Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear waves, patterns and spatio-temporal chaos in cellular neural networks

Spatio-temporal pattern formation occurring in discretely coupled nonlinear dynamical systems has been studied numerically. In this paper, we review the possibilities of using arrays of discretely coupled nonlinear electronic circuits to study these systems. Spiral wave initiation and Turing pattern formation are some of the examples. Sidewall forcing of Turing patterns is shown to be capable of driving the system into a perfect spatial organization, namely, a rhombic pattern, where no defects occur. The dynamics of the two layers supporting Turing and Hopf modes, respectively, is analysed as a function of the coupling strength between them. The competition between these two modes is shown to increase with the diffusion between layers. As well, the coexistence of low- and high-dimensional spatio-temporal chaos is shown to occur in one-dimensional arrays.
A Novel DEM Imaging Algorithm for Complex Terrain Based on GNSS-InBSAR System

Global Navigation Satellite System (GNSS) based interferometric synthetic aperture radar(InSAR) can be considered as a distributed system. It is required to use digital elevation model (DEM) to obtain high-accuracy interferometric phase. However, using coarse DEM directly will make the edge of target's PSF distorted due to its low two-dimensional resolution. In this paper, a DEM imaging method that combines fitting and smoothing processing is proposed. The fitting processing is based on moving least square and the gaussian kernel with symmetrical transformation is used to realize the smooth effect. The experimental results show that the correlation coefficient of the associated points in imaging pairs has been improved, which proves the effectiveness of the algorithm.
An Efficient Continuous Imaging Framework for Bistatic SAR in Strip-map Mode

Fast factorized back-projection (FFBP) is a clas-sic fast time-domain algorithm (FTDA) for bistatic synthetic aperture radar (BiSAR) in spotlight mode. However, when conventional FFBP is employed for BiSAR in strip-map mode, the processing efficiency for continuous imaging of multiple full-apertures will be decreased significantly. In this paper, an efficient continous imaging framework is developed for BiSAR in strip-map mode. In the proposed framework, echo data are divided into several full-apertures data blocks then processed separately by FFBP to reduce redundant BP operations, so that the new continuos imaging framework has the higher efficiency than conventional FFBP. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm.
Lightweight End-To-End Deep Learning Model For Music Source Separation

In this work, we propose a lightweight end-to-end music source separation deep learning model. Deep learning models for audio source separation based on time-domain have been proposed for end-to-end processing. However, the proposed models are complex and difficult to use when the computing resources of the device are limited. Additionally, long delays may be expected since long-term inputs are required to obtain adequate results for separation, making the models unsuitable for applications that require low latency. In the proposed model, Atrous Spatial Pyramid Pooling is used to reduce the number of parameters, and the receptive field preserving decoder is utilized to enhance the result of separation while the input context length is limited. The experimental results show that the proposed method obtains better results than previous methods while using 10% or fewer parameters.
Time-varying Vibration Compensation for FMCW Lidar Signals

Frequency modulation continuous wave (FMCW) lidar 3D imaging system may suffer from time-varying vibrations which will affect the accuracy of ranging and imaging of a target. The system uses only a single-period FMCW lidar signal to measure the range of each spot; however, traditional methods may not work well to compensate for the time-varying vibrations in a single period signal. To solve this problem, we propose a time-varying vibration compensation method based on segmented interference. We first divide the time-varying vibration errors into primary errors caused by the vibrations with a constant velocity and quadratic errors. Second, we estimate the coefficients of quadratic vibration errors by using a segmented interference method and build a quadratic compensation filter to eliminate the quadratic vibration errors from the original signals. Finally, we use the symmetrical relations of signals in a triangular FMCW period to estimate the vibration velocity and establish a primary compensation filter to eliminate the primary vibration errors. Numerical tests verify the applicability of this method in eliminating time-varying vibration errors with only one-period triangular FMCW signal.
Long-Length Accumulation Unit with Efficient Biasing for Binary Weight CNNs

Binary weight convolutional neural networks (BCNNs) that achieve qualified classification accuracy with reduced operation complexity have been progressively used in convolutional neural networks (CNNs). For BCNN accelerators, long-length accumulation units (AUs) can be considered as essential components. This paper proposes an efficient biasing scheme for BCNN AUs based on probabilistic derivation, aiming to reduce the operation-bit-width by truncating several least significant bits (LSBs) of all operands while compensating for the truncation error. With the pre-calculation using pre-known binary weight values, an AU that enables the proposed biasing method can be realized using minimal adder resources. Through the evaluation of accuracy and hardware performances for an $(8\ \times 100)$ -length 16-bit AU, our design can achieve a great reduction in the area-delay and power-delay products than the level of accuracy degra-dation for 3- to 10-bit truncated LSBs, relative to approaches using the post truncation method.
Stress Identification in Online Social Networks

