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In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process. In this study, using channel state information (CSI) data, a novel cross-person activity recognition (CPAR) method is proposed by a deep learning approach with generalization capability. Combining one of the state-of-the-art deep neural networks (DNNs) used in activity recognition, i.e., attention-based bi-directional long short-term memory (ABLSTM), the snapshot ensemble is the first to be adopted to train several base-classifiers for enhancing the generalization and practicability of recognition. Second, to discriminate the extracted features, metric learning is further introduced by using the center loss, obtaining snapshot ensemble-used ABLSTM with center loss (SE-ABLSTM-C). In the experiments of CPAR, the proposed SE-ABLSTM-C method markedly improved the recognition accuracies to an application level, for seven categories of activities.

In recent years, non-contact biometric identification has garnered significant attention owing to its flexibility and its capacity to ensure privacy and confidentiality. Previous research has predominantly centered on harnessing cardiac radar signals detected by radar systems. This paper aims to compare two methods for person identification utilizing heartbeat signals extracted from radar data. In the first approach, we directly employ the heartbeat time series data as input for a deep learning model called InceptionTime dedicated to person identification. The second method explores the viability of using spectrograms, which are images generated from cardiac radar heartbeat signals, combined with other deep learning models, ResNet and a small convolutional neural network (CNN) model built by us, to achieve the same goal. Additionally, we assessed the robustness of both methods by introducing white Gaussian noise into the signals and evaluating their performance under different noise levels. For the second method, we implemented three image denoising methods: autoencoder, deep image prior (DIP), and Dilated-Residual U-Net (DRU-Net) to denoise the spectrograms after adding noise in the time domain. Experimental results reveal that the accuracy of person identification using spectrograms is 100% without adding noise based on the public dataset. The identification accuracy is the same as that using time series data. Following the introduction of noise, the accuracy of both methods decreases; however, the method based on spectrograms exhibits a more pronounced decline in accuracy compared to the approach using time series data directly especially for the small CNN model we built. The image-based method with DIP image denoiser shows outstanding performance when the value of noise is high compared with InceptionTime.

High-altitude platform stations (HAPSs) that provide direct communication services to smartphones on the ground are garnering significant attention as innovative mobile communication platforms suitable for ultrawide coverage areas. The deployment of a multi-cell configuration is essential for enhancing communication capacity over large geographical regions. However, designing and optimizing multi-cell configurations proves challenging due to the numerous antenna parameters involved, such as beamwidth and beam direction. HAPSs also hold promise in serving as reliable networks during disasters, as they can quickly restore coverage when terrestrial base stations (BSs) are in failure, allowing users to maintain connectivity via HAPSs using the same frequency band as terrestrial networks. Despite this advantage, HAPSs can potentially cause interference in terrestrial networks during such scenarios, necessitating careful considerations to avoid disrupting existing terrestrial networks. However, the optimization of cell configurations in the coexistence of HAPSs and terrestrial networks remains largely unexplored. To address this gap, this paper proposes a design scheme that leverages a genetic algorithm (GA) to optimize antenna parameters for each cell, thereby maximizing both user coverage and area coverage while ensuring coexistence with terrestrial networks. We optimize antenna parameters to increase user coverage and area coverage as a multi-objective optimization problem, resulting in a Pareto front that illustrates the trade-off choices. Additionally, the proposed method protects terrestrial networks by setting the initial beam direction of each cell to avoid interference with terrestrial BSs. Consequently, the GA can initiate optimization with candidate combinations that adhere to the interference constraints of terrestrial networks, thus expediting the optimization process and yielding improved coverage. Simulation results demonstrate that the proposed scheme outperforms conventional schemes which solely control beam direction, leading to superior coverage. While the results show trade-off between user coverage and area coverage, it is also clarified that considering both types of coverage show the potential to achieve significant improvements in both aspects simultaneously. Moreover, the results highlight the efficacy of the initial beam direction setting in enhancing coverage rates and significantly reducing the number of combinations required for the GA to converge.

