Miracle-dz/UniFAR-FTUs huggingface dataset:
EINI: A Large-scale Environment for Intelligent Network Infrastructure

EINI, the Environment for Intelligent Network Infrastructure, is a science and technology infrastructure construction project in the field of communications and information system. It is committed to making breakthroughs in core equipment and key technologies of the network and providing large-scale verification environment for the research on the basic theory and critical technology of network architecture, which help to explore scientific model and development mechanism for continuous evolution of the network. EINI will comprehensively enhance the forward-looking research capabilities in the field of network science and technology, This article will give a brief introduction to EINI, including capabilities, architecture and implement of the EINI. At present, the test facility has achieved interoperability between heterogeneous networks and can support various types of experiments and efficiently carry existing network services. In addition, we will show the flow of experiment in EINI.
Energy Efficient Coyote optimizer-based Multipath Routing in MANET

MANETs use multi-hop wireless connections to connect nodes, and the topology often changes as nodes move around. Therefore, dynamic routing techniques are required. A multipath routing strategy all better services network factors, including delay, energy consumption, and network throughput in dynamic contexts, than using a single path routing system. Each protocol addresses a specific use case in a networking environment. This can be caused by various factors, such as random loss; delaying packet delivery due to this data loss would harm real-time applications. This study offers guidance on how to deal with the confluence of serious issues that plague MANETs. Among the Ad-hoc On-demand Multipath Distance Vector (AOMDV) routing protocol, this study proposes Coyote optimizer-based Multipath Routing to choose the most efficient route from the available options. The suggested optimizer has a novel algorithmic structure and techniques for balancing exploitation. When data packets are randomly lost, these protocols use an optimization procedure to identify well-organized routes with the highest fitness values, resulting in the least data traffic, shortest path, and residual energy possible. Here, a mechanism is introduced that allows the suggested model to use the TCP Congestion Control Improvement for Random Loss (TCP CERL) to improve the effective route.
Low-Complexity Architecture of Finding First Four Minimum Values for Non-binary LDPC Decoders

Looking for a set of minimum values is considering as an important part in reducing the computational complexity introduced by the conventional non-binary low-density parity-check (NB-LDPC) decoding algorithms. In which, looking for a set of four min-value has been using i $n$ recent NB-LDPC decoders. In this paper, a low-complexity architecture is proposed for the first four minimum values finder. Experimental results confirmed that the proposed design is able to achieve a high flexibility in adapting to changing input sequence size while maintaining a low hardware complexity.
Safety Helmet and Mask Detection in substation Based on Deep Learning

Safety helmet is the basic protective measure for electric power workers in the construction work. In the current epidemic situation, mask is also an essential protective equipment. But in the complex construction environment, the detection of safety helmet and mask is easy to produce occlusion, resulting in information loss. In order to reduce the safety hazards of power workers and the risk of epidemic transmission, this paper carried out real-time detection for wearing safety hats and masks in power scenarios, the dataset was annotated and enhanced. At the same time, we redesigned the network structure of YOLO V5, the loss function is changed, and the attention mechanism is added at the end of the backbone network. The experimental results show that the proposed method can accurately detect occluded masks and safety helmets with strong generalization in dynamic video detection. The improved algorithm outperforms the original algorithm in terms of accuracy, real-time performance and the size of the network model, and can realize the real-time detection of safety helmets and masks in power construction sites.
Frequency Analysis for Non-uniformly Data in a Smart Radar Warehouse

Numerous radar warehouses equipped with Internet of Things (IoT) sensors have recorded a large number of environmental monitoring data. These valuable data can be used to test decision systems and help to modify them. When the measured data are applied in real scenarios, weak components always appear to interfere irregular event surveillance Lots of missing samples, however, exist in IoT data, leading to a serious disturbance in frequency domain which may probably veil the weak components. Traditional algorithms obtain very limited effects when there exist quantities of missing samples. Based on this, a new weak components extraction algorithm is proposed in this paper for real temperature data acquired by IoT sensors installed in the radar warehouses. This algorithm suppresses missing samples disturbance by minimizing the mean squared error of inverse-transformed data and original data in available samples. Its effect is better than any other interpolation method, particularly when missing samples exist consecutively. Some experimental results verify the validity of the proposed method.
A Robot Manipulator Grasping Method Based on Improved YOLOX

The overlap and coverage of objects can affect the grasping success rate for robot manipulator grasping in multi-object scenarios. We propose an enhanced grasping algorithm based on YOLOX-that can predict the bounding box with a smaller aspect ratio, thereby more accurate spatial location. Due to the limits of sensor's environment and physical factors, the depth map will lose some depth values. We propose a depth value repair algorithm based on the FMM algorithm, through which the lost depth values in the grasping region can be repaired. In pose estimation, we use the aspect ratio of the bounding box to determine the rotation angle of the robot manipulator jaws. We use a six-axis robot manipulator combined with a depth camera to achieve object grasping in multi-object scenes. The experimental results show that the enhanced grasping algorithm makes the grasping area prediction more accurate, and the distance between the object and the camera is obtained more accurately.
Estimation of 2-D DOA for Coherently Distributed Non-circular Sources

