare, Blessen Varghese Mathew
Abstract: This work presents a performance analysis of various power electronic converters with RL load to reduce the total harmonic distortion. The power converters inspected are: ZETA converter, single-ended primary inductance converter, often written SEPIC converter and Z SOURCE converter. The objective is to analyze which power electronic converter exhibits less total harmonic distortion (THD) and more efficiency in order to select the suitable converter for electric vehicle propulsion system. Three above mentioned converters are designed, modeled, and simulated. The zeta converter is advantageous over other converters inspected and have reduced total harmonic distortion , higher output power and hence improved efficiency. The simulations are done with MATLAB/SIMULINK and the results are presented.
Keywords: Total Harmonic Distortion (THD); MATLAB simulation ,Z-SOURCE converter (ZSC); Zeta converter; Sepic converterrnrn.
SVM Classification of Brain images from MRI Scans using Morphological Transformation and GLCM Texture Features
by Usha Ramasamy, Perumal K
Abstract: This paper introduces a novel HTT based GLCM texture feature extraction procedure for an automatic MRI (Magnetic Resonance Images) brain image classification. The method has three phases, (1) Hierarchical Transformation Technique (HTT), (2) texture feature extraction and (3) classification. The new proposed HTT method incorporates optimum disk-shaped mask selection, top-hat and bottom-hat morphological operations, and some mathematical operation for both image pre-processing and enhancement. The gray level co-occurrence matrix is computed to extract statistical texture features such as contrast, correlation, energy, entropy, and homogeneity from an image. And these extracted images features of co-occurrence matrix can very well be fed into SVM (Support Vector Machine) for further MRI brain normal and abnormal image classification. The alternate approach of the HTT based GLCM also compared with conventional GLCM texture feature extraction method.
Keywords: magnetic resonance images; classification; texture feature extraction; grey level co-occurrence matrix; support vector machine; top hat transform; bottom hat transform.
Performance Analysis of Lyapunov Stability Based and ANFIS Based MRAC
by Kalpesh Pathak, Dipak Adhyaru
Abstract: Analysis of two adaptive controller parameter adjustment laws for a model reference adaptive controller has been discussed in this paper. The comparison has been done about applying Lyapunov stability rule and using adaptive Neuro fuzzy inference system (ANFIS) to adjust parameter for model reference adaptive control. Discussion of the nature of system, adaptive controller, basic block diagram and control law has been presented. For intense analysis two case studies have been considered. Simulation of two bench-mark process control applications, level control in coupled tank and concentration control in biochemical reactor (BCR) has been discussed. Comparative results have been plotted and discussed for each proposed algorithm. Considered systems have mutual parameter interaction and nonlinear parameter dynamics. Introduction part discusses literature survey, development of the topic and importance of the work. Initially Lyapunov rule based technique has been applied for control in both cases. With ANFIS based algorithm, new values of adjustment parameter have been generated. Results shows that performance of ANFIS based MRAC gives improved results in presence of system uncertainties.
Keywords: Coupled Tank; Biochemical Reactor; Model Reference Adaptive Control; Lyapunov Stability; ANFIS.
An Efficient Load Balancing Mechanism with Deadline Consideration on GridSim
by DEEPAK KUMAR PATEL, CHITA RANJAN TRIPATHY
Abstract: GridSim is a very popular Grid simulation tool. The GridSim toolkit is used to simulate application schedulers for different parallel and distributed computing systems such as Clusters and Grids. Many researchers have proposed various load-balancing techniques in Grid, but all those cannot be used in GridSim due to the structural differences of Grid. In this paper, we propose an enhanced load balancing method called Enhanced GridSim with Load Balancing based on Deadline Consideration (EGLBD) for GridSim. The proposed algorithm balances the load by providing an effective selection method for efficient scheduling of Gridlets among heterogeneous resources which maximizes the utilization of the resources and increases the efficiency of the Grid system. Also, the proposed algorithm shows the details of the load estimation method for every levels of GridSim. We simulate the proposed algorithm on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of finished Gridlets, unfinished Gridlets, total execution time and resubmitted time. The simulation results are presented.
Keywords: GridSim; Gridlet; Gridresource; Load Balancing.
Optimal Control of Magnetic Levitation System based on Cuckoo Search Algorithm
by Ananthababu Palaparthi
Abstract: The classical Proportional-Integral-Derivative (PID) controller and its variants remain the controllers of choice in many applications, despite the efforts put in the development of advanced control schemes over the past two decades. Magnetic levitation or MagLev systems are highly nonlinear and unstable systems. Because of the inherent nonlinearities of the MagLev systems, position tracking of Magnetic Levitation System (MLS) is a challenging task. In the present paper, a noval method of global optimization algorithm, cuckoo search algorithm is proposed, to tune the parameters of PID controller. The proposed PID controller is tested on real time magnetic levitation system. Simulation results show the effectiveness of the cuckoo search based PID controller for position tracking of magnetic levitation system.
