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This paper investigates the performance of solar PV system by implementing MPPT algorithm. The IV and PV characteristics of solar photovoltaic system have been analyzed for various parameters of temperature and irradiation. From the characteristics, the peak power variations with respect to physical parameters have been identified using algorithmic approach. To validate the MPPT operation, the P&O algorithm has been implemented and verified the operation under varying environmental conditions. And also, the same has been compared with theoretical approach in this paper.

Partial discharge (PD) monitoring of electrical substations could provide early warning of insulation failures. Among the various technologies, Radio Frequency (RF) based PD monitoring system could be a promising solution. The RF-based monitoring system detects PD sources in the substation and can also localise the PD sources. The time difference of arrival (TDOA) based PD localisation system primarily require arrival time of the impulsive RF signal. Though many localisation algorithms have been proposed in the recent past to overcome the TDOA estimation errors, less attention has been given to the accurate estimation of RF PD signal arrival time. This paper presents the AT's automatic labelling in the RF PD signal using Bi-Long Short-Term Memory (Bi-LSTM) network applied on the continuous wavelet transformed (CWT) signal. Further, it also shows PD signal augmentation to overcome the problem of limited representative training data set. The behaviour of the radiated RF signals is influenced by many factors and has almost stochastic characteristics. The proposed system has been validated with laboratory-based experimental signals and the data set obtained from different electrical substations. The results show that the improved performance is obtained from the combination of a multilayer Bi-LSTM model and an augmented training set.

Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

Facial expressions are an important form of non-verbal communication as they can depict the internal mood and emotions of an individual. In Automatic Facial Expression Recognition (AFER) system, the main task is to extract features that can best classify the expressions into various categories. The existing local based approaches fail in obtaining different feature values for edge, corner and flat image regions. In this work, Chess Pattern, a game based feature descriptor is proposed based on the movements of the chessmen such as Rook, Bishop and Knight and also the combinations of Rook_Knight, Rook_Bishop and Knight_Bishop are considered for feature extraction. Apart from using binary weights, new weighting schemes such as fibonacci weights, prime weights, natural weights, squares weights are also proposed for facial feature extraction. The Chess Pattern with different weights is applied on JAFFE, MUG, TFEID, KDEF, WSEFEP and ADFES datasets for six and seven expressions. Also, for SFEW, TFEID and ADFES datasets the experiments are conducted for seven, eight and ten expressions respectively. The experiments are conducted in person independent setup, in order to simulate a real world scenario. The comparison results shows the efficiency of the proposed approach when compared to other existing methods.

This paper presents stability enhancement of a test system that is connected with a Wind Farm (WF) by using Power System Stabilizer (PSS) for Synchronous Generator (SG) and Power Oscillation Damper (POD) for Static Var Compensator (SVC). This paper also proposes a coordination mechanism for the controller to effectively damp out the oscillations and make the power system more stable by considering the uncertainties. The uncertainty is considered as wind speed variation and wind power penetration and different locations. The Particle Swarm Optimization (PSO) is used to overcome the controller parameter tuning drawbacks and controller coordination. The SG rotor speed deviation is selected as an objective function with various constraints for PSO. The transient stability analysis is carried out by considering large disturbance that is a three-phase fault. The nonlinear dynamic simulation results are obtained by integrating WF and SG replacement with the same rating WF. Evaluation and analysis are performed for various cases and different combination of without and with controllers. From the simulation results, it is noticed that oscillations in the system are minimized, and stability is enhanced at the maximum level. It also observed that the capability of SG and DFIG under three-phase fault is intensified by using PSO for optimized coordinated controller parameters. The robustness and effectiveness of the proposed approaches are evaluated on the IEEE-11 bus test system.

Wireless Sensor Networks (WSNs) are a resource-constrained network class recognized as a major energy consumer. Wireless sensor technologies are used in many commercialized industrial automation processes and other real-world applications. The WSN protocol is well-suited to harsh situations where deployment is difficult or impossible, such as the battlefield, a toxic chemical plant, the cloud, fog computing, and the Internet of Things, but not in a high-temperature network infrastructure environment. WSNs have introduced various Energy-Efficient Routing Protocols based on network (NW) organization and protocols in recent years. Various WSN routing options for energy efficiency are explored in this work. The WSN Energy Efficient Routing Protocol is compared to other routing systems. We also compare and investigate better WSN routing algorithms for cloud computing, fog computing, and the Internet of Things.

