Showing papers in "Circuits and Systems in 2016"

Multi-Criteria Decision Making Using ELECTRE

Abstract: Requirements prioritization is one of the key factors in deciding the success of the project and hence the software industry. One of the major concerns in software prioritization techniques is that the existing ranking techniques have a very modest support to different criteria used by stakeholders to present their ranking. The current techniques are not suitable for arriving at anoptimized view of multiple stakeholders using multiple criteria. This research analyzes the issues in existing techniques. A web based decision support model using ELECTRE as the method for prioritization is proposed. ELECTRE is a multi-criteria decision making model that is proved to be effective in ranking several decision making problems. The proposed system takes input from multiple stakeholders using 100-point method. An optimized ranking is obtained using ELECTRE method. The developed system is validated using a pilot project and is found to be efficient in terms of saving cost of implementation and man-hours needed for implementation.

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Abstract: The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequency-magnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the earthquakes of magnitude between 4.0 and 6.0.

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Abstract: Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

Performance-Economic and Energy Loss Analysis of 80 KWp Grid Connected Roof Top Transformer Less Photovoltaic Power Plant

Abstract: In India most part receives 4 - 7 kWh of solar radiation per square meter per day with 200 - 250 sunny days in a year Tamilnadu state also receives the highest annual radiation in India In this paper, the grid connected photovoltaic plant has a peak power of 80 KWp supplies electricity requirement of GRT IET campus during day time (7 hrs) and reduces load demand and generates useful data for future implementation of such PV plant projects in the Tamilnadu region Photovoltaic plant was installed in April 2015, monitored during 6 months, and the performance ratio and the various power losses (power electronics, temperature, soiling, internal, network, grid availability and interconnection) were calculated The PV plant supplied 64,18286 KWh to the grid from April to September 2015, ranging from 11,510900 to 10,2009 kWh The final yield ranged from 143886 (h/d) to 12751 (y/d), reference yield ranged from 2016 (h/d) to 15531 (h/d) and performance ratio ranged from 713% to 821%, for a duration of six months, it had given a performance ratio of 8382%, system efficiency was 416% and the capacity factor of GRT IET Campus for six months was 1826% Payback period in years = 9 years 4 months, energy saving per year = 204,400 KWh, cost reduction per year = 1,737,400, Indian rupee = 26,19730 USD and total CO2 reductions per year = 102,200 tons CO2/year

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

Abstract: This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Abstract: Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).

Energy Consumption Power Aware Data Delivery in Wireless Network

Abstract: In this paper, we present the virtual desktop that uses a novel methodology and related metrics to benchmark thin client based on Data Delivery Networks (DDN) in terms of scalability and reliability. Most studies of the wireless networks mainly focus on system performance and power consumption circuit system; the main target has been separated in terms of Data operation and GUI operation by DDN. The communication protocol for wireless communication may play a major role in energy consumption and other important factors. The portable devices like Personal Digital Assistance (PDA) and others are mainly focusing on the efficient energy consumption (power control) in wireless networks. Here we focus on energy efficiency, algorithmic efficiency, virtualization and resource allocation; these are the main aims of the authors. The foremost research in the direction of wireless computing in saving energy and reducing carbon foot prints is also the challenging part. This is the study proof of brief account of wireless networks.

Feature Selection Based on Enhanced Cuckoo Search for Breast Cancer Classification in Mammogram Image

Abstract: Proposed system has been developed to extract the optimal features from the breast tumors using Enhanced Cuckoo Search (ECS) and presented in this paper. The texture feature, intensity histogram feature, radial distance feature and shape features have been extracted and the optimal feature set has been obtained using ECS. The overall accuracy of a minimum distance classifier and k-Nearest Neighbor (k-NN) on validation samples is used as a fitness value for ECS. The new approach is carried out on the extracted feature dataset. The proposed system selects only the minimum number of features and performed the accuracy of 98.75% with Minimum Distance Classifier and 99.13% with k-NN Classifier. The performance of the new ECS is compared with the Cuckoo Search and Harmony Search. This result shows that the ECS algorithm is more accurate than the other algorithm. The proposed system can provide valuable information to the physician in medical pathology.