People Identification is a critical aspect in developing modern vehicles, aimed at enhancing safety and comfort levels. Most traditional methods of people identification in vehicles use RGB images or videos. In this study, we introduce a novel methodology for identifying individuals in private car scenarios, utilizing 3D Light Detection and Ranging (LiDAR) technology, generative image inpainting based on Contextual Attention, and the YOLOv5 model. Initially, we gather data utilizing a 3D-LiDAR instrument and subsequently convert the acquired depth data into depth images. Following this, the depth images are annotated manually to indicate the positions and identifiers of various individuals occupying distinct seats. This annotated data serves as the training material for the YOLOv5 model, facilitating the recognition and categorization of subjects. However, given that individuals seated in the back often have parts of their bodies occluded by the front seats and the passengers in them, we employ generative image inpainting techniques to reveal the occluded portions. This step significantly enhances the precision in detecting and identifying individuals situated in the back seats. We implemented our strategy on a restricted group of four participants, conducting training and testing phases within identical environments. Prior to the inpainting process, the classification’s F1 score stood at 66.5%. After inpainting, we observed a notable surge in the F1 score for the rear-seat passengers increased by 17.1%.

Local model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models. In the industry, practitioners focus not only on how these explanations can validate their models but also on how they can help maintain trust from end-users. Some studies attempted to measure this ability by quantifying what they refer to as the explainability or interpretability of ML models. In this paper, we introduce a new method for measuring explainability with reference to an approximated human model. We develop a human-friendly interface to strategically collect human decision-making and translate it into a set of logical rules and intuitions, or simply annotations. These annotations are then compared with the local explanations derived from common XAI tools. Through a human survey, we demonstrate that it is possible to quantify human intuition and empirically compare it to a given explanation, enabling a practical quantification of explainability. By relying on this new method, we identified several potential flaws in today’s ML selection process. Furthermore, we demonstrate how our method can help to better evaluate ML models.

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar’s coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution.

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person’s position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one’s cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level.

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

Heartbeat detection could enable various applications in the medical and health care fields. In particular, non-contact heartbeat detection can be acceptable for those who have difficulty wearing devices, such as burn patients and infants. A Doppler sensor could be a key device to realize heartbeat detection without any wearable devices. Many researches in recent years have focused on heartbeat detection using a Doppler sensor with a single beam Doppler sensor. However, when the SNR (Signal-to-Noise Ratio) of heartbeat components is low for the beam direction, the heartbeat detection accuracy is likely to degrade. In this paper, for more accurate heartbeat detection, we propose a heartbeat detection method based on beam diversity using a multibeam Doppler sensor. Through the preliminary experiments, we clarified that the SNRs of heartbeat components differ from one beam to another. This means that when the SNR is low for one beam, the SNR could be high for the other beams. Inspired by this fact, the proposed method extracts heartbeat components from all peaks detected by the multi-beam signals. To verify the heartbeat detection accuracy of our method, we conducted the experiments for different detection ranges. The obtained results show that compared to the conventional methods using a single beam, our proposed method using multibeam detected heartbeat more accurately. This indicates the benefit of exploiting beam diversity, i.e., the heartbeat detection accuracy can be improved by utilizing beam diversity.

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, we run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.

In recent years, scientists are paying much attention to the research on automatic dementia detection that could be applied to the speech samples of dementia patients. In a related context, recent research has seen the fast development of Deep Learning (DL) and Natural Language Processing (NLP). The techniques developed for text classification or sentiment analysis have been applied to the field of early dementia detection by many researchers. However, text classification and sentiment analysis are different tasks from dementia detection, which makes us believe that for dementia detection, some adjustments would help improve the performance of the machine learning models. In this work, we implemented experiments with various language models including traditional $n$ -gram language models, Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) models, and attention-based models to evaluate the speech data of dementia patients. Unlike traditional works where the text is stripped from stop words, we propose the idea of exploiting the stop words themselves, since they offer non-context information which helps to identify dementia. As a result, 3 different language models are prepared in this work: a model processing only context words, a model processing stop words and Part-of-Speech (PoS) tag sequences, and a model processing both of them. By performing the aforementioned experiments, we show that both grammar and vocabulary contribute equally to classification: The 3 models achieve an accuracy equal to 70.00%, 76.16%, and 81.54%, respectively.

