A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

With the help of computer-aided diagnostic systems,cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease.However,the early diagnosis of cardiac arrhythmia is one of the most challenging tasks.The manual analysis of electrocardiogram(ECG)data with the help of the Holter monitor is challenging.Currently,the Convolutional Neural Network(CNN)is receiving considerable attention from researchers for automatically identifying ECG signals.This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute(ANSI)standards and the Association for the Advancement of Medical Instruments(AAMI).The Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia dataset is used for the experiment.The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0%and a positivity predictively 99.2%in the detection of a Ventricular Ectopic Beat(VEB).Moreover,it also gained a sensitivity of 99.0%and positivity predictively of 99.2%for the detection of a supraventricular ectopic beat(SVEB).The overall accuracy of the proposed model is 99.68%.

New Modified Controlled Bat Algorithm for Numerical Optimization Problem

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization problems.BA leverages the echolocation feature of bats produced by imitating the bats’searching behavior.BA faces premature convergence due to its local search capability.Instead of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search capability.In this work,we proposed an improved variation of BA by applying torus walk to improve diversity and convergence.The proposed.Modern Computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test problems.The finding of our technique shows promising performance as compared to the standard PSO and standard BA.The proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of UCI.Simulation results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional methodologies.The MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR(Continuous stirred tank reactor)is employed in process control and chemical industries to improve response characteristics and system efficiency.It has a highly nonlinear characteristic that includes complexities in its control and design.Dynamic performance is compassionate to change in system parameterswhich need more effort for planning a significant controller for CSTR.The reactor temperature changes in either direction from the defined reference value.It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature,and deviation from reference values may cause degradation of biomass quality.Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential.In this paper,a conventional Proportional Integral Derivative(PID)controller is designed.The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters.Hence,A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller’s limitation.In the proposed technique,PID parameters are tuned by Particle Swarm Optimization(PSO).It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response.In this article,a multi-objective function is proposed for PSO based controller design of CSTR.

Application of Fourth Industrial Revolution Technologies to Marine Aquaculture for Future Food:Imperatives,Challenges and Prospects

This study was undertaken to examine the options and feasibility of deploy­ing new technologies for transforming the aquaculture sector with the ob­jective of increasing the production efficiency.Selection of technologies to obtain the expected outcome should,obviously,be consistent with the crite­ria of sustainable development.There is a range of technologies being sug­gested for driving change in aquaculture to enhance its contribution to food security.It is necessary to highlight the complexity of issues for systems approach that can shape the course of development of aquaculture so that it can live-up to the expected fish demand by 2030 in addition to the current quantity of 82.1 million tons.Some of the Fourth Industrial Revolution(IR4.0)technologies suggested to achieve this target envisage the use of re­al-time monitoring,integration of a constant stream of data from connected production systems and intelligent automation in controls.This requires ap­plication of mobile devices,internet of things(IoT),smart sensors,artificial intelligence(AI),big data analytics,robotics as well as augmented virtual and mixed reality.AI is receiving more attention due to many reasons.Its use in aquaculture can happen in many ways,for example,in detecting and mitigating stress on the captive fish which is considered critical for the success of aquaculture.While the technology intensification in aquaculture holds a great potential but there are constraints in deploying IR4.0 tools in aquaculture.Possible solutions and practical options,especially with re­spect to future food choices are highlighted in this paper.

Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging effects.That is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical industry.In this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain Kd.The controller is used to control Mobile Robot System(MRS)at the required set point.The FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system response.The FF is touted as providing an easy,reliable,and efficient tuning technique for PID controllers.The most suitable ideal performance is accomplished with FF-PID,according to the display in the time response.Further,the observed response is compared to those received by applying GA and conventional off-line tuning techniques.The comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.