Miracle-dz/UniFAR-FTUs huggingface dataset:
SAR Target Recognition Using Improved Monogenic-Based Feature Extraction Framework

Applying computer vision methods to synthetic aperture radar (SAR) image recognition is a research trend in recent years, and a series of valuable results have been achieved. In order to use machine learning classifiers for recognition, it is necessary to extract effective features, and most of these features are directly extracted based on grayscale SAR images. SAR data is usually rare, and more difficult to collect than optical images. Therefore, the problem of recognition using a small-size training set for SAR is more challenging to practical pattern recognition methods. The monogenic signal is an extended version of analytic signals in high-dimensional space which has attracted attention. In this paper, a new recognition framework which is based on feature dimensionality augmentation using combined multi-scale monogenic components and histogram of oriented gradient (HOG) feature is proposed. Proposed feature is named as MONO-HOG. This letter focuses on recognition under both standard operating condition (SOC) and small-sample scene. Experiments on moving and stationary target automatic recognition (MSTAR) data set show that our proposed framework has satisfying performance.
Multi-human Separation Method Based on CNN-Mask-PIT

Human activities classification based on micro-Doppler effect is a hot topic in the field of radar detection. However, when there are multiple humans, the motion mechanism of the multi-human target is complex, the components are mixed, and the micro motion features of each component are highly similar, which lead to difficulties in the classification of the multi-human activities. In order to overcome these problems, a multi-human separation method based on convolution neural network Mask generation and permutation invariant training (CNN-Mask-PIT) is proposed in this paper. In this method, the multi-human target is separated on the time-frequency (TF) images. The Mask matrixes are generated by CNN, and then the generated multiple Mask matrices are multiplied with the original TF image to obtain the separated TF images, so as to realize the effective separation of multi-human separation. The permutation invariant training (PIT) is introduced to solve the label arrangement problem by minimizing the separation error. Finally, the effectiveness and robustness of the proposed method are verified on the multi-human TF images generated based on motion capture data.
Improved Method of Human Vital Sign Parameters Estimation Based on Hilbert-Huang Transform

In this paper, we propose a novel method for estimating parameters of human vital signs in a frequency modulated continuous wave (FMCW) radar system. Specifically, we first introduce the signal model of human vital signs in the FMCW radar system. Then, the Hilbert-Huang transform (HHT) and variational mode decomposition-Hilbert transform (VMD-HT) algorithms are used on the signal components after pre-processing to estimate respiratory rate (RR) and heart rate (HR), respectively. Finally, the simulation and experimental results validate the effectiveness and superiority of the proposed method.
MsdoBot: An Extensible Chabot Platform for Microservice Development and Operations

The microservice architecture (MSA) is among the most popular systems in use today; however, development and operations are hindered by the variety of languages, platforms, and tools, as well as difficulties in collecting and organizing information scattered throughout complex DevOps environments. This study developed a chatbot system to facilitate the development and maintenance of MSAs under various usage scenarios dealing with single/multiple microservices, single/multiple tools, and atomic/composite functionality. The proposed MicroService Development and Operation Bot (MsdoBot) integrates numerous auxiliary tools to make available the information required for system development, as well as real-time system status reports for monitoring and maintenance. Notably, MsdoBot preserves extensibility by making it easier for users to add new functionality and integrate existing tools simply by altering configuration files.
Improved Virtual Inertia Damping Adaptive VDG Control Strategy for DC Microgrid Hybrid Energy Storage Converter

When the power supply or load changes, the traditional control strategy of hybrid energy storage bidirectional DC-DC converter has the shortcomings of low inertia and weak damping, which has a great impact on the frequency and voltage of the system. To solve this problem, an improved adaptive VDG control with inertia damping is proposed, in which different adaptive control strategy is employed according to the working characteristics of the supercapacitor and the battery. The simulation results show that when system is disturbed, the improved control strategy effectively reduces the amplitude of bus voltage fluctuation and recovery time compared with other control strategies, which provides a conducive solution for the safe and stable operation of DC micro-grid hybrid storage system.
Deep Learning Aided Emotion Recognition from Music

Emotion identification by audio signal is a contemporary study area in Human Computer Interaction domain. The desire for improving the communication interface between people and digital media has increased. The emotion of the song is detected through music. Music is a great medium for conveying emotion. The practice of determining emotions from music snippets is known as music emotion recognition. Audio dataset is collected from the Kaggle. Researchers are now increasingly concerned towards increasing the precision of emotion recognition techniques. However, a complete system that can discern emotions from speech is not yet developed. This research work has suggested a novel emotion recognition technique, where the neural networks are trained to identify emotions based on the retrieved information. The performance of neural networks is then compared to the performance of baseline machine learning classification algorithms. The obtained results show that MFCC characteristics combined with deep RNN perform better for instrument emotion identification. The results also reveal that MFCC features paired with a deep neural network outperform other emotion recognition methods. It also shows that the class has a major influence on the mood evoked by music. To make human-computer interaction more natural, the computer should be able to perceive different emotional states. The voice of a person is very essential in assessing individuals. The emotion of the individual is detected through the person's speech. These audio types are further classified as joyful, sad, neutral, or fearful.
Keystone Transform Based UAV Detection by Utilizing MillimeterWave Radar

