Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization of Railway Scene Classification Model via Grad-CAM

Railway detection task produces a large number of images, the lack of effective image classification method makes it difficult to analyze detection image deeply. Using convolutional neural networks (CNN) to realize railway image scene classification is an effective technical means. This paper propose a method to reduce the bias of database by Gradient-weighted Class Activation Mapping(Grad-CAM) to effectively improve scene classification accuracy, and achieve accuracy of 95.3%(top3) on Railway12 database. Our approach combines two insights: (1) Small quantity of railway scene database make it hard for CNN to achieve high performance, transfer pre-trained ImageNet-CNN to fine-tune on railway scene database (2) Introduce Grad-CAM visualization method to analyze model’s classification pattern and intuitively show the possible bias of database, provide an intuitive way to reduce bias of dataset.
k-NN and LDA based Motor Imagery EEG Classification using Phase Features

A Brain-Computer Interface (BCI) based on motor imagery (MI) can be developed to translate the motor intent of a person for controlling some device. For this purpose, changes occurring in alpha and beta bands are exploited. In this paper we utilize measurement of coherence and phase synchrony (Phase features) to inspect the activities of sensory motor cortex during imagination of hand and feet movement. An attempt is made to establish relationship between changes occurring in the motor cortex with respect to phase. After bandpass filtering and spatial filtering for noise removal analysis of phase characteristics of these signals is carried out. These features are classified with the help of Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) classifiers. The classification performance suggests that phase and coherence features prove to be an additional set of features for differentiating between various motor imagery signals.
Single Stage Transformer Less Reconfigurable Inverter for PV Applications

Solar photovoltaic system has an important role from the embryonic stage of renewable energy to maintain the electricity demand because of their several peculiarities, so that Photovoltaic system associated with inverters are widely used in several applications. Based on the size and power rating different topologies are familiar with previous studies. This paper proposes a single stage transformer-less reconfigurable inverter for utilizing PV system efficiently, which overcome the drawbacks of conventional topologies. The reconfigurable topology enables the system to be operated in PV-load, battery-load, PV-battery-load, PV-battery. To analyze the system MATLAB/Simulink is used.
Introduction de l’algue rouge Compsopogon aeruginosus (J. Agardh) Kützing dans le port de Rouen, Normandie, France

Nous rapportons une premiere observation en France metropolitaine de l’algue rouge Compsopogon aeruginosus (J. Agardh) Kutzing, dans le port de Rouen (Seine-Maritime, France) en septembre 2012. L’espece, d’origine circum-tropicale, n’avait ete a ce jour trouvee en Europe qu’en Pologne et en Autriche. Son introduction dans le bassin de la Seine pourrait provenir d’un rejet d’aquarium.
Comparison of Methods for the Simulation of Dynamic Power Flows in the International Grid Control Cooperation

The international grid control cooperation (IGCC), which consists of eleven transmission system operators (TSO), provides an optimized secondary control power (SCP) activation [1]. According to the European guideline for electricity balancing [2], the optimization in this field shall be expanded in the future. Consequently, the amount of exchanged control power will rise and the relevance of the IGCC congestion management (CM) will be increased. Studies on the CM require a model, which allows the simulation of the power flows under consideration of the IGCC and the CM. A model for this purpose is suggested in [3]. This model is based on standard approach for the analysis of load-frequency controls, representing the overall system inertia with one equivalent generator [4]. This model is combined with a Newton-Raphson based power flow calculation program for the simulation of the dynamically changing power flows. Since the focus of the model is to represent dynamic processes on the long-time-scale, the underlying assumptions seem acceptable and the model delivers plausible results in first studies on new concepts for the CM [5]. Due to its simplicity, the model is easy to use and shall be utilized in further studies on the IGCC and its CM. In this paper, the simplified model is compared to a standard modeling approach for quasi-steady-state simulation with distributed rotating mases. For this purpose, both models are implemented in MATLAB, the advantages and limits of the simplified model are discussed and the differences are illustrated in various case studies. Focus of the case studies is the impact of the modeling approach on the results regarding the assessment of different CM concepts.
Network Intrusion Detection Based on Kernel Principal Component Analysis and Extreme Learning Machine

This study is aimed at the problem that the detection rate of intrusion detection technology based on Extreme Learning Machine (ELM) algorithm is not high and the intrusion detection technology based on Support Vector Machine (SVM) algorithm is slow. An intrusion detection method based on Kernel Principal Component Analysis (KPCA) and extreme learning machine algorithm is proposed. Using the KPCA algorithm to reduce the dimension of the extracted feature matrix, and using the ELM algorithm to perform multi-classification detection on four common types of attacks. Simulation results show that the proposed method is more efficient and faster than intrusion detection based on extreme learning machine algorithm and intrusion detection based on support vector machine algorithm. Finally, the accuracy, false alarm rate, detection rate, and detection time in intrusion detection technology are improved.
Pedestrian Re-identification Based on Image Enhancement and Over-fitting Solution Strategies

