Miracle-dz/UniFAR-FTUs huggingface dataset:
Skeleton Based Graph Convolutional Network Method for Action Recognition in Sports: A Review

Human action recognition has gained significant attention and skeleton-based methods has become a hot research topic owning to its resilience to environmental variations. Graph Convolutional Networks (GCN) have improved action recog-nition accuracy by operating graph convolution on graph-structured data, surpassing conventional models like CNN and RNN. However, current works primarily relies on CNN and RNN for sport action recognition, with limited explo-ration of skeleton-based GCN methods.This paper provides a comprehensive review of the development of skeleton-based GCN methods for action recognition. Additionally, we evaluate these methods on a fine-grained sport dataset and identify three crucial considerations for skeleton-based action recognition: incorporating sport-specific local actions, utilizing skeleton angles, and designing multi-scale network structures. Furthermore, we highlight challenges and oppor-tunities in sport action recognition and hope our work would inspire more excellent research works in this area.
Segment anything model for subgrade distress detection with GPR

The detection of distresses with ground-penetrating radar (GPR) images is a highly challenging task due to their complex structure, blurred boundaries, and varying scales. The Segment Anything Model (SAM) has demonstrated impressive performance in natural image segmentation tasks with its zero-shot segmentation capability and high efficiency, which effectively reduces annotation time. However, the applicability of SAM for GPR image analysis remains till now. To this end, we construct a GPR image dataset comprising 395 samples with segmention, which are jointly confirmed and annotated by multiple domain experts. We comprehensively evaluate the performance of various mainstream deep learning image segmentation models and different SAM models on this dataset, and analyze the results in detail. Our experiments confirm that SAM's performance in detecting underground radar distresses is limited or even completely inadequate. These findings demonstrate that SAM's current zero-shot segmentation capability is insufficient for direct application in subgrade distress with GPR data.
Comparison of Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram Techniques to Classify Industrial Machine Sound

The utilization of sound recognition to detect the faulty machines are getting the interest to be explored with the technology advancement in IR 4.0. In this paper, we investigated the performance of two acoustic features in representing the sound of a fan in industrial machines, that is acquired from the MIMII dataset. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram techniques are explored and compared in this paper. The classification is performed using Convolutional Neural Network (CNN) with the input of images from both techniques. The dataset of the input images are the audio files of 6dB fans sound in industrial machine. The results suggest that Mel Spectrogram is an effective feature for faulty machine sound recognition when used with CNN model.
Reputation and the league standing effect: the case of a split season in minor league baseball

ABSTRACT Split season league design resets standings at the midpoint of the season, thus allowing for two periods in which a team can potentially achieve success in a single season. This context allows us to test both the reputation of the first half winner and the league standing effect on demand. Examination of game-level data from the 2010 Southern League reveals fans are unaffected by measures of both team quality and league standing in the second half of the season. On the other hand, the first half winners saw an 11% increase in attendance as a percent of stadium capacity, suggesting that in the second half of the season winners matter more than winning.
The effect of noise injection method on DRL-based controller robustness of human gait model

Human gait models in the dynamic environment have been studied to understand the fundamental neuronal control system of the human. Previously, imitation learning methods with reinforcement learning could successfully reproduce human gait motions using skeletal models. However, imitation learning-based controllers could have lacks of the ability to flexibly adapt to a wide range of state spatial scenarios such as instabilities and falling. Recently, noise injection methods have been introduced to increase the flexibility and robustness of the controller for robot systems. Therefore, the objectives of this study were to train human gait controllers with the noise injection method and to analyze the effect of noise injections on the balance recovery against external forces. A three-dimensional skeletal human gait model and two gait controllers with and without noise injections were developed. The robustness of the gait controllers against external forces was tested via forward dynamics-based gait simulation. The number of simulations without falling against external forces increased when the gait controller was trained with the noise injection method. In this study, the noise injection method during imitation learning could enhance the robustness and stability of the human gait controller.
Fusion of Spectral and Connectivity Features to Detect Depressive Disorder using EEG Signals