Keywords: Cuckoo Search algorithm;Magnetic Levitation; Optimization algorithms; PID Controller; tuning controller parameter;.
EMOTION RECOGNITION BY DANCE MOVEMENT-A SURVEY
by PALLAVI JO
Abstract: Abstract-Emotion plays an important role in our daily lives. Recognize emotions such as happy, sad, fear, surprise etc from facial expressions is an easy task for human. But it is a complex task for computers. Many researches have done to recognize emotions from users facial expression by computers. In that, a challenging thing today is recognize emotions from body pose. In this paper, we review the methods of emotion recognition by body pose. Body pose means, it may be dance movement or person person communication. In this paper, first we review some methods of emotion recognition from dance movements. Secondly, we propose our future method of recognize emotions from the Indian classical dance bharathanatyam navarasas i.e. nine emotions. We will use the techniques of orientation filtering, max pooling and template filtering for recognizing navarasas of bharathanatyam dance.
Keywords: Keywords- kinect sensor; log gabor filter; max pooling; template matching.
REVENUE MAXIMIZATION MODEL USING CUSTOMIZED PLANS IN COMPUTER SERVICE ALLOCATION
by Showkat Ahmad Dar
Abstract: The rapid development of the Internet and the emergence of computing technologies like grid or cloud computing have enabled a novel trend of purchasing and consuming Information Technology services. These services are offered at different prices using various pricing schemes and techniques. End users will favor the service provider offering the best quality with the lowest price. Therefore, applying a fair pricing model will attract more customers and achieve higher revenues for service providers. This work focuses on a novel static/dynamic pricing model which is able to satisfy advance users requirements based on normal fixed price model. This paper considers many factors that affect pricing and user satisfaction, such as fairness, QoS, SLA, and more, by highlighting their importance in recent markets and propose a flexible model which tries to utilize all resources to the highest capacity and offers low prices for underutilized resources.
Keywords: Computer Resource; Pricing; Resource Allocation.rn rn.
A Survey on Word Embedding Techniques and Semantic Similarity for Paraphrase Identification
by Divesh Kubal, Anant Nimkar
Abstract: In Natural Language Processing (NLP), Paraphrase Identification (PI) determines the relatedness between the pair of sentences having fewer or negligible lexical overlap but still pointing towards the same meaning. The major challenge faced while attempting to solve this problem is the many possible linguistic variations conveying the same purpose. This paper aims to provide a detailed survey of traditional similarity measures, Statistical Machine Translation metrics, Machine Learning and Deep Learning techniques and a well-defined flow between them. This article encompasses various word embedding methods and step-wise derivation of its learning module. This survey paper also provides a definite flow pointing towards the evolution of Deep Learning in an unambiguous manner. A comparative analysis of various techniques to solve PI is presented and it will provide research directions to work in the similar domain.
Keywords: Paraphrase Identification; Word embedding; Deep Learning; Convolutional Neural Network; Semantic Similarity.
IMAGE STYLE TRANSFER USING CONVOLUTIONAL NEURAL NETWORKS BASED ON TRANSFER LEARNING
by Varun Gupta, Rajat Sadana, Swastikaa Moudgil
Abstract: The purpose of an image style transfer system is to extract the semantic image content from the target image and then using a texture transfer procedure display the semantic content of target image in the style of the source image. The uphill task in this context is to render the semantic content of an image but with the advent of Convolutional Neural Networks, image representations have been made much more explicit. In this work, we explore the method for image style transfer using transfer learning from pre-trained models of Convolutional Neural Networks (CNN).Use of these models gives us the power to produce images of a high perceptual quality that are a union of the content of an arbitrary image and the appearance of renowned artworks. Further, this paper compares pre-trained CNN models for image style transfer task and highlights the potential of CNN to deliver appealing images using modern manipulation techniques.
Keywords: Image Style Transfer; Convolution Neural Networks; Transfer Learning; Deep Learning; Machine Learning; Artificial Intelligence.
