A Substrate Integrated Waveguide Based Filtenna for X and Ku Band Application

In this paper Substrate Integrated Waveguide-basedfiltenna operating at Ku band is proposed.The model is designed on a low loss dielectric substrate having a thickness of 0.508 mm and comprises of shorting vias along two edges of the substrate walls.To realize a bandpassfilter,secondary shorting vias are placed close to primary shorting vias.The dimension and position of the vias are carefully analyzed for Ku band frequencies.The model is fabricated on Roger RT/duroid 5880 and the performance characteristics are measured.The proposed model achieves significant impedance characteristics with wider bandwidth in the Ku band.The model also achieves a maximum gain of 7.46 dBi in the operating band thus making it suitable for Ku-band applications.Substrate Integrated Waveguide(SIW)Structures possess most of the advantages over conventional radiofrequency waveguides since they have high power management capacity with self-consistent electrical shielding.The most noteworthy advantage of SIW,it can able to integrate all the components on the same substrate,both passive and active components.

Development of Energy Management System for Micro Grid Operation

The introduction of several small and large-scale industries,malls,shopping complexes,and domestic applications has significantly increased energy consumption.The aim of the work is to simulate a technically viable and economically optimum hybrid power system for residential buildings.The proposed micro-grid model includes four power generators:solar power,wind power,Electricity Board(EB)source,and a Diesel Generator(DG)set,with solar and wind power performing as major sources and the EB supply and DG set serving as backup sources.The core issue in direct current to alternate current conversion is harmonics distortion,a five-stage multilevel inverter is employed with the assistance of an intelligent control system is simulated and the optimum system configuration is estimated to reduce harmonics and improve the power quality.The monthly demand for residential buildings is 13-15 Megawatts.So,almost 433 Kilo-Watts(KW)of electricity is required every day,and if it is used for 8 h per day,50-60 KW of electricity is needed per hour.The overall micro-grid model’s operation and performance are established using MATLAB/SIMULINK software,and simulation results are provided.The simulation results show that the developed system is both cost-effective and environment friendly resulting in yearly cost reductions.

A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

Diabetic Retinopathy(DR)is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina,leading to blindness or loss of vision.Morphological and physiological retinal variations involving slowdown of blood flow in the retina,elevation of leukocyte cohesion,basement membrane dystrophy,and decline of pericyte cells,develop.As DR in its initial stage has no symptoms,early detection and automated diagnosis can prevent further visual damage.In this research,using a Deep Neural Network(DNN),segmentation methods are proposed to detect the retinal defects such as exudates,hemorrhages,microaneurysms from digital fundus images and then the conditions are classified accurately to identify the grades as mild,moderate,severe,no PDR,PDR in DR.Initially,saliency detection is applied on color images to detect maximum salient foreground objects from the background.Next,structure tensor is applied powerfully to enhance the local patterns of edge elements and intensity changes that occur on edges of the object.Finally,active contours approximation is performed using gradient descent to segment the lesions from the images.Afterwards,the output images from the proposed segmentation process are subjected to evaluate the ratio between the total contour area and the total true contour arc length to label the classes as mild,moderate,severe,No PDR and PDR.Based on the computed ratio obtained from segmented images,the severity levels were identified.Meanwhile,statistical parameters like the mean and the standard deviation of pixel intensities,mean of hue,saturation and deviation clustering,are estimated through K-means,which are computed as features from the output images of the proposed segmentation process.Using these derived feature sets as input to the classifier,the classification of DR was performed.Finally,a VGG-19 deep neural network was trained and tested using the derived feature sets from the KAGGLE fundus image dataset containing 35,126 images in total.The VGG-19 is trained with features extracted from 20,000 images and tested with features extracted from 5,000 images to achieve a sensitivity of 82%and an accuracy of 96%.The proposed system was able to label and classify DR grades automatically.

