Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven Method for In-band Network Telemetry Monitoring of Aggregated Traffic

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks.
Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Myoelectric control has gained much attention which translates the human intentions into control commands for exoskeletons. The electromyogram (EMG)-driven musculoskeletal (MSK) model shows prominent performance given its ability to interpret the underlying neuromechanical processes among the MSK system. This model-based scheme contains inherent physiological parameters, e.g., isometric muscle force, tendon slack length, or optimal muscle fiber length, which need to be tailored for each individual via minimizing the differences between the experimental measurement and model estimation. However, the creation of the personalized EMG-driven MSK model through the evolutionary algorithms is time-consuming, hurdling the use of the EMG-driven MSK model in practical scenarios. This article proposes a computationally efficient optimization method to estimate the subject-specific physiological parameters for a wrist MSK model based on the direct collocation (DC) method. By constraining control variables to the experimentally measured EMG signals and introducing the physiological parameters into control variables, fast optimization is achieved by identifying the discretized parameters at each grid simultaneously. Experimental evaluations on 12 healthy subjects are performed. Results demonstrate that the proposed method outperforms the baseline optimization algorithms used in the literature, including genetic algorithm, simulated annealing algorithm (SA), and particle swarm optimization (PSO) algorithm. The proposed DC method shows the possibility to alleviate the costly optimization procedure and facilitate the use of the MSK model in practical applications.
Automated Major Depressive Disorder (AMDD) Detection using Audio-Visual Feature Discriptor and CNN

The severe forms of depression, a widespread mental health issue, can result in self-harm or suicide. Clinical diagnosis and early intervention of depression are greatly aided by an automatic depression detection system. In this study, we suggest a novel automated major depressive disorder (AMDD) detection technique that makes use of audio-visual parameters from patients. PCA is used as dimension reduction for feature selection. After these two types of signals' effective characteristics were extracted and selected, we trained classifiers on each modality. SVM is taken into account for classification. According to our research, fusing features from different data modalities performs better than using just one, and combining audio-visual data modalities' features results in the highest classification accuracy. Comparing the accuracy of the feature selection approach to other methods, PCA significantly increased the accuracy. Also, SVM gives the best in class accuracy of 99.15% on DAIC-WOZ dataset.
Automatic endometrial segmentation in ultrasound images using deep learning

Endometrial segmentation plays a vital role in the computerized evaluation of uterine ultrasonic images. Accurate segmentation of endometrial regions may improve the accuracy and efficiency of diagnosis. Recent studies have been focused on the employment of deep learning in medical image segmentation. In this study, we compared six models, including five convolutional neural networks with different network architectures (UNet, Segnet) and backbones (Resnet50, Vanilla CNN, VGG16) for the segmentation of endometrium, and one model called deep dual-resolution networks (DDRNets). The training and test datasets were composed of 840 and 210 images from 302 and 68 cases, respectively. Through validation, DRRNets demonstrated the best performance for endometrial segmentation with an average Dice coefficient (DSC) of 0.895.
Evaluation of Different Microarchitectures for Energy-Efficient RISC-V Cores

The increase in Internet of Things $(\text{IoT})$ applications has triggered the development of energy-efficient embedded SoCs that can utilize limited energy sources. Relatively simple general-purpose processor cores are a vital component of SoCs, and op-timizing the power consumption, performance, and area is a key issue in the design of $\text{SoCs}$. Therefore, this study quantitatively compared the power, performance, and area of several 32-bit RISC-V cores with different microarchitectures. The simulation evaluations were performed for each processor with different pipeline configurations, with and without a multiplier and divider. The benchmark execution performance of the processors in a register transfer level (RTL) design, as well as the estimated power consumption and area based on logic synthesis and place-and-route using various CMOS process technologies are presented. Based on the results, we provided a brief guideline for the selection of microarchitectures for energy-efficient embedded SoCs.
Fuel Cell Stack Model for Real-Time Simulation of Grid-Connected Applications

Fuel cell stacks coupled with electrolyzers and hydrogen storage sites can be a promising category of distributed energy resources for both grid-connected and stand-alone power systems. However, because of high costs, at-scale hardware testing of fuel cell stacks for grid-connected applications is not economically viable at present. A model-based system that can accurately captures the steady-state and dynamic response of fuel cell stacks over long time periods (hours), is needed. This paper demonstrate a real-time electromagnetic transient model of a megawatt-scale, grid-connected proton exchange membrane fuel cell stack, coupled with a mass-based hydrogen storage system. This model can emulate the electrical steady-state and dynamic response of grid-connected fuel cell stacks. We validate the model using the response of commercial hardware fuel cell stacks and analytical models in the literature – using a digital real-time simulator (RSCAD). The proposed real-time model is then used to simulate cases spanning different time horizons and to design controller-hardware-in-the-loop experiments to evaluate controllers for hydrogen stations.
Visual system for oil sampling robot based on YOLO v5 and OpenCV model

