Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. Firstly, this paper considers improving the quality of the remote Photoplethysmography (rPPG) signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Secondly, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.

This paper proposed a novel deep learning-based relay selection scheme in millimeter wave (mmWave) Device-to-Device (D2D) communication underlying the fifth generation (5G) cellular networks. Relay selection seems to be a promising solution to extend the coverage and solve the blocking problem of mmWave direct communication. In the case of direct path blocking, the base station (BS)/user equipment (UE) has several candidate devices to be selected as a relay. Despite that, conventional scheme when the direct path is blocked, the direct communication link is handover from mmWave to a lower frequency band using a fast session transfer (FST) technique. Such a blocking problem can be solved multi-hop communications by relaying data based on select another device as a relay. Motivated by the importance of selecting the optimal relay which increases reliable connectivity in mmWave communication and expansion of coverage. The proposed deep learning model is developed to overcome the challenges of selected the optimal relay based with low complexity and high efficiency. The proposed scheme considers a deep learning model learns how to predict the best relay for relaying the data in high-reliability communication. The in-depth learning approach is recommended due to its capability in constructing an intelligent model that can take successful decisions and make precise predictions. Simulation results show that the proposed relay selection algorithm outperforms the conventional relay selection in D2D technique in the spectral efficiency and the energy efficiency.

A growing attention is given to exploiting Photoplethysmography (PPG) signals in non-invasively measuring of many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted in a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available in both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.