Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Recognition of Millimeter Wave Radar Based on Local Texture Features

As a biological feature, the gait can be utilized to distinguish different individuals and different behaviors. Compared with other biological features, the gait has the characteristics of long distance and not easy to camouflage, which makes it a very promising biological feature. In this paper, human gait data collected from a millimeter wave radar is exploited to distinguish different behaviors. The micro-Doppler spectrogram of the corresponding gait is obtained via the Fast Fourier transform (FFT). In order to better characterize the gait features, the Local Binary Pattern (LBP) are exploited to represent the local texture features of the micro-Doppler spectrogram of different gaits, which can automatically extract micro-Doppler features and also reduces the amount of data required. In addition, Support Vector Machines (SVM) is employed for classification. The actual data from three gaits that include empty-handed walking, walking with a carton, and walking with one carry-on baggage by one hand and carrying a backpack, demonstrate that the classification accuracy rate of the proposed method can reach 92.59 %.
Sparse Aperture ISAR Imaging Based on UNet++

This article proposes a modified UNet++ for effective sparse aperture inverse synthetic aperture radar (ISAR) imaging. Firstly, Mapping from the true distribution of scattering centers to the original ISAR images is established. Then, modified UNet++ is used for sparse aperture ISAR imaging. The optimal network parameters can be obtained by end-to-end training using simulated data with different SNR. Experimental results have shown that the UNet++ can achieve noise-stable sparse aperture ISAR imaging with superior performance on both simulated and measured data.
Chemical Sensors based on Organic Field-Effect Transistors for Real-Sample Analysis

Organic field-effect transistors (OFETs) are promising candidates for chemical sensor platforms because of their beneficial features including switching properties, mechanical flexibility, and applicability to printing methods for high-throughput manufacturing. OFETs functionalized with molecular recognition materials quantitatively show changes in transistor characteristics through the detection of analytes in aqueous media. In other words, the detectability of the OFET-based chemical sensor relies on the selection of molecular recognition materials. Herein, we propose a comprehensive approach for accurate real-sample analysis by using the OFET-based chemical sensors functionalized with biogenic and artificial molecular recognition materials.
Urine Crystal Classification Using Convolutional Neural Networks

This study focuses on classifying different types of urine crystals using Convolutional Neural Networks (CNN). 1100 data samples are collected from medical books and hospitals and divided as training and testing datasets in a 70:30 percentage ratio. To yield an optimized reliability rate in classifying the types of urine crystals, CNN, a deep learning algorithm is used. First, the images underwent preprocessing stage to eliminate noise, to smooth, and to convert it as a binary image. In the segmentation process of the system, some images that contains overlapping urine crystals, indefinite in shape and colorless crystals become major factors and caused these images not to be optimally segmented. Layers of CNN are trained in a way that it can detect patterns from simple to further complex patterns. A convolution examines the entire image in search of information required for greater prediction accuracy. The system’s overall reliability is to be equal in 87.88%. The error rate for classification was often caused by the overlapping of urine crystals in the test image and differences of some urine crystals in terms of its shape and appearance.
Flow Measurement Methods Applied to Hydro Power Plants

Efficiency and maximal power output are two of the most important goals to analyze in hydraulic turbines. Turbines normally operate in variable head conditions; therefore, tests to analyze performance are frequently realized for a selected number of power plant heads. Usually it is limited to three heads: low, medium and high. The efficiency of water turbines is most frequently expressed using the weighted average efficiency or arithmetic mean efficiency, calculated from the measured results in the examined heads. To perform the calculation of efficiency is necessary to know several parameters such as kinetic and potential energy of water due to the position, because of this is required to know the flow rate entering the turbine. The flow rate of water through the turbine (Q) is determined by the volume of water flowing in time unit. The measurement of this quantity is one of most difficult tasks in water turbine tests. Three basic methods of flow rate measurement and results of them will be presented in this chapter.
Air Pollutant Detection System Utilizing an IoT-based Electronic Nose for Air Purifier

Air pollution is a substance that is present in the atmosphere that may cause a possible threat to human health. It can come from vehicles, factories, dust, volcanoes, mold spores, and wildfire. The most common pollutants in the air are Ozone, particulate matter, Carbon monoxide (CO), Nitrogen dioxide (NO2), and Sulfur Dioxide (SO2). This study developed an electronic nose that detects CO and PM2.5, and PM10, and it is integrated with the air purifier to purify the air pollutants present in the air. The Arduino Uno is the microprocessor for creating the electronic nose, and the MQ-7 gas sensor detects carbon monoxide. The particulate matter was detected using a PMS5003 sensor. An application developed using the MIT app inventor for the graph and numerical values of the air pollutants to be able to see it in real-time. The testing area device is in the different household parts: the kitchen, dining room, living room, and bedroom. There were ten trials; the trials were per minute. The results recorded before and after air purification concluded a significant decrease in air pollutants after the air purification. Verified the results by using a paired t-test statistical treatment.
Nonlinear Modeling of the VSC Grid-Connected System with Controller Saturation