The aging problem of transformer oil in Ultra-High Voltage (UHV) substation is crucial in long-time operation. The transformer oil can be removed and tested regularly to avoid the problem of power system breakdown. This paper proposes a visual system for oil sampling robot based on YOLO v5 and OpenCV model under the specific environment of substation. Firstly, YOLO v5 algorithm is used to identify and locate the object in the local view. OpenCV is used to estimate the distance, size and pose of the object by using modules such as morphological operation and minimum enclosing rectangle. Finally, the positioning information is transmitted to control the movement of the manipulator. Experiments show that the proposed method can recognize, classify and locate objects. The UR5E manipulator and end-effector can be used to grasp and control the oil valve accurately. The average time of one full grasp can reach 2.47s, and the success rate of grasp can reach 98%.
Joint Placement and Passive Beamforming Design for Aerial Reconfigurable Intelligent Surface Enhanced NOMA Systems

This paper studies a new framework of aerial reconfigurable intelligent surface (ARIS) assisted non-orthogonal multiple access (NOMA) for wireless communication systems. The base station transmits superimposed signals to multiple users with different channel gains through ARIS which can be deployed flexible. The placement of the unmanned aerial vehicle (UAV) and the passive beamforming of the ARIS are jointly optimized to maximize the sum rate. The non-convex problem is decomposed into two subproblems and solved alternately through the successive convex approximation (SCA). The numerical results show that our proposed NOMA-ARIS framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.
Infrastructure and Security for Supporting Smart City: A Systematic Literature Review

Today's Smart City is increasingly encouraged to be built. The ease of public access to information makes smart cities a target for the development of many cities. Smart City itself cannot be separated from the term internet of things which connects various objects in the same network. The application of IoT in a city presents a new term called Smart City. With the ease of living in a city that carries IoT and a Smart City, it is hoped that the implementation of this IoT can be carried out throughout the city. However, many things need to be prepared and of course, in terms of the security of the existing devices. In addition to the readiness of human resources, supporting infrastructure must also be designed very carefully, especially if a city does not yet have adequate internet access for IoT implementation. Through this research, we found that building a Smart City IoT requires infrastructure, at least a good internet network, various sensors, data storage, and applications. Security and privacy in a smart city are also as important. Many common vulnerabilities by OWASP, such as Broken access control, Security misconfigurations, Identification and Authentication Failures, Insecure Design, Injection attacks, Cryptographic Failures, Vulnerable and Outdated Components, Software and Data Integrity Failures, Security Logging and Monitoring Failures, and Server-Side Request Forgery (SSRF) are still underestimated, people also need be aware of privacy since the apps collect your data constantly.
SmallRhex: A Fast and Highly-Mobile Hexapod Robot

Relying on the unique C-leg structure, RHex robots have good mobility and traffic ability when having relatively simple structures. Based on the existing RHex robots, taking into account the performance and cost, this design develops a small hexapod robot, named smallRhex, with low cost but strong performance. This paper mainly introduces the mechanical structure, robot gaits, simulation, and physical performance tests of smallRhex robots. The hardware is mainly based on the raspberry pie microcomputer and Robomaster motor and accessories. The high power density meets the dual requirements of performance and cost. Then cooperates with 3D printing and sheet metal and machined parts processing to complete the design of the mechanical structure and assembly of the robot. At the control level, raspberry pie directly controls the movement of 6 motors. The gait design includes basic motion gait, which includes straight walking and turning, and other gaits of complex motion: stair climbing, jumping, and high obstacle climbing. They are simulated in Webots. Finally, the performance test and the gait test of the robot are carried out. Then the gait design is further optimized, and the basic design of smallRhex robot is completed.
Dynamic Parameters Identification for Manipulators Considering Physical Feasibility and Nonlinear Friction

The dynamic modeling and parameters identifying of a manipulator is extremely important for its control performance and interaction capability. The nonlinear viscous friction is always neglected in parameters identification which usually uses a linear model, particularly, it is easy to cause that the parameters lose physical feasibility. A dynamic parameters identification method considering nonlinear viscous friction is proposed which can ensure the physical feasibility at the same time. Then an identification framework and an experiment platform are built to validate this new method. The results of verification tests show the method proposed has better results in verification trajectories whose relative error is 7.266% which is lower than classic method with 10.217%.
Research Progress of Smart Products for the Elderly: A Bibliometric Analysis