Facial expressions are extremely important in the social interaction as they can display the internal emotions and intentions of an individual. Accurately classifying the facial expressions into various categories is the main task in Automatic Facial Expression Recognition (AFER) systems. The existing local based techniques, at times suffer and generate same feature values for different image portions such as edge, corner and flat regions. To address this issue, Radial Mesh Pattern (RMP), a local texture based approach based on the chess game rules is proposed. With reference to the center pixel in a $$5\times 5$$ neighborhood, the possible positions of Rook, Bishop and Knight are determined and based on these positions, the features are extracted. In this paper, not only binary weights, but also other weights such as fibonacci, prime, natural, squares, odd and even weights have been utilized for feature extraction. To validate the efficiency of the proposed method, RMP is implemented on six ‘in the lab’ datasets. The performance is measured through recognition accuracy and the results obtained from experiments demonstrate the efficiency of RMP over standard existing methods.

If The stand-alone dc microgrid system with a PVA i.e solar renewable energy source is operated without any supportive energy storage sources like battery and supercapacitor, then it will lead to an unstable operation of a DC microgrid, so it necessitates the usage of energy storage devices for maintaining stability in the system and also to improve the efficiency of PVA we have used an MPPT controller with P&O algorithm which provides a required duty ratio for DC-DC boost converter and this converter sees that the maximum power can be transmitted from PVA to loads. In this paper, we present how we performed a simulation study by integrating Simulink models like PVA, MPPT, battery, and Supercapacitor at Point of common coupling with DC loads and observed the stability of the system with different conditions like the change of irradiances during charging and discharging processes of storage devices and observed how is the power-sharing from PVA, Battery, and supercapacitor concerning change in load.

Due to intermittency in the natural resources and discrepancies in DC load, the stable power supply demands a storage device, generally low power density battery storage used, but the demand of quick power surges increases its rate of charge & discharge cycle which leads to a reduction in life of the battery (High energy density device), so which is compensated by using a supercapacitor (High power density device) which delivers high power in a short period, whereas battery delivers energy for a longer duration, however in this project work we have used an ideal Dc voltage source to simulate how the adopted control strategy is making power-sharing from battery & supercapacitor to DC bus and to observe their charging and discharging processes, also prevents them from overcharging and undercharging.

Viral infection causes a wide variety of human diseases including cancer and COVID-19. Viruses invade host cells and associate with host molecules, potentially disrupting the normal function of hosts that leads to fatal diseases. Novel viral genome prediction is crucial for understanding the complex viral diseases like AIDS and Ebola. While most existing computational techniques classify viral genomes, the efficiency of the classification depends solely on the structural features extracted. The state-of-the-art DNN models achieved excellent performance by automatic extraction of classification features, but the degree of model explainability is relatively poor. During model training for viral prediction, proposed CNN, CNN-LSTM based methods (EdeepVPP, EdeepVPP-hybrid) automatically extracts features. EdeepVPP also performs model interpretability in order to extract the most important patterns that cause viral genomes through learned filters. It is an interpretable CNN model that extracts vital biologically relevant patterns (features) from feature maps of viral sequences. The EdeepVPP-hybrid predictor outperforms all the existing methods by achieving 0.992 mean AUC-ROC and 0.990 AUC-PR on 19 human metagenomic contig experiment datasets using 10-fold cross-validation. We evaluate the ability of CNN filters to detect patterns across high average activation values. To further asses the robustness of EdeepVPP model, we perform leave-one-experiment-out cross-validation. It can work as a recommendation system to further analyze the raw sequences labeled as 'unknown' by alignment-based methods. We show that our interpretable model can extract patterns that are considered to be the most important features for predicting virus sequences through learned filters.