Design and Implementation of Bi-Directional DC-DC Converter for Wind Energy System

Abstract: This paper proposes a design and implementation of the bi-directional DC-DC converter for Wind Energy Conversion System. The proposed project consists of boost DC/DC converter, bi-directional DC/DC converter (BDC), permanent magnet DC generator and batteries. A DC-DC boost converter is interface with proposed wind system to step up the initial generator voltage and maintain constant output voltage. The fluctuation nature of wind makes them unsuitable for standalone operation. To overcome the drawbacks an energy storage device is used in the proposed system to compensate the fluctuations and to maintain a smooth and continuous power flow in all operating modes to load. Bi-directional DC-DC converter (BDC) is capable of transforming energy between two DC buses. It can operate as a boost converter which supplies energy to the load when the wind generator output power is greater than the required load power. It also operates in buck mode which charges from DC bus when output power is less than the required load power. The proposed converter reduces the component losses and increases the performance of the overall system. The complete system is implemented in MATLAB/SIMULINK and verified with hardware.

Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

Abstract: An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

Finite Element Analysis (FEA) for the Beam-Column Joint Subjected to Cyclic Loading Was Performed Using ANSYS

Abstract: This paper analyses the seismic performance of exterior beam-column joints strengthened with unconventional reinforcement detailing. The beam-column joint specimens were tested with reverse cyclic loading applied at the beam end. The samples were divided into two groups based on the joint reinforcement detailing. The first group (Group A) of three non-ductility specimens had joint detailing in accordance with the construction code of practice in India IS456-2000, and the second group (Group B) of three ductility specimens had joint reinforcement detailed as per IS13920-1993, with similar axial load cases as the first group. The experimental studies are proven with the analytical studies carried out by finite element models using ANSYS. The results show that the hysteresis simulation is satisfactory for both un-strengthened and ferrocement strengthened specimens. Furthermore, when ferrocement strengthening is employed, the strengthened beam-column joints exhibit better structural performance than the un-strengthened specimens of about 31.56% and 38.98 for DD-T1 and DD-T2 respectively. The analytical shear strength predictions were in line with the test results reported in the literature, thus adding confidence to the validity of the proposed models.

ECBK: Enhanced Cluster Based Key Management Scheme for Achieving Quality of Service

Abstract: Wireless sensor networks consist of many small nodes with distributing devices to monitor conditions at different locations. Usually wireless sensor nodes are sprinkled in a sensor field grouping limited areas. This paper highlights the Enhanced Cluster Based Key management (ECBK) protocol to achieve secure data delivery based on clustering mechanism. This protocol gives more importance to Cluster Coordinator node, which is used to coordinate the members and provide protective communication among the sensor nodes to enhance reliability. In Enhanced Cluster Based Key management two types of nodes are deployed. The high power nodes form clusters with surrounding nodes to enable the routing process without interference. This paper introduces ECBK protocol that balances the load among the clusters, achieves high throughput, end to end delay will be reduced, routing overhead also reduced and also it prolongs the network lifetime. Simulation results show that the presence of high transmission nodes reduces the delay, load balance, routing overhead, and enhances the throughput increased by 45% compared to other similar methods.

FPGA Implementation of a Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines

Abstract: Restricted Boltzmann Machines (RBMs) are an effective model for machine learning; however, they require a significant amount of processing time. In this study, we propose a highly parallel, highly flexible architecture that combines small and completely parallel RBMs. This proposal addresses problems associated with calculation speed and exponential increases in circuit scale. We show that this architecture can optionally respond to the trade-offs between these two problems. Furthermore, our FPGA implementation performs at a 134 times processing speed up factor with respect to a conventional CPU.

Comparative Methodical Assessment of Established MOSFET Threshold Voltage Extraction Methods at 10-nm Technology Node

Abstract: Threshold voltage (VTH) is the most evocative aspect of MOSFET operation. It is the crucial device constraint to model on-off transition characteristics. Precise VTH value of the device is extracted and evaluated by several estimation techniques. However, these assessed values of VTH diverge from the exact values due to various short channel effects (SCEs) and non-idealities present in the device. Numerous prevalent VTH extraction methods are discussed. All the results are verified by extensive 2-D TCAD simulation and confirmed through analytical results at 10-nm technology node. Aim of this research paper is to explore and present a comparative study of largely applied threshold extraction methods for bulk driven nano-MOSFETs especially at 10-nm technology node along with various sub 45-nm technology nodes. Application of the threshold extraction methods to implement noise analysis is briefly presented to infer the most appropriate extraction method at nanometer technology nodes.