Because of its application value and research significance, pedestrian re-identification (re-ID) technology has become more and more popular at present. This article has studied two common problems that affect the re-identification rate of pedestrians. Firstly, the pedestrian data collected in a specific scene has more blurred and obscured pedestrian images. The second is that directly using the data set to train deep neural networks is prone to over-fitting, resulting in poor performance of trained models. These problems make the final pedestrian recognition rate lower. This article is mainly based on logarithmic enhancement, histogram equalization and MSRCR algorithm (LHEMR) to enhance the blurred pedestrian image in the data set. This paper uses the random erasing technique and fine tuning for the second problem (Fine-tune). The combination of network model strategy to solve the problem of direct training deep neural network prone to over-fitting, the experimental results show that the proposed method is very effective and can improve the overall pedestrian recognition rate.
Short-Term Power Data Prediction Model Based on IA-RBF Neural Networks

This paper puts forward a kind of based on immune algorithm and radial basis function (IA-RBF) neural network with the combination of electric power system short-term load forecasting model. The model uses the global convergence of immune algorithm(IA) to find the number of hidden layer centers of RBF neural network and uses negative gradient descent method to determine the weight of output layer. Thus, the convergence speed and accuracy of RBF are improved. The model is trained and predicted using simulation tools and it reduced the overall Mean Absolute Percentage Error(MAPE) value by 1.25% and the quantitative index Mean Absolute Error(MAE) decreased by 192.94 compared with the traditional RBF model based on clustering, which verified the accuracy and effectiveness of this model.
A Novel DWT and CTSM-Based Image Watermarking Method

In this paper, a novel image watermarking method on the basis of discrete wavelet transform (DWT) and coupled tent-sine map (CTSM) is proposed. Firstly, CTSM is introduced and then three chaotic sequences are generated by CTSM. The first two sequences are used to encrypt the cover image and the watermark in order to improve the security, and the third one is used to find the embedding and extracting positions in the watermarking method. After that, in the watermarking embedding process, the encrypted watermark is embedded into the found positions, and then the watermarked cover image is gained. Finally, the extracted watermark is obtained by the watermarking extraction process. The security performance test results and the comparison results show that the proposed image watermarking method has good performance in the aspects of invisibility and robustness.
A rapid face detection method based on skin color model and local binary gradient feature

This paper proposed a fast facedetection method based on the skin color feature and local binary gradient feature. First, according to the clustering of human skin color in the YCbCr color space, the skin color area is detected in the image. Then, it is coarsely fast judged whether if the face is in the skin color area. Finally, the local binary gradient feature is used to judge face accurately, and the weights of the local binary gradient feature is found by using AdaBoost train algorithm. For improved the efficiency of algorithm, the algorithm is accelerated by using the method of the integral image, the cascade classifier and the search order from big to small. The algorithm was tested by a set with 450 color images which the size is 896 ×592. It can find that the average detection time of new algorithm has reduced about 17.1% compare with the face detection algorithm of Paul Viola. The detection accuracy is similar with algorithm of Paul Viola.
Method for Creating Virtual Copies of Real Labs in a 2D Environment, Representing a 3D Virtual Reality

The study presents a method for creating a graphical virtual copy of real labs. The method is developed for the Engine for Virtual Electrical Engineering Equipment (EVEEE). , which has its own limitations for the virtual components - equipment surfaces. , which hold potentiometers. , indicators and pluggable sockets should be perpendicular to the line of sight. Initially. , an appropriate point of view is chosen and a photo of the lab is taken. Next. , the position of each equipment is marked, and a 3D model is created for each equipment, based on high quality photos. The 3D model is exported as a 2D PNG image with a shadow included in the alpha-channel. The modelled equipment is scaled down to fit into the markings of the original image in order to keep the actual dimensions ratio between the equipment. The obtained results show that the method ensures all requirements are met: those of the engine and those that ensure the sense for reality of the virtual reality.
A Guided Filtering Method Based on Shen-Castan Operator

