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The increasing penetration of PhotoVoltaic (PV) generation has a significant impact on the transient stability of power systems. Power electronics interface, control strategies and lack of inherent rotational element are the main factors that distinguish PV generation from conventional synchronous machine-based generation. In addition, the time constants of the PV control loops and Phase Locked Loop (PLL) are of the same order unlike the synchronous generators. The conventional equal area criterion based stability analysis is not applicable for PV generators with DC-link voltage and reactive power control. The transient stability analysis of power system with PV generation is not well explored in the literature. This paper proposes a criterion to assess the transient stability of a grid connected VSC-based PV generator with DC-link voltage and reactive power control. The PV generator is equipped with Low Voltage Ride Through (LVRT) capability, voltage and frequency support functionalities. The grid is modelled as a Synchronous Machine (SM) with finite inertia. The transient stability is assessed considering the dc link capacitor, outer and inner PV controls, PLL and grid dynamics. The proposed criterion is derived using two nonlinear functions and the Voltage Support (VS) signal of PV generator. The proposed criterion is applicable to all types of short-circuit faults with/without fault resistance. The criterion is validated through time-domain simulations. The Critical Clearing Time (CCT) calculated using the proposed criterion matches with the CCT obtained from time-domain simulations.

COVID-19 virus can be detected with the help of medical images such as X-Ray and CT Scan of human lungs. The unavailability of proper resources might result in the delay of future proceedings. Henceforth, this paper analyzed and comprehended the medical images for COVID-19 lungs and tried to figure out the practical application of that procedure with the overall ecosystem. The whole paper is split into major categories such as: analyzing medical images, classifying and detecting the presence of COVID-19, and building the ResNet-18 model. The present paper creates a base for these terminologies and presents the implementation of a Classification Model on the Kaggle dataset for the X-Ray images. The current work serves the purpose of identification and detection of the Coronavirus, Pneumonia, and normal lungs with the help of ResNet-18. The model was implemented with the Transfer Learning technique. In brief, the work addresses the identification or detection of the COVID-19 reports and differentiates them from reports having symptoms synonymous with the task at hand. The accuracy obtained was 97.78% with 96.33% average sensitivity rate and 98.21% average specificity rate. The conclusion of this report ideally focuses on delineating the result coherently.

We critically analysed and systematically comprehended the Images of X-ray and CT Scans for COVID19 Lungs. We tried to Figure out the practical application of that procedure with the prevailing ecosystem while also considering the user friendliness. The whole major piece has been split into 3 umbrella terms namely, X-Ray, CT Scan and COVID-19. The present paper creates a base for these terminologies. Henceforth we can say that this paper creates such an atmosphere that elevates a prudent man’s understanding to understand the complex and high indulgent of science and technology.It is true to say that the paper serves the purpose of being called analogical paper to such a height that the further research of the paper addresses the identification/detection of the COVID-19 reports and also differentiating them with reports having symptoms that are synonymous to task in hand. The conclusion of this paper ideally focuses on delineating the result coherently.

This paper introduces the idea of using social media streams like Twitter to identify occurrences of crop diseases. Climate change and changes in agriculture practices have contributed to a change in crop disease dynamics leading to an increase in crop damages. Monitoring crop disease occurrences across regions is helpful for farmers to prepare for such adverse situations and make effective use of crop protection products thus ensuring enough produce for the growing population and protection of the environment. We investigate Machine Learning and Natural Language Processing techniques in order to spot agricultural discussions on Twitter; then analyze, categorize, and group them; so they can be used by a stakeholder to identify crop disease incidences, patterns, and trends at the regional scale. Current systems using keyword based search of agricultural diseases do not always yield agriculturally relevant tweets and those that do could talk on a range of sub-topics. Therefore, text classification forms the core component of this work. A two fold classification process is employed, classifying agriculturally relevant tweets from the rest and then performing fine-grained categorization on them. The resulting model for agricultural tweets classification performs with 93% accuracy and the fine grained categorization model that categorizes tweets into 6 categories gives 75% accuracy. A prototype of an interactive web based disease monitoring application is also presented. The location estimation is not always accurate but nonetheless, this work acts as a proof of concept for the introduction of social media as a novel data source in precision farming. ISBN för värdpublikation: 978-1-7281-7031-2

