Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiogram Detection with a Millimeter-wave Radar Sensor

In this work, we proposed and verified that a noncontact measurement to the human cardiogram could be implemented based on the inner connection between the bioelectrical signals that control the diastole and systole of human hearts and the volume change of atriums and ventricles taking place at the surface of skin. A K-band, board-integrated, highly linear and sensitive Doppler radar sensor was designed and implemented to detected such weak motions. Experimental results showed that by linearly retrieving the Doppler information from the signals scattered by the skin, the cardiogram can be measured. While the correspondence between this kind of cardiogram and the conventional ECG can be observed. And the cardiogram also includes velocity and strength information of the atrial and ventricular contractions and relaxations. Such characteristics are highly desirable for hospitalization and personal daily healthcare.
Examining Permission Patterns in Android Apps using Kernel Density Estimation

Effective detection of malware apps requires thorough knowledge of behavioral and structural patterns of both benign and malware apps. In this paper, we focus on permissions which allow or restrict access to system services in Android mobile operating system. After a preliminary statistical analysis of different permissions classified into normal, dangerous, and signature ones, we apply Kernel Density Estimation, a nonparametric method for estimating probability distribution of data. Our analysis is based on the distinct permission patterns in both benign and malware apps from a research database containing nearly 120,000 sample apps. The research presented here is the necessary first step towards building a cost-effective malware app detector for the Android system.
Temperature and Resistance Profile on HTS Tape During Fault and Recovery in Modular SFCL Unit

A new modular configuration of resistive superconducting fault current limiter (SFCL) unit is developed to study the temperature and resistance profile on copper laminated 2G high-temperature superconductor (HTS) tape during fault and recovery at 77 K using a lab-scale resistive modular SFCL unit. The thermal characteristics and their transition behavior, i.e., the resistance profile on the HTS tape are investigated using five cycles of the fault of peak currents 1.8, 2.2, and 4 kA. The temperature of the HTS tape is measured during fault and recovery under load. The fault limiting characteristics are analyzed for various output voltages (20, 40, and 60 Vrms) across the modular SFCL unit. The resistance profile on the HTS tape is estimated analytically, which is compared with the experimental measurement of resistance. The recovery time is measured and it is correlated with the peak temperature on the HTS tape and the associated heat transfer in the liquid nitrogen bath. The study will be useful for developing a higher rating of SFCL using this modular configuration.
Output Feedback Energy Control of the Sine-Gordon PDE Model Using Collocated Spatially Sampled Sensing and Actuation

The present investigation focuses on the output energy control of the nonlinear sine-Gordon model in a practical situation where only spatially sampled sensing and actuation are available. The speed-gradient method that has corroborated its utility for the sine-Gordon model, controlled through the manipulable parameters of the external field and boundary actuation, is now generalized to the spatially sampled sensing and actuation, aiming to pump/dissipate the energy of the model to a desired level. Luenberger-type observers are additionally developed in the present nonlinear PDE setting to be involved into the output feedback synthesis over collocated state (position and/or velocity) measurements. Capabilities of the proposed synthesis are illustrated in numerical simulations.
Translational Invariant Bistatic SAR Based on Tower Crane: Experiments and Results

Compared with monostatic Synthetic Aperture Radar (SAR) systems, bistatic SAR systems own obvious advantages, and have attracted more and more attentions from academe. A number of experiments with various configurations and platforms have verified the application potential of bistatic SAR. However, the bistatic SAR system is far more complicated than monostatic one, which means more non-ideal factors may be encountered during bistatic SAR experiments, such as motion deviation of receiver and transmitter platforms. To keep the motion state of the receiver and transmitter stable and simplify the process of implementing bistatic SAR experiments, we employ a translational invariant bistatic SAR system whose transmitter and receiver are suspended on the arm of a tower crane. In this case the transmitter and the receiver can only move along the arm of the tower crane, which makes it more convenient to control the motion state of receiver and transmitter. With the translational invariant bistatic SAR system based on a tower crane, we conducted real-scene experiments in Lianyungang, Jiangsu Province, China and some meaningful imaging results are obtained.
A grating and side lobe suppression method for near range MIMO array

In near range multiple-input multiple-output array imaging, due to the sparsity and limited length of the array, there are high grating lobes and sidelobes in the imaging result, which greatly reduce the image quality. In this paper, aperture domain weighting is utilized to suppress the grating lobes and sidelobes. In order to effectively suppress them, the different weighting functions are used to weight the transmitting and receiving array prior to imaging. To overcome the mainlobe broadening caused by weighting without increasing the redundancy of the array, the multiapodization technology is adopted, which takes the minimum value of the imaging results weighted by different functions as the output for each pixel. The measured data verify its performance.
Multi-RAT multi-connectivity active steering antenna technology for IoT, Wi-Fi, LTE, and 5G