A guided filtering method based on Shen-Castan operator is proposed to solve the problem of a large amount of noise attached to the ultrasound image. In this method, the edge detection result of the Shen-Castan operator was used to reduce the edge halo phenomenon of the guided filter. Apply the Shen-Castan operator to perform edge detection on the noisy image; the edge detection result will be returned to the original image to obtain an edge enhanced guide image; this image will be used as a guided image of the noisy image for guided filtering. Through the simulation experiment, the guided filtering method based on the Shen-Castan operator can not only maintain the original function of smoothing noise, but also improve the signal-to-noise ratio and structural similarity of the passenger peak.
Speeding optimization considering the fuel consumption in the mooring period

This paper aims to establish a nonlinear speeding optimizing model for minimizing the fuel consumption. Considering the fuel consumption of the vessel both in sailing and mooring, the traditional speeding optimizing model with fuel consumption only in sailing consideration is improved. Not only the relationship between fuel consumption and speed in sailing is fitted by a power function, but also a linear function was used to fit the relationship between fuel consumption and time in mooring. Based on the two functions above, a new speeding calculating formula which is more practical is proposed. The simulation experiments prove the speeding optimizing model and formula proposed can reduce the fuel consumption and emission more effectively.
Smart Cap - Wearable Visual Guidance System for Blind

Science and technology always try to make human life easier. The people who are having complete blindness or low vision faces many difficulties during their navigation. Blindness can occur due to many reasons including disease, injury or other conditions that limit vision. The main purpose of this paper is to develop a navigation aid for the blind and the visually impaired people. In this paper, we design and implement a smart cap which helps the blind and the visually impaired people to navigate freely by experiencing their surroundings. The scene around the person will be captured by using a NoIR camera and the objects in the scene will be detected. The earphones will give a voice output describing the detected objects. The architecture of the system includes the processor Raspberry Pi 3, NoIR camera, earphones and a power source. The processor collects the frames of the surroundings and convert it to voice output. The device uses TensorFlow API, open-source machine learning library developed by the Google Brain Team for the object detection and classification. TensorFlow helps in creating machine learning models capable of identifying and classifying multiple objects in a single image. Thus, details corresponding to various objects present within a single frame are obtained using TensorFlow API. A Text to Speech Synthesiser (TTS) software called eSpeak is used for converting the details of the detected object (in text format) to speech output. So the video captured by using the NoIR camera is finally converted to speech signals and thus narration of the scene describing various objects is done. Objects which come under 90 different classes like cell phone, vase, person, couch etc are detected.
Cooperative Ad Hoc Node Localization with Ground-Level Suburban Measurements via Machine Learning Techniques

Ground-to-ground radio measurements in an ad hoc network are utilized to assess the potential of cooperative in-network node localization. The measurements include pair-wise signal quality (measured by demodulator SNR), ToA-based range estimates and GPS availability in a plurality of anchor nodes. The employed data were gathered in specific field-test campaigns. The focus is on the performance assessment of a pragmatic solution combining straightforward machine-learning tools along with a standard maximum-likelihood geo-location metric. Experimental conclusions compare various options (such as the added value of a map versus only GPS), plus the Cramer-Rao lower bound baseline. Successful link classification proves important for good performance, a fact already known for other environments.
An Energy-Efficient Resource Allocation Algorithm Based on Multi-user and Multiantenna Large Scale MIMO System

With the increase of users' demand and the increase of transmission energy consumption, this paper extends the model to double antennas in the case of large-scale MIMO system equipped with single antenna in the user terminal, and proposes an energy-efficient resource allocation algorithm based on multi-antenna and multi-user large-scale MIMO system. In the situation of meeting the users' minimum rate and highest transmission power limit, using zero forcing receiver and adjusting the transmitting power of users and the number of base station's transmitting antennas to establish MIMO system's energy efficiency function by the method of convex optimization, and then the optimal transmit power vector and base station's antenna subset can be obtained. Finally, the simulation results show that the energy efficiency by zero forcing receiving is higher than by maximal-ratio combiner, the number of optimal antennas is more, and the system throughput is also larger.
The Verification Method of Reference Signal Components for PBR

In this paper, the problem that how to confirm the components of reference signal is investigated based on the passive bistatic radar (PBR). If the clutter/multi-path signals, or target echoes are in presence of reference signal, it leads to significant degradation of the cancellation and detection performance. A novel method is proposed to determine whether above signals exist or not in reference channel (RC), and quantify above signals. Utilizing beamforming technique in surveillance channel (SC), the strong direct signal can be extracted. Then, we regard it as a new reference signal and original reference signal in RC can be viewed as echo signal. Finally, the distribution of above signals can be obtained by introducing the cancellation and detection process. Experimental results validate effectiveness of the proposed method.
Nodes Availability Analysis of Heterogeneous Wireless Sensor Networks Based on NB-IoT Under Malware Infection

