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.

In this paper, a beat-based autoencoder is proposed for mapping photoplethysmography (PPG) to a single-lead electrocardiogram (single-lead ECG) signal. The main limiting factors represented in uncleaned data, subject dependency, and erroneous beat segmentation are regarded. The dataset is cleaned by a two-stage clustering approach. Rather than complete single–lead ECG signal reconstruction, a beat-based PPG-to-single-lead-ECG (PPG2ECG) conversion is introduced for providing a simple lightweight model that meets the computational capabilities of wearable devices. In addition, peak-to-peak segmentation is employed for alleviating errors in PPG onset detection. Furthermore, subject-dependent training is highlighted as a critical factor in training procedures because most existing work includes different beats/signals from the same subject’s record in both training and testing sets. So, we provide a completely subject-independent model where the testing subjects’ records are hidden in the training stage entirely, i.e., a subject record appears once either in the training or testing set, but testing beats/signals belong to records that never appear in the training set. The proposed deep learning model is designed for providing efficient feature extraction that attains high reconstruction quality over subject-independent scenarios. The achieved performance is about 0.92 for the correlation coefficient and 0.0086 for the mean square error for the dataset extracted/cleaned from the MIMIC II dataset.

Recently, reconfigurable/large intelligent surface (RIS/LIS) has been emerged as an essential key enabler for 6G communication scenarios. The surface can be programmed by software for forming and directing the electromagnetic (EM) wave in any desired direction. Furthermore, cooperative RISs can be dynamically aligned for routing the EM beams through some short line-of-sight (LOS) paths. This gives rise to the birth of a new physical layer networking (PHY0) concept. However, it is still limited to indoor scenarios where the whole walls and ceilings are coated with metasurface material. The routing hops are limited to short distances up to 10 meters per hop. In this paper, an extreme scenario of EM beam routing is explored under the restrictions imposed by the outdoor environment (small number of limited RISs). Specifically, the focusing functionality of RIS is employed. The inherent trade-off between the achieved beam waist and the focal length is leveraged for maximizing the routing hop. So, the placement of the focal plane will be adapted for confining the routed beam between RIS surfaces as being as a guided wave. Main equations, governing near-field focused RIS, are investigated for maximizing focusing gain. The routing problem is formulated as problem of finding a single-source shortest path (SSSP). Simulations are performed on outdoor scenarios under a size-limited RISs network. The impact of the relative orientation between RIS nodes and surface aperture is considered in our simulations. Compared to short indoor hops (limited to a few meters), the maximum hop between RIS nodes is enlarged up to $47\;m$ and $94\;m$ for RIS areas of $2\;m^{2}$ and $4\;m^{2}$ , respectively under optimal relative orientation.

Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively. Graphical Proposed camera-based blood pressure monitoring system

Reconfigurable intelligent surface (RIS) is regarded as one of the main enablers in the context of 6G wireless communications. It converts the conventionally uncontrolled wireless channel into a programmed channel. The current interest in these surfaces is extended from just exploring the capabilities of single RIS to further exploring opportunities of employing multiple RIS cooperatively for enhancing network coverage and capacity. Unfortunately, almost all current work of multiple RIS-assisted networks are limited to either cascade or parallel topologies without regarding the most proper distribution according to the corresponding channel qualities and mutual orientations. So, in this paper, we are aiming to gain both benefits of cascaded and parallel topologies through a hybrid RIS networking structure. While cascade topology minimizes path loss and enhances multiplicative gain, the parallel topology is exploited for enriching scattering signatures in the interested region (cluster). First, cascaded grouping is resolved based on consecutive channel qualities through optimal routing technique. Then, a joint active and passive beamforming (JAPBF) problem is assumed over the grouped parallel routes. The spatial diversity problem is formulated as fractional programming (FP) optimization problem. The superiority of the proposed hybrid network is demonstrated through the performed simulation results represented in maximizing the overall achievable sum rate and exploring the sensitivity to location shift due to receiver mobility.