This paper presents an active steering antenna system that intuitively beam steers for the best RF signal, achieving ubiquitous coverage and throughput enhancement. The direction of antenna radiation pattern can be arbitrarily rotated in accordance with the passive and active tuning elements. Using 802.11ac Wi-Fi, or Wi-Fi 5 as an example, the authors present a demonstration and experimental result of an active steering antenna feedback system and how it can improve the coverage and throughput. The sensing and control algorithm of the active steering antenna system is applicable for different communication protocols, makes it feasible in a multi radio access technology (RAT), multi connectivity scenario with aggregation of traffic flows from different sources such as IoT, Wi-Fi 6, and 5th generation new radio (5G NR).
Design and Comparative Analysis of 2.45 GHz Rectangular microstrip Patch Antenna

Over the years, there has been plenty of development in communication systems which requires the development of low cost, minimum weight and low profile antennas that are efficient for maintaining great performance over a wide spectrum of frequency. The aim of this paper is to simulate, design and fabricate a rectangular microstrip patch antenna for analytical studies and global WLAN applications. The antenna is designed to operate at 2.45 GHz resonant frequency. FR-4 is used as a dielectric substrate with relative permittivity of 4.4 and thickness of 1.6 mm. The antenna proposed here have been modeled and simulated using High-Frequency Structure Simulator (HFSS) and MATLAB. The obtained results of antennas simulation indicates that the designed antenna fulfills the requirements and characteristics for different frequency bands. Also, results shows a good agreement with comparative analysis.
Outlier Processing in Multimodal Emotion Recognition

Automatic emotion recognition plays a key role in human-computer interactions. Multimodal emotion recognition has attracted much attention in recent years. When multimodalities are used, different modalities interact with each other and the obtained results tend to be accurate in general. However, there are also cases of unimodal anomalies. Most of the existing studies do not take into account the existence of outliers in the multimodality, which leads to low accuracy of the prediction results. This paper proposes fuzzy weighted support vector machine for regression (FWSVR) to deal with outliers and prediction errors. We design an automatic affective recognition model structure to analyze continuous dimension emotions based on multimodality (audio and visual). The LIRIS-ACCEDE database is used in this work. Experimental results indicate that the concordance correlation coefficient (CCC) is 0.9456 for arousal and 0.9183 for valence on the test set. The fusion result obtained when using fuzzy weighting is much better than the direct fusion one.
A Stepped Frequency Modulated Radar Imaging method based on Convolutional Neural Network

In this paper, we propose a new high resolution range profiles (HRRP) imaging method of stepped frequency modulated (SFM) radar based on convolutional neural network (CNN). In the traditional SFM radar imaging methods, the incomplete motion compensation may incur some phenomena, e.g., position shift, amplitude loss and false peaks. In order to solve these problems, and motivated by the great merits of deep learning, we make an interesting attempt to use CNN to learn the mapping from SFM radar echo to HRRP. The basic principle of SFM radar is given firstly. Then the CNN structure and the training process are detailed. Finally, simulation experiments are presented to demonstrate the efficiency of the proposed method.
Synthetic Aperture Radar Imaging Response of Three-dimensional Moving Target

In this paper, we propose the two-dimensional imaging response of three-dimensional moving target via synthetic aperture radar (SAR) with back-projection imaging algorithm. Firstly, we analyzed the imaging response for each single echo, which is the intersection of Doppler isodop and iso-range contour. Then, we come up with a focus condition for moving target SAR imaging. Besides, we find that the Doppler frequency will cause a shift and the motion trail of moving target will draw a curve in the imaging result over synthetic aperture time. Meanwhile, we provide the numerical simulation experiments of moving point targets via linear SAR and circular SAR with X-band and THz-band, respectively, to verify the conclusion of moving target imaging response.
Unsupervised Protocol-based Intrusion Detection for Real-world Networks

Anomaly-based Intrusion Detection Systems (IDSs) are rarely deployed in real networks, because of their high false positive rate. Their ability to detect unknown attacks is, however, very valuable in a context where new threats are emerging almost daily. This paper presents an unsupervised anomaly-based intrusion detection solution focused on protocol headers analysis. This approach is tested on a recent and realistic dataset (CICIDS2017) over a 4-day period. Each protocol is converted to a set of normalized numeric features, which are processed by 5 neural network architectures: deep autoencoders, deep MLPs, LSTMs, BiLSTMs, and GANs. The output of these algorithms is an anomaly score, which is normalized and combined with the anomaly scores of other protocols. We argue that this classification problem is very different from the actual problem of intrusion detection and requires new metrics. In particular, packet anomaly scores must be refined in a post-processing step to aggregate anomalies into continuous attacks. This approach successfully detects 7 out of 11 attacks not seen during the training phase, without any false positives. It is thus possible to consider deployments in real-world networks of such IDSs, capable of reliably detecting zero-day attacks.
Dynamic UL/DL Mode Selection and Resource Allocation in Multi-Cell MIMO TDD Systems

