Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of an AoA-based Wi-Fi Indoor Positioning System

Location-based services (LBS) has emerged as a key computing application service for various industries as computing system is moving closer to real world human physical interaction. In the last ten years, global positioning system and object tracking have taken various industries by storm. Applications such as global positioning, navigation, logistics, object tracking, or even museum guide are developed around the LBS technology. The invention of the Global Positioning System (GPS) allows the method of trilateration to be used in full effect to pinpoint anyone’s location on Earth. However, the same technique cannot be used effectively when an individual is inside a building. A large portion of GPS signals from satellites cannot penetrate through walls and buildings. Various methods in all layers of conventional communication system have been introduced, and indoor positioning using Wi-Fi is leading the competition at the moment.This research aims to analyze the performance of Wi-Fi indoor positioning system in infrastructure mode. According to various studies and fundamental knowledge, the installation positions of the infrastructure access points to target detection areas of objects to be tracked pay the most significant role. In addition, bandwidth, types of Wi-Fi client devices, and number of devices in the area greatly affect the performance of the system. This work shows how these factors affect the positioning error the system. The results allow us to conclude that the current Wi-Fi indoor positioning system in the infrastructure mode can only be used in some applications that may not require extremely accurate positions such as navigation.
CMRS: A Classifier Matrix Recognition System for Traffic Management and Analysis in a Smart City Environment

The application of the Internet of Things (IoT) in a smart city improves its efficiency in terms of communication and installation costs by scaling geographical distance through intelligent devices and digital information. Different applications in a smart city, including health care, road safety, industry and home automation, rely on the IoT. Considering the significance of the IoT in smart city road applications, this manuscript introduces a classifier matrix recognition system (CMRS) for improving real-time traffic optimization. This classifier matrix system performs an independent and matching analysis of the real-time traffic images and constructs a decision factor for deriving its conclusion. The conclusion is served as responding notifications through the connected IoT systems for the users employing roadside-communication–assisted applications. CMRS exploits the advantages of block classification and matrix operations for improving the correlation accuracy and similarity index. The experimental results indicate that the proposed CMRS improves correlation accuracy with a high similarity index and less processing time and dissimilarity rate.
Grid Adaptive Sparse Bayesian Learning for 2D-DOA Estimation with L-shape Array

Sparsity based methods have gained its popularity in two-dimensional (2D) direction-of-arrival (DOA) estimation in recent years. However, these methods suffer from the off-grid problem, and also need additional angle pairing process, which results in degraded performance when applied in practice. In this paper, to address these problems, a novel and effective method named grid adaptive sparse Bayesian learning (GASBL) is proposed for 2D-DOA estimation with L-shape array from the perspective of sparse Bayesian learning. Specifically, an off-grid DOA model is proposed to enable grid adaptive refinement, and the auxiliary compound electric angle (CEA) is introduced to achieve automatic angle pairing of the elevation angles and azimuth angles. Then, a hierarchical probability framework with Laplacian prior is imposed. Finally, the 2D-DOA estimation problem is solved by Bayesian inferences. Compared with the state-of-the-art approaches, numerical results highlight the proposed method with more superior performance in terms of high angle resolution and robustness against the noise.
Energy Savings by Task Offloading to a Fog Considering Radio Front-End Characteristics

Fog computing can be used to offload computationally intensive tasks from battery powered Internet of Things (IoT) devices. Although it reduces energy required for computations in an IoT device, it uses energy for communications with the fog. This paper analyzes when usage of fog computing is more energy efficient than local computing. Detailed energy consumption models are built in both scenarios with the focus set on the relation between energy consumption and distortion introduced by a Power Amplifier (PA). Numerical results show that task offloading to a fog is the most energy efficient for short, wideband links.
High-Performance SAR Automatic Target Recognition Under Limited Data Condition Based on a Deep Feature Fusion Network

Deep learning algorithms have been widely applied to the recognition of remote-sensing images due to their excellent performance on various recognition problems with sufficient data. However, limited data on synthetic aperture radar (SAR) images degrade the performance of neural networks for SAR automatic target recognition (ATR). To address this problem, this paper presents a new deep feature fusion framework by combining the Gabor features and information of raw SAR images and fusing the feature vectors extracted from different layers in our proposed neural network. Gabor features improve the richness of SAR image features. The number of free parameters of neural networks is largely reduced by utilizing large-scale convolutional kernel factorization and global average pooling. Moreover, the fusion of feature vectors from different layers helps improve the recognition performance of neural networks. Experimental results on the MSTAR dataset demonstrate the effectiveness of our proposed method. The proposed neural network can achieve an average accuracy of 99% on the classification of ten-class targets and even achieve a high recognition accuracy on limited data and noisy data.
Assessment of Frequency Support from Wind Turbines under Alternative Control Schemes

