Optimum Placement of Relay Nodes in WBANs for Improving the QoS of Indoor RPM System

This paper analyzes the performance of a cognitive underlay system over Nakagami-m fading channels, where maximal ratio combining (MRC) is employed at secondary destination and relay nodes. Under the condition of imperfect channel state information (CSI) of interfering channels, system performance metrics for the primary network and for the secondary network are formulated into exact and approximate expressions, which can be served as theoretical guidelines for system designs. To verify the performance analysis, several analytical and simulated results of the system performance are provided under various system and channel settings.

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Sum-Rate Optimization Constrained by Consumed Power for Multi- Antenna Non-Regenerative Relay Network

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666190219125535 ◽

2020 ◽

Vol 10 (2) ◽

pp. 143-152 ◽

Cited By ~ 1

Author(s):

Maryam Alibeigi ◽

Shahriar S. Moghaddam

Keyword(s):

Relay Network ◽

Minimization Algorithm ◽

Amplify And Forward ◽

Relay Nodes ◽

Convex Problem ◽

Semidefinite Programming Problem ◽

Total Leakage ◽

Sum Rate ◽

Rate Optimization ◽

Novel Algorithm

Background & Objective: This paper considers a multi-pair wireless network, which communicates peer-to-peer using some multi-antenna amplify-and-forward relays. Maximizing the throughput supposing that the total relay nodes’ power consumption is constrained, is the main objective of this investigation. We prove that finding the beamforming matrix is not a convex problem. Methods: Therefore, by using a semidefinite relaxation technique we find a semidefinite programming problem. Moreover, we propose a novel algorithm for maximizing the total signal to the total leakage ratio. Numerical analyses show the effectiveness of the proposed algorithm which offers higher throughput compared to the existing total leakage minimization algorithm, with much less complexity. Results and Conclusion: Furthermore, the effect of different parameters such as, the number of relays, the number of antennas in each relay, the number of transmitter/receiver pairs and uplink and downlink channel gains are investigated.

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Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽

10.3390/s21030776 ◽

2021 ◽

Vol 21 (3) ◽

pp. 776

Author(s):

Xiaohui Tao ◽

Thanveer Basha Shaik ◽

Niall Higgins ◽

Raj Gururajan ◽

Xujuan Zhou

Keyword(s):

Mental Health ◽

Machine Learning ◽

Radio Frequency Identification ◽

Patient Monitoring ◽

Vital Signs ◽

Remote Patient Monitoring ◽

Rfid Technology ◽

Rpm System ◽

Frequency Identification ◽

Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

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