Introduction: Transomental hernia (TOH), a type of intra-abdominal hernia formed as a result of incarceration through abdominal greater omental hiatus, is a rare disease that lacks specific physical features, which makes its preoperative diagnosis difficult. This study aims the clinical characteristics and radiological findings of 15 cases of TOH. Methods: From 2009 to 2021, we encountered 15 cases of TOH in which multi-detector row computed tomography (MD-CT) with multiplanar reconstruction (MPR) was helpful for the preoperative diagnosis. We performed a clinical study of patients with TOH. Results: The average age of the 15 patients (male, n=9; female, n=6) was 73 years (range, 58-91 years). The preoperative diagnosis, based on MD-CT, was TOH in 11 patients and internal hernia in 4 patients. The preoperative diagnosis of TOH was confirmed in 11 of 15 patients (73%). MD-CT showed dilatation of the small intestine on the ventral side of the ascending colon and the descending colon through the omental hiatus, closed loop, and the convergence of the mesentery or small intestine. Conclusion: The possibility of TOH should be considered when treating patients with obstruction who have no history of laparotomy. MD-CT with MPR is useful for the preoperative diagnosis of TOH.

In this paper, we optimize the secrecy capacity of the legitimate user under resource allocation and security constraints for a multi-antenna environment for the simultaneous transmission of wireless information and power in a dynamic downlink scenario. We study the relationship between secrecy capacity and harvested energy in a power-splitting configuration for a nonlinear energy-harvesting model under co-located conditions. The capacity maximization problem is formulated for the vehicle-to-vehicle communication scenario. The formulated problem is non-convex NP-hard, so we reformulate it into a convex form using a divide-and-conquer approach. We obtain the optimal transmit power matrix and power-splitting ratio values that guarantee positive values of the secrecy capacity. We analyze different vehicle-to-vehicle communication settings to validate the differentiation of the proposed algorithm in maintaining both reliability and security. We also substantiate the effectiveness of the proposed approach by analyzing the trade-offs between secrecy capacity and harvested energy.

Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) array sensors. Alongside with the main challenge which is how to further improve the performance of IR array sensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR array sensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors ( $32\times24$ pixels), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including “walking”, “standing”, “sitting”, “lying”, “falling”, and the transition between the activities which is referred to as “action change”. This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of activities. Through experiments, we show that the classification accuracy of the ceiling data, wall data, and combined data with the LSTM reach 0.96, 0.95, and 0.97, respectively.

Sentiment analysis refers to the process of automatically identifying the emotions expressed by people. Its accuracy is highly dependent on the amount of training data. However, it takes time and cost for humans to collect a large number of data. Many research works used generative models to generate a large amount of data based on a small amount of data for sentiment analysis. However, training on long texts and inaccurate sentiment information that might be generated are two severe challenges. It is difficult to improve the sentiment analysis accuracy effectively. In this paper, we propose a novel data augmentation framework based on Sequence generative adversarial networks (SeqGAN) to improve the sentiment analysis accuracy when the dataset already has a certain amount of data and contains long texts. Penalty-based SeqGAN is used to generate high-quality and diversified text data. Long short-term memory (LSTM) networks with attention mechanisms are used to conduct sentence compression for the training data of SeqGAN. A sentiment dictionary is used to retain the sentiment words for compressed data. We also propose a data screening method to obtain more accurate data from the generated data. The results of the usability, novelty, and diversity of the generated data show that the proposed sentence compression method can help SeqGAN learn more information from the long text data. The data generated by the proposed framework improve the classification accuracy of four classifiers applied on two distinct text datasets.

In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) is required to obtain accurate estimation of channel state information (CSI) for a user terminal (UT). Because of the time-varying nature of the channel, the length of pilot signals is limited and the number of orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by pilot contamination from the neighboring cells. The minimum mean square error (MMSE) channel estimation can be used to reduce the influence of pilot contamination. However, it needs to know the covariance matrix of channels for all the UTs, which is unknown to the BS in practice. In this paper, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. The former method is better in terms of the training speed, however, the latter one can estimate the channel more accurately. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and covariance estimation methods via normalized mean square error (NMSE). In addition, we also investigate the impact of channel aging, and show that including some expected data into training datasets can avoid the great degradation of estimation quality.