The paper presents a novel estimator for two-dimensional (2-D) direction-of-arrival (DOA) of coherently distributed (CD) non-circular signals utilizing two uniform linear arrays (ULAs). The proposed method firstly divides the two ULAs into three subarrays, thus three output matrices can be obtained and the generalized steering vector has the rotational invariance characteristic. Then extend the array output matrices using the non-circularity of the signals, and the extended generalized steering vector still has the rotational invariance characteristic. At last, the central DOAs can be calculated by ESPRIT-like method. This paper also provides examples to validate the performance of the novel estimator. Simulation results validated that the proposed estimator owns the best performance compared with two existing methods.
Anomalous Reflection and RCS Reduction Based Ultra-wideband Polarization Conversion Metasurface

In this paper, a new metasurface (MS) reflection polarization converter was came up for combining with Pancharatnam-Berry (PB) phase theory in realizing anomalous reflection and Radar Cross-Section (RCS) reduction. The basic unit of the polarization converter was composed of a dielectric substrate, a metal patch and a metal backplane, which enabled the linearly polarized incident waves to cross-polarized wave with relative bandwidth of 43.1%, polarization conversion rate (PCR) of almost 100% from 9.8GHz to 19.9GHz by simulation. Furthermore, a phase gradient (GM) MS was designed to react on anisotropic reflection and satisfy the generalized Snell's law based on the PB phase principle. Finally, we designed two arrangement structure to verify the RCS reduction. The structure plays an important role in electromagnetic stealth and antenna designs.
Multichannel Emotional Perception in Chinese Female: Faces, Voices, and Bodies

Visual cues and social variables influence speech perception. Emotions, as a fundamental aspect of everyday communication, can be expressed through a variety of channels and are also influenced by social traits like gender. What influence do different channels have on emotion perception? Does gender affect perception? This study used a mismatched body-face-speech experiment on female native Mandarin speakers to evaluate the influence of channels and the expresser’s gender. We discovered that female Mandarin native speakers concentrated more on facial expressions independent of the expresser’s gender. This conclusion urges related academics to focus more on machine facial expression models for human-machine emotion interactions, and it supplements prior research on the gender-culture impact and the McGurk effect in emotion perception.
Space mapping based extracts parasitic parameters of movable lead interconnection in microwave circuits

As a bridge between circuit modules, microwave circuit interconnection not only needs to meet the reliability of mechanical connection, but also needs to ensure the complete transmission of electromagnetic signals between circuits. With the increase of the operating frequency of microwave circuits, circuit interconnection has significantly restricted the improvement of microwave circuit signal transmission performance. Aiming at the active phased array antenna T/R component circuit movable lead interconnection structure, this paper establishes the movable lead interconnection parametric characterization model and equivalent circuit model. The space mapping method is used to extract the parameters of the interconnected equivalent circuit, so that the response of the equivalent circuit model is close to the response of the fine structure electromagnetic model, and the correctness of the equivalent circuit model is verified. This paper can quickly and accurately obtain the equivalent circuit parameters of the movable lead interconnection, so as to provide theoretical guidance for the accurate and rapid realization of electromagnetic transmission performance prediction and interconnection optimization design control.
Dynamic Dispatch of Integrated Wind, Solar and Hydrogen Energy Systems Based on Single Layer Variable Steps

In the context of double carbon, Integrated Wind, Solar and Hydrogen Energy Systems (IWSHES) are attracting a lot of attention because of their good environmental and economic performance. The issue of optimal dispatch is key to the economic and safe operation of integrated energy systems. Model Predictive Control (MPC) has good effectiveness in solving optimal dispatch problems, but there are still problems such as inflexibility and computational inefficiency. In this paper, an IWSHES is studied and a dynamic dispatch model based on MPC rolling optimization of the integrated energy system is developed. And a single layer variable step dispatch method is adopted. The results show that: (1) The Average Deviation Indicator (ADI) can reflect the degree of variation in net load to some extent, and the larger the ADI, the more refined the dispatch time step is required to cope with rapid fluctuations in net electrical load. (2) A dispatching strategy that takes into account the dynamic characteristics of the equipment allows the actual heating power to follow the changes in heat load better and with less power deviation. In the example calculation, the total heat energy deviation is as low as 3.60 kWh. (3) The single layer variable step strategy only changes the number of scheduling nodes in the prediction horizon in each rolling optimization, and the calculation time of the case with single layer variable step is saved by 27.47%, achieving smooth and efficient operation of the system.
Conv-AdaRNN:A Power Load Forecasting Method Based on CNN and AdaRNN

