Design of a Controller for Simultaneous Control of Multiple Systems in Wireless Scenario

Wireless technology is becoming an ever-emerging part of human life with new services and products being released every month. Thus wireless communications brings huge benefits to the user or users. The used Radio Frequency (RF) Module is basically an Advanced Virtual RISC (AVR) microcontroller based communication system. The RF Module used in our project contains two units transmitter and receiver. The transmitter module converts parallel data into serial by using HT12E encoder prior to wireless transmission. The encoded data get received by receiver and converts or decodes the serial data into parallel by using HT12D decoder. After converting the data into parallel form which is made use by AVR16A micro controller to generate instructions for operation of relays connected to two different bulbs

Design and Evaluation of Composite Fault Diagnosis Protocols for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are spatially distributed battery-operated devices interconnected wirelessly to support various applications. However, due to well-known deployment issues such as human-inaccessible environment, environmental hazards, the devices are susceptible to faults leading to failures. These faults can be either hardware fault or computational fault or sometimes both. The undesirable behaviors of the sensor node caused by these faults affect the computational efficiency and quality of service (QoS).Detection, identification, and isolation of faults in WSNs could improve assurance of quality, reliability, and safety. Automated fault diagnosis is a well-studied problem in the research community. Fault diagnosis is considered as a very challenging problem in WSNs research. The works in this thesis attempt to solve these problems. Mostly, the works concentrate on the design of fault diagnosis methodology to build highly accuracy detection method by considering the constraints and resources of WSNs. The simulations (using NS–2.35) and testbed experiments are conducted for the performance measurement of the proposed methods. The set of works that have been conducted in this thesis are summarized as follows. A neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes four different kinds of faults such as hard, soft, intermittent, and transient faults. The fault diagnosis protocol designed are based on (1) gradient descent and (2) evolutionary algorithm (gradient-free) approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Furthermore, a feed forward neural network based on gradient descent is modeled for automatic detection of link quality in a sensor network. Simulation results show that the proposed protocol using gradient-free optimization performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency. A composite fault diagnosis protocol is proposed for wireless sensor networks using statistical and neural network approach. The proposed protocol consists of three phases, such as clustering phase, fault detection phase, and fault classification phase to diagnose the composite faulty nodes in the WSNs. The protocol strategy is based on a timeout mechanism to detect the hard faulty nodes, and analysis of variance method (ANOVA test) to detect the soft, intermittent, and transient faulty nodes in the network. To test a method of probabilistic classification, a feed forward probabilistic neural network (PNN) technique is implemented to classify the different types of faulty nodes in the network. The performance of the proposed composite fault diagnosis protocol is evaluated. The evaluation of the proposed model is also carried out by the testbed experiment in an indoor laboratory and outdoor environment. A lightweight and less-overhead approach is proposed to automatically diagnose hard and soft faults in wireless sensor networks. Precisely, a lightweight checksum method is implemented for hard or crash fault detection. Such a method is capable of detecting multiple hard faults within a single path with the help of a timeout mechanism. For diagnosis of soft faults such as permanent, intermittent, and transient faults, we implement the Anderson-Darling statistical method. The Anderson-Darling test analyzes how the sensor readings are fitted in a specific distribution for a tested significance level. To validate the hypotheses and implementation, many testbed experiments are conducted. These experiments essentially report performance of proposed methods. Some of the performance parameters include fault detection accuracy, false alarm rate, and false positive rate and these parameters have also been studied with varying fault probabilities in a sensor network. The most important and interesting observation is that the proposed lightweight schemes can diagnose both hard and soft faults in O(1) message complexity over the network, which makes the schemes adoptable in practice. A graph-theoretic distributed protocol is proposed to detect simultaneously the faults and cuts in the WSN. The proposed approach is accomplished mainly in four phases, such as initialization phase, hard fault and cut detection phase, soft fault detection phase, and fault tolerance phase. The protocol is an iterative method where at every time iteration, the node updates its state to calculate the potential factor. We introduced two terminologies such as a safe zone or cut zone of the network. The proposed method diagnoses the different types of faulty nodes such as hard and soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The proposed method is evaluated with regard to various performance evaluation measures by implementing the same in the network simulator. The obtained results show that the proposed graph-theoretic approach is simple yet very powerful for the intended tasks. The experimental evaluation of the fault tolerance module shows promising results with R-squared of 0.99. For the periodic fault such as intermittent fault, the proposed method also predicts the possible occurrence time and its duration of the faulty node so that fault tolerance can be achieved at that particular time period for better performance of the network