Due to the small radar cross section (RCS), the detection of unmanned aerial vehicles (UAVs) is a challenge task for traditional surveillance radar. In this paper, a linear frequency modulation continuous wave (FMCW) millimeter-wave radar is presented for UAV detection and a keystone transform (KT) based long-time coherent integration method is employed for the sake of improving the output signal-to-noise ratio, which is essential for UAV detection. Specifically, the KT is first utilized to correct the range migration induced by the movement of UAV. Then, the two dimensional fast Fourier transform (2D-FFT) along the fast time and slow time directions is addressed to realize the long-time coherent integration. After that, the constant false alarm rate (CFAR) detection is used to achieve the UAV detection. Finally, the simulation results are given to verify the effectiveness of the proposed method.
SA-FGDEM: A Self-adaptive E-Learning Performance Prediction Model

With the rapid development of computer computing power and the severe challenges brought by the COVID-19, e-learning, as the optimal solution for most students and other learner groups, plays an extremely important role in maintaining the normal operation of educational institutions. As the user community continues to expand, it has become increasingly important to guarantee the quality of teaching and learning. One way to ensure the quality of online education is to construct e-learning behavior data to build learning performance predictors. Still, most studies have ignored the intrinsic correlation between e-learning behaviors. Therefore, this study proposes an adaptive feature fusion-based e-learning performance prediction model (SA-FGDEM) relying on the theoretical model of learning behav-ior classification. The experimental results show that the feature space mined by fine-grained differential evolution algorithm and the adaptive feature fusion combined with differential evolution algorithm can support e-learning performance prediction more effectively and is better than the benchmark method.
A Quadtree Beam-Segmenting Fast Backprojection Algorithm

The computation of the backprojection (BP) algorithm for spotlight synthetic aperture radar (SAR) is expensive since the reflectivity reconstruction is based on pixel-by-pixel interpolation operation. Based on dechirp, a quadtree beam-segmenting fast BP algorithm for spotlight SAR is proposed. This fast BP algorithm is to generate multiple sub-beam down-sampled raw data by using dechirp imaging algorithm to filter the whole beam data, and the filtering process is recursively implemented like a quadtree. For any narrow sub-beam, the corresponding sub-image can be reconstructed by a regular backprojection. The pixel numbers of each sub-image is small and the down-sampled sub-beam raw data is small, so it can significantly accelerate the regular BP algorithm. The processing flows are derived in detail and the algorithm is validated by simulated data.
Efficient Distributed Algorithms for Minimum Spanning Tree in Dense Graphs

In recent years, the Massively Parallel Computation (MPC) model capturing the MapReduce framework has become the de facto standard model for large-scale data analysis, given the ubiquity of efficient and affordable cloud implementations. In this model, an input of size $m$ is initially distributed among $t$ machines, each with a local space of size $s$. Computation proceeds in synchronous rounds in which each machine performs arbitrary local computation on its data and then sends messages to other machines. In this paper, we study the Minimum Spanning Tree (MST) problem for dense graphs in the MPC model. We say a graph $G(V,\ E)$ is relatively dense if $m=\Theta(n^{1+c})$ where $n=\vert V\vert$ is the number of vertices, $m=\vert E\vert$ is the number of edges in this graph, and $0 < c\leq 1$. We develop the first work- and space-efficient MPC algorithm that with high probability computes an MST of $G$ using $\lceil\log\frac{c}{\epsilon}\rceil+1$ rounds of communication. As an MPC algorithm, our algorithm uses $t=O(n^{c-\epsilon})$ machines each one having local storage of size $s=O(n^{1+\epsilon})$ for any $0 < \epsilon\leq c$. Indeed, not only is this algorithm very simple and easy to implement, it also simultaneously achieves optimal total work, per-machine space, and number of rounds.
MMDL: A Novel Multi-modal Deep Learning Model for Stock Market Prediction

Stock markets play an important role in modern financial systems. Stock market analysis is of significant interest to both academia and industry. One of the main challenges in this area is modelling multiple sources of information for market forecasting. In this research, we propose MMDL, a Multi-modal Deep Learning model, which utilizes both unique and joint information of the financial news modality and the market data modality for stock predictions. In experiments on real-world datasets, our model clearly outperforms current existing models, which demonstrates that our proposed technique is highly effective for multi-modal stock market predictions.
Target Detection with Optimal Power Allocation and Quantization for Distributed MIMO DFRC System