Accurate power load prediction is crucial for power dispatch and response. In order to improve the accuracy of power load prediction, this paper proposes a new method called Conv-AdaRNN which combines Convolutional Neural Network and Adaptive Learning and Forecasting for Time Series(AdaRNN) models. The method firstly uses CNN to extract valid information from effective factors, that affect the power load, such as temperature, humidity, and electricity price to construct feature vectors. Then, the data set is divided into the least relevant segments in time series according to the Time Series Distribution law. Finally, the AdaRNN, whose hidden states are assigned different weight to match time distribution, analyzes feature vectors belong to each segment and forecasts power load. Taking a certain area's power load as an example, the predicting results of the Conv-AdaRNN model were compared with those of gated recurrent unit (GRU) and AdaRNN models. The results show that, compared with other single prediction models, the Conv-AdaRNN achieved the better forecast precision.
A Deep Learning Approach to Analyze Diabetic Retinopathy Lesions using Scant Data

One of the most dangerous effects of diabetes is diabetic retinopathy, which, if ignored, results in lifelong blindness. Early detection, which is crucial for successful treatment outcomes, is one of the key obstacles. Unfortunately, it takes a skilled human to accurately evaluate fundus images to determine the exact diabetic retinopathy stage. Millions of people can benefit from the detecting step's simplification. The identification of diabetic retinopathy is one area where convolutional neural networks (CNN) have been successfully utilized. Early detection may prevent the possibility of permanent and complete blindness. Therefore, an efficient screening system is needed. We proposed a system that can classify various stages of diabetic retinopathy to facilitate the screening process.
Convolutional Neural Networks (CNN) based Marine Species Identification

There are more than 5600 species of fish in the world. Studies show that crowds can be larger and many people are less knowledgeable about the characteristics of marine life. This project is to raise public awareness and generate knowledge about Aquaculture. In fisheries education, it is important to minimize human error in the process of fish observation and analysis, where an automatic system recognition is required. Image processing is also known as the process of taking image as an input, analyzing it and generating a category or probability of a particular class of output type. Fish image recognition research is an important area, especially in marine biology and aquaculture. Recently, the identification of different classes of animals based on computer vision and deep learning has been widely used by analysts. There is one of the ultimate powerful forms of deep learning architecture for conceptual classification based on visual features. It is reported that there are many kinds of clean water fish in world level. Most of the existing work has been developed for the identification of fewer fish species and used for specific user groups. This detection system is used to identify and classify images of fish species found in clean water as well as in sea water.
Novel SAR Autofocus Method Based on Deep Learning with GAN Network

This paper focuses on SAR autofocus based on deep learning. Firstly, considering the influence of phase on radar imaging results, aiming at the problem of defocus of SAR imaging results and poor focusing effect of some SAR imaging results by traditional autofocus methods, a SAR autofocus complex generation adversarial network model is constructed by using the method of deep learning, and the key parameters of the network are given. Then, the network training set and test set are constructed, and using the realistic measured SAR imaging results to train and test the network. Finally, the autofocus results of the network are displayed, and the autofocus results of GAN network are evaluated by using image entropy as an objective evaluation index, which proves the feasibility of using deep learning to solve the problem of SAR autofocus.
Fast Implementation of Signal Reconstruction based on Sparse Bayesian Learning with Fourier Dictionary

Sparse Bayesian learning (SBL) is a robust algorithm for sparse signal reconstruction (SSR) and widely applied in many fields. Unfortunately, the conventional SBL algorithm suffers from heavy computational complexity due to the matrix inversion involved in each iteration, which is proportional to the cube of the observed data length and thus prevents it solving large-scale data. In this paper, based on the Fourier Dictionary (FD)-based SBL, abbreviated as FD-SBL, we propose an efficient FD-SBL method to implement SSR for uniformly sampled one-dimensional (1D) case. By utilizing the property that the matrix to be inverted is a Toeplitz matrix, we can exploit the Gohberg-Semencul (G-S)-type factorization to reduce the computational complexity of the FD-SBL algorithm. To be noted, there is no approximation in our proposed method, and the computational cost is reduced by several orders of magnitude compared with the conventional SBL algorithm. Finally, the experimental results verify the effectiveness of the proposed method.
Exactly Solvable Models of Biological Invasion