Depressive disorder (DD) has a high morbidity and death rate, contributing to suicide, the incidence and unfavorable effects of medical disease, and drug addiction. DD necessitates long-term monitoring, changes in symptom presentations, and subjective evaluation, which opens the door to analyze brain signals using modern neuro-signal processing and machine learning techniques. To detect depression in an automatic and noninvasive fashion, we have chosen electroencephalogram (EEG) signals from 199 DD adults and 95 healthy control (HC) adults. We have used a machine learning classification framework which uses spectral and functional connectivity features and with their feature-level fusion. Compared to the individual features, the fusion-based feature outperforms (98.06 ± 1.19 % accuracy) at a significant p-value (<0.05) for distinguishing DD from HC. Artificial neural network (ANN) performs better than different kernel-based support vector machines (SVM). In frequency band-wise analysis, using our fusion method, the delta frequency band (1-4 Hz) demonstrates the most discriminant features. Using connectivity networks and their strength, our findings reveal that the left posterior and right anterior brain regions are the most linked in depression. The improved performance signifies the clinical relevance of the work providing reliable assistance for depression detection and has the potential to find fusion-based connectivity patterns of depression.
Mitigating Non-ideality Issues of Analog Computing-In-Memory in DNN-based designs

Analog Computing-in-memory (ACIM) has emerged as a promising approach to enhance energy efficiency and throughput in matrix-vector multiplications (MVMs) for deep neural network (DNN) applications. Albeit low-power features and high parallelism of ACIM, the non-ideality arising from process variation hampers classification accuracy. To address this issue, conventional circuit-level methods augment more circuit resources, which sacrifices energy and area efficiency. Alternatively, various non-ideality-aware frameworks have been proposed to leverage the redundancies and error tolerance inherent in DNN-based designs in recent years; i.e., mitigating the impact of non-ideality at both algorithm and architecture levels. The algorithmic innovations improve the accuracy while maintaining its computational efficiency. Moreover, at the architecture level, a sequential but error-free near-memory-computing (NMC) mode has been introduced in ACIM for flexible adjustment of computing reliability based on different application scenarios. This paper presents the design challenges and foundational concepts of various non-ideality-aware frameworks in a comprehensive way. Besides, the representative related works are reviewed and summarized. Finally, we conclude the paper and point out future works.
A Survey on Doppler Mitigation Approaches in Satellite Communications

Satellite communication has attracted considerable attention as a future communication technology due to its advantages such as wide coverage, which is expected to bring new opportunities to improve various services in the Internet of Things (IoT). Low Earth orbit (LEO) satellites have been adopted as a core technology for next-generation communications, beyond 5G (B5G) and 6G, due to their low latency and high service density. However, there exist problems that arise because of technical differences with existing IoT services and Doppler shift caused by fast movement. In this paper, we review the existing studies on innovative approaches to solve the problems in satellite communication.
Best State Estimate for the Phase Angles of Busbars in Power Systems via Circuit Modeled with DC Load Flow

State estimate is a digital processing method that gives many of the central control and dispatch operations in a power system a real-time data basis. It is essential to do state estimates to get rid of probable errors and boost system reliability. State variables, which are utilized to further calculate other power system characteristics, are necessary to determine the state of a system. Based on measurements, or input, such as active power, reactive power, voltage, and current magnitude collected from the system, these state variables are given a value. Voltage and phase angle are often utilized as state variables. The method used for state estimation will, however, affect the choice of state variables. In addition to input parameters, network data such as topology and impedances are also necessary. The measurements are made with a meter that has a predetermined full-scale reading that can be used as a starting point for calculations. When estimating the state, there may be errors that cause the reading from a meter to differ from the genuine value. Error in measurement is taken good care of here. Where measurement error, measurement and estimated value for the meter reading is considered. The best approximation of the voltage angles and power flow is obtained using it. The Weighted Least Squares Method is a widely used method for estimating system state.
Exploring Social Identity and Self-Categorization within Niche Networks: The Effect of Group Commitment on Sexual Identity Commitment among Men who have Sex with Men

Niche networks for sexual minorities are becoming increasingly popular and have garnered significant scholarly attention. Adding to the ongoing conversation regarding the uses and effects of niche networks, we explore the motivations for and impact of engaging with GPS-based hookup applications on sexual identity and group commitment. Using a cross-sectional survey of a nationwide sample ( N = 102) and social identity theory (Tajfel, 1978), we investigate the effect of group commitment and motivations on daily hookup application use and sexual identity commitment among men seeking men. Findings demonstrate that group commit - ment and sexual identity commitment are positively correlated among GPS-based hookup application users, and that group commitment moderates the relationship between daily application use and sexual identity commitment. Further, the asso - ciation between group commitment and sexual identity commitment is moderated by users’ motivations. Specifically, long-term motivations for using GPS-based hookup applications strengthens the association between group commitment and sexual identity commitment among app users. A significant association existed between group commitment and sexual identity commitment for participants whose primary motivation was long-term, but no association existed for partic - ipants whose primary motivation was short-term. This study extends niche net - work research and social identity theory by situating hookup applications as a social context for experiencing cognitive connections with group members and exploring sexual identity.
Trust-minimized Integration of Third-Party Intellectual Property Cores