In this paper, the problem of detection in a power-bandwidth-constrained distributed MIMO Dual-Functional Radar-Communication (DFRC) system is considered, an optimal power allocation fusion scheme is investigated by joint analyzing the influences of interference and quantization error on detection performance. Firstly, we describe the influence of interference and quantization error on detection, and derive the numerical relationship with detection probability. On this basis, a constrained maximization problem of detection probability is proposed to balance the trade-off between these two factors in order to obtain higher detection performance, by using Yalmip optimization tool, the approximate optimal power allocation is derived. Finally, numerical results confirm the superiority of our method.
A FPPT Scheme Based on Reinforcement Learning in Photovoltaic Power Systems

For Photovoltaic (PV) power systems to capture the maximum power from solar energy, the maximum power point tracking (MPPT) is necessary. Since the intermittent nature of solar energy and the ever-changing environment, MPPT may cause power and voltage fluctuations which may cause problems for grid stability. Furthermore, the fluctuation may increase the PV converter's heat load and reduce its service life. To limit power fluctuations, a flexible power point tracking (FPPT) scheme is proposed. As a result, the system is more dependable. This article proposes a FPPT scheme based on reinforcement learning (RL) to increase the FPPT performance in terms of high speed and precision. RL makes the system hardware structure simpler and enables faster tracking speed and higher accuracy than traditional methods. The open source OpenAI Gym platform is applied to construct the PV environment. The constructed simulation environment is applied to conduct experiments to evaluate the proposed control scheme's robustness in various environmental scenarios. The simulations results show the feasibility of the scheme with fast response and stable performance.
Optimal Control and Trunk Reservation in Loss Networks

Consider a stochastic loss network, where calls or customer types arrive and have to find a path through the network to a given destination, and where our aim is to maximize the gain (suitably defined) from the network. In general there will be a number of paths available, and when a call arrives the two questions to answer are first, should the call be accepted, and secondly, if it is accepted which route should it take? The answer to the first question is in some sense harder than the second, and all dynamic routing or control policies have some explicit or implicit mechanism for rejecting calls and so answer the question in some way.
Dynamic Routing Reconfiguration for Low-Latency and Deadlock-Free Interconnection Networks

For interconnection networks of large-scale super-computers, it is reported that low-diameter randomized networks are effective in reducing network latency. Since these networks with irregular structures have many cyclic connections, deadlocks in packet routing lead to the deterioration of communication performance. This research proposes a dynamic routing reconfiguration method that can alter multiple spanning trees based on different root nodes to support both deadlock freedom and low latency. Using our proposed method, the average number of hops was improved by 5.41 % at maximum under random permutation traffic. Moreover, it is found that for communication traces of parallel applications, the average packet latency can be reduced by up to 3.61 % by using our proposed dynamic routing reconfiguration.
NCache: Neighborhood Cooperative Caching in Named Data Networking

Improved response time and resilience to network failures are heavily influenced by the routing and caching design strategies in content centric networking. In-network caching improves response time in the network. On-path caching techniques are simple and commonly used. In this paper we present NCache, a neighborhood cooperative caching technique which brings cooperation amongst neighborhood routers both in searching/retrieval of the content and also in caching decisions. It retains the simplicity of on-path caching techniques while leveraging the cooperation amongst the routers in the neighborhood to deliver improved performance. We formalize the interaction of a router with its neighbors as an extension to the interest packet structure of Named Data Networking. We perform simulation experiments with both custom and standard network topologies using a discrete event simulator. Our results show that NCache provides significant improvement in performance in terms of cache hit ratio, response time (content access latency) and overall cache utilization when compared with the state-of-the-art on-path caching techniques.
Efficient Spatio-Temporal Randomly Wired Neural Networks for Traffic Forecasting

Designing neural architecture is very important for traffic forecasting. In the past decade, it mainly depends on human experts. Recently, neural architecture search (NAS) has been leveraged to automatically find the optimal candidates for diversity spatio-temporal forecasting applications. However, it still depends on experts' experiments to narrow down the search space to make a trade-off between accurate forecasting and the high computational cost. In this paper, we try to generate the neural architecture for spatio-temporal forecasting. We propose a novel and efficient spatio-temporal randomly wired neural network generation method, termed ST-RWNet. ST-RWNet is not a fixed neural architecture. It can produce a bunch of spatio-temporal neural networks. ST-RWNet includes a spatio randomly neural network generator, a temporal randomly neural network generator, and a randomly bipartite neural network generator. Those neural network generators are based on random graphs, which control the attributes of neural networks, e.g., the number of pathways, the length distribution of pathways. The spatio and temporal neural network generators are designed to capture the spatio and temporal dependencies respectively. And the randomly bipartite neural network generator is designed to fuse the complicated intermediate spatio-temporal features. Extensive experiments on real-world traffic forecasting datasets have demonstrated that the performance is competitive with state-of-the-art methods. And our method achieves up to $6\times$ speed up compared to other NAS-based methods.
RFI Localization Using Joint Sparse Recovery in Synthetic Aperture Interferometric Radiometry