This paper analyzes the possibility of applying data fusion combined with artificial neural networks (ANN) on a dataset combining hard and soft data for prediction of one of the most devastating crop diseases of winter wheat, i.e., Septoria Tritici (Zymoseptoria tritici). In advanced decision support systems for crop protection choices, disease models form a major component.They reproduce the biophysical processes of disease development and temporal spread as a set of rules or processes to predict disease risk value. However, the adaptation of these rules or processes to incorporate the effects of climate change is complex and requires extensive rework. To remedy this issue, statistical machine learning techniques have been introduced to model disease severity percentage for some diseases.However, the use of artificial neural networks has been limited (mainly to image data) and is unexplored for Septoria Tritici.This paper explores the use of Feed Forward neural networks on fused tabular data for the task of disease severity modelling. First, ten years of trial data ranging from 2008 to 2018 across Europe is used for the creation of the new tabular dataset with a fusion of all important data sources baring impact on disease development: Field-specific data, weather data, crop growth stages, and disease severity observation made by human trial operators (response variable). %Correlation and regression analyses and domain expert knowledge were used for the selection of useful predictor variables from these data sources. Next, two implementation architectures of Feed Forward neural networks on tabular data are employed: a) standard architecture with backpropagation, drop out regularization, and batch normalization and b) advanced architecture with improvements such as cyclic learning rate and cosine annealing.%A comparison of generic two layer feed forward network vs the same with the incorporation of the latest advances to improve the performance of the architecture is presented. The advanced architecture is able to better model the data and make estimations of disease severity with a difference of +-10\% giving a better quantifiable estimate of disease stress. For better outreach to farmers, a technique to incorporate such modelling techniques into the well established Decision Support Systems is also presented. ISBN för värdpublikation: 978-0-578-64709-8, 978-1-7281-6830-2

Historically, FPGA designers have used integer processing whenever possible because floating-point processing was prohibitively costly due to higher logic requirements and speed reduction. Therefore, fixed-point processing was the norm. Recently, Intel introduced the Arria 10 FPGA which is the industry's first FPGA that includes single-precision hardened Floating-Point Units (FPUs) on DSP blocks. With the advent of hardened floating-point, FPGA designers have largely abandoned fixed-point processing. This paper introduces a series of arithmetic tests to evaluate whether fixed-point processing is obsolete considering the FPGA performance. A performance metric is developed to calculate the FPGA performance in terms of logic utilization and kernel speed. All programs are tested with Intel Stratix V FPGA which does not have hardened FPUs and Intel Arria 10 FPGA for comparison. The performance metric indicates that, on average, there is a 20.18% performance increase when Stratix V processes fixed-point operations and 27.17% performance increase when Arria 10 processes fixed-point operations. Even with hardened FPUs, it is shown that the Arria 10 FPGA exhibits a significant logic reduction when processing fixed-point operations. The results clearly indicate that the FPGAs perform better when processing converted fixed-point arithmetic operations compared to floating-point arithmetic regardless of whether they include hardened FPUs.

Infant cardiac distress is the leading cause of neonatal deaths in the world. Cardiotocography (CTG) is a diagnostic tool used for recording fetal heartbeat and uterine contractions during pregnancy to determine cardiac distress. To avoid the need of continuous monitoring by on-site medical personnel, researchers have been working on several machine learning tools to automate the process. Most of these approaches discover statistical trends in data to predict target variables. However, being reliant on these trends makes them prone to overfitting and other statistical perils. In this paper, we demonstrate the usage of a modified deep neural network to learn about 2 seemingly disjointed tasks in the field of cardiotocography. The proposed model acquires predictive power in one task whilst being trained on a separate yet related task in the same field. Further, it establishes that regularization facilitates the sharing of knowledge across tasks. The resulting model mimics the human learning process by demonstrating the ability to acquire domain knowledge.

With the growing penetration of renewables, it is essential to determine the transient stability of power systems with renewable generators. This paper focuses on the transient stability analysis of Photovoltaic (PV) generator connected to infinite bus. A stability criterion is proposed for Voltage Source Converter (VSC) based two stage PV generator with Active and Reactive Power (PQ) control. The proposed criterion is developed considering the equations of LC filter and PQ control of the PV generator. An algorithm is also developed to determine the Critical Clearing Time (CCT) of the Single PV Infinite Bus (SPIB) system using the proposed stability criterion. In this paper, the Weddle's rule of numerical integration is used to calculate the CCT using proposed stability criterion. The time-domain simulations are carried out to verify the proposed stability criterion.