The increasing penetration of wind generation in power grid introduces challenges for the system operation and stability, highlighting the importance of the frequency support from wind turbines (WTs). In this context, this paper proposes a novel control framework for the frequency support from WTs in power systems. The recovery effect of WTs associated with the frequency support delivering is explicitly included in the system frequency dynamics such that the secondary frequency dip can be eliminated. Various control schemes (constant power, inertia response and droop response) are analyzed and compared in terms of the effectiveness to replace primary frequency response(PFR) while maintaining the system frequency nadir above the limit. The proposed control design is verified through Matlab/Simulink simulations. A significant reduction of PFR requirement is observed especially in a system with high penetration of wind generation.
Development of the Gait Planning for Stability Movement on Quadruped Robot

Legged robot has an increasing attention from researchers in this recent year in the form of humanoid or animaloid robot. One of the animaloid robot type is quadruped. this paper will discuss about crawling gait planning that will be proposed on a Quadruped robot. This gait is expected to be able to maintain the balance of the robot pitch and roll angle movement when walking. The trajectory planning used in this case is linear translation and sinusoidal shaped gait trajectory. In this study, there are no obstacles and just walk on a flat terrain. This study focuses on the gait trajectory generation and timing adjustment to minimize the rolling and pitching movement of the robot while walking. This study will not only show the simulation, but also the implementation results of the crawling-gait planning to the robot.
Music Signals Beat Tracking Based on Bidirectional Long-Short Term Memory

In this research we developed a music signals beat tracking based on Bidirectional Long-Short Term Memory (BLSTM) method. Predicting the beat of music is one of the Music Information Retrieval (MIR) research field. Humans can find the beats of the music automatically, like when we clap our hands or stomp our feet. Finding the beats of the music is a difficult task for machine or actuator. The music signal must be processed first to get the beats pattern as the time sequence. Various methods were proposed at the feature extraction, pattern recognition, and peak detection stages. BLSTM is applied to the magnitude spectrogram as the music feature which obtained from the feature extraction stage, and then to estimate the tempo we use autocorrelation as the peak detection stage. We used the ISMIR 2012 dataset as the training data and 10 songs of Ballroom and GTZAN dataset which has different musical character for each dataset as the testing data. The accuracy is proven by the calculation of f-measure as a standard evaluation of beat tracking on music signals. The result shows that for the training data reach 86.3% f-measure accuracy, Ballroom data 73.4%, and GTZAN 65%.
ACO-OFDM Transmission over Underwater Pipeline for VLC-based Systems

Visible light communication (VLC) technology supports high-speed, reliable, flexible, and cost-effective designs which are demanded most of the underwater wireless communication (UWC) systems. Therefore, in this study, a comprehensive and completely adaptive asymmetrically clipped optical-OFDM based underwater VLC system is designed and demonstrated with widely used and commercially available light emitting diodes (LEDs) and photodiodes (PDs). The designed system is evaluated under twelve different test scenarios with varying fundamental system parameters such as sampling rate, modulation technique and order, and coding rates. In the light of experimental results, it is shown that the system is capable of transmitting and receiving the data supporting both QPSK and 16-QAM modulation techniques with sampling rates of 2 Mbps,5 Mbps, and 10 Mbps. The experimental results also show that bit-error-rate (BER) could still be maintained on the order of 10−6 over 6.5 m long underwater pipeline channel. In addition, the performance analyses of cold white, blue and green LEDs at wavelengths of 430 nm, 470 nm, and 530 nm, respectively, are evaluated and compared. Their advantages and drawbacks are discussed in detail.
Capillary blood sampling: How much pain is necessary?. Part 3: Pricking the finger can be less painful

A new lancing device (Softclix®) with an adjustable penetration depth was compared to the Glucolet® which had proven to be the least painful device in an earlier study1. At the lowest depth setting for obtaining just enough blood for a glucose test (⩽20μl), the new device was significantly less painful. This is mainly due to the fact that the Glucolet® lacks low and intermediate depth settings.
Joint User Grouping and Power Allocation for MISO Systems: Learning to Schedule