This paper considers joint uplink/downlink mode selection and resource allocation problem in multi-cell systems for flexible TDD frame structures. To combat the difficult interference characteristics of such systems, a dynamic mode selection and resource allocation scheme is derived using Lyapunov optimization. The result is a cross-layer optimization scheme, where a long-term time-average utility function of flow rates and interference penalty is optimized by solving a series of instantaneous subproblems. The proposed dynamic algorithm provides a solution for the UL/DL mode selection and resource allocation, guided by the accumulating network layer queues and virtual queues. Furthermore, a heuristic method is considered to decouple the difficult power constraint in the resource allocation subproblem.
Real-time Unstable Approach Detection Using Sparse Variational Gaussian Process

Worldwide, Air Navigation Service Providers (ANSP) are striving to exceed the desired safety levels. The Terminal Manoeuvre Area (TMA) is one of the most safety-critical areas in ATM as it encompasses the most critical phase of flight, i.e., departure and landing. An aircraft, during the final approach phase, is required to remain in a stable configuration and prevent any undesired state such as an unstable approach, which may subsequently lead to incidents/accidents such as Go-Around, Runway Excursions, etc. In this paper, we propose a data-driven framework to model the aircraft 4D trajectories in the final approach phase by adopting sparse variational Gaussian process (SVGP) model. The model is trained to learn the aircraft landing dynamics from Advanced Surface Movement Guidance and Control System (A-SMGCS) data, during the final approach phase. We experimentally demonstrate that SVGP provides an interpretable probabilistic bound of aircraft parameters that can quantify deviation and perform real-time anomaly detection. The findings of this work can increase situational awareness of the air traffic controller and has implications for the design of a new approach procedure in complex runway configurations such as parallel approach.
Fundamental Issues of Future Internet of Things

Internet of Things (IoT) revolutionizes our daily life by incorporating the Internet and Wireless Sensor Network (WSN) together. IoT provides us healthier, comfortable, and easy life than before. Due to these reasons, IoT is now becoming the center of attraction for not only the research community but also for marketers and business owners. Due to its expected massive contribution in the future industry and academia, a lot of research has been made on this topic that discussed its different applications and future research direction. Still, none of the research paper raised its fundamental issues. These issues have significant importance while transferring this concept to reality. We will not be able to delve correctly into this phenomenon without considering its main contributors, Internet, and WSN. This paper mainly focusses on the fundamental issues of IoT by considering the differences between the Internet and WSN with IoT. The identified differences can be viewed as future research directions to bridge the gap between the IoT concept and its implementation in real-world scenarios. In this way, we will be able to deploy the IoT for the betterment of human beings successfully.
Promotion from 1-Bit Quantization Model to Multi-Bit in SAR Imaging

Recently, a single-frequency threshold(SFT) based 1-bit quantization method had been proposed for synthetic aperture radar(SAR) imaging. Such a method uses a SFT to quantize echo data. Compared with the traditional high precision quantization method, the SFT based 1-bit quantization model can effectively reduce the data storage and calculation pressure while ensuring the imaging quality. Based on the SFT 1-bit quantization model, this paper promoted it to multi-bit and studied the characteristics of multi-bit quantized signals in the terms of harmonics. The work in this paper provides theoretical support to the performance analysis and improvement for subsequent low bit quantization.
Glycemic Index and Breast Cancer Risk and Phenotype

Ecological studies and results from our low-fat, high-carbohydrate dietary intervention trial suggest that different carbohydrates are associated with breast cancer risk in different ways. We examined the association of diet glycemic index (GI), a ranking of carbohydrate containing foods based on their blood glucose raising potential, with breast cancer risk and phenotype. GI was calculated from multiple food records from subjects in our intervention trial using a nested case-control design (220 cases, 440 controls). GI was not associated with risk of total or estrogen receptor positive breast cancer, tumor size or nodal status. GI was strongly positively associated with hormone negative breast cancer. This finding is potentially important as little is known about the etiology of hormone negative breast cancer, which has a worse prognosis than hormone positive breast cancer. However, this finding is based on a small number of cases and should be replicated in a larger sample.
ReMCW: Reduced Bandwidth FMCW Radar for Autonomous Driving