With the development of power electronics technology, the power system is now evolving into a new-generation power system dominated by renewable energy. Due to the physical limitations of power electronics, controller saturation is widely adopted in wind turbines and photovoltaics. In this paper, a nonlinear system model considering output saturation is proposed in the view of general dynamic systems. By defining saturation-activation vector L, all the saturations of the system are uniquely represented. Then, the proposed modeling method is applied to a voltage source converter (VSC) grid-connected system. Based on RTDS simulation, this paper revealed that a class of oscillations at a specific frequency are caused by the activation of the phase-locked loop (PLL) saturation. The frequency of the oscillation depends on the top and bottom boundaries of PLL saturation.
RLF: Directed Fuzzing based on Deep Reinforcement Learning

Fuzzy testing is widely used in the field of vulnerability mining due to its ease of use and low false positive errors. However, traditional fuzzy tests suffer from low efficiency, blind test sample generation, and huge resource consumption. In order to solve these problems, we propose RLF‐‐a deep reinforcement learning-based directed fuzzing. RLF uses program instrumentation to obtain the execution path and calculates the distance between the path and the target. RLF will apply a deep reinforcement learning network that performs optimization to guide the selection of test samples basing on the information. Experimental results show that the method has better efficiency than other fuzzy testing tools in terms of vulnerability reproduction and significantly improves the orientation of fuzzy tests.
On saturation and related parameters following Guth's ATD colour‐vision model

In this work, we have examined the influence of different parameters both on perceived saturation and saturation discrimination with unrelated colours, on the basis of S. L. Guth's CA90 colour-vision model and its subsequent modifications. Our analysis of perceived saturation covered (1) spectral saturation functions at constant luminance, (2) saturation functions in constant colorimetric purity loci, (3) saturation vs. colorimetric purity functions, (4) saturation vs. luminance functions in the 1–1000 td range, and, finally, (5) the equal saturation loci in the xy color diagram. Regarding saturation discrimination, we focused on (1) saturation thresholds from white and from the locus, (2) the number of just noticeable steps from white to the spectral locus as a function of dominant wavelength, and (3) the colorimetric purity incremental threshold vs. colorimetric purity at constant dominant wavelength. Hunt's 1991 colour-appearance model and the colour-discrimination model by Yeh et al. (1993) were used for comparison purposes. The behavior predicted by Guth's and Hunt's models is qualitatively very similar in many issues, but they especially disagree on the dependence of saturation on luminance. For the discrimination task, Guth's model predicts spectral discrimination functions comparable with those predicted by Yeh et al., both from white and from the locus. Nevertheless, the models disagree on intermediate colorimetric purities for most wavelengths. The model is basically consistent with the experimental data except for the luminance dependence. On the other hand, the literature on discrimination is insufficient to determine which model is right. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 305–321, 2001
Prognosis of Dementia Using Convolutional Neural Networks

Dementia is a common neurodegenerative disorder caused in elderly people. People who have mild cognitive impairment are at risk of causing this disorder. It gradually becomes worse from mild to severe, making it difficult for the person to do any work without others’ help. Early diagnosis of dementia is important to diagnose the condition of the patient and to get intensive medical care. Manual diagnosis requires considerable effort, is time-consuming and is prone to misdiagnosis. To address this problem, we applied Image processing techniques like convolutional neural network (CNN) to predict the disease in aged people. The dataset comprises a total of 5121 imaging sessions categorized as Non-Demented, Very Mild Dementia, Mild Demented, and Moderate Demented. The proposed methodology splits the dataset as train and test data. The data is preprocessed by balancing and normalizing and performs well with the best accuracy using the sequential model. The Proposed model is trained with 10 hidden layers which can accurately assess the disease. The model’s performance has an accuracy of 94 per cent.
CFAR-Guided EfficientDet with Improved Loss Function for SAR Target Detection