In this paper, we propose a new model combing the joint sparse recovery (JSR) with multiple snapshots data of the satellite named Soil Moisture and Ocean Salinity (SMOS) to get the geolocation of radio frequency interference (RFI) sources. The JSR method has been used to solve this model by exploiting the temporal redundancy of multiple snapshots data. Numerical simulation shows that the performance of the proposed model is much better than the conventional inversion algorithm based on single snapshot.
Distributed ISAR spatial spectrum analysis and its influence on sparse recovery

The echo of distributed ISAR is modeled, and its spatial spectrum structure is analyzed under different array structures and different motion forms of target. Furthermore, when sparse recovery is used for cross-range imaging, the relationship between spatial spectrum structure and sparse recovery performance is discussed. Using the average coherence coefficient of the observation matrix as an index, the influence of accumulation time and sensor position is analyzed. These analyses are helpful to optimize the sparse recovery performance.
A study of the Dream Net model robustness across continual learning scenarios

Continual learning is one of the major challenges of deep learning. For decades, many studies have proposed efficient models overcoming catastrophic forgetting when learning new data. However, as they were focused on providing the best reduce-forgetting performance, studies have moved away from real-life applications where algorithms need to adapt to changing environments and perform, no matter the type of data arrival. Therefore, there is a growing need to define new scenarios to assess the robustness of existing methods with those challenges in mind. The issue of data availability during training is another essential point in the development of solid continual learning algorithms. Depending on the streaming formulation, the model needs in the more extreme scenarios to be able to adapt to new data as soon as it arrives and without the possibility to review it afterwards. In this study, we propose a review of existing continual learning scenarios and their associated terms. Those existing terms and definitions are synthesized in an atlas in order to provide a better overview. Based on two of the main categories defined in the atlas, “Class-IL. ” and “Domain-IL”, we define eight different scenarios with data streams of varying complexity that allow to test the models robustness in changing data arrival scenarios. We choose to evaluate Dream Net - Data Free, a privacy-preserving continual learning algorithm, in each proposed scenario and demonstrate that this model is robust enough to succeed in every proposed scenario, regardless of how the data is presented. We also show that it is competitive with other continual learning literature algorithms that are not privacy preserving which is a clear advantage for real-life human-centered applications.
A Comparative Study of Single-polarized and Polarimetric SAR Images Applied on Unsupervised Classification

SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre one, we use GF3 data for experiments and adopt the Deep Clustering with Convolutional Autoencoders (DCEC) algorithm for single-polarized SAR image unsupervised classification. Using both the Kappa coefficient and mutual information derived from the confusion matrix, the results of single-polarized SAR data and PolSAR data are compared. It shows that the classification results of single-polarized SAR data match with the polarimetric ones to some extent, and their results are comparable in some specific applications. This paper presents the potential of information mining from single-polarized SAR images, as well as gives some references for the selection and trade-off between single- and full-polarized SAR data.
Emotion Recognition from Telugu Speech using Probabilistic Support Vector Machines

Recognizing emotional state of a person from his/her speech is one of the crucial tasks in Human Computer Interaction (HCI). Several classifiers were used for emotion recognition from speech and Support Vector Machine (SVMs) is an important one among them. In several works SVMs were used to recognize the emotions from speech, but they were not used with probabilistic outputs. This paper discusses a way to produce probabilistic outputs from an SVM followed by the discussion of modifying standard One-vs-One (OVO) model to use probabilistic SVM. The classification accuracy of the proposed model is analyzed on a Telugu Emotional Speech Corpus and compared with three of the existing methods.
An HLS implementation of on-the-fly randomness test for TRNGs

In this paper, we present an FPGA implementation of a randomness test called the count-the-ones test, which can be used to assure or control the quality of true random number generators (TRNGs) on-the-fly. Since our implementation adopts fixed-point arithmeric operations, reducing their precision is a trade-off between the amount of hardware and computational error. We used high level synthesis (HLS) to assess this trade-off easily. Based on our evaluation, we focused on two versions of circuits. A high-precision circuit required 607 LUTs and 536 FFs for 0.06% of an average error ratio of $\chi^{2}$ value, whereas a resource-saving circuit reduced the amount of hardware to 377 LUTs and 430 FFs with 1.48% of an error ratio.