Volatile nature in renewable power output, stochastic nature of load and continuous change in system structure are the key factors affecting the stable operation of microgrid (MG), particularly in frequency control. Appropriate control schemes are indispensable to reduce the frequency fluctuations in MG under such scenarios. Concerning to these issues; this paper proposes a novel two-level control mechanism approach to mitigate the frequency fluctuations in the wind-diesel standalone MG. In the first level, from wind turbine generator (WTG) side, a fixed wind power command is replaced by an optimum adaptive wind power command based on the power system operating conditions and wind speed conditions. To perform this task, the fuzzy logic approach (FLA) is employed by considering change in wind velocity and frequency deviation as inputs and change in WTG power as output. In the second level, a robust FLA tuned Proportional Integral (PI) controller is proposed to regulate the output power of the diesel engine generator (DEG) for the further reduction in MG frequency fluctuations. With this proposed two-level approach, the MG frequency fluctuations are within the limits in all possible operating conditions, which include some critical operating scenarios. Moreover, the superiority of proposed approach in dynamic performance enhancement against load and wind power changes is proven by comparing with various powerful controllers in the literature.

Emerging NVM are replacing the conventional memory technologies due to their huge cell density and low energy consumption. Restricted writes is one of the major drawbacks to adopt PCM memories in real-time environments. The non-uniform writes and process variations can damage the memory cell with intensive writes, as PCM memory cells are having restricted write endurance. To prolong the lifetime of a PCM, an extra DRAM shadow memory has been added to store the writes that comes to the PCM and to level out the wearing that occurs on the PCM. An extra address directory will store the address of data written to the DRAM and a counter is used to count the number of times the blocks are written into. Based upon the counter values, the data will be written from DRAM to the PCM. The data is written to the DRAM from the PCM, based on the data requirement. Experimental results show the reduction in overall writes in a PCM, which in turn improves the lifetime of a PCM by 5% with less hardware and power overhead.

As the solar PV system (SPVS) suffered from an unavoidable complication that it has nonlinearity in I–V curves, the optimum maximum power point (MPP) measurement is difficult under fluctuating climatic conditions. For maximizing SPVS output power, MPP tracking (MPPT) controllers are used. In this paper, a new adaptive fuzzy logic controller (AFLC) based MPPT technique is proposed. In this proposed AFLC, the membership functions (MFs) are optimized using the Grey Wolf Optimization (GWO) technique to generate the optimal duty cycle for MPPT. Four shading patterns are used to experiment with the performance of the proposed AFLC. The proposed approach tracks the global MPP for all shading conditions and also enhances the tracking speed and tracking efficiency with reduced oscillations. The effectiveness and robustness of proposed AFLC based tracker results over P&O and FLC are validated using Matlab/Simulink environment. The proposed AFLC overcome the drawbacks of the classical P&O, and FLC approaches.

Solar power is free from environmental pollution and also offers economic benefits to the investors. In order to optimize at most solar power, tracking schemes are employed and traditional schemes from the family of perturbation suffer from slow response, due to specific step size disruption and oscillations around operating point. Therefore, this paper proposes an effective design and implementation of a hybrid Maximum Power Point Tracking (MPPT) scheme based on Linear Tangents - Neville Interpolation (LT-NI) techniques. Two tangents are drawn on the P-V characteristics at 75% and 90% of open circuit voltage (VOC).The chosen two tangents intersect and gives rise to a third voltage (N) point. In continuation, the three voltage points (0.75VOC, N & 0.9VOC) are computed using Neville Interpolation. The performance of the proposed LT-NI scheme is compared with Perturb & Observe (P&O) and advanced Divide and Conquer (DC) algorithms.

In the field of Wireless Sensor Networks (WSNs), after the deployment of sensor nodes, transmission of data with an effective energy is becoming a critical issue due to the limited battery of sensor nodes. To solve this issue, most of the past developed approaches focused on the implementation of clustering in which the sensor nodes forward their data through Cluster Heads (CHs) to Base Station (BS). However, the main issue is to select an optimal CH or CHs such that every sensor node is ensured with at least one path to base station, directly, through CHs or through relay nodes. Towards this objective, we have proposed a new method called Multi-Objective Assisted Clustered Relay Node Placement (MOCRNP) to achieve improved energy efficiency along with an enhanced network lifespan. Firstly, to select the CH, the Multi-Objective Genetic Algorithm is introduced based on energy, distance and node density. Secondly, in the process of rely node placement, Multi-objective firefly algorithm is introduced and for this purpose, two constraints such as Coverage and Connectivity are considered. We have conducted several experimentations using several setups of the WSN and the performance is evaluated in terms of Network lifetime, average energy consumption, average residual energy, and throughput. On an average, the proposed approach has gained an improvement of 150 sec network lifetime, 104 Kbps Throughput when compared with state-of-art methods.