In the context of an aging society and the development of intelligent technology, smart products are increasingly used by the elderly in everyday life. This study reviewed the current research status and hotspots of smart products for the elderly by visually analyzing 377 publications from the core collection database of the Web of Science. The annual number of articles published, the article production regions, and the hot keywords of the selected publications are analyzed in this study. And the research hotspots in terms of user research, technology development, and application scenarios are summarized. In addition, possible research directions are proposed to provide a reference for developing smart elderly products in the future.
Supporting Deaf and Hard-of-Hearing People to Watch Sports by Detecting Excitement using Mobile and Wearable Devices

In this paper, we propose an application for supporting deaf and hard-of-hearing (DHH) people to watch sports by detecting excitement. The application detects a crowd cheering sound with a machine learning technique and notifies it using mobile and wearable devices. We implemented a prototype of our proposed application using a commercially available smartphone and a smartwatch. We then conducted two experiments to evaluate the accuracy of excitement detection. The results demonstrated that our trained model could detect the level of excitement with a practical accuracy, showing the feasibility of our approach in supporting for DHH people to enjoy sport events.
10GHz Low Current Mismatch Pseudo-Differential Charge Pump Phase-Locked Loop Circuit Design

Design a 10GHz low current mismatch, low jitter fast locking charge pump phase-locked loop based on UMC 28nm HLP CMOS process. This design reduces the charge pump current mismatch by using a negative feedback loop method; the voltage-controlled oscillator uses a pseudo-differential structure to improve the frequency tuning range and proposes a low voltage high matching current mirror applied to the tail current switching control array to improve the robustness of the voltage controlled oscillator in a low voltage environment and reduce the phase noise of the voltage-controlled oscillator. The simulation results show that: the charge pump charge/discharge current mismatch is less than 2%; the voltage-controlled oscillator frequency tuning range is from 9.6 to 10. 4GHz, and the phase noise is -86.7dBc/Hz at the output frequency of 1MHz; the phase-locked loop lock time is 0. 75us, and the jitter is 2ps.
Movie Oriented Positive Negative Emotion Classification from EEG Signal using Wavelet transformation and Machine learning Approaches

Electroencephalography (EEG) sensor plays an important role in developing brain-computer interfaces (BCI) to enhance human-computer interaction (HCI). Nowadays, various types of research works are performed to develop EEG-based HCI systems for controlling and monitoring systems. However, researchers are still facing challenges in developing this system due to noise from the physiological and internal and external artefacts. This study proposed a method to find useful electrodes and extract potential information from the brain nerves for the classification of positive or negative emotions. The collected emotion's EEG signal is recorded using 14 electrodes from the 30-younger people. Two movies were used for positive and negative emotions. In the proposed method, we first extracted the five bands wavelet transform from the EEG and then calculated the standard deviation (SD), average power (AVP) and mean absolute value (MAV) of the five bands wavelet information. Finally, we applied an extra tree classifier (ETC), random forest (RF), and support vector machine (SVM) to classify the emotion based on the feature vector. Among three classifiers ETC achieved higher performance accuracy in F3, FC5, T8, FC6, F8, and AF4 electrodes. This indicates that the F3, FC5, T8, FC6, F8, and AF4 electrodes carry potential information in positive-negative emotion classification.
Rapid Sorting Robot System for E-commerce Warehouse

Aiming at the problem of slow sorting speed of sorting robots in warehousing environment, this paper proposes a new rapid sorting robot system for e-commerce warehousing. The main purpose of the design of the system is to improve the speed of the sorting robots, at the same time to ensure a high accuracy of object detection and pose estimation in the process of sorting. The system is mainly composed of a 6-DOF manipulator and an RGB-D camera. The self-developed control software is used to remotely control and monitor the system, and a suction cup is selected as the end-effector. The development of the system further improves the sorting speed of the sorting robot system, and the average sorting speed reaches 612.7 pieces/hour under the condition that the average sorting distance is 0.9m. The system provides technical reference for the improvement of automation level of warehousing and logistics industry.
Deep Reinforcement Learning Approach Using Customized Technical Indicators for A Pre-emerging Market: A Case Study of Vietnamese Stock Market