Target detection has been a hot topic in the field of synthetic aperture radar (SAR) image analysis. In this paper, the one-stage object detector EfficientDet is applied in the SAR target detection. Since the labeled SAR data is limited and the SAR scene contains only a small number of target areas, the imbalance problem between positive and negative samples is serious in the network training process. To solve this problem, we introduce the Average-Precision Loss (AP-Loss) into the loss function of the class prediction net of EfficientDet. In addition, we integrate the binary indicator map obtained by the two-parameter constant false alarm rate (CFAR) detection into EfficientDet to guide the generation process of anchors. The experimental results on the miniSAR data demonstrate the effectiveness of the proposed method.
Research on Low-carbon Technology for 110kV Urban Indoor Substation

To further reduce the lifetime carbon emission index of substations, a design scheme of low-carbon 110kV indoor substation is proposed. In the proposed design scheme, building carbon reduction technology, renewable energy utilization, green energy power equipment is analyzed and used to achieve the goal of green, low carbon and near zero energy consumption of substation. Furthermore, simulation models are established for lighting simulation, noise simulation and heat dissipation simulation to analyze and optimize the design scheme. Through the simulation results, the substation design scheme deficiencies in the environmental impact are intuitively shown to put forward reasonable optimization measures of the substation carbon emission index improvement. And with the calculation of carbon reduction economical index, result shows that the proposed design scheme is feasible for the establishment of 110kV indoor low-carbon substation.
Computational interpretability of multilayer preceptron used for SAR image target recognition

The achievement of deep neural networks (DNNs) in the computer vision has aroused great concerns in the synthetic aperture radar (SAR) automatic target recognition (ATR) field. As a simple but effective model, the multilayer perceptron (MLP) is widely used in SAR image target recognition. However, the black-box problem could limit the development of DNNs in SAR ATR. In this paper, we explore the interpretability of MLP from the perspective of computation process of its forward propagation. By using the matrix representation, the function is studied that the angles between parameters and features as well as features magnitudes. Besides, the feature statistics is adopted to discuss the effect of nonlinear activation functions. Finally, some experiments on the MSTAR datasets are carried out and analyzed to demonstrate the effectiveness of the proposed method.
Multi-Spectral-Attention Dual-Channel Network for Radar-Based Human Gait Recognition

Deep learning techniques have gained success in radar-based human gait recognition in recent years. To exploit the time-varying micro-Doppler information of human gait, most existing methods generate samples using short time Fourier transform (STFT). By extracting the features of STFT images with different sliding window lengths, dual-channel deep convolutional neural network (DC-DCNN) can obtain high accuracy for human gait. To further improve the performance of the network, this paper designs a multi-spectral-attention dual-channel network for gait recognition. Combined with the channel attention mechanism, the proposed method can achieve higher recognition accuracy without incurring additional radar resources. Experimental results have demonstrated the effectiveness of the method.
Lightweight energy-efficient offloading framework for mobile edge/cloud computing

Energy efficiency is one of the most critical aspects of the modern computing paradigm, such as edge and cloud computing, due to minimizing carbon footprint and lowering operational costs. In order to achieve efficiency, it is essential to address the energy consumption problem of the computing nodes. Conventionally, power in the edge/cloud paradigm could be conserved by diminishing under-utilized resources through various virtual machine consolidation techniques. This operation can be performed more effectively if the resource management component acquires some knowledge of the system workload. In this paper, we would like to present our research on developing an energy-efficient framework to optimize and offload computationally intensive tasks to the edge/cloud system. This objective was achieved based on a two-fold effort. Firstly, an adaptation and modification were introduced to an offloading framework to make it work with heterogeneous edge/cloud systems. This modification consists of the functionalities of resource allocation and control. Subsequently, a lightweight resource scheduling algorithm, namely the Minimal Margin-Based Scheduling Algorithm, was developed to orchestrate the deployment of offloaded tasks to the best-suited container. After that, an extensive evaluation of real equipment was conducted to confirm the proposal's effectiveness. In fact, the results of practical experiments showed that the developed framework and algorithm could efficiently manage computing nodes in response to the change in the workload and reduce energy consumption.
Influencers Selection Model Using Association Rules

Since social media users are on the rise, influential users play a crucial role in online social networks. As a result, social media influencers can be an effective marketing tool for brands. This study proposes a model for evaluating the associations between social media influencers based on association rules. In this paper, we propose an association rules model using FP growth algorithm (Frequent Pattern growth algorithm) to detect relationships between influential users and help bands to best select multiple influencers for their marketing campaigns.
Weight-Training Ensemble Model for Stock Price Forecast