Polar code is one of the channel codes and is used in the 5th generation of mobile communication system (5G). This encoding scheme is based on the operation of channel polarization, and it is possible to achieve the capacity of arbitrary binary-input symmetric discrete memoryless channels. Compared with Turbo codes and LDPC (Low-Density Parity-Check) codes, the implementation of the encoder and decoder is easier. BP (Belief Propagation) decoding, one of the decoding methods of the polar codes, can be performed at high speed because it can decode in parallel. However, the disadvantages of the BP decoding are the hardware and computational complexities. The technique of quantization can be used to reduce complexities of hardware and calculation. One of the quantization methods is the information bottleneck method, which allows an observation variable to compressed one while trying to preserve the mutual information shared with a relevant variable. As a novel approach, the information bottleneck method is used in the design of quantizers for the BP decoding of LDPC codes. In this paper, we propose a discrete BP polar decoder that can use only unsigned integers in the decoding process by using the information bottleneck method. Thus, we can replace complex calculations of BP decoding with simple lookup tables. We also investigate the minimum bit width for quantization with negligible degradation and the suboptimal $E_{b}/N_{0}$ for designing lookup tables, where $E_{b}$ and $N_{0}$ denote energy per bit and noise power density, respectively. The simulation results show that the proposed method can achieve almost the same error correcting capability compared with the BP decoding without compression in the range of low $E_{b}/N_{0}$ . Besides, we show that the proposed decoder can compress both channel outputs and BP messages with small loss compared with the uniform quantization decoder.

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. Main results: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection.

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

Massive multi-user multiple-input multiple-output (massive MU-MIMO) technology is considered as a promising enabler to fulfill the rapid growth of traffic requirement for wireless mobile communications. The massive MU-MIMO system can achieve unlimited capacity when the base station (BS) has accurate channel state information (CSI). In time-division-duplex (TDD) mode, the BS estimates CSI by receiving pilot signals sent from user terminals (UEs). However, because of using non-orthogonal pilots, pilot contamination happens to degrade the quality of the CSI estimation. To deal with pilot contamination problem, a low-complexity subspace minimum mean square error (MMSE) estimation method is proposed in this paper. Specifically, our approach operates the MMSE estimation in a low-dimensional subspace to avoid large matrix manipulation. Meanwhile, subspace projection helps to discriminate the desired signal and interfering signals in the power domain. Interference analysis shows the MMSE estimation can achieve interference-free estimation even in a low-dimensional subspace with a large number of BS antennas, and non-overlapping angles of arrival (AoAs) between desired and interfering UEs. Furthermore, thanks to the low-rank property of the channel covariance matrix in massive MU-MIMO systems, a two-step covariance matrix subspace projection method is proposed for further computational complexity reduction. The complexity analysis and simulation results indicate that our proposed approach has better channel estimation accuracy with lower complexity than the conventional MMSE estimation when the number of BS antennas is large.

In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.

Heartbeat detection is one of key techniques to monitor our health condition in daily life, and demands for this technique have increased year and year. Thanks to the non-contact and non-invasive features, various Doppler sensor-based detection methods have been investigated so far. However, the heartbeat detection accuracy of the conventional methods could get degraded due to the low SNR (Signal-to-Noise Ratio) of heartbeat components. Thus, even after some signal processing, non-heartbeat components still remain over such processed signal, which could degrade the heartbeat detection accuracy. In particular for the subjects with low HR (Heart Rate), the estimated HR tends to be higher than the ground truth HR due to such non-heartbeat components, though the conventional methods have mainly focused on the heartbeat detection against the subjects with the normal HR higher than 50 bpm (Beats Per Minute). In this paper, to accurately detect heartbeat even with low HR via a Doppler sensor, we propose a heartbeat detection method based on heartbeat signal reconstruction with convolutional LSTM (Bidirectional-Long Short-Term Memory). In the proposed method, to reconstruct a heartbeat signal based on the periodicity of heartbeat and the spectrum distribution peculiar to heartbeat, successive spectrograms that might be due to heartbeat is used as an input to convolutional LSTM. In addition, for better reconstruction of a heartbeat signal, the previously estimated RRI (R-R Interval) is also used as a feature in the proposed deep learning model with convolutional LSTM. Through the experiments, we confirmed that our proposed method accurately detected heartbeat against 17 subjects including the ones with the HR lower than 50 bpm.

