Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Improvements of Parallel Applications thanks to MPI-4.0 Hints

HPC systems have experienced significant growth over the past years, with modern machines having hundreds of thousands of nodes. Message Passing Interface (MPI) is the de facto standard for distributed computing on these architectures. On the MPI critical path, the message-matching process is one of the most time-consuming operations. In this process, searching for a specific request in a message queue represents a significant part of the communication latency. So far, no miracle algorithm performs well in all cases. This paper explores potential matching specializations thanks to hints introduced in the latest MPI 4.0 standard. We propose a hash-table-based algorithm that performs constant time message-matching for no wildcard requests. This approach is suitable for intensive point-to-point communication phases in many applications (more than 50% of CORAL benchmarks). We demonstrate that our approach can improve the overall execution time of real HPC applications by up to 25%. Also, we analyze the limitations of our method and propose a strategy for identifying the most suitable algorithm for a given application. Indeed, we apply machine learning techniques for classifying applications depending on their message pattern characteristics.
Distance Distributions and Trip Behavior in Defined Regions

This paper discusses applications of the statistical distribution of distances between random pairs of points in bounded regions. The existing literature on convex figures is first described, and applications of the analytically tractable distributions to point pattern analysis are given. New statistics are then introduced with applications in shape analysis and trip distribution studies. Finally a brief discussion is made of directional bias, and of alternative sampling rules and distance metrics.
Joint Sparse Channel and Clipping Level Estimation in OFDM-based IoT Networks: A Bayesian Learning Approach

Orthogonal frequency division multiplexing (OFDM)-based internet of things (IoT) networks undergo severe nonlinear distortion at the transmitter side owing to saturation of the high power amplifiers. Compensation of RF impairments at the receiver requires precise knowledge of the clipping amplitude level which in turn necessitates an accurate channel estimate. This paper leverages the inherent temporal sparsity associated with frequency selective wireless channels to develop a novel joint sparse channel and clipping amplitude estimation framework based on the popular sparse Bayesian learning (SBL) algorithm. The proposed scheme iteratively obtains the maximum likelihood estimate of the clipping amplitude followed by an expectation maximization (EM)-based sparse channel vector estimate. Numerical simulations are demonstrated to validate the superiority of the proposed technique over a sparsity agnostic scheme in terms of mean squared error (MSE) of channel, clipping amplitude estimates and symbol error rate (SER).
Scheduling of the Uruguayan Football and Basketball Leagues

Football in Uruguay is the most popular sport, representing an important part of the country’s culture. It is not surprising that the Uruguayan football championship serves as entertainment for a large part of the population. The process of designing a concrete fixture has many implications, for instance: is the tournament sportingly fair? is it attractive for the fans? is it economically profitable? In this work, we tackle the scheduling of fixtures for both Uruguayan football and basketball leagues to improve the fairness of the competitions. We address this problem following a multi-objective approach and use the well-known NSGA-II, as well as an Integer Programming solver for considering each objective independently. The results obtained show the effectiveness of both algorithms studied to find high-quality solutions. The resulting fixtures are far superior to the ones that are actually being used in the leagues.
Mainlobe Interference Suppression Method Based on Blocking Matrix Preprocessing with Low Sidelobe Constraint

Adaptive beamforming is widely used in phased array radar for interference and noise suppression. However, when mainlobe interference exists, mainlobe distortion, peak offset and sidelobe level rise will occur, which seriously deteriorate the performance of adaptive beamforming. To address this issue, this paper proposes a mainlobe interference suppression method based on blocking matrix preprocessing (BMP) with low sidelobe constraint. In the method, singular value decomposition (SVD) method is firstly utilized to estimate the angle of the mainlobe interference and blocking matrix is constituted to suppress the mainlobe interference. Then, under the restriction of low sidelobe level, a convex optimization problem is solved to further suppress the sidelobe interferences. Numerical simulations are conducted, and the results show the effectiveness and robustness of the method.
Calibration and optimization of non-linear 6D force/torque cells

Multi-dimensional force/torque sensors have a wide range of applications in the field of automation technology and in the steadily growing field of robotics. Through the development of new applications, the demands on multi-axis force/torque sensors and their calibrations are growing. Therefore, the focus of our research target lies in the optimization of the sensor’s measuring cell geometries and strain gauge positioning. Further our intention is to create a calibration that is resistant to ageing and temperature with maximized application accuracy using FEM-Models and Machine Learning. In this paper, we investigate the requirements for AI calibration procedures of six-dimensional force/torque sensors, also against the background of generating an optimal calibration model.
Full-Angle Digital Predistortion Technique for 5G Millimeter-Wave Integrated Phased Array

In this paper, a full-angle digital predistortion (DPD) technique is proposed to linearize fifth-generation (5G) millimeter-wave (mm-Wave) integrated phased array transmitters with low complexity. The far-field main beam is captured as the feedback signal for the DPD system which is cascaded by the ΔDPD array and the Ref-DPD block. The former is implemented to compensate the differences between different angles and the latter is used to linearize the whole system. Applied in a 64-channel integrated phased array working at 28 GHz, the proposed method can enhance the EIRP of transmitter by more than 6 dB under the constraints of EVM and ACPR. The number of coefficients can be reduced by 64% without obvious performance degradation.
A Multi-Objective Perceptual Aware Loss Function for End-To-End Target Speaker Separation

Recent years the most commonly used loss function in end-to-end monaural target speaker extraction, especially for time domain deep neural networks, is Scale-Invariant Signal-to-Distortion Ratio (SISDR). This objective function shows powerful optimization efficiency in speaker extraction. However, the evaluation result of extraction models trained by SISDR shows deficiency in speech quality and intelligibility especially in perceptual and spectral scales. Hence we propose a multi-objective loss function for end-to-end target speaker network training, consisting of objectives from time domain, spectral domain and the human perceptual field, which is able to make improvement in terms of speech quality and intelligibility in various measures, and at the same time ensure that its performance in SISDR will not drop. We select SpEx+ as our baseline network, and experimental results on the WSJ0-2mix-extr dataset show that our proposed loss function achieves superiority in multiple quality measures compared with the baseline system.
A multi-scale EEGNet for cross-subject RSVP-based BCI system

In the cross-subject classification task, a subject-agnostic model is trained for the classification task of other subjects, according to the prior knowledge from EEG data of some subjects. It is one of the challenges for ERP classification in the RSVP-based BCI system. So far, convolutional neural networks (CNNs) for RSVP classification only use a fixed-size kernel for each layer to extract features in the temporal domain, which limits the ability of the network to detect ERP. In this work, a multi-scale EEGNet model (MS-EEGNet) for cross-subject RSVP classification task was proposed, which adopted parallel convolution layers with multi-scale kernels to extract discrimination information in the temporal domain, and increased the robustness of the model. The proposed model was used for the BCI Controlled Robot Contest in the World Robot Contest 2022 and achieved good results. The UAR of the A and B datasets got 0.493 and 0.528, respectively. Compared with other CNN algorithms including EEGNet and PLNet, the proposed model had better classification performance.
Proactive Deployment of Cache-Enabled Aerial Base Stations for Optimized Energy-Delay Cost

Unmanned aerial vehicles (UAVs) via caching have been envisioned as a promising technology for many applications. In cache-enabled drone small cell (DSC) networks, file delivery delay and energy consumption are two critical performance metrics. Firstly, power control is an essential mechanism to mitigate inter-cell interference in DSCs. Secondly, the UAV deployment and content placement are vital for the quality of experience (QoE) of users. In this paper, we propose the proactive cache-enabled drone management (PCDM) framework including mobility prediction model and proactive optimization mechanisms to reduce the energy consumption and file delivery delay cost of the system. We build an accurate mobility prediction model by supervised learning, which uses historical user location to train the model. In the proactive optimization mechanisms, the mobility-aware movement and power control (M2PC) scheme for DSCs and mobility-aware content placement (MCP) for cache devices are developed. In our simulation results, the proposed method effectively reduces the average energy and delay cost.
A Novel Inductive Power Transfer System for Medium-low Speed Maglev Train Based on Double-ended Inverter

Inductive power transfer (IPT) system for medium-low speed maglev train can effectively overcome the drawbacks of traditional contact power supply system. However, in the existing IPT system, the primary track is excited by single inverter. When the inverter fails, the IPT system can not supply power anymore which will lead to shutdown of the maglev train. In order to improve the reliability of power supply system, three novel IPT system topologies based on double-ended inverter is proposed in this paper. The primary track can be excited by both double-ended inverters simultaneously or either of them independently. The output voltage and current characteristic of the asymmetric LCC-S topology is analyzed in detail. Finally, the effectiveness of the system is simulated for a power level of 30kW. The results verified the performance of the proposed IPT system.
Torque Ripple Reduction of Small Inductance BLDCM Based on Instantaneous Voltage Control

Conventional pulse width modulation will cause large torque ripple of small inductance brushless DC motor during the conduction region. And the periodic commutation prudeces the commutation torque ripple. It will cause torque ripple in the conduction region too. A two levels DC-DC circuit based on instantaneous voltage control is used to eliminate the PWM torque ripple and commutation torque ripple for small inductance brushless DC motor. The simulation results show the proposed topology is an attractive option to make the torque keep constant during the conduction and commutation region at low-and high-speed range.
Effect of Electrooculography on Electroencephalography Classifying Accuracy in Deep Learning and Reducing Number of Channels in Motor-Imagery Brain-Computer Interface

Recently, brain-computer interface (BCI) control using deep learning has been investigated. The BCI control is realized by analyzing brain activity through a computer and reflecting it to an effector. There are several methods to measure brain activity such as electroencephalography (EEG) and functional magnetic resonance imaging; however, EEG-based BCI has been developed and studied because it is easier to measure. However, EEG signals contain many artifacts such as electrooculography (EOG), electromyography (EMG) and commercial power supply noise, which deteriorate the SNR. Further, it is not possible to identify the frequency of EOG and EMG forehand; therefore, it is not possible to remove them completely from EEG. This is a critical problem for implementing BCI to residential environments because it is impossible to stop the movements of the user's body for practical use. Here, we focused on EOG and investigated the effect of pseudo-EOG on classifying accuracy by adding pseudo-EOG to steady state visual evoked potential (SSVEP) and motor imagery (MI). The results showed that EOG had no effect on the classifying accuracy of SSVEP but had an effect on the classifying accuracy of MI. For the fast classification of MI, the number of channels was reduced to lower the computational cost for implementing edge computing for the practical use in residential environment. Only 2% degradation was observed between the accuracy of deep learning with 22 channels and those with 15 channels.
StellaUAV: A Tool for Testing the Safe Behavior of UAVs with Scenario-Based Testing (Tools and Artifact Track)

When we allow Unmanned Aerial Vehicles (UAVs) to perform their missions autonomously in the near future, we need to ensure their safe behavior. To generate relevant test cases that can reveal potential faults in the tested UAVs, we propose to leverage scenario-based testing from the automotive domain. For a systematic application of this methodology, we present StellaUAV, a tool for testing the safe behavior of UAVs with scenario-based testing. With our proposed tool, we can describe relevant test situations, generate test cases for these situations that can reveal potential faults in the tested UAV, and evaluate the performance of different optimization algorithms and their combinations. To demonstrate its applicability, we apply StellaUAV to generate test cases for various situations and discover several safety distance violations of the tested exemplary UAV in the presence of dynamic obstacles. These experimental results indicate that the given system under test can handle situations with only static obstacles rather well, while it encounters problems when facing dynamic ones. Further, we detect that a combination of optimization algorithms can find safety distance violations for a logical scenario that the widely used algorithm NSGAII deemed safe for the tested system. Overall, our results show that StellaUAV can effectively detect potential faults in the tested UAV.
A Monolithically Integrated Tunable Comb Source and Filter

Modern optical networks employ hundreds of lasers that fill up the limited bandwidth. Each laser generates a channel and each channel is separated by a spectrally inefficient region of space called a guard band. Optical frequency comb sources (OFCS), can potentially reduce or eliminate the use of these guard bands by creating coherent superchannels with a precise and stable frequency. In this paper we demonstrate a monolithically integrated comb source which is integrated with a filter with the intent to be used as a de-multiplexer.
Slotted Concatenated Coding Scheme for Asynchronous Uplink Unsourced Random Access with a Massive MIMO Receiver

This paper considers a concatenated coding scheme of sparse regression codes (SPARC) and tree code for asynchronous uplink unsourced random access. The encoding of SPARC is limited in a single sub-carrier to counter the unknown delay due to asynchronization. A slotted structure, where both channel uses and potential users are divided into slots, is added to reduce the overall computational complexity and improve its reliability when the length of the coherence block is limited. An efficient two-stage decoder is applied for further computational complexity reduction at the cost of slightly higher per-user probability of error. Asymptotic and numerical results are provided to demonstrate the effectiveness of the proposed scheme.
Analysis of epileptic EEG based on improved sample entropy algorithm

The physiological state of the system can be evaluated by quantifying the complexity of pathological time series. For physiological time series with large amplitude changes, traditional methods often do not work. An improved sample entropy (iSampEn) algorithm is proposed based on wave mode to measure the complexity of nonlinear system. Through the analysis of the complexity of epileptic electroencephalogram (EEG), it is concluded that iSampEn algorithm can obviously distinguish epileptic EEG from normal EEG. The results of this study are helpful for clinical diagnosis.
Markov Image with Transfer Learning for Malware Detection and Classification

Malware attack is a severe problem that can cause a considerable loss. To prevent the malware attack, different malware detection and classification method have been implemented in recent years. This paper proposed a new method based on Markov image and transfer learning on machine learning. Also, an experience comparing the performance on malware detection and classification between the proposed and grayscale methods was done. The accuracy and loss of malware detection and classification by using the proposed method are 0.973 and 0.076, 0.987 and 0.062 respectively. The accuracy and loss of malware detection and classification using the grayscale method are 0.989 and 0.037, 0.973 and 0.202 respectively. Although the grayscale method has done better in malware detection, the proposed method's accuracy is over 0.97. Therefore, the result shows that the proposed method are suitable for malware detection and classification.
Automated Classification of Lung Injury from X-ray Images using Deep Learning Network

This paper presents a new approach for supporting the diagnosis of lung injury from chest X-ray images. Specifically, this paper proposes using the DarkCovidNet model for classifying the damage as Covid-19 or other causes. Since the approach also takes into account the dataset feature to train classification models, data augmentation using an interpolation algorithm was also researched and used to enrich the dataset. The experimented results done on different databases show that the proposed method help to improves the the accuracy of lung injury classification models.
A Method of Short-Term Load Prediction of Renewable Energy Power System Based on CNNLSTM

In the context of the development of new energy power systems, a large number of distributed power sources are connected to power grid, and power users have the ability to actively participate in grid regulation. Under the electricity spot trading market mechanism, in order to achieve the balance of supply and demand in the power grid, demand side management encourages users to participate in load regulation based on the guidance of electricity price. This paper conducts research on the short-term net load forecasting model under the dual background of a large number of grid-connected distributed power generation and the spot trading market. CNN-LSTM model is used for the prediction of short-term net load of the new energy power system under the market mechanism. The comparison of predicted results with actual load data shows that the prediction accuracy of the CNN-LSTM model considering market factors is higher than that of the traditional regression analysis model, which verifies the effectiveness of the proposed method.
Vibration Reduction of IPMSM with Asymmetric Rotor Shape under Load Condition

In this study, the interior permanent magnet synchronous motor employing the asymmetric rotor is proposed to reduce the vibration under load conditions. The asymmetric rotor is created by modifying the arrangement of permanent magnets and flux barriers. By employing the proposed asymmetric rotor, harmonic magnitudes of the radial air-gap magnetic flux density are reduced under load conditions. As a result, the vibration can be reduced as radial electromagnetic pressures with low vibration orders are decreased. To confirm the vibration reduction of the asymmetric model, stator displacements of the symmetric and asymmetric models are compared under two load conditions via a vibration analysis.
Iron Doping for Transfer Printed High Speed EAM

An iron doped InP layer is adopted to achieve a micro transfer printing (MTP) compatible high-speed electro-absorption modulator (EAM). An equivalent circuit model analysis of the transfer printed EAM is presented with a simulated speed up to 45.6 GHz. Fabrication details are also discussed.
PPG-GAN: An Adversarial Network to De-noise PPG Signals during Physical Activity

Quality photoplethysmographic (PPG) signals are essential for accurate physiological assessment. However, the PPG acquisition process is often accompanied by spurious motion artefacts (MAs), especially during medium-high intensity physical activity. This study proposes a generative adversarial network (PPG-GAN) to create de-noised versions of measure PPG signals. The Adaptive Notch Filtration (ANF) algorithm, which enables the extraction of accurate heart rates (HR) and respiration rates (RR) from PPG signals, is used as the approximate reference signal to train the PPG-GAN. The generated PPG signals from test inputs provide a heart rate (HR) with a mean absolute error of 1.68 bpm for the IEEE-SPC dataset. A comparison with gold-standard HR and RR measurements, for our in-house dataset, show the errors in absolute value of less than 5%. The generated PPG signals, for the test clips, show a very strong correlation with their reference values, R ≈ 0.98. The results suggest that PPG-GAN could be a paradigm for MA-free PPG signal processing specifically for personal healthcare, even during high intensity activity.
Automatic Identification of Printed Circuit Board Vias based on YOLO Algorithm

As the core component of small electronic products, the Printed Circuit Board (PCB) is of multi-layer structure to ensure complex circuit design. In a multi-layer PCB, the electrical signals between the layers are transmitted through the vias. Accordingly, the quality of vias would be highly related to the signal integrity of the PCB. In this study, an automatic via identification model based on YOLO algorithm is developed. The objective is to help engineers quickly locate the via on the section image of a multi-layer PCB which is necessary for subsequent processing. For four kinds of vias, the presented identification model can not only ensure reasonable recognition results (89% mAP), but also a high processing speed (26FPS). It has practical application prospects in PCB manufacturing process.