Enhanced Portable LUT Multiplier with Gated Power Optimization for Biomedical Therapeutic Devices

Digital design of a digital signal processor involves accurate and high-speed mathematical computation units.DSP units are one of the most power consuming and memory occupying devices.Multipliers are the common building blocks in most of the DSP units which demands low power and area constraints in the field of portable biomedical devices.This research works attempts multiple power reduction technique to limit the power dissipation of the proposed LUT multiplier unit.A lookup table-based multiplier has the advantage of almost constant area requirement’s irrespective to the increase in bit size of multiplier.Clock gating is usually used to reduce the unnecessary switching activities in idle circlet components.A clock tree structure is employed to enhance the SRAM based lookup table memory architecture.The LUT memory access operation is sequential in nature and instead of address decoder a ring counter is used to scan the memory contents and gated driver tree structure is implemented to control the clock and data switching activities.The proposed algorithm yields 20%of power reduction than existing.

Bus Encoded LUT Multiplier for Portable Biomedical Therapeutic Devices

DSP operation in a Biomedical related therapeutic hardware need to beperformed with high accuracy and with high speed. Portable DSP hardware’s likepulse/heart beat detectors must perform with reduced operational power due to lack ofconventional power sources. This work proposes a hybrid biomedical hardware chip inwhich the speed and power utilization factors are greatly improved. Multipliers are thecore operational unit of any DSP SoC. This work proposes a LUT based unsignedmultiplication which is proven to be efficient in terms of high operating speed. For n bitinput multiplication n*n memory array of 2 n bit size is required to memorize all thepossible input and output combination. Various literature works claims to be achieve highspeed multiplication with reduced LUT size by integrating a barrel shifter mechanism.This paper work address this problem, by reworking the multiplier architecture with aparallel operating pre-processing unit which used to change the multiplier and multiplicandorder with respect to the number of computational addition and subtraction stages required.Along with LUT multiplier a low power bus encoding scheme is integrated to limit the powerconstraint of the on chip DSP unit. This paper address both the speed and power optimizationtechniques and tested with various FPGA device families.

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

Routing Protocol in Underwater Wireless Acoustic Communication Using Non Orthogonal Multiple Access

The underwater wireless communication with the complexity of attenuation and low propagation speed makes resource constraints in networking sensor nodes and sink.Underwater Sensor Transmission with Attenuation Calculation using Non Orthogonal Multiple Access(UWSTAC-NOMA)protocol has been proposed.This protocol calculates channel gain along with attenuation in underwater channels and provides internetworking sensor for rate allocation minimizing interference.Successive Interference Cancellation has been used at the receiving sensor to decode the information sent.The network level performance of sensors and increasing the data rate improves the overall throughput.Simultaneously,connecting several sensors to sink based on its depth region of deployment has been achieved using Underwater Sensor Transmission with Attenuation Calculation using Non Orthogonal Multiple Access(UWSTAC-NOMA).The analytical background of attenuation never confuted the simulation results of the proposed protocol in NS2 simulator.Simulation results shows that the throughput,average bit error rate and residual energy of sink performance.

Comparative Analysis of Quantum based Receivers with Linear and Nonlinear Adaptive Receivers

In this paper,we propose a new linear multiuser receiver for synchronous code-division multiple-access (CDMA) systems,referred to as the orthogonal multiuser(OMU) receiver.Unlike the linear minimum mean-squared error(MMSE) receiver,the OMU receiver depends only on the signature vectors and does not require knowledge of the received amplitudes or the channel signal-to-noise ratio(SNR).Here we develop methods that construct an optimal set of vectors with a specified inner product structure,from a given set of vectors in a complex Hilbert space.The optimal vectors are chosen to minimize the sum of the squared norms of the errors between the constructed vectors and the given vectors.An algorithm has been developed using the principles of quantum parameters and some of its axioms and constraints.In place of the classical matched filter(MF) receiver we propose a modified receiver.This approach assumes that improving the accuracy will necessarily result in im- proved performance.The simulation results provided here suggests that in certain cases the OMU and POMU receivers can significantly increase the probability of correct detection with low error rate over the MF receiver.