Online social networks have become one of the primary ways of communication to individuals. It rapidly gen-erates a large volume of textual and non-textual data such as images, audio, and videos. In particular, textual data plays a vital role in detecting mental health-related problems such as stress, depression, anxiety, and emotional and behavioral disorders. In this paper, we identify the mental stress of online users in social networks using a transformers-based RoBERTa model and an autoregressive model, also called XLNet. We implement this model in both a constrained system and an unconstrained system. The constrained system maintains the gold standard datasets such as training, validation, and testing. On the other hand, the unconstrained system divides the given dataset into user-specific training, validation, and test sets. Our results indicate that the proposed transformers-based RoBERTa model achieves a better result in both constrained and unconstrained systems than the state-of-the-art models.
Microfluidic technology for making string-like cell tissues

In recent years, attention has been focused on techniques for creating small organs in microfluidic devices for medical and pharmaceutical applications. Especially in the research field of new drug development, it is strongly desired to put an organ device into practical use because animals for the drug efficacy test can be replaced with an artificial organ in the device. On the other hand, the development of those devices requires various techniques to shape multiple types of cells into the desired figures and place them in specific locations within the microchannel. Manipulating cells sized from a few micrometers to tens of micrometers in a microchannel is difficult and often requires specialized skills for arranging cells by type. Therefore, we devised a simple method for culturing aligned cells in a microchannel. Anyone can easily align and culture cells at specific position in the microchannel by simply dropping 20 - 100 μL of cell solution onto the inlet port of the channel. We have demonstrated the functionality of this microchannel by culturing HUVECs in the device for 3 days to 1 week. The cells were gently cultured in the microchannel by changing the medium at a flow rate of several μL / min. After 3 days, it was confirmed that the aligned cells were connected to each other by adjacent cells. As a result, we succeeded in culturing elongated cellular tissues in an environment with low shear stress using our original microfluidic device. As a future development, we are planning to create an artificial organ by combining multiple types of string-like cell tissues.
Deep Domain Adaptation for Powe Transformer Fault Diagnosis Based on Transfer Convolutional Neural Network

The power transformers are important devices in power systems. Some issues still exist and have been well addressed in the traditional methods. The traditional data-driven methods use the training data samples to train the samples and ignore the data distribution differences. This decreases the classification performance of the trained model on the testing data set. To address this problem, we proposed a transfer convolutional neural network (TCNN), which considers both of the classification loss on the domain data samples and the domain transfer loss. In this way, the proposed model has higher transferability and generalization ability on the testing samples, and thus the classification performance has been improved. tal results validate the effectiveness of the proposed method.
Performance Enhancement of Kesterite Cu2ZnSn(S,Se)4 Thin Film Solar Cell

Work presented in this paper is based on findings of SCAPS simulation of Kesterite CZTSSe thin film solar cell. The main objective of this work is to investigate and improve overall performance of nanostructured Cu2ZnSn(S1-x,Sex)4/CdS/ZnO solar cell through optimization of thickness, band gap and doping concentration in absorption layer. These are major parameters which determine performance of the cell on large extent. The highest efficiency (simulation) reported for CZTSSe/CdS/ZnO before is 13%. Materials with large absorption coefficient in visible spectrum of solar irradiance, regular crystal structure and optimum band gap are being searched by researchers and industrialists for solar applications. There are less number of material compositions used for solar cell thin film application with concerns of cell-efficiency, device flexibility, manufacturing cost and availability of source materials in nature. Because extracting elements from its ores and then making a compound for the required applications raised manufacturing cost significantly. Adequacy of Kesterites in nature, less toxic, easy extraction of CZTS as compared to chalcogenide CIGS make it competent for the application. Removal of toxic elements introduced additional challenges which can be future objective of this work. The proposed optimizations have increased the cell-efficiency by 22.76% w.r. t. the reference work.
An Adaptive SVT-Based Matrix Completion Method for DOA Estimation in Nonuniform Noise

In this paper, we consider the problem of direction-of-arrival (DOA) estimation in the presence of nonuniform noise. It is known that in most of the existing DOA estimation methods are based on the uniform white noise assumption. In many applications, however, the noise across the array may be nonuniform. To tackle this problem, the matrix completion via adaptive singular value threshold (SVT) is used to find the noise-free covariance matrix. On this basis, the signal and noise subspaces are obtained from the eigendecomposition of the noise-free covariance matrix. Finally, traditional subspace-based DOA estimation can be employed to determine the DOAs. Numerical simulation results are performed to demonstrate the effectiveness of the proposed method.
Application of Sensing Technology in Electrical Automatic Control System