The present study proposes a method to calculate Critical Clearing Time (CCT) for Photovoltaic (PV) Generators using energy functions. The Voltage Source Converter (VSC) based grid connected two stage PV generator with Active and Reactive power (PQ) control is considered for the study. The proposed method is developed based on the fault trajectory of the system during the most severe fault on the system. Two energy functions are identified based on the feedback control strategy of PV generator. One energy function corresponds to the P control loop and the other energy function corresponds to the Q control loop. A criterion is proposed using the energy functions to determine the stability of system after fault clearance. The proposed method for CCT calculation is validated using time-domain simulations. The impact of control parameters and DC link capacitance on CCT is also investigated. The proposed method of CCT calculation requires lesser computations than calculating CCT through time-domain simulations.

Electric fence play a vital role in the present-day security systems. They are commonly used to protect premises such as buildings, cattle, large farm areas and international borders. As grid supply may not be available in remote areas, diesel generators are mostly used for energizing the fence. But transportation of diesel to remote areas is often expensive and also a difficult task. However, remote areas often have renewable energy sources. This paper proposes a high-power electric fence energizer using standalone PV generators for remote areas. The proposed energizer is based on standalone PV system with battery backup and Marx generator circuit with high gain resonant converter. An energy management system (EMS) has been implemented for efficient operation of the energizer and proper utilization of solar and battery power. The performance of the proposed energizer has been evaluated for different load and source conditions. The proposed fence energizer can be used to cover large fence areas.

This paper proposes robust control theory based stabilizers utilizing Energy Storage System (ESS) to improve the damping of power systems with high level Renewable Energy (RE) penetration. The proposed stabilizer can damp the electromechanical modes as well as the oscillation modes introduced by Converter Control Based Generators (CCBGs). A new weight-function for complementary-sensitivity function of system is proposed to improve robustness of the stabilizer. The weight function is developed by utilizing information from different operating points of the power system so as to improve the robustness. Controllability and observability indices are used to evaluate the effective input-output signals and the placement of ESS stabilizer. Detailed dynamic model of ESS representing energy storage, converter, PWM and filter dynamics is considered in the design procedure of the stabilizer. The physical limitations of ESS are considered in the stabilizer design by incorporating a new sensitivity function in to the objective function of H-infinity control. The IEEE-39 bus system is modified to represent 50–70% RE penetration level. Small-signal studies and time-domain simulations of the modified system are performed to verify the effectiveness of the proposed ESS stabilizer. Cost benefit analysis and sizing of the stabilizer falls out of the scope of this paper.

Nickel oxide (NiO) thin film was made using solvent casting method by keeping polyvinyl alcohol (PVA) as a base. Transparent film of Nickel oxide (NiO) was formed which was further examined by Vector Network Analyzer (VNA) for understanding its properties in electromagnetic frequency. The measurement was carried out X-band for understanding its application microwave devices. The result indicated the decrease in dielectric constant with 0.5% concentration doping in PVA, the dielectric loss was about 0.15 in 9.5 GHZ. The scattering parameters (S11 and S12) were measured which give good reflection coefficient of -45dB and lower transmission value of -0.1dB. This material is suitable for microwave reflecting material.

For reliable operation of power system, the system should be stable not only in the fundamental domain but also in harmonic domain. Power electronic converters of renewable generators inject harmonics into the power systems. So, at higher renewable penetration levels, study of power system stability in harmonic domain i.e. harmonic stability is required. The Phase Locked Loop(PLL) is used for grid synchronization of renewable generators. PLL affects the dynamic behavior of power electronic converters in harmonic domain also. This paper analyzes the impact of PLL on the harmonic stability of renewable dominated power systems. A small signal state space model is developed for PLL in harmonic domain to analyze the behavior of PLL. The obtained state space model is a periodic Linear Time Varying (PLTV) system, making the state space model of the whole power systems also a PLTV system. As the obtained state matrix is periodic in nature, the concept of time varying eigen-values is employed to analyze the harmonic stability. The standard IEEE-39 bus system is modified to enable high renewable penetration level and the modified system is used to carryout the harmonic stability analysis. Impact of PLL parameters on harmonic stability is analyzed using the modified IEEE 39 Bus system. The impact of renewable penetration level on harmonic stability for given PIA, parameters is also analyzed. It is observed that the PLL parameters and renewable penetration level have impact on the harmonic stability of power system.

The uncertainty in the time series data like wind speed, network traffic, stock price etc. makes the prediction of these data a very tedious task. In order to improve the performance of prediction, several models have been invented. In this paper, some of the models like autoregressive models and Holt-Winters have been discussed. Further, the various steps involved in obtaining the results and comparing the performance of above model have been examined.