Small cells (SCs) offer a promising approach to meeting the exponentially growing data rate demands. However, dense deployment of SCs can degrade the energy-efficiency (EE) of the network due to the additional deployed base stations (BSs). Under heterogeneous network (HetNet) deployments, SCs can be dynamically switched off for energy saving when traffic load decreases. We do this by defining a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, with Device-to-Device (D2D) communication, mobile users in proximity can establish a direct link and bypass the BSs, thereby offloading the network infrastructure and providing further improvement of EE. In this paper, the objective is to rely on both D2D communications and sleeping SC BSs to offload traffic from the mains powered macrocell BSs leading to energy saving in the network. Furthermore, in order to consider D2D requirements under the practical application scenarios, we assume a framework for wireless video sharing, where users can store popular video files and share files via D2D communication. To begin with, we derive the EE expression of the cache-enabled D2D-aided HetNet. Then, to maximize the EE of the cache-enabled D2D-aided HetNet, the optimal load-dependent TPC for SC BSs is obtained under constraints on both network's coverage and rate. Using simulations, we will investigate the potential effects of the TPC defined for SC BSs as well as the introduced D2D layer on the network EE.

An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

Sentiment analysis refers to the automatic collection, aggregation, and classification of data collected online into different emotion classes. While most of the work related to sentiment analysis of texts focuses on the binary and ternary classification of these data, the task of multi-class classification has received less attention. Multi-class classification has always been a challenging task given the complexity of natural languages and the difficulty of understanding and mathematically "quantifying" how humans express their feelings. In this paper, we study the task of multi-class classification of online posts of Twitter users, and show how far it is possible to go with the classification, and the limitations and difficulties of this task. The proposed approach of multi-class classification achieves an accuracy of 60.2% for 7 different sentiment classes which, compared to an accuracy of 81.3% for binary classification, emphasizes the effect of having multiple classes on the classification performance. Nonetheless, we propose a novel model to represent the different sentiments and show how this model helps to understand how sentiments are related. The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

Blink duration is one of the useful indicators to estimate drowsiness and fatigue. A Doppler sensor could be a key device to realize the non-contact blink duration estimation, which is very useful for the drowsiness and fatigue monitoring in real life. However, none of the blink duration estimation methods has been proposed so far. In this paper, we propose a novel Doppler sensor-based blink duration estimation method based on the analysis of eyelids closing and opening behavior on a spectrogram. When one blinks, the energies caused by eyelids closing and opening behavior appears on a spectrogram. Hence, the blink duration can be estimated by integrating such energies and then detecting the timings when the integrated energy caused by the eyelids closing behavior appears and when the energy caused by the eyelids opening behavior disappears. To precisely detect such timings, a spectrogram with a short time window is better. However, thorough the preliminary experiments, we confirmed that when a shorter time window is used, more noise appears around such timings, which makes it difficult to detect such timings. To deal with this issue, the proposed method multiplies several integrated energies for spectrograms with different time windows. By multiplying the integrated energies with such noise and the ones without such noise, the energy level of such noise gets significantly declined over the product. Furthermore, the levels of the redundant blink energies also decline to the noise level, where the redundant blink energies denote the ones that appear before and after the eyelids closing and opening timings because of a long time window. The results of the experiments on 8 subjects watching a movie showed that our method achieved an average root mean squared error (RMSE) of 49.3 ms.

Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-subspace structure and sparse representation in the time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an optimization problem on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture of TF vectors with the fewer computational cost. The experiments are designed on the observations that are generated from an underdetermined mixture, and that is collected with some direction angles in a virtual room environment. The proposed approach exhibits higher separation accuracy.

Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZALMS) algorithm and the block 10-norm LMS (B10-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B10-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.

With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more “cyber”conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

Nonlinear blind source separation (BSS) is one of the unsolved problems in unsupervised learning, because the solutions are highly non-unique when there is no prior information for the mixing functions. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear BSS problem with a few assumptions. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such a representation is built, a final output layer is constructed by solving a convex optimization problem. Relying on the multi-layer architecture, the algorithm transforms a time-invariant nonlinear BSS to the local linear problem with a tolerable computational cost. Then, the projected data can break the nonlinear problem down into the version of a generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experiments show that the proposed algorithm has a higher estimation accuracy on audio data sets from the real world for separating various nonlinear mixtures.