The Vietnamese stock market is a challenge for applying algorithmic trading. However, the advance of Machine Learning, especially Reinforcement Learning, has provided a new opportunity to develop better trading models. In this work, we proposed an ensemble strategy, namely S-A-P, combining three deep reinforcement learning models (i.e., SAC, PPO, and A2C). The best model at each quarter is used for trading in the incoming quarter based on the Sharpe ratio. Moreover, the list of technical indicators is also proposed to represent the variation in this market. Our approach shows better performance than the baseline and VN30INDEX in both profits (55% in cumulative return) and risk management (0.77 in Sharpe ratio). Additionally, this approach can perform appropriately during two high-turbulence periods, which the baseline cannot detect. The extension of this work may consider a novel Machine Learning approach for representing the stock market and a different metric for building ensemble strategy.
Prediction of hepatocellular carcinoma using a machine learning algorithm

The prevention of hepatocellular carcinoma (HCC), which is rated third for causing death due to cancer in the world, and the selection of more effective treatment have necessitated the development of HCC diagnosis and prediction systems using artificial intelligence. The presented paper examines the possibility of applying machine learning algorithms to predict liver cancer. Machine learning methods such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) are used to predict HCC. The HCC Dataset taken from the website Kaggle (Kaggle.com) is referenced for the realization of prediction. This research uses the libraries scikit- learn, Pandas, NumPy, etc. in the Jupiter programming environment to conduct experiments. The results of the experiments are compared, and the RF classifier is estimated to perform the highest result. Referring to this fact, the importance of using the RF method in building an initial HCC diagnosis and prognosis system is justified.
A 10-bit 50-MS/s SAR ADC with Binary-Scaled Recombination Weighting Capacitor Array

A 10-bit 50MS/s successive-approximation-register (SAR) analog-to-digital converter (ADC) in TSMC 65nm CMOS process with a 1.2-V supply voltage. The DAC capacitor array combines the split monotone control scheme with the monotone control scheme to reduce the offset of the comparator due to the variation of the common-mode input voltage. The DAC structure combined with the binary redundancy recombination method can introduce redundancy to calibrate the errors in the quantization process, reduce the overall quantization time, improve the quantization speed and accuracy. Simulation results show that the SAR ADC achieves 53.36 SNDR and 8.57b ENOB at the input signal of 24.21875 MHz and the sampling rate of 50 MS/s, and consumes 2.72 mW of power.
A 13-Bit 150-Ms/s Hybrid SARADC Based on VTC on the Fine Stage

A 13bit150Ms/s hybrid ADC is proposed in this paper. The first stage is designed to be a 10-bit SARADC to coarsely quantize the input level, and then the voltage residual is transferred to the time domain through a current adjustable VTC, and then TDC is used for the remaining 3bits quantify. In order to reduce the mismatch caused by the capacitor array, the DAC is divided into a 7-b BDAC controlled by the high-order thermometer code and a 3-b SDAC controlled by the low-order binary code, helping to improve the capacitor turnover speed and utilization rate, mismatch and so on, which means that the subsequent quantization can be completed without re-sampling the residual with another set of capacitors. The characteristic of good linearity helps to improve the quantization accuracy. In the T40 CMOS process, simulations show that the ADC achieves 68. 3dB SFDR and 9. 2mW power consumption.
A Low-Power Cardiac Signal Processor for Atrial Fibrillation Detection with a Sensitivity of 93.02%

The paper presents a cardiac signal processor for power-efficient atrial fibrillation (AF) detection. The processor detects R-wave and AF based on Pan and Tompkins algorithm and Kolmogorov-Smirnov test (KS-test), respectively. Validated by MIT-BIH arrhythmia database, the AF detection sensitivity (SE) and specificity (SP) of 93.02% and 80.00% are achieved, respectively. The system operates at a clock frequency of 256 Hz significantly reducing the dynamic power, which makes it suitable for implantable cardiac monitor applications. The processor is implemented in a 180-nm CMOS process. It occupies an area of 1.45 mm2 and consumes 11.758 $\mu$W at a 1.8-V supply.
EEG Feature in Amputee with Five Projected Fingers: A Comparison Study

The somatosensory ERP provides a tool to understand the tactile sensation mechanism for the projected finger. A total of four subjects (1 amputee & 3 able-bodied) were recruited. The tactile sensation is evoked via TENS. Through applying the stimulation electrodes on the target fingers, simultaneously the EEG data was recorded. Two major findings are concluded as follows. First, the somatosensory ERP of the early-stage in three groups are similar, and then it could infer that the projected finger may have a similar delivery pathway as the natural finger. Second, since three groups show similar cortical processing characteristics, it could infer that the projected finger evoke similar somatosensory neuro information as the natural finger. This tactile sensation mechanism study shows the uniformity between the natural finger and the natural finger.