Automotive radar is an all-weather sensing technology that makes direct measurements of target motion thereby aiding in unmanned driving. Continuous-wave (CW) radars which use linear frequency modulation (FM) have been the most popular automotive sensing systems because of lower cost, higher range resolution, and lower transmit power than a pulse Doppler radar. The available spectrum for vehicular systems is limited and, therefore, avoiding mutual interference from multiple automotive radars operating in the same spectrum in a crowded traffic scenario is a major challenge. To address this, we present a Reduced bandwidth FMCW (ReMCW) radar that consumes less spectral resources without decreasing the range resolution and enables interference-free operation. Our CW radar waveform transmits few randomly chosen slopes within the original FMCW sweep. This waveform avoids range- Doppler coupling encountered in conventional FMCW radars. The parameters used for the design of this waveform conform to current automotive radar requirements. Numerical experiments with ReMCW show great savings in spectrum over the conventional FMCW radar.
Adaptive Directional Neighbor Discovery Schemes in Wireless Networks

The use of fully directional links in ad-hoc networks improves the overall network performance but complicates the process of neighbor discovery. In most of the state-of-the-art schemes, nodes switch their direction either randomly or in a predefined sequence, without considering the result of past discovery attempts in each sector. This introduces high discovery latency and severe long tail problem, especially for high density networks and/or narrow beams. To overcome these limitations, we propose two novel decentralized, and low complexity reinforcement learning-based neighbor discovery schemes in this paper. In these schemes, the neighbor discovery is mapped to a stochastic multi-player game, where each node independently adjusts its policy via the Q-learning based scheme to minimize the discovery latency. Effectiveness of the proposed schemes is assessed through numerical results, and it is observed that these proposed schemes are able to achieve a significantly faster network-wide neighbor discovery, while incurring low overhead and computational complexity.
A Unique Satellite Formation of Super-Polyhedron for Multistatic SAR

Spaceborne multistatic synthetic aperture radar has wide applications, including digital beamforming (DBF), cross-and along-track interferometry, multi-angle imaging et al. Nevertheless, classical satellite formations are very difficult to meet the application requirements of multistatic SAR synchronously. Therefore, Super-Polyhedron (SP), a unique satellite formation, is presented to avoid those weaknesses and provide more potential for the rapid development of multistatic SAR. Moreover, Super-Tetrahedron (ST), as a specific instance, is exhibited to highlight the advantages of the formation by compared with other satellite formations with the same number of satellites.
On Stability of Linear Estimators in Poisson Noise

This paper considers estimation of a random variable in Poisson noise. Specifically, the main focus is to assess optimality and near optimality conditions for linear estimators.In the first part of the paper, it is shown that linear estimators are optimal if and only if the underlying prior is a gamma distribution and the dark current parameter is zero.In the second part of the paper, a stability analysis of linear estimators is undertaken. Specifically, it is shown that if an optimal estimator is close to a linear estimator in an Lp,p ≥1 distance, then the underlying prior distribution is approximately gamma in the Lévy metric and the Kolmogorov metric.
Incremental Robust Non-negative Matrix Factorization for SAR Image Recognition

In the face of incremental learning of synthetic aperture radar (SAR) image recognition, recognition model is easy to affect by the increase of training samples. In this paper, incremental robust non-negative matrix factorization(IRNMF) scheme is proposed to relieve the problem. The noise and outliers transform the projection direction from the original space to feature space as the increase of training samples. Thus conventional non-negative matrix factorization(NMF) is easily prone to noise and outliers since its loss function includes the squared errors. The loss function of IRNMF is defined by using $\mathrm{L}_{2,1}$-norm so that the proposed algorithm can avoid the influence of the squared errors. In the process of incremental learning, some experiments on Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset prove that our scheme consistently outperforms NMF and RNMF.
RESTful Web of Things for Ubiquitous Smart Home Energy Management

Emergence of smart things has matured the Internet of Thing (IoT) notion. Extensive attention drawn towards IoT has inspired the concept of Smart homes. However, actual implementation of smart home architecture is highly challenged by the heterogeneity of smart devices, platform incompatibilities, and energy management demands. Web of Things (WoT) is a common solution platform, which exposes these heterogeneous services in an effective and a transparent manner. Hence, in this paper, we propose a WoT inspired smart home that occupies representational state transfer (REST) architecture for data collection and sharing. The proposed smart home architecture was tested through computer simulation to evaluate its performance efficiency with respect to energy efficiency of domestic appliances, device discovery performance, response time, and failed transmissions.