In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system.Systems based on c...In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system.Systems based on clustering strategies,instead of each node sending information by itself,utilize cluster heads to collect information inside the clusters for forwarding collective information to sink.This can effectively minimize the total energy loss during transmission.The environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission bandwidth.Round base clustering strategy works in rounds,where each round comprises three fundamental stages:cluster head selection,grouping or node association,and data aggregation followed by forwarding data to the sink.In UWSN,the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous works.In this paper,the cluster head setup period has been considered the main contributor to extra energy utilizer.A numerical channel model is proposed to compute extra energy.It is performed by using a UWSN broad model.The results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.展开更多
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 cardi...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%.展开更多
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...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.展开更多
文摘In an Underwater Wireless Sensor Network(UWSN),extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system.Systems based on clustering strategies,instead of each node sending information by itself,utilize cluster heads to collect information inside the clusters for forwarding collective information to sink.This can effectively minimize the total energy loss during transmission.The environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission bandwidth.Round base clustering strategy works in rounds,where each round comprises three fundamental stages:cluster head selection,grouping or node association,and data aggregation followed by forwarding data to the sink.In UWSN,the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous works.In this paper,the cluster head setup period has been considered the main contributor to extra energy utilizer.A numerical channel model is proposed to compute extra energy.It is performed by using a UWSN broad model.The results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.
基金supported by Faculty of Computing and Informatics,University Malaysia Sabah,Jalan UMS,Kota Kinabalu Sabah 88400,Malaysia.
文摘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%.
文摘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.