Power generated by PV panels is highly vulnerable to uncertain weather conditions, and impedance connected to its terminals. Therefore, to maximize the energy productivity from panels by controlling output impedance, a power electronic converter capable of adopting maximum power point tracking (MPPT) technique is required. This paper includes a comprehensive review of basic and advanced MPPT techniques proposed to address the variations in temperature, irradiance, partial shading conditions. Power processing equipment such as dc/dc converters and inverters are mandatory in extracting power from PV panels and utilizing either for standalone systems or grid integration. Grid integration is a major focus where access to utility line ranging from domestic micro-inverters ( MW). A centralized inverter topology interfaces a MW power rating PV farm consisting several parallel strings of series connected PV panels to the grid. This review article contributes on presenting an overview of the state-of-the-art power electronics systems for integration of PV panels to the grid. Various interfacing power electronic architectures covering micro-inverter, central inverter up to modular inverter approach operating at low switching frequency and high switching frequency with resonant and pulse-width modulated (PWM) soft-switching or hard-switching are reported. Various voltage-fed and current-fed multi-level inverters for such architectures are studied and discussed.

Active power filter is a power electronic converter used for improving the quality of supply by eliminating the effect of harmonics due to non-linear loads. This paper recommends a concept for shunt active power filter (SAPF) using a single power source fed to a cascaded multilevel inverter (CMI) with 3-φ transformers. Apart from traditional transformer based topologies, the required number of transformers are substantially reduced, resulting in less space requirement, which leads to low cost and simple control system. The proposed CMI based SAPF has a capacity to compensate for contaminated load with high harmonic and a low power factor. The effective i d -i q theory is used to calculate compensation currents. DC link voltage regulator analyzed through delay time and controller gain. The tasks of the controller in the SAPF can perform all necessary actions for correct operation in SAPF. A wide range of computer simulation results demonstrated and validated the results with the prototype experimental setup.

The maximum power point tracking (MPPT) in the PV system has become complex due to the stochastic nature of the load, intermittency in solar irradiance and ambient temperature. To address this problem, a novel Grasshopper optimized fuzzy logic control (FLC) approach based MPPT technique is proposed in this paper. In this proposed MPPT, grasshopper optimization is used to tune the membership functions (MFs) of FLC to handle all uncertainties caused by variable irradiances and temperatures. The performance of the proposed grasshopper optimized FLC based MPPT is studied under rapidly changing irradiance and temperature. The proposed MPPT overcomes the limitations such as slow convergence speed, steady-state oscillations, lower tracking efficiency as encountered in conventional methods viz. perturb & observed (P&O) and FLC techniques. The feasibility of the proposed MPPT is validated through experimentation. The effectiveness of the proposed scheme is compared with P&O and also with FLC MPPT.

An fuzzy classification rules extraction model for online analytical mining (OLAM) was explained in this article. The efficient integration of the concept of data warehousing, online analytical processing (OLAP) and data mining systems converges to OLAM results in an efficient decision support system. Even after associative classification proved as most efficient classification technique there is a lack of associative classification proposals in field of OLAM. While most of existing data cube models claims their superiority over other the fuzzy multidimensional data cubes proved to be more intuitive in user perspective and effectively manage data imprecision. Considering these factors, in this paper we propose an associative classification model which can perform classification over fuzzy data cubes. Our method aimed to improve accuracy and intuitive ness of classification model using fuzzy concepts and hierarchical relations. We also proposed a generalization-based criterion for ranking associative classification rules to improve classifier accuracy. The model accuracy tested on UCI standard database.