Performance Analysis and Evaluation of Software Defined Networking Distributed Controllers in Datacenter Networks
by Worku Muluye, Mesfin Abebe, Satheesh Kumar Nagineni
Abstract: A computer network is a critical issue in our day to day activity; however, this day it works under several problems. To solve these problems OpenFlow-based programmable Software-Defined Network (SDN) was released. OpenFlow is a protocol for SDN which vertically separates the control plane from the data plane of the network devices. In SDN the controllers are the brains of the network that manages and controls the network devices. Datacenter network is one of the application areas that required successful integration of distributed OpenFlow controllers by making the network more consistent. However, the need for high-performance SDN controllers increases with the increasing of network devices in the datacenter network. SDN distributed controller is a controller which can increase or decrease the number of controllers according to the change of traffics. The aim of this paper is to perform a study and performance analysis of SDN distributed controllers in a datacenter networks by considering three designed topologies and a different number of devices. Initially, has been performed a detailed study and comparison of SDN distributed controllers and selected the best ONOS (Open Networking Operating System), controller. Then, created three ONOS controllers in a cluster using Linux Container (LXC) and designed tree topologies using python in Mininet. Then tested and evaluated three ONOS controllers throughput and latency and presented the results of single, two and three ONOS controllers. The single ONOS controller has 60665.22 to 82566.85 flow/s throughput and 62.89 to 94.45 responses/ms latency. Two ONOS controllers have 105564.30 to 118911.20 flow/s throughput and 117.53 to 147.91 responses/ms latency. Three ONOS controllers have 216816.80 to 229388.90 flows/s throughput and 297.61 to 245.32 responses/ms latency. Single, two and three ONOS controllers have high throughput and high response per ms. Finally, based on these results the distributed ONOS controller better performance than single SDN controllers in a datacenter networks.
Keywords: Software-defined networking; SDN distributed controller; OpenFlow; Open networking operating system; Datacenter networks; Tree topologies.
Pipel: Exploiting Resource Reorganization to Optimize Performance of Pipeline-Structured Applications in the Cloud
by Vinicius Meyer, Rodrigo Da Rosa Righi, Vinicius Facco Rodrigues, Cristiano André Da Costa, Guilherme Galante, Cristiano Both
Abstract: Workflow has become a standard for many scientific applications that are characterized by a collection of processing elements and an arbitrary communication among them. In particular, a pipeline application is a type of workflow that receives a set of tasks, which must pass through all processing elements (also named here as stages) in a linear fashion, where the output of a stage becomes the input of the next one. To compute each stage, it is possible to use a single compute node or to distribute its incoming tasks among the nodes of a cluster. However, the strategy of using a fixed number of resources can cause under- or over-provisioning situations, besides not fitting irregular demands. In addition, the selection of the number of resources and their configurations are not trivial tasks, being strongly dependent of the application and the tasks to be processed. In this context, our idea is to deploy the pipeline application in the cloud, so executing it with a feature that differentiates cloud from other distributed systems: resource elasticity. Thus, we propose Pipel: a reactive elasticity model that uses lower and upper load thresholds and the CPU metric to on-the-fly select the most appropriated number of compute nodes and virtual machines (VMs) for each stage along the pipeline execution. This article presents the Pipel architecture, highlighting load balancing and scaling in and out operations at each stage, as well as the elasticity equations and rules. Based on Pipel, we developed a prototype which was evaluated with a three stages graphical application and four different task workloads (Increasing, Decreasing, Constant and Oscillating). The results were promising, presenting an average gain of 38% in the application time when comparing non-elastic and elastic executions.
Keywords: Cloud elasticity; Pipeline applications; Performance Optimization; Dynamic Resource Management; Adaptivity.
An Efficient Payload Distribution Method for High Capacity Image Steganography
by Sandeep Rathor, Anand Singh Jalal, Soumendu Chakraborty
Abstract: To produce higher level of security most of the irreversible and reversible image steganography techniques stress upon encrypting the secret image (payload) before embedding it to the cover image. In this case the steganographic system requires more computation time if a payload is large. Therefore, technique that can have lower computation time, higher embedding capacity and provoke same level of distortion, as any state of art encryption technique, can enhance the performance of stego systems. In this paper we propose a payload distribution method for secure secret image sharing that has lower computation time. The proposed scheme can be consider as a better option than any encryption technique. In the proposed scheme the payload is distributed over sign map (SM), error factor (EF) and normalized error (NE) using primary cover image. A payload is a grayscale image which is divided into signmap, error factor and normalized error then embedded into RGB secondary cover image using high capacity steganography algorithm. Proposed method is an alternate for encryption which reduces the overall complexity of the stego system. Result analysis shows that the distortion introduced into the resulting signmap, error factor and normalized error is significant enough to conceal the original payload with lower computation time than any state of art encryption schemes.
Keywords: Irreversible Steganography; Payload; Cover Image; LSB; Sign Map Error Factor.