In this paper, we address ajoint user scheduling and power allocation problem from a machine-learning perspective in order to efficiently minimize data delivery time for multiple-input single-output (MISO) systems. The joint optimization problem is formulated as a mixed-integer and non-linear programming problem, such that the data requests can be delivered by minimum delay, and the power consumption can meet practical requirements. For solving the problem to the global optimum, we provide a solution to decouple the scheduling and power optimization. Due to the problem’s inherent hardness, the optimal solution requires exponential complexity and time in computations. To enable an efficient and competitive solution, we propose a learning-based approach to reduce data delivery time and solution’s computational delay, where a deep neural network is trained to learn and decide how to optimize user scheduling. In numerical study, the developed optimal solution can be used for performance benchmarking and generating training data for the proposed learning approach. The results demonstrate the developed learning based approach is able to significantly improve the computation efficiency while achieves a near optimal performance.
Hyperspectral and Multispectral Image Fusion based on a Non-locally Centralized Sparse Model and Adaptive Spatial-Spectral Dictionaries

Hyperspectral (HS) imaging systems are useful in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of a HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is a HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem. The dictionaries are learned from the observed data taking advantage of the high spectral correlation within the HS image and the non-local self-similarity over the spatial domain of the MS image. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm. Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.
Acoustic Source Position Estimation Based On Multi-Feature Gaussian Processes

Gaussian Processes, representing a Bayesian frame-work for regression, were already previously shown to allow effective range estimation in highly reverberant and noisy scenarios from a single pair of microphones when using the Coherent-to-Diffuse Power Ratio as a feature. In this work we investigate how Gaussian Process regression can jointly estimate range and Direction of Arrival by using the Coherent-to-Diffuse Power Ratio and an additional Direction of Arrival estimation feature (e.g., MUSIC) to achieve an estimate of the source position, based on a single concentrated array requiring only two sensors as a minimum.
Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data

In this paper, the problems of indoor sound source localization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm is proposed. Using time delays for the direct paths from candidate source locations to microphone nodes, the proposed algorithm estimates the most likely source location. To reduce the amount of data that must be exchanged between microphone nodes, a Gaussian measurement matrix is multiplied on to each channel and the proposed method operates directly on the compressed data. This is achieved by exploiting sparsity in both the frequency and space domains. The performance is analysed in numerical simulations, where the performance as a function of the reverberation times in investigated, and the results show that the proposed algorithm is robust to reverberation.
Perfect is the Enemy of Good: Lloyd-Max Quantization for Rate Allocation in Congestion Control Plane

Decoupling congestion control plane from datapath expedites the development of new congestion control solutions and creates opportunities for explicit rate allocation schemes. However, dealing with large numbers of flows remains a major challenge. Max-min fairness - the gold standard for rate allocation - has a running complexity proportional to the number of flows, which might be prohibitive in large-scale networks. To accelerate explicit rate allocation, we suggest using rate quantization, i.e. mapping the continuous range of flow rates to a small number of bins. We use Lloyd-max, a quantization method that generates bins according to the distribution of flow rates, to dynamically adjust the quantization bins over time. Our experimental evaluation shows that the distortion caused by this quantization scheme is small, while reducing the max-min rate allocation running time by 60 − 90%.
FOSS EKV2.6 Verilog-A Compact MOSFET Model

The EKV2.6 MOSFET compact model has had a considerable impact on the academic and industrial community of analog integrated circuit design, since its inception in 1996. The model is available as a free open-source software (FOSS) tool coded in Verilog-A. The present paper provides a short review of foundations of the model and shows its capabilities via characterization and modeling based on a test chip in 180 nm CMOS fabricated via Europractice.
Evaluation of an informatics educational intervention to enhance informatics competence among baccalaureate nursing students

Concerns around quality of care and patient safety have been key drivers behind the increased interest in improving informatics competencies among health care providers. The purpose of this study was to develop an informatics educational intervention for baccalaureate nursing students and compare outcomes associated with vodcasting and face-to-face methods for delivering this material. Following a pilot test, we used a three-group posttest only design to test the effect of the intervention on knowledge gain, confidence and attitude outcomes toward the electronic health record. Forty-two individuals participated in this study. Findings showed that the intervention had a large effect on knowledge gain (0.444), but no effect on confidence or attitudes, and that vodcasting was equally effective to face-to-face methods for delivering informatics content. Following refinement of the knowledge gain instrument used in this study, we urge replication of this study in other settings with a larger sample. https://doi.org/10.34105/j.kmel. 2015.07. 026
NetzKapa: Simulation Tool for Assessing the Integration of Electrical Vehicles in Distribution Grids