The visual similarity-based techniques detect the phishing sites based on the similarity between the suspicious site and the existing database of resources such as screenshots, styles, logos, favicons etc. These techniques fail to detect phishing sites which target non-whitelisted legitimate domain or when phishing site with manipulated whitelisted legitimate content is encountered. Also, these techniques are not well adaptable at the client-side due to their computation and space complexity. Thus there is a need for light weight visual similarity-based technique detecting phishing sites targeting non-whitelisted legitimate resources. Unlike traditional visual similarity-based techniques using whitelists, in this paper, we employed a light-weight visual similarity based blacklist approach as a first level filter for the detection of near duplicate phishing sites. For the non-blacklisted phishing sites, we have incorporated a heuristic mechanism as a second level filter. We used two fuzzy similarity measures, Simhash and Perceptual hash for calculating the similarity score between the suspicious site and existing blacklisted phishing sites. Each similarity measure generates a unique fingerprint for a given website and also differs with less number of bits with a similar website. All three fingerprints together represent a website which undergoes blacklist filtering for the identification of the target website. The phishing sites which bypassed from the first level filter undergo second level heuristic filtering. We used comprehensive heuristic features including URL and source code based features for the detection of non-blacklisted phishing sites. The experimental results demonstrate that the blacklist filter alone is able to detect 55.58% of phishing sites which are either replicas or near duplicates of existing phishing sites. We also proposed an ensemble model with Random Forest (RF), Extra-Tree and XGBoost to evaluate the contribution of both blacklist and heuristic filters together as an entity and the model achieved a significant accuracy of 98.72% and Matthews Correlation Coefficient (MCC) of 97.39%. The proposed model is deployed as a chrome extension named as BlackPhish to provide real time protection against phishing sites at the client side. We also compared BlackPhish with the existing anti-phishing techniques where it outperformed existing works with a significant difference in accuracy and MCC.

India's economy is built on agriculture. Farmers are currently confronted with a slew of challenges when it comes to irrigating their crops. Advanced irrigation systems must be employed to address these challenges. The "Smart Irrigation Motor System" project provides an IoT-based system that allows farmers to turn on and off irrigation motor from any location through a mobile application. In reality, even if the power supply is given to the motor, there is a 3 in 10 chance that motor might not turn on. In such cases, farmer has to be physically present at the motor to identify the problem. So, the use of a webcam allows farmers to monitor the working of irrigation motor. If there is a power outage when irrigating the field, the farmer must wait until power is restored before turning on the motor. However, this idea uses the GSM module to send a Phone call to the farmer notifying power outages and restorations. So that the farmer may review the camera input and turn on the motor when power is restored. The farmer's task is made easier as a result of this effort.

The novel Coronavirus outbreak has created a massive economic crisis, and many succumb to death, disturbing the lives of mankind all over the world. Currently, there are no viable treatment for this condition, drug development approaches are being pursued with vigor. The major treatment options are to repurpose existing drugs or to find new ones. Traditional methods for drug discovery take a longer time, so there is an urgent need to develop some alternative techniques that reduces search space for drug candidates. Towards this endeavor, we propose a novel drug discovery method that leverages on long short term memory (LSTM) model to generate novel molecules that are adept at binding with novel Coronavirus protease. Our study demonstrates that the proposed method is able to recreate novel molecules that correlate very much with the properties of trained molecules. Further, we fine-tune the model to generate novel drug-like molecules that are active towards a specific target. We consider 3CLPro, the main protease of novel Coronavirus, as a therapeutic target and demonstrated in silico screening to assess target structural binding affinities with docking simulations. We observed that 80% of generated molecules show docking free energy of less than −5.8 kcal/mol. The top generated drug candidate has the highest binding affinity with a docking score of −8.5 kcal/mol, which is very much lower when compared to approved existing commercial drugs including, Remdesivir. The low binding energy indicates that the generated molecules could be explored as potential drug candidates for Covid-19., Graphical abstract Image 1

In recent years, improper image manipulation has become a significant issue across many sectors, such as in the education field, politics, entertainment sector, and social media platforms. In various society sectors, images play a crucial role in interpreting the facts and number of fraud incidents. The fraud and fact can be detected by image manipulation, which is gaining more and more attention in today’s world. Detecting such manipulations images is a major challenging problem concerning many fragmented images’ unavailability as training data. A learning algorithm has been proposed for recognizing many visually spliced images and manipulations in the image. The proposed algorithm also utilizes the existing recorded image Exchangeable Image File Format (EXIF) metadata as a supervisory image for training the model to identify the image is self-consistent, i.e., whether the content of the photo has been generated using a single imaging pipeline. The proposed algorithm is implemented to the errand of identifying and localizing the image splices. An excellent performance improvement has been observed on several benchmarks, despite not using any spliced image data while training. This model will stand out as a vital source to detect morphed images in various industries.