Our society relies on smart digital systems that fulfil not only performance and energy-efficiency demands, but also address security and privacy goals. At the same time, vendors must deal with stringent time-to-market challenges and tight budget constraints. This leads to the use and integration of intellectual property (IP) cores. Vulnerabilities in these cores may compromise the system’s security. This paper presents a novel architecture concept that allows for a secure integration of IP cores with only minimal security assumptions. This is achieved by a hardware component called trusted communication unit (TCU) that isolates all components on the chip. A post-routing implementation shows only small security costs in terms of TCU area overhead of around 13%.
Towards the Extension of FPG-AI Toolflow to RNN Deployment on FPGAs for On-board Satellite Applications

In recent years, research in the space community has shown a growing interest in the deployment of Deep Neural Networks on-board satellites, mostly driven by system miniaturization and commercial competition. Field Programmable Gate Arrays (FPGAs) have proven to be competitive accelerators for these algorithms and works proposing methods for automating the design on these devices have acquired relevance. The common purpose is to enable a wide range of users without specific skills to accelerate DNN models on FPGA with reduced development times. This paper describes the process of improving FPG-AI, a novel technology-independent toolflow that efficiently automates DNN deployment on FPGAs belonging to different vendors and with desperate resource budgets. The proposed techniques are meant to be a first step towards the extension of FPG-AI to Recurrent Neural Networks (RNNs), a subset of DNNs designed for processing sequences of data. As a first step, we discuss model compression strategies for enabling energy-efficient hardware acceleration of Gated Recurrent Units (GRUs), one of the most popular RNN layers. The article then presents a GRU-based hardware accelerator designed to be compliant with the identified quantization algorithm. The architecture features an optimized dataflow to enable hardware reuse over time and maximize resource efficiency without affecting the performance of the architecture. Preliminary results are reported for a state-of-the-art RNN-based model for Fault Detection Isolation and Recovery (FDIR) onboard the satellite to evaluate the proposed methods. A comparative analysis is also performed with the NVIDIA Jetson Nano Embedded GPU to characterize our accelerator with respect to a state-of-the-art platform for RNN inference.
A Digital LDO with Adaptive Loop Control and Reset-Voltage Optimization for Comparator

This paper presents the implementation of digitally designed low dropout voltage regulator (DLDO) with coarse-fine control and dynamic comparator with optimized reset voltage. The coarse-fine loops based on digital logic adjust the thermal-weighted passgates with automatic loop control to reduce the voltage ripple at the output voltage, and the proposed comparator makes possible to reduce the output error with less than 1mV input voltage difference. With helps of proposed design techniques, the DLDO operates with 200MHz external clock source, and the ripple voltage at the output is 0.49mV. Also, the latency caused by the comparator is reduced within 1 clock cycle, and the setting time when the load current changes from 1mA to 10mA is 904ns. The proposed DLDO is designed using 180nm CMOS process, and the power efficiency is 95.2%.
The METASAT Hardware Platform: A High-Performance Multicore, AI SIMD and GPU RISC-V Platform for On-board Processing

The METASAT Horizon Europe project which is funded by the European Commission and started in January 2023, will enable model-based design methodologies in order to manage the complexity of upcoming hardware and software for space on-board processing. As a representative high performance platform for on-board processing, METASAT will design a multi-core platform featuring accelerators prototyped on an FPGA. This includes both an AI SIMD accelerator tightly integrated with the CPU, as well as a GPU. All hardware components of the METASAT platform will be open source and based on the RISC-V open ISA. In this paper, we provide an overview of the platform architecture as well as preliminary implementation decisions, current development status and early results.
Scalable Compact Model for High-frequency GaN-HEMTs

This paper presents a compact model for GaN-HEMT high-frequency operations that is scalable over dc bias, geometry, and doping concentrations. Specifically, a "surface-potential-based" compact model for (intrinsic) GaN-HEMTs is extended to model the "sub-surface" (substrate) and access (extrinsic) regions of the GaN-HEMT for capturing its nonlinear current/charge behaviors that are important at high-frequency operations. A quiescent-bias (Q-point) scalable RF model is not easily achievable by conventional sub-circuit approaches in which different R/C element values need to be extracted at different Q-points. Preliminary results show that the developed RF model is scalable for different Q-points as well as gate and gate-source/drain lengths and substrate doping concentrations without refitting the model. The scalable GaN-HEMT RF model is validated in comparison with S-parameters from numerical simulations and available measurement data. The proposed concepts and methodology will lead to new approaches to RF compact modeling, and it can be extended to Si-MOSFETs RF modeling as well.
Espresso-like Iterative Algorithm for Three-Level Logic Minimization