This paper presents the architecture and functionalities of a grid simulation tool called NetzKapa. NetzKapa is a tool used for assessing distribution grids performance for different electrical vehicle configurations (e.g. distribution of charging infrastructure, charging profiles) by means of Monte-Carlo simulations. The Monte-Carlo method allows assessing significant amount of grid variations and obtains statistical information on different performance indicators. One of the contributions of this paper is the implementation of a concept call region of interest (ROI) in the Monte-Carlos simulations, which allows reducing the number of simulated configurations during the estimation of the grid capacity.
Estimation and Reservation for Autonomous Resource Selection in C-V2X Mode 4

In this paper, the decentralized resource selection for Cellular Vehicle to Everything (C-V2X) communication is investigated. The immunity against packet collisions and selection of resource allocation are examined. It is found that the improvement of collision ratio and packet reception ratio (PRR) with the estimation and reservation of resource allocation, is the best way to improve the message broadcasting reliability. However, Sensing-based Semi-Persistent Scheduling (S-SPS), has been proposed for C-V2X Mode 4. The S-SPS faces several serious problems, like half duplex errors, hidden terminal errors, and packet collision errors. We propose an algorithm to enhance the performance aimed at decreasing the collision ratio and increasing PRR. The idea is that each vehicle lists all resource locations of the received packets that have the same random counter. This allows each terminal to estimate which resource location will be free in the next resource selection. Also, the announced next location in the list will be the current location when the random counter reaches zero. This way the vehicle does not need to depend on the sensing process, and resources utilization is improved. Our simulations show a significant improvement in the performance of our proposed algorithm as compared to S-SPS.
Data-Dependent Ensemble of Magnitude Spectrum Predictions for Single Channel Speech Enhancement

Applying a predicted time-frequency mask to the noisy speech spectrogram and directly predicting the clean speech magnitude spectrogram are two common deep learning-based speech enhancement approaches. Ensemble techniques such as averaging and neural network-based fusion of magnitude spectra obtained with these two approaches have been shown to improve the objective perceptual quality of speech using synthetic mixtures of data. This work generalizes the averaging ensemble approach by proposing neural network layers to predict time-frequency varying weights for the combination of the two magnitude spectra obtained by time-frequency masking and by direct prediction. In order to combine the best individual magnitude spectrum estimates, the proposed weight prediction layers are trained after the time-frequency mask and magnitude spectrum networks layers have been separately trained for their corresponding objectives and their weights have been fixed. Using the publicly available CHiME3-challenge data, which consists of both simulated and real speech recordings in everyday environments with noise and interference, the proposed approach leads to significantly higher noise suppression in terms of segmental source-to-distortion ratio over the alternative approaches. In addition, the approach achieves similar improvements in the average objective instrumentally measured intelligibility scores with respect to the best achieved scores.
Detection Method Against Fake Message Attacks in Sparse Mobile Ad-Hoc Networks

In this paper, we consider a detection method against fake messages in sparse mobile ad-hoc networks, where nodes are temporally isolated. In these networks, messages are delivered to their destination nodes using store-carry-forward routing, where they are relayed by some nodes. Therefore, when a node has messages in its buffer, it can easily rewrite them. When there exist malicious nodes in the network, they can alter messages to fake message and launch fake message attacks, spreading the fake messages over the network. In this paper, we first model system dynamics as a continuous-time absorbing Markov chain, and clarify how fake message attacks affect the system performance using Markov analysis. We then propose a detection method that uses the hash value of messages. Whenever a node that has a message and its hash value encounters another node, it forwards only one of them to that node. By doing so, the message and its hash value are delivered to the destination node via different paths. Through simulation experiments, we show the effectiveness of our proposed method.
Source Enumeration in Non-White Noise and Small Sample Size via Subspace Averaging

This paper addresses the problem of source enumeration by an array of sensors in the challenging conditions of: i) large uniform arrays with few snapshots, and ii) non-white or spatially correlated noises with arbitrary correlation. To solve this problem, we combine a subspace averaging (SA) technique, recently proposed for the case of independent and identically distributed (i.i.d.) noises, with a majority vote approach. The number of sources is detected for increasing dimensions of the SA technique and then a majority vote is applied to determine the final estimate. As illustrated by some simulation examples, this simple modification makes SA a very robust method of enumerating sources in these challenging scenarios.