INTRODUCTION Why exactly solvable models are important Intra- and inter-species interactions and local population dynamics Basic mechanisms of species transport Biological invasion: main facts and constituting examples MODELS OF BIOLOGICAL INVASION Diffusion-reaction equations Integral-difference models Space-discrete models Stochastic models Concluding remarks BASIC METHODS AND RELEVANT EXAMPLES The Cole-Hopf transformation and the Burgers equation as a paradigm Further application of the Cole-Hopf transformation Method of piecewise linear approximation Exact solutions of a generalized Fisher equation More about ansatz SINGLE-SPECIES MODELS Impact of advection and migration Accelerating population waves The problem of critical aggregation DENSITY-DEPENDENT DIFFUSION The Aronson-Newman solution and its generalization Stratified diffusion and the Allee effect MODELS OF INTERACTING POPULATIONS Exact solution for a diffusive predator-prey system Migration waves in a resource-consumer system SOME ALTERNATIVE AND COMPLEMENTARY APPROACHES Wave speed and the eigenvalue problem Convergence of the initial conditions Convergence and the paradox of linearization Application of the comparison principle ECOLOGICAL EXAMPLES AND APPLICATIONS Invasion of Japanese beetle in the United States Mount St. Helens recolonization and the impact of predation Stratified diffusion and rapid plant invasion APPENDIX: BASIC BACKGROUND MATHEMATICS Ordinary differential equations and their solutions Phase plane and stability analysis Diffusion equation References Index
FDA-SAR Imaging Algorithm Based on Beam Scene Matching

Traditional SAR imaging system is always accompanied by excessively high sidelobe levels, which cannot improve the comprehensive detection probability of the target. However, The traditional windowing method cannot balance the main lobe widening and sidelobe suppression. Frequency Diverse Array(FDA) radar has received wide attention from academic community because of the range-angle-time three-dimensional coupling characteristics of their transmitting beams. Based on previous studies, this paper proposes the concept of imaging scene and beam energy matching, and improves the frequency diverse array radar echo baseband signal model, so that the FDA can be better applied to synthetic aperture radar imaging. In addition, this paper proposes a FDA-SAR range-dimension matched filtering method. This method can better gather the point target energy in range dimension, without increasing the time complexity of the algorithm; it has a better sidelobe suppression effect while narrowing the mainlobe width, so that the FDA-SAR system can still obtain high-quality and clear scene images under strong noise.
A Comparison of Ambulance Redeployment Systems on Real-World Data

Modern Emergency Medical Services (EMS) benefit from real-time sensor information in various ways as they provide up-to-date location information and help assess current local emergency risks. A critical part of EMS is dynamic ambulance redeployment, i.e., the task of assigning idle ambulances to base stations throughout a community. Although there has been a considerable effort on methods to optimize emergency response systems, a comparison of proposed methods is generally difficult as reported results are mostly based on artificial and proprietary test beds. In this paper, we present a benchmark simulation environment for dynamic ambulance redeployment based on real emergency data from the city of San Francisco. Our proposed simulation environment is highly scalable and is compatible with modern reinforcement learning frameworks. We provide a comparative study of several state-of-the-art methods for various metrics. Results indicate that even simple baseline algorithms can perform considerably well in close-to-realistic settings. The code of our simulator is openly available at https://github.com/niklasdbs/ambusim.
Data Augmentation for Infant Cry Classification

Classification of normal vs. pathological infant cries is a challenging task as infant cry has higher fundamental (or pitch) frequency $(F_{0})$ and hence, distantly-spaced pitch source harmonics that sample vocal tract spectrum resulting in poor spectral resolution. This paper first presents infant cry classification system on DA-IICT infant cry corpus using two cepstral features, namely, Mel Frequency Cepstral Coefficients (MFCC), and Constant-Q Transform Cepstral Coefficients (CQCC) on statistical classifiers, namely, Gaussian Mixture Model (GMM). Results are then presented on cross-dataset scenarios on Baby Chilanto and DA-IICT datasets and found to degrade substantially. The classification results are also presented by merging these two corpora, where results were found to degrade for a certain parameter setting in CQCC. To mitigate this degradation in performance, we present three data augmentation methods, namely, signal perturbation using tempo, volume, and speed. The results obtained, indicated that the tempo perturbation enhances the performance by a small margin of 0.2 % and 0.98 % using MFCC and CQCC, respectively. However, results are found to degrade using speed perturbation by 3.49 % and 21.93 % using MFCC and CQCC, respectively. This indicates that F0 and it’s dynamics are very crucial acoustic cues for normal vs. pathological infant cries and which is why cry modes based on F0 were used historically for infant cry classification in the literature. Additionally, the performance of the various feature sets are compared using J-statistics.
Data extraction from flash memory and reverse engineering using Xilinx 7 series FPGA boards

In this paper, we introduce a general methodology of reverse engineering for Xilinx FPGA devices using flash memory for programming. Based on the structural analysis of the flash memory used for the most recent 7-Series FPGA, data extraction for reverse engineering is carried out targeting the flash memory with the help of a logic analyzer. The accuracy of the extracted bitstream is finally verified using the in-house reverse engineering tool.