This paper presents a modern control strategy for the speed control of a Brushless DC (BLDC) motor based on Convolution Neural Network (CNN). The PID loop employed to manage the quickness of the BLDC motor is attenuated using the multi layered perception-based CNN. The modern technique is successful in optimizing the parameters of integral square error of the PID controller. A convolution model is created, compiled and trained based on the verified data and the evaluated model performance. The sequence of speed-controlled data from the controller is to be multiplexed into convolution region, Rectified Linear (ReL) Unit, pooling zone, finally the rest of the error is minimized in fully connected area.

Monitoring partial discharge (PD) activities inside electrical substation using few ultra high frequency (UHF) sensors mounted around the periphery of the substation could be a useful and economical tool. In this work, an experimental study has been conducted in the laboratory and in substations to investigate the localisation ability of the UHF PD detection system. For localisation, one needs to calculate time difference of arrivals (TDOAs) between the UHF waveforms obtained from the multiple sensors mounted around the substation. After that these TDOAs are used for localisation of the PD sources. In this paper a novel joint entropy (JE)-based TDOAs calculation method is proposed and also compared with the existing techniques. For localisation of PD sources, a modified direction of arrival (DOA)-based method for the near field is proposed, as found to be suitable when the sensors are mounted around the substation. Obtained results in TDOA estimation using the proposed JE method is in line with other technique; however, it was found to provide consistent results and is useful in an extreme situation. Modified DOA-based localisation algorithm found to provide improved performance due to the elimination of approximation as required in previously reported far-field-based localisation system for the air-insulated substations.

Photovoltaic (PV) panels are equipped with Maximum Power Point Tracking (MPPT) schemes to extract utmost available power even during dynamic weather conditions (DWC) and partial shaded conditions (PSC). Though P-V and I-V characteristics of a PV system are affected by DWC and PSC, they have a constant current region (CCR) and constant voltage region (CVR). To achieve the optimal operating point of a PV system, the existing conventional MPPT schemes have to pass through CCR and CVR regions which consume time. Hence, this paper presents a novel MPPT scheme that eliminates perturbation in constant regions in any condition by projecting tangents over P-V characteristics thereby approaches an estimated point on the top of the curve quickly. Further, the iterative procedure involved in the proposal achieves the exact optimal point according to operating conditions of the system. Experimental and simulation results of the proposed methodology provide enough evidence to prove the superiority of the proposed method over conventional Perturb and Observe (P&OC) scheme. Keywords: Tangent perturbation on P-V characteristics, Global Maximum Power Point (GMPP), Constant current region (CCR), Constant voltage region (CVR), Maximum Power Point Tracking (MPPT) and Partial shaded condition (PSC)

This paper studies the stability issues of the Double Fed Induction Generator (DFIG) based wind farm in the power system with variable wind speed in strong and weak grid. With the intermittent generation of the wind power, it experiences instability and malfunction. The nature of Electromechanical Modes of Oscillations (EMOs) for DFIG incorporated into the multi-machine system under various conditions is explored considering system uncertainties. In this paper, the impact of Power System Stabilizer (PSS) and Static Series Synchronous Compensator (SSSC) on the stability of wind power system is analyzed by utilizing modified IEEE 14-bus test system. The rotor speed deviation is the objective function for particle swarm optimization and is formulated based on the transient and small signal stability. The transient and small signal stability investigations are performed by using time domain simulation and eigenvalue analysis respectively. The sensitivity analysis is carried out with degree of compensation of SSSC and coefficient of damping gain of PSS as sensitivity parameter. The performance of the modified test system is examined for transient and small signal stability for DFIG based wind farm for variable wind speed and grid strength considering different combinations of PSS and SSSC. From the results, it is noticed that oscillations in the system are minimized, damping, transient and small signal stability are enhanced and also the capability of synchronous generator and DFIG under extreme disturbance is intensified by optimization the coordinated controllers’ parameters. The analyses also show that damping ratio is improved for most of the EMOs. Sensitivity analysis identifies EMOs that have detrimental and beneficial impacts on the system.

Due to the rapid advances of technologies, the scaling of parameters are decreasing. In VLSI (Very Large Scale Integration) technology, the feature size of integrated circuits (IC) has driving reduced in terms of power, speed, area and cost characteristics. The decreasing the sizes in sub-quarter microns, spacing between the components on-chip VLSI design and the signal switching time in terms of pico seconds or even less. As a result, the signal integrity (SI) issues are occurring at higher frequencies and high data rates. The evaluation of crosstalk noise between the coupled interconnect is one of the prominent issue in designing of high-speed ICs. In this paper, investigated the crosstalk noise of coupled copper (Cu) interconnect models with analytically at 32 nm technology nodes. Also, investigated the crosstalk reduction with shield insertion technique and increasing physical spacing between the coupled lines. For the low power VLSI applications, the shield insertion technique is preferable for reducing the crosstalk effects in coupled interconnects.

Driven by the trends of emerging technologies in memories, a non-volatile memory (NVM) has been considered as an alternate technology to replace SRAM in an on-chip cache. Spin-transfer torque random access memory (STT-RAM), a new type of NVM technology has low leakage power and huge density of cells. Besides having advantages, the emerging NVM technologies have limited writes and huge error rates. The write endurance and error rates are influenced by the cache replacement algorithm, which ultimately dictates the lifetime of a cache. Hence, it becomes necessary to develop or modify the cache replacement algorithms for improving the lifetime of the STT-RAM caches. The proposed Hybrid-Cache-Replacement (HCR) policy reduces the impact of the replacement algorithm in such a way that its write endurance improves with reduction in error rate. The incoming data are appropriately placed in the existing block, which have minimum error rates and less number of writes. It has been implemented by comparing the incoming bits with the existing bits in a cache set. The simulation results show that the lifespan of the STT-RAM caches improves by 135%, 165% and 27% along with 2%, 2% and 1% performance overhead when compared to the existing methods.

In the field of molecular biology, gene expression is a term that encompasses all the information contained in an organism’s genome. Although, researchers have developed several clinical techniques to quantitatively measure the expressions of genes of an organism, they are too costly to be extensively used. The NIH LINCS program revealed that human gene expressions are highly correlated. Further research at the University of California, Irvine (UCI) led to the development of D-GEX, a Multi Layer Perceptron (MLP) model that was trained to predict unknown target expressions from previously identified landmark expressions. But, bowing to hardware limitations, they had split the target genes into different sets and constructed separate models to profile the whole genome. This paper proposes an alternative solution using a combination of deep autoencoder and MLP to overcome this bottleneck and improve the prediction performance. The microarray based Gene Expression Omnibus (GEO) dataset was employed to train the neural networks. Experimental result shows that this new model, abbreviated as E-GEX, outperforms D-GEX by 16.64% in terms of overall prediction accuracy on GEO dataset. The models were further tested on an RNA-Seq based 1000G dataset and E-GEX was found to be 49.23% more accurate than D-GEX.

Efficiency of the photovoltaic (PV) power generating system is affected during partial shaded condition (PSC). The power-voltage characteristic of PV system exhibits multiple peaks during PSC. It is the task of maximum power point tracking (MPPT) controller to track global maximum power point (GMPP). Conventional MPPT schemes stop at first peak and fail to accomplish GMPP during PSC. Metaheuristic algorithms developed to track GMPP are complex, costly and require much time to track GMPP. Hence, this study put forwards a novel GMPPT scheme for effective tracking based on shading pattern identification using artificial neural network (ANN). In this scheme, ANN is used to estimate the shading pattern on PV panels and a two-dimensional lookup table supplies the MPP voltage corresponding to the shading pattern. By maintaining this voltage across PV panel, maximum power is extracted. The proposed scheme is compared with the existing artificial bee colony and particle swarm optimisation algorithms under different shading configurations to verify their performance under PSC. It is observed that the proposed scheme extracts maximum power effectively under various partial shading conditions. The proposed scheme is implemented in field-programmable gate array (FPGA) controller and the experimental results prove effectiveness of the proposed scheme.