With the development of science and technology, the research on sensing technology and electrical aspects has been deepened, and great progress has been made in processor performance, switch performance and control technology. Under such technical conditions, the performance of DC speed regulation has been Far from meeting the needs of people, the AC motor speed regulation system has gradually developed, and its performance far exceeds that of the DC speed regulation. It is now widely used in the speed regulation system. The main purpose of this paper is to study the application of sensing technology in electrical automatic control system. In this paper, key issues such as flux linkage identification and rotational speed estimation in speedless vector control systems are studied. The defects in the process of calculating the rotor flux linkage using the voltage-type flux linkage observer are analyzed in detail, and the principle and advantages of observing the rotor flux linkage based on the ESO method are introduced. Experiments show that the fitting formula can be obtained between the actual voltage value and the value after AD digital-to-analog conversion: Ureal = (AD reading+21.28)/3.94, and the actual bus voltage is calculated according to the AD reading.
A Simple Method for Contact modelling in an Arbitrary frame of Reference within multi-Physics Software

A method to simulate contact between two boundaries of structures which are not aligned to predefined axes when using Comsol Multi-physics (v3.3, Comsol Ltd, Cambridge, UK) is presented. This method was developed because of limitations in the existing default contact modelling. Some of these limitations were recently addressed in a separate study; however, the method exploited symmetry across an axis of the coordinate system. The method presented here enables contact modelling with arbitrarily aligned structures within such a coordinate system. The contact method presented is then applied to a simple model with two deformable structures that come into contact, and compared over a range of positions. Results show a minimal variation in peak stress and contact pressure with model orientation, demonstrating that results are independent of orientation. Therefore, the contact method enables contact simulations in Comsol Multi-physics without assuming symmetry about an axis for contract. The method is compatible with the true transient contact method defined.
An Adversarial Reinforcement Learning Framework for Robust Machine Learning-based Malware Detection

Empowered by the recent development in Ma-chine Learning (ML), signatureless ML-based malware detectors present promising performance in identifying unseen mal ware variants and zero days without requiring expensive dynamic malware analysis. However, it has been recently shown that ML-based malware detectors are vulnerable to adversarial malware attacks, in which an attacker modifies a known malware exe-cutable to trick the malware detector into recognizing the modi-fied variant as benign. Adversarial malware example generation has become an emerging area in adversarial ML that studies creating functionality-preserving adversarial malware variants. Advancements in this area have led to an eternal game between the adversary and defender. While the area has attracted much attention in the security community, a large body of these studies merely focuses on attack methods against ML-based malware detectors. There has been little work on understanding how these adversarial variants can be systematically used by the defender to strengthen the robustness of these detectors and stand ahead of the adversary. Latest efforts have led to emergence of adversarial learning. In this work, we propose a simple wargame approach to empirically conduct the adversarial minimax optimization underlying in the adversarial learning for improving the robustness of ML-based malware detectors. Our proposed approach employs adversarial malware variants generated from a reinforcement learning-based adversarial attack policy in a minimax game alternating between strengthening the attack policy and improving the detectors' robustness. We evaluated the effectiveness of our approach on a testbed with 33.2 GB working malware collected from VirusTotal. Despite the sub-optimal nature of our method, it was able to surprisingly enhance the robustness of three known open-source ML-based malware detectors (LGBM, MalConv, and NonNeg) against the adversarial malware variants by 4, 7, and 11 times, respectively.
A Potential Human Activity Detection Method Based on Multi-polarization InSAR

Human activity detection refers to detect, locate and extract potential activities from the observation scene, which employs the coherent change detection (CCD) method to detect the subtle change between two data acquisitions. The polarization information of Synthetic Aperture Radar (SAR) system can represent the scattering characteristics of the target more comprehensively. This paper adopts a coherent change detection method based on multi-polarization interferometric coherence to detect potential human activity in the scene. The effectiveness of the method is further validated by the actual airborne repeat-pass C-band Interferometric Synthetic Aperture Radar (InSAR) data that acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS). Moreover, the results are compared with the coherent change detection method based on single-polarization InSAR. The results show that the method proposed in this paper has better detection performance than the traditional coherent change detection method based on single-polarization.
Layered Media Inversion Network Applied in Ground Penetrating Radar

Ground-penetrating radar (GPR) is a mainstream detection tool for layered media inversion. However, due to the multiple reflections and complicated refraction effect, traditional inversion method can't achieve accurate parameter inversion. For the issue, a transformer-based layered media inversion network with GPR is proposed in the paper. The network utilizes velocity spectrum as the data set to enhance feature identification and utilizes transformer-based self-attention mechanism as the backbone to reallocate attention resources based on the degree of feature importance. It focuses more attention on reflected features adaptively and maps the velocity spectrum to the permittivity image. Extensive simulated results demonstrate that the proposed network has the superiority in reconstructing the underground structure and inversing the multilayered parameters.
Multi-channel SAR moving target detection based on simulation samples and transfer learning

