3D Reconstruction for Motion Blurred Images Using Deep Learning-based Intelligent Systems

Zhang, Jing; Yu, Keping; Zheng, Wen; Qi, Xin; Paul, Anup Kumar

doi:10.32604/cmc.2020.014220

Cited by 74 publications

(29 citation statements)

References 13 publications

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“…It shows that the PLR is high at a very tight WLQ level for TQMoS, LTRT, and MCARM and is decreasing as its threshold level is becoming low. However, it remains almost consistent for MCARMR at different WLQ [40] threshold levels. Moreover, MCARM results in slightly poor performance compared to MCARMR [41] and significantly good performance when compared with TQMoS and LTRT.…”

Section: Packet Loss Ratio (Plr)supporting

confidence: 54%

“…e reliability-conscious procedure is called for CD and RCD packets and after receiving DP and NHNWLQ or NHNPD, it looks at the received list, and NNs with PRi,j, LC ≥ PRthre are recorded into NHNPR (Next-Hop Neighbors with acceptable path reliability) (lines [39][40][41][42]. SNH is the NN with the highest PRi,j, LC if NHNPR is empty (lines 43-45) and RCD packet is sent towards RCQ (lines 46-47) while CD packet is sent towards CDQ (lines 48-39).…”

Section: Qos-conscious Next Hop Selector (Qos-cnhs)mentioning

confidence: 99%

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Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Bangash

Khan

et al. 2021

Journal of Healthcare Engineering

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The merger of wireless sensor technologies, pervasive computing, and biomedical engineering has resulted in the emergence of wireless body sensor network (WBSN). WBSNs assist human beings in various monitoring applications such as health-care, entertainment, rehabilitation systems, and sports. Life-critical health-care applications of WBSNs consider both reliability and delay as major Quality of Service (QoS) parameters. In addition to the common limitations and challenges of wireless sensor networks (WSNs), WBSNs pose distinct constraints due to the behavior and chemistry of the human body. The biomedical sensor nodes (BMSNs) adopt multihop communication while reporting the heterogeneous natured physiological parameters to the nearby base station also called local coordinator. Routing in WBSNs becomes a challenging job due to the necessary QoS considerations, overheated in-body BMSNs, and high and dynamic path loss. To the best of our knowledge, none of the existing routing protocols integrate the aforementioned issues in their designs. In this research work, a multiconstraint-aware routing mechanism (modular-based) is proposed which considers the QoS parameters, dynamic and high path loss, and the overheated nodes issue. Two types of network frameworks, with and without relay/forwarder nodes, are being used. The data packets containing physiological parameters of the human body are categorized into delay-constrained, reliability-constrained, critical (both delay- and reliability-constrained), and nonconstrained data packets. NS-2 is being used to carry out the simulations of the proposed mechanism. The simulation results reveal that the proposed mechanism has improved the QoS-aware routing for WBSNs by adopting the proposed multiconstraint-aware strategy.

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Section: Packet Loss Ratio (Plr)supporting

confidence: 54%

Section: Qos-conscious Next Hop Selector (Qos-cnhs)mentioning

confidence: 99%

Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Bangash

Khan

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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“…There are limitations in its effective feature extraction and feature weighting methods, which limits the classification ability to a certain extent. Therefore, we use 5G technology [ 32 , 33 ] and deep learning [ 34 , 35 ] to propose a real-time medical monitoring system for cardiovascular diseases, which is used to process real-time data streams transmitted from wearable devices to predict the health of patients in real time and send timely information to patients.…”

Section: Related Workmentioning

confidence: 99%

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach

Tan

Bashir

et al. 2021

Neural Comput & Applic

Self Cite

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Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient’s cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

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“…μ WiFi for WiFi AP 11,13,14,16,12, and 15 are set to 2.39 and 1.43 (ms), respectively. These average packet arrival intervals correspond to LTE subframe numbers 3 and 5, respectively.…”

Section: Existing Problems and The Enhanced Proposed Schemementioning

confidence: 99%

“…The goal is to achieve fair medium access by giving more transmission opportunities to WiFi systems. Based on [11] and recent advances in learning techniques [12][13][14], a Q-learning-based muting period selection scheme was proposed for fair LTE-WiFi coexistence in [15]. However, it focuses on maximizing the LTE throughput and can only work in single channel scenario.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum

Kishimoto

Wang

Umehira

2021

Wireless Communications and Mobile Computing

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In recent years, to cope with the rapid growth in mobile data traffic, increasing the capacity of cellular networks is receiving more and more attention. To this end, offloading the current LTE-advanced or 5G system’s data traffic from licensed spectrum to the unlicensed spectrum that is used by WiFi systems, i.e., LTE-Licensed-Assisted-Access (LTE-LAA), has been extensively investigated. In the current LTE-LAA system, a Listen-Before-Talk (LBT) approach is implemented, which requires the LTE user also perform carrier sense before the transmission. However, fair LTE-WiFi coexistence is still hard to guarantee due to their unbalanced frame sizes and traffic loads. In the LTE-LAA system, the optimal channel selection and subframe number adjustment are the keys to realize efficient spectrum utilization and fair system coexistence. To this end, in this paper, we propose a reinforcement learning-based joint channel/subframe selection scheme for LTE-LAA. The proposed approach is implemented at the LTE access points with zero knowledge of the WiFi systems. The results of extensive simulations verify that the proposed approach can significantly improve the fairness and packet loss rate compared with baseline schemes.

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3D Reconstruction for Motion Blurred Images Using Deep Learning-based Intelligent Systems

Cited by 74 publications

References 13 publications

Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Multiconstraint-Aware Routing Mechanism for Wireless Body Sensor Networks

Toward real-time and efficient cardiovascular monitoring for COVID-19 patients by 5G-enabled wearable medical devices: a deep learning approach

Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum

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