Miracle-dz/UniFAR-FTUs huggingface dataset:
Reduction of Inter-process Communication in Distributed Simulation of Road Traffic

A detailed computer simulation is an important tool for the managing of road traffic. Since it can be very time consuming, it is often performed in a distributed computing environment. The simulated road traffic network is then divided into sub-networks simulated by processes on the nodes of the distributed computer. The inter-process communication necessary for the vehicle transfer and the synchronization can then significantly influence the performance of the distributed road traffic simulation. In this paper, two efficient communication protocols for distributed road traffic simulation, which we developed during our previous research, are compared. These protocols - the Long Step (LS) protocol and the Long Step Binary (LSB) protocol - reduce the inter-process communication using an aggregate message transfer and/or a lossy data compression. Semi-centralized, centralized, and distributed variants of both protocols were thoroughly tested and compared to a reference communication protocol representing a common protocol of distributed road traffic simulators. The tests indicate significant savings of the number of transferred messages and, more importantly, of the total computation time.
3D Mapping of X-Ray Images in Inspections of Aerospace Parts

In this work we present an industrial system for the inspection of composite parts in the aerospace industry, based on X-ray sensors and robotic manipulators. Such system is designed to identify any type of defects such as, missing gluing, core cell deformation, cracks or foreign objects, which may occur between layers of which these objects are composed. The inspection process involves back-projection of X-ray images onto the 3D CAD model of the inspected part, to directly locate the defects on the part itself. The complete system has been implemented in a real industrial workcell that involves two synchronized robots equipped with a X-ray source-detector system. The two robots move autonomously along a pre-computed trajectory without any human intervention, and the back-projection of the acquired images is efficiently performed at run-time using the proposed algorithm. The experiments demonstrate that the X-ray images back-projection is successful and can effectively replace standard manually guided inspections. This has a high impact on the factory automation cycle since it helps to reduce the effort and time needed for each inspection task. This work is part of a EU funded project called SPIRIT.
University of Groningen mRNA and protein dataset of autophagy markers (LC3 and p62) in several cell lines Gómez-Sánchez, Rubén; Yakhine-Diop, Sokhna M S; Rodríguez-Arribas, Mario; Bravo-San

We characterized the dynamics of autophagy in vitro using four different cell systems and analyzing markers widely used in this field, i.e. LC3 (microtubule-associated protein 1 light chain 3; protein recruited from the cytosol (LC3-I) to the autophagosomal membrane where it is lipidated (LC3-II)) and p62/SQSTM1 (adaptor protein that serves as a link between LC3 and ubiquitinated substrates), (Klionsky et al., 2016) [1]. Data provided include analyses of protein levels of LC3 and p62 by Western-blotting and endogenous immunofluorescence experiments, but also p62 mRNA levels obtained by quantitative PCR (qPCR). To monitor the turnover of these autophagy vier Inc. This is an open access article under the CC BY license /j.ab. 2015.02. 020 e's Balanced Salt Solution; FBS, Fetal bovine serum; GAPDH, Glyceraldehyde sts; LC3, Microtubule-associated protein 1 light chain 3; MEFs, Mouse line; qPCR, Quantitative PCR; SB, Sample buffer; SDS, Sodium dodecyl sules), rosapolo@unex.es (R.A. González-Polo). rk. R. Gómez-Sánchez et al. / Data in Brief 7 (2016) 641–647 642
PoseNetwork: Pipeline for the Automated Generation of Synthetic Training Data and CNN for Object Detection, Segmentation, and Orientation Estimation

The latest developments and research of convolutional neuronal networks (CNNs) have proven the feasibility of their use in industrial applications that require object detection and pose estimation in unknown environments. Nevertheless, the end-users have neither the required resources for model-training nor the expertise to efficiently implement such applications. On the one hand, our work proposes a pipeline that focuses on the automated generation of training data by using synthetic images. On the other hand, we introduce a deep neural network to estimate the orientation of a reference object by using a one-shot image. We demonstrate the use of PoseNetwork by detecting and estimating the 5D-Pose of a workpiece in a robot-based inspection cell.
Voice Gender Recognizer Recognition of Gender from Voice using Deep Neural Networks

The proposed research work has used a Multilayer Perceptron (MLP) based deep learning model for deploying a gender-based user classification. The input to proposed model is a set of acoustics features, which was extracted by various packages. This work has developed a dataset consisting of 3168 data points, which were extracted from the recorded voice samples of both men and women. The voice samples are produced by using acoustics analysis. The proposed model is trained with different set of parameters and finally comes up with an MLP model that achieves an accuracy of 96 percent on the test dataset.
Personalized Location Privacy Protection for Location-Based Services in Vehicular Networks