Armor on Digital Images Captured Using Photoelectric Technique by Absolute Watermarking Approach
by Suresh Annamalai
Abstract: Nowadays digital image captured through Photoelectric Technique have undergone with malicious modifications. The proposed paper tells on a high quality recovery of digital document using absolute watermarking approach. The recoveries of lost informations are identified using bit values. Whereas the problem on recovering scaled, rotated and translated images exist still. Thus the absolute watermarking approach assures the recovery of digital images from any format of manipulations providing high quality pictures. The different sort of bits used for this purpose is classified into audit bit, carrier bit and output bit. Here the consistent feature of the original image is coded and the output bit is protected using a carrier encoder. This enables the audit bit to detect the erasure locations and retrieve the manipulated areas of the image with high quality pictures in low cost.
Keywords: Carrier Encoding; Tamper Proofing; SIFT; Predictive Coding; Spoofing Detection; Compression.
Special Issue on: Soft Computing Approaches in Wireless Networks with IoT and Medical Health Care System (SI-SWNMS)
Reduced Mutual Coupling MIMO Antenna
by Hari Krishna, MATURI THIRUPATHI
Abstract: Abstract: In this paper, a reduced mutual coupling 1x2 inset feed rectangular patch antenna is presented. The antenna elements are separated by a distance of λ0/4 exhibiting excellent isolation of -55 dB at 5 GHz band. To improve the isolation between closely placed antennas, a compact planar meander line based Electronic Bandgap Structure (EBG) behaves like a double negative (DNG) material is placed between them. The proposed EBG structure is implemented on the MIMO antenna with continuous as well as discontinuous ground plane. It is found that the EBG structure with discontinuous ground plane improves at least 6 dB isolation between antenna elements than continuous ground. The proposed antenna structures are fabricated showing good agreement between simulated and measured results.
Keywords: Keywords: EBG Structure; MIMO Antenna; Miniature Antenna; Wideband Antenna.rn.
Hybrid Converter for Electric Vehicle Battery Charging with Power Quality Features
by B.R. Ananthapadmanabha, Rakesh Maurya, Sabharaj Arya
Abstract: The power factor correction converters for electric vehicle battery chargers result in higher conduction losses and reverse recovery losses when fed from low voltage supply line.To overcome the above problem, ahybrid switched capacitor Cuk converter based power factor correction converter is proposed which has high step-up gain and low-voltage stress across the switch.The converter offers a very significant efficiency improvement at low voltage supply line over the single-switch buck-boost converters (including fly-back, SEPIC and Cuk topologies) and conventional two-switch buck-boost cascaded converters. In this paper, the design and simulation of the proposed converter is carried out in continuous current mode (CCM).The modeling and simulation are done in Matlab-Simulink environment. The power quality indices like THDs (vTHD, iTHD), PF are evaluated to demonstrate the performance of the converter. The converter is evaluated both in steady state and transient conditions.
Keywords: Battery charging; Cuk converter; Harmonics; Electric vehicle; Power factor.
Automation of home and its Management Using IoT
by Hari Krishna, VANGA YASHWANTH REDDY, E. Naga Booshanam
Abstract: This paper proposes a smart home management system that depicts automation of home and its management. IoT (Internet of Things) is used to post the information to physical devices that are interfaced to environment. The acquisition of data considered in this work is humidity, temperature, smoke or hazardous gas detection, water level indication. This collected data is sent to internet using network and protocols. The proposed system works on real time monitoring of data and maintains security of home. Controlling action taken through internet. Reducing power consumption is major achievement and energy conservation happens with the implementation of automation. ARM 7 LPC2148 microcontroller is used in this work.
Keywords: IoT; Temp; LDR; IR Sensor,GPRS,WI-FI.
Analysis of particle swarm and artificial bee colony optimization based clustering protocol for WSN
by Ankit Gambhir, Ashish Payal
Abstract: Wireless sensor networks (WSNs) have attracted may research scholars in recent years. WSNs are significantly resource-restrained by their bound power supply. Due to which, energy utilization is a key issue in the designing of protocols for WSNs. Current researchers proposed the appropriate uses of routing protocols to increase network life. To such as hierarchical routing (clustering such as LEACH) is an efficient approach, in which cluster has been organized; each cluster has numerous nodes and lone cluster head (CH). Node transmits their sensed data to CH; CH cumulates that information and forwards that to sink. Soft-computing (SC) techniques such as nature inspired algorithms (particle swarm optimization, ant colony optimization, artificial bee colony optimization etc) vastly tackle their compatibility and adaptableness to deal with the complex constraints in WSNs. In this paper, performance of different versions of LEACH, obtained by applying soft computing approaches has been evaluated. Comparative analysis has been also presented.
Keywords: wireless sensor network; soft computing approaches; particle swarm optimization; artificial bee colony optimization;.