Fault Identification and Islanding in DC Grid Connected PV System

Abstract: Nowadays, the DC distribution system has been suggested, as a replacement for the AC power distribution system with electric propulsion. This idea signifies a fresh approach of issuing energy for low-voltage installations. It can be used for any electrical application up to 20 MW and works at a nominal voltage of 1000 V DC. The DC distribution system is just an extension of the multiple DC links that previously available in all propulsion and thruster drives, which typically comprise more than 80% of the electrical power consumption on electric propulsion vessels. A fault detection and islanding scheme for DC grid connected PV system is presented in this paper. Unlike traditional ac distribution systems, protection has been challenging for dc systems. The goals of this paper are to classify and detect the fault in the PV system as well as DC grid and to isolate the faulted section so that the system keeps operating without disabling the entire system. The results show the measured values of power at PV panel and DC grid side under different fault condition, which indicates the type of fault that occurs in the system.

Think Home: A Smart Home as Digital Ecosystem

Abstract: This work brings all new and advanced technology which is proposed for refinement and improvement in the existing electrification system at domestic as well as commercial levels including hotels, commercial complexes, apartments, rented floors and rooms. This advanced module will not only convey means of luxury but will also accomplish real-time energy monitoring and cost es-timation. This developed module will rule out entire re-wiring and will be fruitful at places where installation of a new meter was a problem. The new system after installation will offer means of comfort to the consumer, elderly as well as handicapped and disabled people in operating electric load with ease and comfort. Apart from this, it would also benefit the apartment/hotel owner’s and business personnel who have rented their property or portion of property and face problems in calculating energy bill.

Fuzzy Logic Based Decision Making Algorithm to Optimize the Handover Performance in HetNets

Abstract: A prominent growth in a wireless communication system provides a wide range of services for a heterogeneous wireless network thus accomplishing user’s needs. These real time services are delay sensitive which requires a continuous internet connection. Assuring a seamless end-end connectivity without link outages at the mid of an action is a crucial ongoing problem. Thus the main objective relies on designing a cognitive algorithm to achieve the practical impossibility of having a sustainable net connection while roaming across various technologies. Hence, to fulfill this and degrade vertical handover (VHO) issues an enhanced fuzzy logic based algorithm is proposed. Further, it focuses on improving the transmission quality with low handover latency, less packet loss and reduced false handover. Along with this the algorithm achieves an accurate prediction in the decision making of an optimal network to which the idle node gets connected. The networking environment also has an efficient mobility management, where the functioning network controls the mobility of concerned mobile nodes. Finally the simulation process of this proposal on a comprehensive test-bed infers the fuzzy logic based algorithm, guaranteed seamless connection. Further, the system has a 1.6% improvement in reducing the handover delay and 1.3% in packet loss reduction than the existing approaches, which in turn improves the transmission quality.

Novel Scheme for Compressed Image Authentication Using LSB Watermarking and EMRC6 Encryption

Abstract: In the current era, transmission and storing of medical data in the digital form is of great concern and thus the requirement for content authentication has aroused. As a solution to these, digital watermarking techniques and encryption schemes have been used to secure medical data like medical images. In this paper a combination of two algorithms to provide image authentication for medical images in the compressed format is proposed. In the proposed method, the watermark image is encrypted using the Enhanced modified RC6 block cipher (EMRC6) algorithm and the encrypted watermark image is watermarked using the simple Least significant Bit (LSB) watermarking technique. The watermarked output image shows no visual imparity and the watermark which has been extracted has no visual difference. The test results show that the watermarked image has high quality and the watermark is very secure. Also the PSNR value of proposed method is 44.966 on an average and 43.0633 for the existing system where LSB technique is integrated with MRC6 for security of watermark. Hence the work is aimed to increase the embedding volume and make the watermark more secure which is the basic requirement of medical image security.

ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting

Abstract: The scope of this paper is to forecast wind speed. Wind speed, temperature, wind direction, relative humidity, precipitation of water content and air pressure are the main factors make the wind speed forecasting as a complex problem and neural network performance is mainly influenced by proper hidden layer neuron units. This paper proposes new criteria for appropriate hidden layer neuron unit’s determination and attempts a novel hybrid method in order to achieve enhanced wind speed forecasting. This paper proposes the following two main innovative contributions 1) both either over fitting or under fitting issues are avoided by means of the proposed new criteria based hidden layer neuron unit’s estimation. 2) ELMAN neural network is optimized through Modified Grey Wolf Optimizer (MGWO). The proposed hybrid method (ELMAN-MGWO) performance, effectiveness is confirmed by means of the comparison between Grey Wolf Optimizer (GWO), Adaptive Gbest-guided Gravitational Search Algorithm (GGSA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Cuckoo Search (CS), Particle Swarm Optimization (PSO), Evolution Strategy (ES), Genetic Algorithm (GA) algorithms, meanwhile proposed new criteria effectiveness and precise are verified comparison with other existing selection criteria. Three real-time wind data sets are utilized in order to analysis the performance of the proposed approach. Simulation results demonstrate that the proposed hybrid method (ELMAN-MGWO) achieve the mean square error AVG ± STD of 4.1379e-11 ± 1.0567e-15, 6.3073e-11 ± 3.5708e-15 and 7.5840e-11 ± 1.1613e-14 respectively for evaluation on three real-time data sets. Hence, the proposed hybrid method is superior, precise, enhance wind speed forecasting than that of other existing methods and robust.

A Novel Flower Pollination Algorithm to Solve Load Frequency Control for a Hydro-Thermal Deregulated Power System

Abstract: Load frequency control plays a vital role in power system operation and control. LFC regulates the frequency of larger interconnected power systems and keeps the net interchange of power between the pool members at predetermined values for the corresponding changes in load demand. In this paper, the two-area, hydrothermal deregulated power system is considered with Redox Flow Batteries (RFB) in both the areas. RFB is an energy storage device, which converts electrical energy into chemical energy, that is used to meet the sudden requirement of real power load and hence very effective in reducing the peak shoots. With conventional proportional-integral (PI) controller, it is difficult to get the optimum solution. Hence, intelligent techniques are used to tune the PI controller of the LFC to improve the dynamic response. In the family of intelligent techniques, a recent nature inspired algorithm called the Flower Pollination Algorithm (FPA) gives the global minima solution. The optimal value of the controller is determined by minimizing the ISE. The results show that the proposed FPA tuned PI controller improves the dynamic response of the deregulated system faster than the PI controller for different cases. The simulation is implemented in MATLAB environment.

Performance Analysis of Malicious Node Detection and Elimination Using Clustering Approach on MANET

Abstract: Mobile Ad hoc Network (MANET) is a significant concept of wireless networks which comprises of thousands of nodes that are mobile as well as autonomous and they do not requires any existing network infrastructure. The autonomous nodes can freely and randomly move within the network which can create temporary dynamic network and these networks can change their topology frequently. The security is the primary issue in MANET which degrades the network performance significantly. In this paper, cluster based malicious node detection methodology is proposed to detect and remove the malicious nodes. Each node within the cluster gets the cluster key from the cluster head and this key is used for the data transaction between cluster head and node. The cluster head checks this key for every data transaction from node and match with their cluster table. If match is valid, and then only it will recognize that this node is belongs to this cluster, otherwise it is decided as malicious node. This paper also discusses the detection of link failure due to the presence of malicious node by determining the gain of each link in the network. The performance of the proposed method is analyzed using packet delivery ratio, network life time, and throughput and energy consumption. The proposed malicious node detection system is compared with the conventional techniques as OEERP (Optimized energy efficient routing protocol), LEACH (Low energy adaptive clustering hierarchy), DRINA (Data routing for In-network aggregation) and BCDCP (Base station controlled dynamic clustering protocol).