With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users’ location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don’t provide differentiated protection for users’ different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users’ personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users’ personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user’s service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.
A Generalized Framework Designing Monopulse Tracking of OFDM-aided Aircraft Communication

The upcoming non-terrestrial network communication system will comprise unmanned aerial vehicles (UAVs) and satellites as platforms providing broadband communication service in the future. The conventional UAV monopulse tracking technique at ground station tracking receiver requires dedicated signals which suffer from multipath effect in air-to-ground (A2G) link with low elevation angles. As an essential physical layer technique in 5G wireless networks, the orthogonal frequency division multiplexing (OFDM) under fading channel was not yet investigated well for target tracking in the UAV A2G link. In this paper, we propose a generalized nonlinearity-based angle detection method in tracking receiver of single-channel monopulse tracking (SCMT) system using OFDM signals. Then, we design a generalized framework of monopulse tracking system and evaluate the nonlinearity functions in terms of angle detection S-curve and detection range. Simulations results show that, the logarithmic nonlinearity detection functions exhibit smaller angle estimation root mean squared errors (RMSEs) than square- and absolute-nonlinearity detection functions by 0.03° under fading channels as well as additive white Gaussian noise (AWGN) channels.
Trust-based Incentive for Mobile Offloaders in Opportunistic Networks

Mobile data offloading using opportunistic network has recently gained its significance to increase mobile data needs. Such offloaders need to be properly incentivized to encourage more and more users to act as helpers in such networks. The extent of help offered by mobile data offloading alternatives using appropriate incentive mechanisms is significant in such scenarios. The limitation of existing incentive mechanisms is that they are partial in implementation while most of them use third party intervention based derivation. However, none of the papers considers trust as an essential factor for incentive distribution. Although few works contribute to the trust analysis, but the implementation is limited to offloading determination only while the incentive is independent of trust. We try to investigate if trust could be related to the Nash equilibrium based incentive evaluation. Our analysis results show that trust-based incentive distribution encourages more than 50% offloaders to act positively and contribute successfully towards efficient mobile data offloading. We compare the performance of our algorithm with literature based salary-bonus scheme implementation and get optimum incentive beyond 20% dependence over trust-based output.
Classification of Interference Signal for Automotive Radar Systems With Convolutional Neural Network

When a radar signal generated by another vehicle arrives at an ego-vehicle, mutual interference occurs, which can seriously degrade the detection performance of the radar. To mitigate mutual interference, the type of radar modulation used in the interference vehicle must be identified because the types of radar systems installed in each vehicle are different. Therefore, in this paper, we propose a method for classifying the modulation types of interference signals in automotive fast chirp frequency modulated continuous waveform (FMCW) radar systems. We build a mathematical model of the received signal when the radar signal transmitted by the ego-vehicle interferes with various types of interference signals, such as unmodulated continuous wave (CW), slow chirp FMCW, fast chirp FMCW, pulsed CW, and frequency-shift keying signals. In the fast chirp FMCW radar systems, the received signal is converted into range-Doppler response using two-dimensional Fourier transform. Based on range-Doppler responses of the interference signals, we design a classifier to identify the modulation type of interference signals using a convolutional neural network (CNN). Through our proposed CNN, we can classify five different types of interference signals with an accuracy of over 96%. In addition, compared to conventional feature-based machine learning techniques such as support vector machines, the proposed method can effectively identify the interference signal with fewer input signals in shorter time.
Reliable Broken Conductor Fault Detection for Low Volt Interconnected Distribution Lines

In developing countries like India, most of the low voltage distribution lines feeder are connected via overhead power lines. Such distribution systems, including radical type and low voltage Interconnected networks, face difficulties in fault detection and localization as compared with high voltage transmission systems. One such fault is the broken conductor of live wires, which could cause high impedance current to flow on the ground or object. These faults usually produce very low fault currents, due to low voltage or dry areas, that are usually not detectable by the feeder conventional over-current protection schemes or by the HIF fault detector. As a result, fault detection in Low voltage (LV) overhead lines, due to open (or broken) conductors, is the most challenging problem in interconnected networks as compared to radical feeders. The aim of this paper is to identify a new method to detect such broken conductors in interconnected LV distribution and correctly automates the circuit breaker during faults. The proposed broken conductor fault detection method judges fault based on multiple information to increase accuracy and reliability of trip signal. The trip signal, based on the calculated measurements from the fault finding devices, will be provided to the circuit breaker. Finally, the proposed intelligent electronic devices installed at the bus Circuit Breaker Validates the trip signal before the power completely goes off. Several test cases were simulated to demonstrate the effective results.
A MEMS Turbopump for High Temperature Rankine Micro Heat Engines—Part I: Design and Fabrication

