Blood pressure (BP) monitoring is a basic way to evaluate hypertension and its related diseases. Since non-invasive measurement with cuff is not real-time and invasive measurement with vessel puncture is not practical in daily life, this paper proposes a cuffless BP estimation method using two-dimensional (2D) convolution. Dimensionality increasing algorithms including recurrence plot and Gramian angular field are firstly used to convert electrocardiography (ECG) and photoplethysmography (PPG) signals into 2D images. New fused Gramian angular field (FGAF) and combined Gramian angular field (CGAF) are proposed to reduce the input 2D images data and enhance the signals' relevance. The converted images are used to train 2D convolutional models and estimate BP values. The 2D models effectively improved BP estimation accuracy, and the accuracy of the VGGNet 2D model using Gramian angular difference field (GADF) is improved by 38% compared with the corresponding 1D convolutional model. The proposed FGAF and CGAF can reduce input data by 50% while maintaining estimation accuracy, and the minimum mean absolute errors of the estimated BP values could reach 2.71 and 1.74 mmHg for systolic and diastolic blood pressures, respectively. To reduce model size, the VGGNet BP estimation model is pruned by reducing 60% of channel numbers while maintain the model performance. The pruned VGGNet model using the FGADF is then fine-tuned and validated by MIMIC-III dataset to show its generalization ability. Furthermore, a simple monitor system is built to show the feasibility of signal collection and BP estimation.

Abstract To effectively support low‐latency e‐health applications, a novel artificial intelligence‐based wireless local area network (WLAN) channel access control scheme named intelligent hybrid channel access (IHCA) is proposed and studied. In the IHCA scheme, each beacon interval comprises multiple cycles, with each cycle containing a contention‐free period (CFP) and a contention period (CP). By adopting an artificial neural network (ANN) and utilizing its calculation outputs to (1) decide whether to poll a hub during the CFP and (2) determine the initial backoff count (IBC) of each hub, polling of empty hubs during CFP can be reduced, and collisions during CP can be relieved. The authors’ simulation results show that the IHCA scheme can effectively reduce latency compared to the Hybrid coordination function Controlled Channel Access (HCCA) and the Request based Polling Access (RPA) reference designs.

This paper initially presents the channel modeling for full duplex (FDX) modulating retro-reflector (MRR) freespace optical (FSO) systems under weak-to-strong turbulence conditions. The uplink channel in the MRR FSO communication is a conventional FSO channel that is typically modeled by a Gamma-Gamma (ΓΓ) distribution. The downlink channel is jointly affected by the turbulence-induced fading in two opposite passes as well as the reflection effect of the MRR, and its normalized channel fading can be described as the product of two correlated ΓΓ random variables (RVs). The channel model of the downlink is approximated by a single α-μ distribution via moment matching method. Then, we propose an effective signal modulation and detection scheme to achieve the FDX communication. The bit error rates of the MRR FSO systems are calculated by the analytical formulas that are derived based on the channel models of the two links, and also evaluated by the Monte-Carlo simulations to validate the derived formulas. The effect of system configurations and propagation conditions on the system performance is investigated, and the optimal designs to make a trade-off between the uplink and downlink performance are discussed.