The Narrowband Internet of Things (NB-IoT) is a main stream technology based on mobile communication system. The combination of NB-IoT and WSNs can active the application of WSNs. In order to evaluate the influence of node heterogeneity on malware propagation in NB-IoT based Heterogeneous Wireless Sensor Networks, we propose a node heterogeneity model based on node distribution and vulnerability differences, which can be used to analyze the availability of nodes. We then establish the node state transition model by epidemic theory and Markov chain. Further, we obtain the dynamic equations of the transition between nodes and the calculation formula of node availability. The simulation result is that when the degree of node is small and the node vulnerability function is a power function, the node availability is the highest; when the degree of node is large and the node vulnerability function satisfies the exponential function and the power function, the node availability is high. Therefore, when constructing a NBIOT-HWSNs network, node protection is implemented according to the degree of node, so that when the node vulnerability function satisfies the power function, all nodes can maintain high availability, thus making the entire network more stable.
Geometric Binary Descriptor Based Monocular SLAM

In this work, we develop an essential part of monocular simultaneous localization and mapping which is the matching process. However, the performance of any solution depends on the quality of the descriptor involved into the association approach. The most popular is binary descriptor due to their ability of matching with a high rate of matching in real time. In the present paper, we provide an ameliorated version of our previous work named local binary descriptor basing on 3D polynomial interpolation. The old version is reduced by the proposition of an algorithm of coefficients selection to deal with the real time constraint. This last is involved into a SLAM scheme named inverse depth parameterization for monocular SLAM. The result's quality is acceptable compared to the cross correlation in the old version of this SLAM.
Machine Learning Based Featureless Signalling

Direct-sequence spread-spectrum (DSSS) is commonly used to mitigate the effect of jamming and to operate under an adversary receiver's thermal noise floor in order to avoid signal detection. Unfortunately, the discrete nature and unique distribution of DSSS spreading sequences make it relatively easy to detect the resulting transmitted signals. To overcome this issue, this paper proposes a machine learning based scheme that generates featureless, non-repetitive noise-like spread signals. The proposed scheme provides several benefits over the standard DSSS system including the ability to generate signals with low probabilities of detection/intercept, additional processing gain and also an uncoordinated synchronisation method.
Radar Bistatic Configuration for Soil Moisture Estimation at L-Band Using Global Sensitivity Analysis Method

In this paper, an attempt is made to identify the optimal bistatic configurations for the retrieval of bare soil moisture at L-band based on a global sensitivity analysis using the extended Fourier amplitude sensitivity test (EFAST) algorithm. For this purpose, we apply the Advanced Integral Equation Model (AIEM), which is a well-established theoretical scattering model, to simulate complete sets of bistatic scattering coefficients, over a wide range of surface roughness and dielectric parameters. The sensitivities of radar response to soil moisture and surface roughness are identified and quantified in terms of bistatic geometries and radar parameters. The results show that the autocorrelation functions (ACFs), which significantly affect the distribution of sensitivity indices of surface roughness, do not exert distinct effects on the bistatic sensitivity pattern of soil moisture. In bistatic scattering, $VV$ polarization shows higher sensitivity to soil moisture than $HH$ polarization, particularly at large incident angles. As incident angle increases, in cases of small azimuth scattering angles and large scattering angles, the sensitivity zone expands and tends to move to the forward region at $VV$ polarization. This feature persists regardless of the ACFs, and thus offers promising configurations for soil moisture retrieval. For $HH$ polarization, in contrast, the sensitivity zone gradually moves to the backward region as the incident angle increases.
Identification of Coronary Artery Diseased Subjects Using Spectral Featuries

Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity globally. Nowadays, it is spreading at an alarming rate. Recently, there is an increasing interest to develop simple and non-invasive automated methods for reliable diagnosis of CAD. Studies reported that the use of single-channel phonocardiogram (PCG) signal for detecting weak CAD murmurs caused by the stenosed coronary arteries due to turbulent blood flow. In this work, we introduce a new framework with multi-channel data acquisition system to classify CAD and normal subjects. The proposed method does not require any reference signal such as an electrocardiogram (ECG) signal for PCG signal segmentation as reported in the earlier studies. Subsequently, the study has used five different features, such as spectral moments, spectral entropy, moments of PSD function, autoregressive (AR) parameters, and instantaneous frequency derived from frequency representations of PCG signals. These features have captured the specific details related to the disease. We use an artificial neural network (ANN) for the classification task. Experimental results show that the AR features well-performed. We achieve an accuracy of 74.24% by using multi-channel recorded data where as the best performance obtained using single-channel signal is 69.69%.