With the Increase in the multimedia technology a large amount of video information is collected. Due to time constraint, it is not possible for viewer to watch the entire video. This problem is overcome by using video indexing methods. In this paper, we explain the robust video indexing techniques by using different features extraction model with dominant frame generation for each frame of the input video. The overall system model is divided into two phases: training and testing. Using dominant frames the important seen or events of the video is captured, and the dominant frames are then used in video indexing. SVM classifier is used to classify the set of frames into dominant frames. CLBP and Tensor LPP feature extraction algorithm is used to extract the texture features from the video frame. The performance of the designed model is evaluated and the discussion about the same is presented in result section.

Indian distribution sector is constantly facing energy crises. With the increasing dependency on electrical and electronics appliances the demand of power is rapidly increasing. Extensive work is going on to increase the generation capacity by encouraging renewable energy integration for generation diversification, energy conservation and regulations, development of open access energy markets etc. but simultaneously it is also important to optimally use the existing generation capacity. Electricity theft and meter tampering is one of the most important causes of increasing gap in demand and supply and poor financial condition of distribution sector in the country. Unaccounted energy due to theft is a barrier in the growth of Indian economy. With the advancements in metering technologies different new ideas of illegal electricity theft are also being introduced by consumers. This scenario is very challenging for the profitable operation of distribution sector and putting them in loss. In this paper we have discussed meter tampering and theft issues and their drawbacks. Smart meter can work as the best solution to control theft events due of its features of remote data communication and connect /disconnect ability.

Recent advances in technology, multimedia and social networking sites have led to a massive growth in web video content available for the general population. This results in information overload and management problem of the same. In this context, video summarization plays an important role as it aims to reduce the content size of video and yet present the important semantic concepts in the video. This gives an opportunity to reorganize video content in most succinct form for efficient and on- demand user consumption. Video summarization in its true sense is a hard problem as it involves domain specific semantic understanding of video content and user expectations. Most of the existing approaches relies on segmenting video into contiguous shots & selecting one or more keyframes from each shot and present these keyframes as summary. Such approaches may work well if independent concepts in video appear only once. However, in videos where same concepts are repeated multiple times, these existing approaches may pick repeating summary frames belonging to same concepts. In this paper, we present a novel frame clustering approach for generating very concise summaries by grouping all frames of similar concepts together irrespective of their occurrence sequence. The proposed approach is aimed towards large videos in domains like travel guide, documentaries, dramas where video revolves around few repeating concepts. The approach utilizes multiple video features in a generic way for frame-similarity determination and is extensible for multi-video summarization. Experimental comparative results substantiate the efficiency of the proposed approach in generating concise video summaries on videos with repeating concepts.

This paper presents, a complete energy harvesting system with improved implementation of Fractional Open Circuit Voltage (FOCV) technique, to mitigate obligation of using off chip sampling capacitor, gratuitous periodic open circuit operation for Maximum Power Point Extraction (MPP) from solar cell. A possible solution to minimize the reverse current in solar cell due to abrupt decline in irradiance is also proposed. Thus, usual off-chip sampling capacitance can be effectively replaced by on-chip capacitance as low as 3.75pF. Entire system is implemented in 180nm CMOS technology and the proposed blocks consumes 0.675mm × 0.425mm area and an average power of 340nW.

Power distribution network of the developing nation like India is facing continuous financial crises due to lack of revenue generations. Inspite of many programmes launched by central and state governments to improve efficiency of distribution sector the progress is not upto the mark. Power theft through Energy Meter tampering and manipulation with the meter reading is one of the major causes of revenue losses. Replacement of old Electromechnical meters by Electronic energy meters with anti-tamper features has helped utilities to find out the meter tampering attempts but still there is a long way to go as people are finding new ways to tamper meters. The paper discusses common methods of meter tampering in Indian gray market and features of smart meters useful for minimizing electricity theft.

This paper presents design of an improved start-up circuit for a micro scale solar energy harvesting system. A wide input voltage range (270mV–1.8V) start-up circuit that can work in strong as well as weak illumination levels without causing any stress and reliability issues to the CMOS devices has been proposed. The use of native device (zero-V th ) and ultra-low power Band-gap Reference (BGR) helps to operate the start-up circuit within 1.8V maximum voltage allowed by 40nm CMOS technology. The complete system works with minimum power of 2.841μW at 270mV in start-up mode and works with minimum voltage of 100mV once the system enters into main converter mode. The system uses fractional open circuit voltage method (FOCV) to extract maximum power from solar cell when operating in main converter mode.