Minimization is an important part of any logic synthesis algorithm. Espresso algorithm can be used as a first step for global two-level minimization. Then factorization is performed to obtain multi-level structure and various local optimization algorithms are applied. This paper presents a new algorithm called Xpresso to replace Espresso in described synthesis flow. It works with three-level logic circuit structure in a form of DNF with an extra layer of XORs. Such structure allows more compact representation than pure DNF while keeping global minimization feasible. Proposed minimization algorithm consists of three iterative Espresso-like steps: expand, irredundant and reduce, tweaked to work with three-level structure. Computational complexity stays mostly at original Espresso's level with most expensive part of expand being transferred to GPU. Experimental results are provided for combinational logic circuits from LGSynth'91 benchmark. Xpresso's outputs are compared to those of Espresso by operation count and a few other metrics.
MINSU: Precision Quantity Counter with DNN-based Volume Estimation

This paper introduces MINSU, the first DNN (Deep Neural Network) based quantity counter specifically designed for smart factories. The innovative DNN design of MINSU positions it as a suitable solution for the emerging trend of fully automated, man-less smart factories. By periodically updating inventory status using live images, MINSU eliminates the need for manual, repetitive inventory-checking tasks. This system is easily adaptable to existing industrial environments, as it does not require the installation of new racks or modifications to the current factory floor plan. MINSU is designed to seamlessly navigate within the existing factory setup, achieving a level of performance that surpasses manual inventory counting methods. We have tested MINSU in a lab environment replicating a small-sized factory, using actual bolts as the target inventory. Our experiments demonstrated an accuracy of 84.33%, showcasing the potential for real-world applications in modern smart factories.
Research on single-dual power supply scheme based on Prim greedy algorithm planning and reliability analysis

Distribution security and stability are very important for customers' daily electricity consumption. In this paper, the method of planning the main supply line of 10kV distribution network with 100 customer transformers and 2 power substations by using one-dimensional linear regression is presented for single-supply and dual-supply scenarios, and the method of planning the branch lines of distribution network with 50 customer transformers by using P rim’s greedy algorithm is introduced. The main power supply line of two power sources is connected with a liaison line for a given total investment or a given lower reliability limit, and the main power supply line structure in the form of a double-loop network can significantly improve the reliability of customer transformers.
Spiking Neural Network Based Text Summarization System

Due to information overload, ATS(Automatic Text Summarization) is becoming increasingly important. However, nowadays DNN(Deep Neural Network) and pre-trained language models widely used in ATS cannot fully mimic the operating mechanism of neurons in the human brain. In this paper, we introduce SNN(Spiking Neural Network) into ATS system due to the high biological rationality, low power consumption, and high robustness of SNN. A new extractive summarization model called BERT(Bidirectional Encoder Representation from Transformers) + LSNN(Long short-term memory Spiking Neural Network) is proposed and a set of experiments on CNN/Daily Mail are implemented. Compared with the existing BERT + LSTM(Long Short-Term Memory) model, BERT + LSNN not only improves performance, but also verifies SNN's advantages of low power consumption and high robustness. This work is very promising to expand related research in text summarization.
Comparative Study of Different Regression Models for Estimating Lithium Ion Battery Pack Capacity

In the realm of battery health monitoring, machine learning techniques have gained widespread adoption. However, previous research mandates intricate data preprocessing steps for feature extraction, introducing complexity when integrating them into battery management systems. In this study, we conduct a comparative examination of diverse regression-based machine learning methods for estimating battery capacity. Our regression model relies exclusively on signals like current, voltage, and temperature, readily accessible during regular battery operation. Consequently, these raw data can be directly used as input of the trained model, significantly reducing computational overhead. Multiple error assessment metrics are employed to validate the study's objectives. We partitioned the dataset, allocating 80% for training and reserving the remaining 20% for prediction. Additionally, we evaluated the precision of various regression models, conducting a comparative analysis.
A Framework for Learned Approximate Query Processing for Tabular Data with Trajectory

Approximate query processing has been well established for enhancing performance of aggregation queries on ever-increasing big data by statistically equivalent approximations. Recent popularity of mobile devices creates tremendous spatio-temporal data that require different treatment than relational ones. Among spatio-temporal data, we focus on trajectories in a tabular form and analyzes the problem, its requirements, and suggest a general-purpose framework for learned approximate query processing by providing a common encoding/embedding layer for embracing diverse state-of-the-art ML models, on top of which resides a probabilistic circuit for efficiency and efficacy with error bounds.