We report the design and microfabrication of a MEMS turbopump with thermal insulation, as the core component of a steam power plant-on-a-chip for waste heat recovery based on the Rankine thermodynamic cycle. Based on a first-generation microturbopump for ambient temperature operation, this second-generation device introduces materials for thermal insulation and design improvements to the rotordynamic components for operation at high temperatures. Thermal management is required to prevent water boiling in the pump and vapor from condensing in the turbine, bearings and seals. The device is a five-wafer stack that encloses a 4-mm diameter rotor disk with microturbine blades on one side and a viscous micropump on the other. This second-generation microturbopump implements unique thermal insulation strategies to isolate the pump from the surrounding hot flows. An out-off-plane thick molded glass embedded into the rotor prevents heat conduction from the turbine to the pump. In addition, an in-plane thick array of oxidized trenches prevents heat conduction from the thrust bearing region to the pump. The assembled microturbopump is fabricated and tested, resulting in the first MEMS turbopump demonstrated at high temperature. Design and fabrication are covered herein, whereas experimental demonstration and characterization are presented in Part II of this two-part paper. [2020-0050]
A Novel Power Flow Control Strategy for Heterogeneous Battery Energy Storage Systems Based on Prognostic Algorithms for Batteries

This work focuses on a novel power flow control strategy (PFCS) for a heterogeneous multiple battery energy storage system (BESS) based on prognostic algorithms for batteries and the selection of proper prediction horizons for such algorithms. Unlike existing PFCS, the proposed control strategy copes with the transition between power balancing and state of charge balancing regardless of the scenario, the system configuration or the objective of the operator due to the adaptability of the prediction horizons. MATLAB/Simulink is used to conduct a simulation study based on a heterogeneous multiple BESS. Therefore, the novel PFCS is implemented in a validated simulation model to prove its advantages in terms of the performance and efficiency target indicator in different scenarios and system configurations. The results show, the performance can be enhanced regardless of the scenario or system configuration and the efficiency is at least as high as for the reference PFCS.
Short-Term Cost Forecasting of EV Battery Using LSTM Network

The industry of vehicles is now in the phase of a huge transition from fossil fuels to more clean and green energy fuels. Due to this huge transition towards clean energy, new technology has made its place in the market in the form of smart grids for electric vehicles and the charging points. As the quantity of electric vehicles (EVs) is growing fast, the need for the knowledge of costs occurs by these electric vehicles (EVs) has got huge attention. Recently the deep learning methods have received extensive attention for forecasting in various fields, and are been considered as vital for the future world. Although the forecasting for an individual user is highly challenging due to the high inconsistent behavior of the individual users, the forecasting of the aggregated values is quite promising. In this work, we proposed a LSTMs network based forecasting model in order to predict the short term cost incurred by the battery of a single electric vehicle. Then the proposed work is been tested by correlating it with another benchmark. It has been shown that our work shows better results than the other one for a single user.
Evaluating Tomato Ripeness Using a Neural Network.

A method for evaluating tomato ripeness, utilizing its surface color, was developed using a machine vision system with color image processing capability and a multi layered neural network-based software system. The tomato ripeness was classified into four categories; unripe, half ripe, full ripe and over ripe according to the standard commercial classification for manual sorting. Over ripe means the fruit has lost its freshness. Three color specification values, i. e. , lightness L*, chroma C * and hue H were calculated from the RGB gray levels of a captured color digital image of a tomato by an on-line image processing system. Only 0.2 to 0.5 % of total surface area of a fruit is needed for color image sensing of the classification. The area size representing 0.5% of the total area was covered by 243 pixels of resolution. A three-layered neural network with four hidden layer units gave a satisfactory performance at 18000 times of BP (Back Propagation) learning. The total processing time from the image capturing to the final output for a single fruit was 0.45 seconds. The recognition rate for the ripeness classification using this method was as high as 93 %. A recognition rate of only 77 % was obtained by the multiple regression model tested. The present work provides another example to strengthen the area of neural network application research on machine vision systems including agricultural robotics, postharvest and processing systems.
A Blockchain-Enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks

The rapid growth of renewable energy has increased the requirements of the smart grid for communication and processing capabilities. As an effective solution to collect and manage information, the wireless network can improve the efficiency of renewable energy management. But due to the wireless data transmission risk and centralized power trading, the smart grid employing wireless networks cannot guarantee the security of the electricity market and the high consumption of renewable energy. Recently, as an emerging data-sharing technology, the blockchain has attracted widespread attention and is considered to have the potential to solve above problems. In this paper, we propose a blockchain-enabled secure power trading mechanism for the smart grid employing wireless networks. The blockchain is introduced to record power data collected by the wireless network, and the smart contract can make reasonable trading decisions based on this. The dual-chain structure composed of local energy trading blockchain and renewable energy trading blockchain improves the efficiency of power trading and renewable energy consumption. To strengthen the stability of renewable energy producers and expand their scale, we also design a blockchain-enabled renewable energy incentive mechanism. Finally, the modified Southern California Edison 56 bus test feeder model validated our mechanism.
A Local Coupling Multitrace Domain Decomposition Method for Electromagnetic Scattering From Multilayered Dielectric Objects

In this article, a local coupling multitrace domain decomposition method (LCMT-DDM) based on surface integral equation (SIE) formulations is proposed to analyze electromagnetic scattering from multilayered dielectric objects. Different from the traditional SIE-DDM, where the interactions between subdomains are accounted for using global radiation coupling, LCMT-DDM uses a local coupling scheme. The original multilayered object is decomposed into several independent domains, i.e., the exterior region (free space) and many homogeneous interior regions (dielectrics). The boundaries of subdomains are all touching faces, where only the Robin transmission conditions (RTCs) are enforced to ensure the field continuity. Hence, each subdomain only couples with its neighboring regions, which makes the DDM system a highly sparse matrix, especially when the number of subdomains is large. In each subdomain, the electric-field integral equation (EFIE) and the magnetic-field integral equation (MFIE) for dielectrics are used as the governing equations. By imposing RTCs, well-conditioned equations are formed in each subdomain without invoking the combined-field integral equation (CFIE), which usually causes accuracy issues in dielectric modeling. Since the subdomain matrices are diagonally dominant, the flexible generalized minimal residual (FGMRES) technique is used to accelerate the iterative solution of the whole DDM system. Moreover, an effective preconditioner that can be recursively constructed is proposed.
Multimodal MRI Based Classification and Prediction of Alzheimer’s Disease Using Random Forest Ensemble

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide and it accounts for a significant decrease in the quality of life of patients and their families. Currently, available treatment options for AD is merely palliative and no drugs are available for the inexorable progression of the disorder that is diagnosed during the later stage of the disease. So the early diagnosis of AD is an optimal strategy in formulating the treatment plan. Neuroimaging modalities like Magnetic Resonance Imaging (MRI), resting-state functional Magnetic resonance imaging (rs-fMRI), Diffusion Tensor Imaging (DTI) and Positron emission tomography (PET) are used to diagnose the structural and functional alteration caused by AD. For the past few years, machine learning methods are widely used to analyze the neuroimaging data acquired from MRI imaging modalities for the diagnosis and prediction of neurological disorder. In this work, the random forest classification algorithm is used to classify and predict Alzheimer’s disease. The data set that is used in this study is TADPOLE data set, which has been acquired from Alzheimer’s neuroimaging Initiative (ADNI). In this work, the multiclass classification that distinguishes the different level of Alzheimer’s disease has achieved an accuracy comparable to current research in the prediction of AD.
Data-driven soft sensor for continuous production monitoring: an application to paper strength

This paper presents a set of modeling procedures which are substantial for developing a data-driven soft sensor for paper strength parameters in production environment. Special focus is put on describing methods for efficient data aggregation, data cleaning and feature selection, which were employed to appropriately model the inherently complex process of papermaking and handle the challenging nature of collected data. The resulting soft sensor for tensile index deployed at a paper machine shows solid performance. Moreover, good quality of calculated real-time predictions is also confirmed by means of experiment-based validation.
Nonstationary Noise Reduction in Low SNR Speech Signals with Wavelet Coefficient Feature

Generally, the real-time environments with nonstationary noises and speech enhancement under low signal-to-noise ratio (SNR) remains a challenging task. Consequently, this article proposes a supervised model for speech enhancement by using the wavelet coefficient as an input feature. The performance of speech enhancement improves with the input feature selection (i.e., Wavelet coefficient). Experimental results are observed in terms of speech quality and intelligibility. Quality is evaluated with the perceptual evaluation of speech quality (PESQ). The intelligibility of speech is measured with the score of short term objective intelligibility (STOI). Speech enhancement results in a known and unknown non-stationary noise environments that are compared with conventional and supervised speech enhancement models. The result shows that the proposed feature has an improved intelligibility in diverse environmental conditions.