The indispensable knowledge of Deoxyribonucleic Acid (DNA) sequences and sharply reducing cost of the DNA sequencing techniques has attracted numerous researchers in the field of Genetics. These sequences are getting available at an exponential rate leading to the bulging size of molecular biology databases making large disk arrays and compute clusters inevitable for analysis.In this paper, we proposed referential DNA data compression using hadoop MapReduce Framework to process humongous amount of genetic data in distributed environment on high performance compute clusters. Our method has successfully achieved a better balance between compression ratio and the amount of time required for DNA data compression as compared to other Referential DNA Data Compression methods.

This paper is put forward a novel photovoltaic (PV) inverter topology for maximum solar power utilization, which incorporates a new maximum power point tracking (MPPT) scheme based on shading pattern identification using the artificial neural network, single-input and multi-output (SIMO) converter, and multilevel inverter (MLI). The performance of the proposed MPPT scheme is benchmarked under partially shaded conditions. In continuation, the PV voltage is fed to the SIMO converter, where the converter produces four independent (V1, V2, V3 and V4) voltages with different magnitudes. The MLI converts the dc output voltage of the SIMO converter into ac to feed the utility, with reduced harmonic distortion. This MLI consists of eight switching devices and produces thirty one level ac output voltage with very less harmonic distortion. The salient feature associated with the proposed topology is that the proposed MPPT scheme extracts utmost power in any weather conditions and MLI feeds the utility. An experimental prototype is designed and developed to verify the performance of the proposed PV inverter topology. FPGA Spartan trainer kit is used to program pulses required for the MLI and converter of the proposed topology.

In this paper, the Crosstalk noise analysis of coupled on-chip interconnects have been analyzed. The multiresolution time-domain method (MRTD) is used to analyze the crosstalk noise model. The crosstalk induced propagation time delay and crosstalk peak voltage on the victim line of interconnects have been determined and compared to those of the conventional finite difference time domain (FDTD) method and validated with HSPICE simulations at the 22nm technology node. The results of the proposed method shows that crosstalk induced propagation delay in dynamic in-phase, out-phase and peak voltage timing, as well as the peak voltage value for functional crosstalk in the copper interconnects are an average error of less than 0.53% for the proposed model and HSPICE simulations. The results of the proposed model are closely similar to those of HSPICE simulations. Electromagnetic interference and electromagnetic compatibility of on-chip interconnects can also be addressed using the proposed method.

This article includes understudy participation and workforce participation. The understudy participation is set apart by face acknowledgment. Face identification and face acknowledgment are performed by the raspberry-pi module. The pin camera is associated with the raspberry-pi serial USB port catch of the researchers who are accessible inside the class for face location. The selected images perceive with stored images and will perceive the essences of each understudy, and enrollment will be given to that subject class based on that perception. This interaction is done in each class and understudies are given participation appropriately. The attendance will be set apart with date and time. The participants can check whether there is human intervention or not.

CORDIC (Coordinate Rotation Digital Computer) has been designed and implemented in many variants in the past five decades where the different architectures of the algorithm were used in many diverse applications. CORDIC algorithm is a flexible shift and add algorithm having an important feature of reduced quantization errors in the case of higher word lengths when compared to other algorithms. The major issue with the algorithm is due to its linear rate convergence with the speed of iteration. Its overall performance is also affected due to repeated number of shift and adds operations and thereby leading to high power consumption. The main aim of this work is to use a new integrated adder and subtractor designed using reversible gates in the place of binary adders and subtractors used in the previous design. This improved CORDIC uses an architecture where the rotation angle is split into micro rotation angles, where these angle sets provides faster convergence by reducing the number of iterations. Overall performance of the proposed algorithm is implemented using variety of FPGA families like Virtex-4, Virtex-5 and Artix-7 devices with comparison to parameters like area, frequency and power consumed. It is compared with the Conventional CORDIC and LH CORDIC designs.