Cervical Diseases Prediction Using LHVR Techniques

The stabilizing mechanisms of cervical spine spondylosis are involved in the degenerating segmentation vertebra,which often causes pain.Health of the individual is affected,both physically and mentally.Due to depression,nervous-ness,and psychological damages occur thereby losing their human activity func-tions.The nucleus pulposus of spinal disc herniation is prolapsed through a deficiency of annulusfibrosus.A jelly-like core part of the disc contains proteins that cause the tissues to become swollen when it touches the nucleus pulposus.The proposed Gradient Linear Classification(GLC)algorithm is used for the effi-cient automatic classification of disc degeneration herniation of Inter vertebral/vertebra in a cervical disc.Distance between the disc degeneration is classified through gradient operator and is estimated using the rotation of angles between the correlations.Specialists of the orthopedic spine are searching for high-preci-sion algorithms,which are achieved using proposed Linear Hybrid Vertebra Regression(LHVR)diagnostic techniques to identify the degree of cervical disc degeneration using an accurate location.Our experimental results have been used to determine a high range of classification in predicting the spinal cord which saves handling time and accomplishes high accuracy in detection.

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive applications.However,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious attackers.In order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious attacks.The True Random Number Generator-Pseudo Random Number Generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the hackers.Moreover,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key value.The performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and delay.The input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.

Performance Analysis and Throughput Enhancement of the STET Technique for WLAN IEEE 802.11ad

The IEEE 802.11ad innovation has enabled the impact of remote devices in unauthorized 60 GHz unlicensed frequency band at Giga bits per second information transfer rate in speed concentrated 5G applications.We have presented an innovative work that deals with the upgradation of the ability of IEEE 802.11ad wireless LAN to make it suitable for wireless applications.An exact examination on the IEEE 802.11ad analysis has been carried out in this work to achieve the greatest throughput.This has pulled attraction in broad consideration for accomplishing the pinnacle transmission rate of 8 Gbit/s.IEEE 802.11ad is a convention utilized for extremely high information rates(around 8 Gbit/s)and for short range remote correspondence of around 1 to 10 meters.The Multi-layer with Multi-User Multiple Input Multiple-Output(MLMU-MIMO)innovation has enabled transmission of multiple information packets all the while from the WLAN IEEE 802.11 ad Access Point(AP)to various receivers by means of different wireless applications.To help the MLMU-MIMO innovation at Medium Access Control(MAC),a new method called Simultaneous Transmission for Enhanced Throughput(STET)has been presented for enhancement of the transmission rate of the wireless standard.A mathematical model has been used for the evaluation of the performance of an IEEE 802.11ad AP empowering the STET allocation technique.This STET model can help determination of the throughput improvement of a STET technique,for example,audio information,images,good quality and foundation impact.The main goal of this work is to analyze the improvement of the STET allocation technique in regard to the usage of rare frequency band,when obtaining spectrum access between the various access points.The theoretical and simulation outcomes are also confirmed by Riverbed modeler 17.5 simulation tool.

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

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 64,182.86 KWh to the grid from April to September 2015, ranging from 11,510.900 to 10,200.9 kWh. The final yield ranged from 143.886 (h/d) to 127.51 (y/d), reference yield ranged from 201.6 (h/d) to 155.31 (h/d) and performance ratio ranged from 71.3% to 82.1%, for a duration of six months, it had given a performance ratio of 83.82%, system efficiency was 4.16% and the capacity factor of GRT IET Campus for six months was 18.26%. Payback period in years = 9 years 4 months, energy saving per year = 204,400 KWh, cost reduction per year = 1,737,400, Indian rupee = 26,197.30 USD and total CO2 reductions per year = 102,200 tons CO2/year.