SCSBE: Secured Cluster and Sleep Based Energy-Efficient Sensory Data Collection with Mobile Sinks

Abstract: Wireless sensor networks applications involve a position of inaccessible metropolitan vicinity en-closed by wireless sensor nodes (WSNs)-monitors environmental parameters like battle field surveillance, home applications like fire alarm, health monitoring, etc. Energy plays a vital role in Wireless sensor networks. So, we have to concentrate more on balanced energy consumption for maximizing the network lifetime. Minimizing the whole network overhead and vigor disbursement coupled with the multi-hop data reclamation process that ensuring balanced energy consumption among SNs which results in prolonged network lifetime. This can be achieved by forwarding the sensed data to their cluster heads and then filtering the data before sending it to their tryst nodes, which is located in proximity to MS’s trajectory. Sleep and awakening of nodes periodically helps to retain their energy for some more time. The events occurring in any part of the network should be identified by the nodes, while arrangements sleep and active among the nodes. (i.e.) the nodes should be scheduled to sleep, so that the outstanding nodes can take care of the whole network. The eXtensible Randomized Matrix Arithmetic Coding (XRMAC) Technique has been used to enhance the security among all the nodes in the network. Simulation results show that our Proposed Scheme can have better Lifetime, improved throughput, reduced delay compared to other existing methods.

Simple Simulated Inductor, Low-Pass/Band-Pass Filter and Sinusoidal Oscillator Using OTRA

Abstract: Although a variety of applications of the OTRAs have been reported in literature, the pole of the transresistance gain Rm of the OTRA has been usually considered to affect the performance of the circuits due to being parasitic. In this paper, the pole of the OTRA has been used to evolve some simple OTRA-based active-R circuits for realizing a synthetic inductor, single resistance controlled oscillator and low-pass/band-pass filter. The workability of all the proposed circuits has been verified by SPICE simulations and all the new circuits have been found to work as predicted by theory. The exemplary propositions suggest that it is worthwhile to further investigate new circuit designs using OTRA-pole.

Seasonal Based Electricity Demand Forecasting Using Time Series Analysis

Abstract: Consumption of the electric power highly depends on the Season under consideration. The various means of power generation methods using renewable resources such as sunlight, wind, rain, tides, and waves are season dependent. This paves the way for analyzing the demand for electric power based on various Seasons. Many traditional methods are utilized previously for the seasonal based electricity demand forecasting. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. In this paper, a WEKA time series forecasting is being done for the electric power demand for the three seasons such as summer, winter and rainy seasons. The monthly electric consumption data of domestic category is collected from Tamil Nadu Electricity Board (TNEB). Data collected has been pruned based on the three seasons. The WEKA learning algorithms such as Multilayer Perceptron, Support Vector Machine, Linear Regression, and Gaussian Process are used for implementation. The Mean Absolute Error (MAE) and Direction Accuracy (DA) are calculated for the WEKA learning algorithms and they are compared to find the best learning algorithm. The Support Vector Machine algorithm exhibits low Mean Absolute Error and high Direction Accuracy than other WEKA learning algorithms. Hence, the Support Vector Machine learning algorithm is proven to be the WEKA learning algorithm for seasonal based electricity demand forecasting. The need of the hour is to predict and act in the deficit power. This paper is a prelude for such activity and an eye opener in this field.

Multifunction Filter Employing Current Differencing Buffered Amplifier

Abstract: This paper proposes a new filter biquad circuit, which utilizes three Current Differencing Buffered Amplifiers (CDBA), two capacitors and five resistors, and operates in the trans-resistance mode. This multi-input and single-output multifunction filter uses only grounded capacitors. All the employed resistors are either grounded or virtually grounded, which is an important parameter for its implementation as an integrated circuit. The circuit enjoys independent tunability of angular frequency and bandwidth. The 0.5 μm technology process parameters have been utilized to test and verify the performance characteristics of the circuit using PSPICE. The non-ideal analysis and sensitivity analysis, transient response, Monte-Carlo analysis and calculations of total harmonic distortion have also been shown.

