Non-Invasive Glucose Level Monitoring from PPG using a Hybrid CNN-GRU Deep Learning Network

Every year, humanity loses about 1.5 million persons due to diabetic disease. Therefore continuous monitoring of diabetes is highly needed, but the conventional approach, i.e., fingertip pricking, causes mental and physical pain to the patient. This work introduces painless and cheaper non-invasive blood glucose level monitoring, Exploiting the advancement and huge progress in deep learning to develop a hybrid convolution neural network (CNN) - gate recurrent unit (GRU) network to hit the targeted system, The proposed system deploys CNN for extracting spatial patterns in the photoplethysmogram (PPG) signal and GRU is used for detecting the temporal patterns. The performance of the proposed system achieves a Mean Absolute Error (MAE) of 2.96 mg/dL, a mean square error (MSE) of 15.53 mg/dL, a root mean square Error (RMSE) of 3.94 mg/dL, and a coefficient of determination ($R^2$ score) of 0.97 on the test dataset. According to the Clarke Error Grid analysis, 100% of points fall within the clinically acceptable zone (Class A)

Joint Proportional Fairness Scheduling Using Iterative Search for mmWave Concurrent Transmission

Millimeter wave (mmWave) will play a significant role as a 5G candidate in facing the growing demand of enormous data rate in the near future. The conventional mmWave standard, IEEE 802.11ad, considers establishing only one mmWave link in wireless local area network (WLAN) to provide multi Gbps data rate. But, mmWave has a tenuous channel which hinders it from providing such rate. Hence, it's necessary to establish multiple mmWave links simultaneously by deploying a multiple number of mmWave access points (APs) in 5G networks. Unfortunately, applying conventional standard without any modifications for mmWave concurrent transmission impedes mmWave APs from selecting optimum mmWave concurrent links. Because IEEE 802.11ad standard associates the user equipment (UEs) to mmWave APs using the link that has the maximum received power without considering mutual interference between simultaneous links. In this paper, a joint proportional fairness scheduling (JPFS) optimization problem for establishing optimum mmWave concurrent transmission links is formulated. And, to find a solution to this non-polynomial (NP) time problem, we use exhaustive search (ES) scheme. Numerical simulation proves the effectiveness of using the ES scheme to improve the system performance.