This paper divides antennas at the transmitter into several transmit antenna groups (TAGs) and extends the generalized spatial modulation (GSM) idea to a new precoding-aided multiple-input multiple-output system, which is referred to as precoding-aided GSM (PGSM) system. To mitigate the influence caused by correlated channels, we consider the interleaved grouping scheme which leads to a minimum antenna correlation within the same TAG. To select the receive antenna subset with low complexity, we propose an iterative greedy (IG) algorithm that contributes to a maximum equivalent channel gain with low-computational complexity. To reduce the complexity of maximum likelihood (ML) detector, a low complexity IG algorithm-based ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with an ordered block minimum mean-squared error detector at a given BER of 10−4. It is also shown that the proposed PGSM with the IG-ML detector achieves a similar BER performance in a high signal-to-noise power ratio region compared with that of an ideal precoding for the GSM with an optimal ML detector.

With the rapid growth of online social media content, and the impact these have made on people's behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as ``quantification.”By the term ``quantification,”we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.

Demands for vital sign monitoring are increasing in the field of health care. In particular, the R-R Interval (RRI) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor-based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor-based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the root-mean-squared error of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index low-frequency/high-frequency (LF/HF), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the relative error of the LF/HF.

Spam over Internet telephony (SPIT) is recognized as a new threat for voice communication services such as voice over Internet protocol (VoIP). Due to the privacy reason, it is desired to detect SPITters (SPIT callers) in a VoIP service without training data. Although a clustering-based unsupervised SPITters detection scheme has been proposed, it does not work well when the SPITters account for a small fraction of the entire caller. In this paper, we propose an unsupervised SPITters detection scheme by adding artificial SPITters data to solve the unbalanced situation. The key contribution is to propose a novel way to automatically decide how much artificial data should be added. We show that classification performance is improved by means of computer simulation with real and artificial call log data sets.

Vehicular ad hoc networks (VANETs), which are deployed along roads, make traffic systems safer and more efficient. The existing theoretical results on capacity scaling laws provide insights and guidance for designing and deploying VANETs. As a new paradigm of VANETs, software-defined vehicular ad hoc networks (SDVANETs) separate the data plane from the control plane. For many prospective applications, software-defined technology will be used in VANETs to achieve some general targets, such as network management. Therefore, a capacity analysis is critical and necessary for SDVANETs. In this paper, we propose a new fundamental framework named real vehicular wireless network model (RVWNM), which enables a more realistic capacity analysis in SDVANETs. We first introduce a Euclidean planar graph that can be constructed from any real map of an urban area and that represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph, which considers the transmission interference relations among the nodes in the network. Finally, we theoretically analyze the interference relationships in the interference relationship graph. A practical geometrical structure is used to calculate the asymptotic capacity of SDVANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of social-proximity urban networks. We also consider the social-proximity-based mobility of vehicles, and we derive asymptotic capacity bounds for sparse SDVANETs and constant bounds for high-density SDVANETs.

We consider a multiple-input multiple-output wiretap channel with one transmitter, one receiver, one cooperative jammer, and one eavesdropper whereby each node is equipped with multiple antennas. Eigenbeam-space division multiplexing (E-SDM) and cooperative jamming are known as techniques for improving secrecy rate. E-SDM enables the transmitter and the legitimate receiver to communicate with each other in parallel using channel state information known to them to maximize the legitimate receiver's mutual information. On the other hand, cooperative jamming is to jam only the eavesdropper to degrade the eavesdropper's mutual information. However, co-channel interference and residual interference from the cooperative jammer are caused by channel estimation errors, which degrade the legitimate receiver's mutual information. How these interferences affect the secrecy capacity has not been clarified. In this paper, we derive a lower bound on secrecy capacity using the legitimate receiver's mutual information in the presence of channel estimation errors and the mutual information of the eavesdropper who uses a minimum mean square error receiver. We analyze the effect of co-channel interference and residual interference from the cooperative jammer on the secrecy rate.