Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data.

Stress detection is an emerging field. WESAD is a commonly used public dataset for automated stress detection. It contains physiological signals including ECG, EDA, EMG, ACC, BVP, EDA, and skin temperature. The time window approach is used to extract features from time-series physiological signals. We find in previous studies that a 60-second time window with a 0.25-second window shift is widely used, but such window settings may cause redundancy and over-fitting. Thus, we propose to use (1) new window settings and (2) normalization per subject to tackle this problem. The experiment results show that our proposed methods significantly increase the classification performance.

Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.

Nowadays, the proficiency of Computer‐Aided Diagnosis systems for early diagnosis of malignant nodules in baseline Computed Tomography (CT) scan of chest crucially depends on the authenticity of the segmented nodule. In this study, the authors introduce a new morphological feature called solidity radius (SR). They then employ this feature in the new segmentation framework for the automatic segmentation of nodule and the attached vessels around the seed point on the nodule, delineated by an expert. In the framework, they extract the SR and the curvature features and employ them to determine the candidate pixels of the nodule. They then use the convex‐hull image of the candidate pixels to surround the nodule area. Afterward, using the region growing on the Hessian‐based vesselness enhancement map, the attached vessels are labelled. Finally, they apply the traditional solidity feature of the segmented nodule and the pattern of the related skeleton to prune the false positive pieces. They validate the introduced approach on two datasets, including 56 and 481 CTs (containing 1205 nodules). They show the proficiency of their SR‐based approach compared to the state‐of‐the‐art methods with average Dice Similarity Coefficients of 77.98 and 77.47% for the two datasets, respectively.

Traditional cellular networks are unable to support the delay sensitive applications (e.g. vehicular networks, augmented reality). To cope with these challenges, mobile Edge Computing (MEC) has emerged as a new paradigm with computing capabilities in close proximity to the edge of wireless cellular network. In this paper, we study resource allocation for a multi-user multi-task (MUMT) MEC system based on orthogonal frequency-division multiple access (OFDMA). Each computation task is independent with different priorities. In this regard, we propose a priority based task scheduling policy and jointly optimize the computation and communication resource allocation, so as to maximize profit of mobile network operator (MNO) while satisfying the users quality of service (QoS), power consumption at user and base station (BS), and service rate allocation. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the service rate, transmit power and subcarrier allocation under satisfying maximum power and service rate, and delay constraints. Our proposed algorithms are finally verified by numerical results which show that the proposed approach outperforms other benchmark schemes. For example, in the Priority queuing schemes, the performance can be improved compared to No-priority queuing.

Homogeneous wireless sensor networks (HWSNs) are a class of WSNs in which nodes have identical hardware configurations. A HWSN inherits the same constraints from WSNs such as limited processing capacity and resources. However, the unique characteristic of HWSNs gets more attention for recent security applications. The environment of these applications mandates lightweight security provisions, especially a key management protocol that endures security threats such as node capture attack. On the other hand, the limited computation and communication capabilities of sensor nodes make it hard to deal with such security threats. Thus, in this paper, we introduce a novel clustering paradigm to be used in a key pre-distribution key management scheme designed for HWSNs. Then, we develop a cluster-based key pre-distribution based on this paradigm. Our developed scheme improves the security dramatically by partitioning sensor nodes to the disjointed clusters and letting clusters to be inter-connected via randomly selected nodes as consular nodes from all the clusters to form a universal cluster. As opposed to the adopted schemes, we provide a comprehensive analysis for both sensor node connectivity, and resiliency against node capture attack, and also computational results are presented.

The growing demand for wireless services has led to the introduction of new paradigms in spectrum sharing such as the unlicensed ISMand U-NII bands and dynamic or opportunistic spectrum access through cognitive radios. Coexistence of users in these technologies leads to increases in co-channel interference (CCI) which needs to be appropriately mitigated. CCI is often modeled as a white Gaussian noise process and assumed to simply reduce the signal-to-noise (plus interference) ratio. In this paper we consider the effect of CCI by a careful examination of the samples at the output of the matched filter receiver. We show that the timing offset between the interference and the desired signals may result in the correlation of errors in adjacent symbols. We evaluate the bit error rate (BER) resulting from CCI as well as the distribution of the total number of errors in a packet.

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user's signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal+noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest.

Wireless sensor networks are prone to node misbehavior arising from tampering by an adversary (Byzantine attack), or due to other factors such as node failure resulting from hardware or software degradation. In this paper, we consider the problem of decentralized detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes. Binary hypothesis testing is considered where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious messages. The goal of the FC is to identify the misbehaving nodes and to detect the state of nature. We identify each class of nodes with an operating point (false alarm and detection probabilities) on the receiver operating characteristic (ROC) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved using the expectation maximization (EM) algorithm with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the decentralized hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem which can be resolved if the honest nodes are in majority.

We consider the problem of spectrum sensing in cognitive radios when the secondary user (SU) radio is equipped with multiple antennas. Using an estimate of the cross-correlation among the signals received at different antenna elements, we propose a blind detection method which assumes no prior knowledge of the primary user's (PU) signaling scheme, the noise power, or the channel path coefficients. Detection and false alarm probabilities of the proposed algorithms are evaluated using an asymptotic analysis and the results are compared to simulation results. It is shown that the proposed method outperforms several recently-proposed blind sensing techniques for cognitive radios using multiple antennas.

In this paper, a new joint structure for noise reduction in a reverberant environment will be proposed. The proposed structure consists of an acoustic echo canceller (AEC) followed by a noise reduction stage like generalized side-lobe canceller (GSC). This configuration is called AEC-GSC. It improves noise cancellation of the GSC beamformer in the presence of acoustic echoes in highly reverberant environments where GSC-alone fails to work properly. The AEC section is accomplished by our recently proposed segment variable step-size proportionate normalized least mean squares (SVS-PNIMS) algorithm. The proposed AEC-GSC algorithm is evaluated through both computer simulations and experimental results. The results demonstrate that the proposed AEC-GSC algorithm performs significantly better than GSC-alone in terms of speech distortion parameters and resulting ERLE.