Enhancement of Power Quality for Grid Connected Wind Energy System using Smart-STATCOM

Abstract — When the output of a wind mill is connected to an electric grid affects the power quality. Basically the factors which effects the power quality measurements arethe active power, reactive power, variation of voltage, harmonics, and electrical switching operations. The installation of wind turbine with the grid causes power quality problems can determined by studying this paper. For this Static Compensator (STATCOM) with a Battery Energy Storage System (BESS) at the point of common coupling to mitigate the power quality problems. The grid connected wind energy generation system for power quality improvement by using STATCOM-control scheme is simulated using SIMULINK in power system block set. This relives the main supply source from the reactive power demand of the load and the induction generator in this proposed paper. The improvement in power quality on the grid has been presented here according to the guidelines specified as in International Electro-technical Commission (IEC-61400 standard) provides some norms and measurements

Psychological distress and burnout among healthcare worker during COVID-19 pandemic in India-A cross-sectional study.

BackgroundCOVID-19 has inundated the entire world disrupting the lives of millions of people. The pandemic has stressed the healthcare system of India impacting the psychological status and functioning of health care workers. The aim of this study is to determine the burnout levels and factors associated with the risk of psychological distress among healthcare workers (HCW) engaged in the management of COVID 19 in India.MethodsA cross-sectional study was conducted from 1 September 2020 to 30 November 2020 by telephonic interviews using a web-based Google form. Health facilities and community centres from 12 cities located in 10 states were selected for data collection. Data on socio-demographic and occupation-related variables like age, sex, type of family, income, type of occupation, hours of work and income were obtained was obtained from 967 participants, including doctors, nurses, ambulance drivers, emergency response teams, lab personnel, and others directly involved in COVID 19 patient care. Levels of psychological distress was assessed by the General health Questionnaire -GHQ-5 and levels of burnout was assessed using the ICMR-NIOH Burnout questionnaire. Multivariable logistic regression analysis was performed to identify factors associated with the risk of psychological distress. The third quartile values of the three subscales of burnout viz EE, DP and PA were used to identify burnout profiles of the healthcare workers.ResultsOverall, 52.9% of the participants had the risk of psychological distress that needed further evaluation. Risk of psychological distress was significantly associated with longer hours of work (≥ 8 hours a day) (AOR = 2.38, 95% CI(1.66-3.41), income≥20000(AOR = 1.74, 95% CI, (1.16-2.6); screening of COVID-19 patients (AOR = 1.63 95% CI (1.09-2.46), contact tracing (AOR = 2.05, 95% CI (1.1-3.81), High Emotional exhaustion score (EE ≥16) (AOR = 4.41 95% CI (3.14-6.28) and High Depersonalisation score (DP≥7) (AOR = 1.79, 95% CI (1.28-2.51)). About 4.7% of the HCWs were overextended (EE>18); 6.5% were disengaged (DP>8) and 9.7% HCWs were showing signs of burnout (high on all three dimensions).ConclusionThe study has identified key factors that could have been likely triggers for psychological distress among healthcare workers who were engaged in management of COVID cases in India. The study also demonstrates the use of GHQ-5 and ICMR-NIOH Burnout questionnaire as important tools to identify persons at risk of psychological distress and occurrence of burnout symptoms respectively. The findings provide useful guide to planning interventions to mitigate mental health problems among HCW in future epidemic/pandemic scenarios in the country

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios