Miracle-dz/UniFAR-FTUs huggingface dataset:
Tactical Behaviour of Youth Soccer Players: Differences Depending on Task Constraint Modification, Age and Skill Level

Abstract This study aimed to investigate: i) how Small-Sided and Conditioned Games based on different representation and exaggeration modification strategies, from the Teaching Games for Understanding pedagogical principles, affected team performance and exploratory behaviour; and ii) how teams and players of different ages and skill levels were affected by the use of these different modification strategies. In total, forty-eight youth male soccer players participated in the study (U15, n = 24 mean age = 13.06 ± 1.53 years; U17, n = 24 mean age = 16.89 ± 0.11 years). In both categories, players were organized into three groups according to their tactical efficiency level (Group 01 = High Skilled Players (HSP), Group 02 = Intermediate Skilled Players (ISP), and Group 03 = Low Skilled Players (LSP)). The HSP and LSP groups performed two types of Gk+4vs4+Gk Small-Sided and Conditioned Games (SSCGs) based on different representation and exaggeration modification strategies. The first type of SSCGs was modified by structural constraints (Structural SSCG) and the second type was modified by rule manipulation (Manipulation SSCG). Team performance and exploratory behaviour were analysed through the Offensive Sequences Characterization System and Lag Sequential Analysis, respectively. SSCG modification strategies affected differently tactical performance and exploratory behaviour of teams composed of players of different skill levels. It was found that SSCG modification strategy through rule manipulation provided players and teams with a higher level of difficulty, compromising their performance and inhibiting exploratory behaviour. This information is crucial to practitioners wishing to apply more appropriate pedagogical strategies to improve a specific tactical problem using a player-centred and game-based approach.
Real-time retinal layer segmentation of adaptive optics optical coherence tomography angiography with deep learning

Real time rendering of en face optical coherence tomography (OCT) and OCT-angiography (OCTA) of arbitrary retinal layers in ophthalmic imaging sessions can be used to increase the yield rate of high-quality acquisitions, provide real-time feedback during image-guided surgeries and compensate aberrations in sensorless adaptive optics (AO) OCT and OCTA. However, real-time en face visualizations rely critically on the accurate segmentation of retinal layers in the three-dimensional OCT volumes. Here, we demonstrate a compact deep-learning architecture that segmented batches of OCT B-scans and produced the corresponding OCT and OCTA projections within only 41 ms. The short latency was possible due to a low complexity neural network structure, CNN compression using TensorRT, and the use of Tensor Cores on GPU hardware to accelerate the computation of convolutions. Inferencing of the original U-net was accelerated by 21 times without reducing the accuracy. To the best our knowledge, our work is the first demonstration of an ophthalmic imager with embedded artificial intelligence (AI) providing real-time feedback.
BOSSA: A Decentralized System for Proofs of Data Retrievability and Replication

Proofs of retrievability and proofs of replication are two cryptographic tools that enable a remote server to prove that the users’ data has been correctly stored. Nevertheless, the literature either requires the users themselves to perform expensive verification jobs, or relies on a “fully trustworthy” third party auditor (TPA) to execute the public verification. In addition, none of existing solutions consider the underlying incentive issues behind a rational server who is motivated to collect users’ data but tries to evade the replication checking in order to save storage resources. In this article, we propose the first decentralized system for proofs of data retrievability and replication—<inline-formula><tex-math notation="LaTeX">${\sf BOSSA}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">BOSSA</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-3030063.gif"/></alternatives></inline-formula>, which is incentive-compatible for each party and realizes automated auditing atop off-the-shelf blockchain platforms. We deal with issues such as proof enforcements to catch malicious behaviors, new metrics to measure the contributions, and reward distributions to create a fair reciprocal environment. <inline-formula><tex-math notation="LaTeX">${\sf BOSSA}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">BOSSA</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-3030063.gif"/></alternatives></inline-formula> also incorporates privacy-enhancing techniques to prevent decentralized peers (including blockchain nodes) from inferring private information about the outsourced data. Security analysis is presented in the context of integrity, privacy, and reliability. We implement a prototype based on <inline-formula><tex-math notation="LaTeX">${\sf BOSSA}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">BOSSA</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq3-3030063.gif"/></alternatives></inline-formula> leveraging the smart contracts of Ethereum blockchain. Our extensive experimental evaluations demonstrate the practicality of our proposal.
ML-Based Forward Symbol Timing Offset Estimation for Burst Signals

In order to ensure the performance of burst signal demodulation, forward symbol timing offset estimation algorithm is usually used for symbol timing recovery. Aiming at the shortcomings of common forward symbol timing estimation algorithms, a new algorithm based on maximum likelihood estimation joint trigonometric polynomial interpolation is proposed in this article, which is suitable for the data of four samples per symbol and can resist large frequency offset. Hereafter, in order to make it more suitable for engineering practice, optimize it into an improved data-aided (DA) forward symbol timing offset estimation algorithm with multiple characteristics, e.g., moderate frequency offset capture range, insensitive to shaping coefficient, relatively low complexity, excellent estimation performance, flexible algorithm structure and less sample data required for calculation. The simulation results show that the performance of the improved algorithm in this article can approach the modified cramer-rao bound (MCRB) within the frequency offset capture range, under the conditions of low signal to noise ratio (SNR) and small shaping coefficient.
Development of 7 DOF Robotic Arm with Electronic Bluetooth Stethoscope for COVID Patient Medical Assesment

The study proposed the simulation of the inverse kinematics (IK) controller for the 7 DOF robotic arm custom designed to measure the lung condition of contiguous patients. For this purpose, an electronic stethoscope will be attached to the end-effector of the robot arm. Currently, no robot has been developed for this purpose. In this research, the robot arm hardware was custom designed using the modular design of the motor and the Rapid Prototyping Solidwork to develop an appropriate robot arm. Next, the Robot Operating System (ROS) simulation was performed to evaluate the accuracy and execution of the inverse kinematic controller for the robot arm. The result shows that the IK controller has an accuracy of 0.005 mm and that the execution time was fast within 200 milliseconds.
79-GHz Wide-Beam Microstrip Patch Antenna and Antenna Array for Millimeter-Wave Applications

A wide-beam microstrip patch antenna and antenna array ranging from 77 to 81 GHz are presented in this study for millimeter-wave applications. A substrate-integrated-waveguide probe-fed patch was developed. For beamwidth enhancement, two I-shaped parasitic elements were placed next to the main patch to establish a three-element subarray. The current directions of the parasitic elements were opposite to that of the main patch, leading to beamwidth enhancement in the <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-plane radiation pattern. The proposed I-shaped parasitic element has the advantages of coplanar structure, compact size, and easy adjustment of the induced current. An array-factor method was employed to analyze the effect of the parasitic elements on the beamwidth of the main patch. Three amplitude distributions of the subarray were compared. The distribution of −0.1:1:−0.1 was proved to present the most beamwidth, which can be realized easily by the proposed I-shaped parasitic elements. To achieve both wide beamwidth and high gain, a <inline-formula> <tex-math notation="LaTeX">$1\times8$ </tex-math></inline-formula> patch antenna array with the I-shaped parasitic elements was proposed. The measured results showed that the design can offer a gain of 10.74 dBi and a wide beamwidth of 138° at 79 GHz.
On Angular Sampling Intervals for Reconstructing Wideband Channel Spatial Profiles in Directional Scanning Measurements

Directional scanning measurement is the dominant strategy taken to measure channel spatial profiles for millimeter wave (mmWave) frequency bands. The focus of the paper is on wideband channel spatial profile reconstruction and angular sampling interval (ASI) selection for directional scanning measurement. We propose to employ the trigonometric interpolation technique to reconstruct the spatial channel profile and to use Nyquist sampling theorem and spherical wave mode theory to determine the ASI. Our analysis demonstrated that the half-power beam width (HPBW) of the antenna is a good approximation for the ASI setting to ensure accurate directional scanning measurements. The reconstructed power-angle-delay profile (PADP), composite power angular spectrum (PAS), delay and angle characteristics match well with those of the target in the measured data when the ASI is set to the antenna HPBW.
Fuzzy Logic Control of Electric Vehicles in Changing Braking Conditions

The paper focuses on the issues actual for braking manoeuvres of road electric vehicles. Multiple researches carried out on car braking are classified here on their attitude towards the energy recycling. The designed MATLAB/Simulink™ model concentrates on the gradient fuzzy logic control of braking torque under the tire-road friction uncertainty. The performance variables evaluated are regenerative energy, vehicle and wheel velocities, longitudinal wheel slip, and application torque experienced by the car and distributed between electrical and friction brakes. At this, integrity of both the gradual and the urgent antilock braking processes is ensured based on a united approach with the agreed actions. The solutions found by simulations were compared to the experimental plots obtained from a hardware-in-the-loop test rig. Thereby, the robustness of the system to changes in the tire-road interaction was confirmed.
Method Using IOT Low Earth Orbit Satellite to Monitor Forest Temperature in Indonesia

The ultimate goal of this paper is to ensure the proper functioning of the Monitoring Forest Temperature program in Indonesia using the IoT Narrow-Band Low earth orbit Satellite. As a new technology for monitoring the temperature continue to expand, its implementation in developing countries particularly in Indonesia requires strategic guidance of how the whole process will be executed. Nevertheless, due to this, cross-sectoral partnership in technology, policy, budget, industry is essential to be addressed. The World Bank has recorded the loss from forest fire where 28 million people directly affected including 19 people who died and over 500 thousand people suffered from respiratory problems. Smokes from forest and land fires have also struck Malaysia, Singapore, and Brunei Darussalam respectively. To respond to this, the IoT (Internet of Things) now comes with an extensive feature, using the capability of satellite reach. The Narrow Band Low Earth Orbit Satellite has released a feature for IoT connect to Low Orbit Satellite and transmit the data from the sensor directly. Therefore, we argue that this technology is crucial and needs to be functioned immediately to monitor forest temperature in Indonesia.
Improving the Anomaly Detection by Combining PSO Search Methods and J48 Algorithm

The feature selection techniques are used to find the most important and relevant features in a dataset. Therefore, in this study feature selection technique was used to improve the performance of Anomaly Detection. Many feature selection techniques have been developed and implemented on the NSL-KDD dataset. However, with the rapid growth of traffic on a network where more applications, devices, and protocols participate, the traffic data is complex and heterogeneous contribute to security issues. This makes the NSL-KDD dataset no longer reliable for it. The detection model must also be able to recognize the type of novel attack on complex network datasets. So, a robust analysis technique for a more complex and larger dataset is required, to overcome the increase of security issues in a big data network. This study proposes particle swarm optimization (PSO) Search methods as a feature selection method. As contribute to feature analysis knowledge, In the experiment a combination of particle swarm optimization (PSO) Search methods with other search methods are examined. To overcome the limitation NSL-KDD dataset, in the experiments the CICIDS2017 dataset used. To validate the selected features from the proposed technique J48 classification algorithm used in this study. The detection performance of the combination PSO Search method with J48 examined and compare with other feature selection and previous study. The proposed technique successfully finds the important features of the dataset, which improve detection performance with 99.89% accuracy. Compared with the previous study the proposed technique has better accuracy, TPR, and FPR.
Correlation Between ECG and Heart Sound

In addition to electrical signals from the heart, signals related to heart sound can be received. Electrocardiogram (ECG) data are electrical signals that result in activities in the heart muscles. Phonocardiogram data are the results of turbulence during the opening and closing of the heart valves. In this paper, electrical signals (ECG) and sound signals (FCG) are taken from a healthy heart and the relation of two signals is defined. By defining the relation of two signals, it's aimed to establish the ground for the algorithms to be arranged in the future.
Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method

Esophagus segmentation in computed tomography images is challenging due to the complex shape and low contrast of the esophagus. Fully automated segmentation is feasible with recent convolutional neural network approaches, such as U-Net, which reduce variability and increase reproducibility. However, these supervised deep learning methods require radiologists to laboriously interpret and label images, which is time-consuming, at the expense of patient care. We propose an esophagus segmentation method using a U-Net neural network combined with several variations of backbones. We also propose a semiautomatic labeling method with detection and execution components to solve the labeling problem. The detection component identifies the category to which each slice belongs using the bag-of-features method. The edges in each category are clustered using contour moments and their topological levels as features. In the execution component, the assumed esophageal contours are predicted by the clustered model. A convex hull approach and level set algorithm yield the final esophageal contours, which are employed to train the neural network. Several backbones are implemented as the encoder of the U-Net network to extract features. The predictions are then compared with those obtained via manual labeling by a radiologist and the segmentation results generated by the proposed semiautomatic method. The experimental evaluations demonstrate that the utilization of ResneXt50 and InceptionV3 as backbones with U-Net is more effective than that with other backbones. A three-dimensional rendering of the segmented model is performed to exhibit the prediction. The results demonstrate that the proposed method outperforms previously published methods.
Analysis of Ultra-Broadband Phase-Shifters for 5G User Equipment

As antenna arrays are being deployed in both 5G base-stations and user equipment, phase-shifters have received great attention. This work reviews the requirements for phase-shifters in 5G user equipment and presents ultra-broadband phase-shifting solutions capable of complete coverage of 5G millimeter-wave spectrum.
Multi-model Emotion Expression Recognition for Lip Synchronization

Lip synchronization problem is a significant requirement in the multimedia world. The synchronization plan is in charge of guaranteeing that the sound and video streams are synchronized after handling. In recent years, there is a great deal of research, which was conducted on the issue of employing the recognition operation of emotion expressions, in the process of identifying and finding a solution to the problem of synchronization. This paper is based on the concept of recognizing the problem of synchronization in the multi-models audio/video of emotion expressions for non-synchronizing 3D video film stereoscopic. The proposed work is divided into two-stage: the recognition of audio expressions and the recognition of visual information expressions, with four basic emotion expressions: Anger, Happiness, Sadness, and Surprise. Multi-Layer Perceptron Back Propagation network is used for voice classification and depth image classification. Absolute Distance Differences is used for Intensity Frames of 3D Video (Geometric-based Feature) classification. The classification rate for the isolated audio signal is 90%. The classification rate for the geometric-based process of intensity image, plus depth image for different features of 3D video film is 80%. The classification rate for the geometric-based process of intensity image plus depth image for curvelet features of 3D video film is 82%. The recognition rate for the whole proposed system (audio plus video with curvelet features for depth image) is 80%.
Nanowires Based Artificial Synaptic Networks for Building Neuromorphic Artificial Intelligence

Artificial Intelligence is rapidly adapting to the change in magnitude of computing resources available to us. More powerful processing units are being developed everyday and the data-driven Artificial Intelligence is getting better along with them. However, conventional form of Artificial Intelligence still lags far behind in achieving its parent idea of mimicking human intelligence as the conventional architectures do not offer some necessary properties. This is exactly why the need of developing synthetic brain-like synaptic networks which could deliver them arises. Nanotechnology, at present, is the most viable resource at hand to build complex networks with billions of artificial neurons and highly interconnected synapses that are compact enough to be handled and meet energy constraints as well. Researchers have combined nanotechnology and neuromorphic computing to come up with nanowires based artificial synaptic networks that provide promising results in improving neuromorphic artificial intelligence to a great extent. It is perhaps the most important step in developing next generation artificial intelligence. With this paper, we discuss in-depth the nanowires, neuromorphic systems, synaptic plasticity, biological synapses, current computing technologies along with their shortcomings. We also discuss their need and use in building synthetic synaptic networks and try to interpret their need in building neuromorphic artificial intelligence. Furthermore, we discuss technical challenges and psychological barriers in relevant future research that we, as researchers, felt noteworthy.
Simulation of Emergency Modes in Sensorless Control of Switched Reluctance Motor Drives

This article is continuation of the authors' research of sensorless control of drives with a switched electric motor, in which the phase switching positions are determined by comparing the calculated and measured values of the flux linkage. This study is dedicated to the influence of deviations of the settings of the considered algorithm on the possibility of developing anomalous modes in the drive. Under certain conditions, a progressive accumulation of errors appears when determining the switching position. To identify these conditions, a simulation model of the drive was developed in MATLAB - SIMULINK. Simulation showed that the most adverse situation in the operation of the drive arises with certain deviations of the algorithm's tuning parameters from their calculated values and distortions in the shape of the switching line. In some cases, such a combination can lead to a disruption in the sequence of switching on the phases, or even to a sharp increase in the phase current of the motor.
A Highly Adaptable Ultra-Low Weight Wireless Sensor Network for Tracking and Monitoring of Airborne Vertebrates

This paper gives an overview of the BATS project, which aims to track the behavior of bats by developing an ultra-low power wireless sensor network. It is composed of an ultra-low weight sensor node that is attached to the animal and extensive ground infrastructure for tracking the animals as well as remote data download of locally stored sensor data from the tags. The system achieves its low power consumption by adaptively enabling certain functions based on the current situation. The mobile node, as a key component of the system, contains a variety of sensors, including an electrocardiogram (ECG) to allow precise insights in the animals’ behavior while maintaining a low weight of around 1 g depending on the hardware configuration.
Auditory Locomotion Guidance System For Spatial Localization

Head direction has been identified to anticipate trajectory direction during human locomotion, independently of the visual condition. However, experiments so far have explored this phenomenon with visual instructions. To explore head and gaze anticipation without visual influence, we describe a system for instruction and measurement of auditory instructed locomotion. The objective of this work is to describe the system setup and validate its accuracy for scientific investigations in locomotion research. The system is comprised of an auralization server that plays virtual sound sources via wireless headphones. The auralization is fed with head position and orientation angle measured by a Vicon motion tracking system. Communication between the auralization server and the motion tracking host was facilitating the OSC protocol. Auditory instructed locomotion was performed in two visual condition: eyes open and eyes closed for comparison. First, ten sighted participants localized static virtual sound sources at eight positions to confirm the accuracy of the provided virtual sound cues. Afterwards, they listened and actively followed virtual sound sources that were moving along three trajectories: an eight shape, a circle in clockwise direction and a circle in anticlockwise direction. The virtual sound sources could be localized with an accuracy of 0.16 meters in the eyes open condition and 0.12 meters in the eyes closed condition. Participants also were able to follow the moving sound sources.