A Correlative Study of Perturb and Observe Technique and GA-RBF-NN Method Supplying a Brushless DC Motor

A comparative study is done in regards to the performance of the popular Perturb and Observe algorithm and the Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) algorithm, both incorporating the Interleaved Boost converter. The Perturb and Observe method (P&O) is inarguably the most commonly used algorithm as its advantages pertaining to its ease in implementation and simplicity enable to track the Maximum Power Point (MPP). However, it is absolutely unreliable when subjected to rapidly fluctuating irradiation and temperature levels. More importantly, the system has the tendency to swing back and forth about the Maximum Power Point without reaching stability. At this juncture, the implementation of the Genetic-Assisted Radial Basis Function (GA-RBF) algorithm helps the system achieve MPP at a shorter time when compared to the Perturb and Observe technique. The ever reliable and robust Levenberg-Marquardt algorithm is included along with the MPPT controller that minimizes the Mean Square Error (MSE) and aids in faster training of the neural network. This PV system drives a brushless DC motor (BLDC), employing rotor position sensors.

A New Clock Gated Flip Flop for Pipelining Architecture

The objective of the work is to design a new clock gated based flip flop for pipelining architecture. In computing and consumer products, the major dynamic power is consumed in the system’s clock signal, typically about 30% to 70% of the total dynamic (switching) power consumption. Several techniques to reduce the dynamic power have been developed, of which clock gating is predominant. In this work, a new methodology is applied for gating the Flip flop by which the power will be reduced. The clock gating is employed to the pipelining stage flip flop which is active only during valid data are arrived. The methodology used in project named Selective Look-Ahead Clock Gating computes the clock enabling signals of each FF one cycle ahead of time, based on the present cycle data of those FFs on which it depends. Similarly to data-driven gating, it is capable of stopping the majority of redundant clock pulses. In this work, the circuit implementation of the various blocks of data driven clock gating is done and the results are observed. The proposed work is used for pipelining stage in microprocessor and DSP architectures. The proposed method is simulated using the quartus for cyclone 3 kit.

Solar PV System for Energy Conservation Incorporating an MPPT Based on Computational Intelligent Techniques Supplying Brushless DC Motor Drive

This paper proposes an effective Maximum Power Point Tracking (MPPT) controller being incorporated into a solar Photovoltaic system supplying a Brushless DC (BLDC) motor drive as the load. The MPPT controller makes use of a Genetic Assisted Radial Basis Function Neural Network based technique that includes a high step up Interleaved DC-DC converter. The BLDC motor combines a controller with a Proportional Integral (PI) speed control loop. MATLAB/Simulink has been used to construct the dynamic model and simulate the system. The solar Photovoltaic system uses Genetic Assisted-Radial Basis Function-Neural Network (GA-RBF-NN) where the output signal governs the DC-DC boost converters to accomplish the MPPT. This proposed GA-RBF-NN based MPPT controller produces an average power increase of 26.37% and faster response time.

Experimental verification of chopper fed DC series motor with ANN controller

In this article an artificial neural network （ANN） has been designed for the control of DC series motor through a DC chopper （DC-DC buck converter）. The proportional-integral-derivative （PID）-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady-state and dynamic operating condition with various set speeds and various load torques. The rise time, maximum overshoot, settling time, steady-state error, and speed drops are taken for comparison with conventional PID controller and existing work. The training samples for the neuron controller are acquired from the conventional PID controller. The PID-ANN controller performances are analyzed in respect of various load torques and various speeds using MATLAB simula-tion. Then the designed controllers were experimentally verified using an NXP 80C51 based microcontroller （P89VSIRD2BN）. It was found that the hybrid PID-ANN controller with DC chopper can have better control compared with conventional PID controller.

DSP based fuzzy controller for series parallel resonant converter

In this paper, digital signal processor （DSP） based fuzzy controller for series parallel resonant converter （SPRC） has been estimated, and the performance of the converter is analyzed by using state space model. The method to predict the steady-state and dynamic perfor-mance of the converter with load independent operation has been presented. The proposed converter has been analyzed with the closed-loop and open-loop conditions. The simple form of transfer function for SPRC is developed, and it is used to analyze the stability of the converter with closed-loop operation. The stability analy-sis of the converter is carried out by using frequency response plan. The fuzzy controller regulates the output voltage with change supply voltage and load disturbance. The controller performance of inductance capacitance inductance - T network （LCL-T） SPRC is compared with inductance inductance capacitance - T network （LLC-T） SPRC through simulation TMS320F2407 processor. and experimental studies using