The ensemble model is applied for the stock price prediction in this study. The proposed ensemble model is based on the weighted average estimation of the values predicted by base algorithms. The base algorithms include Linear Regression, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and lightGBM. The performance of the proposed model depends on the weight parameters. The past data are collected to calculate the weigh parameters for base models of the ensemble models. The stock price prediction of Toyota Motor Corporation is considered as the numerical examples. Then LSTM, SVR and LightGBM are built to recognize the trend of the weight sequence data and to predict the most suitable combination weights for ensemble. The experimental results show that any ensemble models achieves significantly better accuracy than each component model. The proposed model also achieved the lowest error than simple average and error-based combination method. Even a tiny difference in choosing associated combining weights can play a crucial role in linear combination of models for prediction.
Distributed Photovoltaic Power Cluster Partition Based on DPC Clustering Algorithm

With the distributed access of large-scale renewable energy, the distribution network is facing more and more serious problems such as overvoltage and power backfeeding. This puts higher demands on the rapidity of voltage control in the current tidal state. Considering the complex control process of voltage centralized control method and the long time consuming traditional zoning method. This paper first analyses the impact of active and reactive power on the voltage of the distribution network after distributed PV access and uses the combined sensitivity as an electrical distance to determine the closeness of the electrical connection between nodes. Secondly, according to the calculated electrical distance as the input of DPC clustering algorithm, the distribution network nodes are clustered to achieve fast partitioning of the distribution network. Finally, the concept of complex network modularity function is introduced to evaluate the partitioning results to obtain the optimal partitioning results. The effectiveness of the method is demonstrated by simulation experiments using IEEE 33 nodes.
Detection of Traffic on the Network based on a Real Dataset for the IIM method and ML-TSDS Algorithm

Anomaly identification is a technique commonly used for monitoring the network traffic with the main goal to distinguish the normal and anomalous network activity using Intrusion Identification Methods (IIM) on an actual network dataset. This study has used the TSDS (Technique for Sampling Difficult Sets) method, which yield good results. ML techniques that can accurately recognize abnormalities based on known data flows can benefit from the input data packets acquired from network devices. The nine different forms of network attacks (traffic issues) defined in the UNSW-NB15 dataset were identified using different ML methods in this article, including KNN, NB, RF, and SVC. The effectiveness of identifying security issues was tested using simulated network attacks and acquired the packet information on actual connected devices. As UNSW-NB15 is a strongly uneven training set of data, Area under the ROC curve (AUC) values were employed to evaluate the effective-ness of segmentation systems. The technique for sampling difficult sets produced the best results in the detection of the sample dataset and the dataset of actual congestion with AUC scores of 91% and 96%, respectively.
An Extensive Review of Machine Learning Techniques for EEG Signal Processing

Electrical brain activity is detected by signals in an Electroencephalogram (EEG). Based on their frequencies, EEG signals are usually put into one of five groups: delta, theta, alpha, beta, and gamma. These signals help find a pattern that can be used to predict when a person will have a seizure. Classifying a seizure is a very important job for a doctor, as it helps them figure out what kind of seizure it is and if there will be any other problems. The goal of seizure classification is to learn as much as possible about the EEG signals. Literature shows that there are a lot of EEG signal pre-processing techniques, selection methods, feature extraction from EEG signals, and classification algorithms that can be used to find out if someone is having a seizure. The most important thing about pre-processing EEG signals is that it improves the quality of raw experimental data, which leads to better datasets, better classifications, and better accuracy. This study gives an overview of recent EEG pre-processing methods, datasets that can be used for experiments, and EEG classification techniques that will help a beginner researcher build on and use the right techniques.
Vital Sign Detection of LFMCW Radar Based on Kernel Density Estimation

In this paper, we consider the problem of vital sign detection for the linear frequency-modulated continuous wave (LFMCW) radar system. We propose a heart rate estimation algorithm based on kernel density estimation to enhance the accuracy. Specifically, first, differences are calculated between the neighbor peaks and valleys of the heartbeat wave. Second, the kernel function is used to characterize the distribution of these differences. Then, based on this statistical characteristic, adaptive threshold values are selected to filter invalid peak-valley pairs. The number of valid peak-valley pairs is the basis to determine the area where we can obtain the final heartbeat estimation result. Finally, experimental data are used to prove the validity of the proposed approach.
Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning

Money laundering has become a great economic problem with huge consequences on society and financial institutions in the last decade. Current anti-money laundering (AML) procedures within the industry are either inefficient due to criminals' increasingly sophisticated approaches or technological advancements. This paper provides an extended abstract to identify and analyze the machine learning methods to detect money laundering through transaction monitoring in the literature. Moreover, the paper identifies research gaps and based on the observed limitations, suggests future research directions and areas in need of improvements.