An Efficient Framework for Indian Sign Language Recognition Using Wavelet Transform

Abstract: Hand gesture recognition system is considered as a way for more intuitive and proficient human computer interaction tool. The range of applications includes virtual prototyping, sign language analysis and medical training. In this paper, an efficient Indian Sign Language Recognition System (ISLR) is proposed for deaf and dump people using hand gesture images. The proposed ISLR system is considered as a pattern recognition technique that has two important modules: feature extraction and classification. The joint use of Discrete Wavelet Transform (DWT) based feature extraction and nearest neighbour classifier is used to recognize the sign language. The experimental results show that the proposed hand gesture recognition system achieves maximum 99.23% classification accuracy while using cosine distance classifier.

Modeling of a Lithium-Ion Capacitor and Its Charging and Discharging Circuit in a Model-Based Design

Abstract: For several years now, electric vehicles (EVs) have been expected to become widely available in the micro-mobility field. However, the insufficiency of such vehicles’ battery-charging and discharging performance has limited their practical use. A hybrid energy storage system, which comprises a capacitor and battery, is a promising solution to this problem; however, to apply model-based designs, which are indispensable to embedded systems, such as the electronic control units used in EVs, a simple and accurate capacitor model is required. Within this framework, a lithium-ion capacitor (LIC) model is proposed, and its charging and discharging performances are evaluated against an actual LIC. The model corresponds accurately to the actual LIC, and the results indicate that the proposed LIC model will work well when used with Model-Based Design (MBD).

An Improved Hybrid Space Vector PWM Technique for IM Drives

Abstract: In this paper, an improved hybrid space vector pulse width modulation (HSVPWM) technique is proposed for IM (induction motor) drives. The basic principle involved in the proposed random pulse width modulation (RPWM) cuddled SVPWM is amalgamating the pre-calculated switching timings for various sections of hexagonal space vector boundary and the random selection of carrier between two triangular signals, in order to disband acoustic switching noise spectrum with improved fundamental component. The arbitrary selection between triangular carriers, which is decided by digital signal states (Low or High) of the linear feedback shift register (LFSR) based pseudo random binary sequence (PRBS) generator. The SVPWM offers a control degree of freedom in terms of positioning of vectors inside every sampling interval and hence it has six possible variants of the voltage vectors arrangements in each sector. The developed HSVPWM is thoroughly analyzed in using the MATLAB? based simulation for all SVPWM variants. From the simulation and experimental results viz. harmonic spectrum, harmonic spread factor (HSF), total harmonic distortion (THD) etc., and the superiority of the proposed scheme such as better utilization of DC bus and the randomization of the harmonic power are evidenced. For the practical implementation, Xilinx XC3S500E FPGA device has been used.

Current-Controlled CFTA Based Fractional Order Quadrature Oscillators

Abstract: This paper presents a study of fractional order quadrature oscillators based on current-controlled current follower transconductance amplifiers (CCCFTA). The design realisation and performance of the fractional order quadrature oscillators have been presented. The quadrature oscillators are constructed using three fractional capacitors of orders α = 0.5. The fractional capacitor is not available on the market or in the PSPICE program. Fortunately, the fractional capacitor can be realised by using the approximate method for the RC ladder network approximation. The oscillation frequency and oscillation condition can be electronically/orthogonally controlled via input bias currents. Due to high-output impedances, the proposed circuit enables easy cascading in current-mode (CM). The PSPICE simulation results are depicted, and the given results agree well with the anticipated theoretical outcomes.

Ride through Strategy for a Three-Level Dual Z-Source Inverter Using TRIAC

Abstract: A new ride through strategy is introduced in a three-level dual Z-source inverter, for isolation under semiconductor switching failure condition. Here the output will have no significant decrease in the amplitude and quality. Instead of diodes, the triacs are added to the inverter source ends, as it can perform a bidirectional power transfer also it can operate well in both low and high voltage operating conditions. The faulted part can be isolated by simply altering the firing pulses for turning on/off the triacs using the carrier based SPWM technique and resulting in a boosting output with zero common mode voltage. Consequently, it forms a common floating point or null point with a zero common mode voltage. It is experimentally verified by using MATLAB, and digital oscilloscope.