Synthetic aperture radar (SAR) moving target detection is always a key technology in the field of radar signal processing. However, due to the unique military background of radar, the real SAR data containg ground moving targets is hardly to obtain, and leading to the lack of labeled moving target data when applying a network. Therefore, an improved convolutional neural network (CNN) is proposed in this paper, in which local maximum mean difference (LMMD) is added to the loss function of the neural network as a measurement method, and neural network parameters are trained by stochastic gradient descending, so that the difference in distribution between the source domain and the target domain is constantly reduced in the representation of the neural network. The results show that the recognition accuracy is better than the previous deep learning, which verifies the effectiveness of the proposed deep transfer learning (TL) model based on the simulation of multi-channel SAR moving target in the case of scarce measured data.
Experimental research on UWB channel and DF algorithm based on EKF

This paper carried out a series of experiments in both the LOS and NLOS scenes, which was based on an unmanned mobile platform, UWB and INS. Throughout experiments, this paper analyzes the ranging error from UWB, verifying that it obeys rice distribution in LOS and lognormal distribution in NLOS respectively; By using TOA combined with GN algorithm, the impact of the number of NLOS links on the positioning error is summarized; Taking the correlation between Links into consideration, their degradation processes was studied; To the end, this paper proposes a DF algorithm based on EKF, whose the positioning error of RMSE is proved to be lower than traditional algorithm.
A Transistor Array for Extracting Total Ionizing Dose Threshold Voltage Shifts

Total ionizing dose (TID) has a significant effect on fully depleted silicon on insulator (FDSOI) transistors, which can be characterized the front gate threshold voltage (Vth) shift. We present transistor array test structure to characterize transistor TID. The resulting structure is one device under test (DUT) on a larger test integrated circuit (IC). The transistor array allows TID testing of a largenumber of devices in a packaged die that is amenable to gamma irradiation and other reliability studies, fabricated on a 22 nm FDSOI process. The approach is however, applicable to any fabrication process.
Virtual Power Plant Scheduling Model Considering Carbon Trading Mechanism and Uncertainty of Source and Load

Aiming at the need of peak-load shifting and the uncertainty of wind and photovoltaic power, this paper proposes a double layer virtual power plant scheduling model that integrates wind, photovoltaic power, gas turbine, power-to-gas device, energy storage devices and cooling heating and power microgrid. A Markov Chain Monte Carlo method is used to deal with the uncertainty of wind and photovoltaic power. In the upper layer VPP scheduling, the source side preferentially uses wind and photovoltaic energy, and the load side guides the load distribution through demand response measures such as TOU price and flexible load compensation. In the lower level VPP scheduling, the NSGA-II algorithm is utilized to the multi-objective optimization model by taking the minimum VPP operation cost and the lowest carbon emission as the objective function. The simulation results show that the proposed VPP scheduling model can effectively deal with the uncertainty of renewable energy output, and take into account environmental protection and economy of the system, so as to realize the low-carbon and economic operation of the system.
Radial Velocity Ambiguity Resolution Method in Azimuth Multichannel HRWS SAR

For moving targets in synthetic aperture radar (SAR) images, the obvious features are defocusing and dislocation. To estimate motion parameters accurately is a premise for the precise imaging of moving targets. However, when the radial velocity of the target exceeds the maximum detectable unambiguous velocity, the estimated value by the existing methods is no longer the real value. A radial velocity ambiguity resolution method based on Imaging Space-Time Adaptive Processing (ISTAP) is proposed in this paper. The proposed method has no search or iterative process and is suitable for the azimuth multichannel SAR with low pulse repetition frequency (PRF). Finally, the simulated data from an x-band azimuth six-channel SAR system verify the feasibility of the proposed method.
Improving Speech Separation with Knowledge Distilled from Self-supervised Pre-trained Models

Large-scale self-supervised learning (SSL) models have shown outstanding ability in many speech processing tasks. Most of the SSL models in the literature are trained with datasets where the single-talker utterances dominate. It may not be optimal to directly apply these SSL models to speech separation tasks. Besides, many computational costs in large-scale SSL models increase the overall complexity of the speech separation system. In this paper, we explore the application of pre-trained SSL models in the speech separation task. Instead of using the SSL model directly, we designed an SSL feature predictor to estimate single-talker’s deep features from the speech mixture. The SSL feature predictor is trained with the knowledge distilled from the pre-trained Wav2Vec2.0 model. Our experiments show that the performance of time-domain speech separation can be improved obviously by leveraging the SSL embedding predictor.