Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Dataflow for Convolutional Generative Models

Convolutional generative models have achieved significant success in the field of artificial intelligence in recent years. These models typically rely on numerous convolution and transposed convolution operators, both of which are highly computationally intensive but present distinct computation patterns. As a result, accelerating the convolutional generative models can be challenging, particularly when the transposed convolution is likely to become performance bottleneck. In this paper, we propose a dataflow for overcoming the challenge and effectively accelerating both computation-hungry operators of convolutional generative models in the unified architecture. Additionally, we have devised specific tiling schemes to flexibly map both convolution and transposed convolution and fully reuse hardware resources. To demonstrate the effectiveness of our approach, we implemented DCGAN and ST on the Xilinx ZCU102 platform. Our experiments show that the proposed dataflow can reduce memory cost by up to 36%, achieving comparable performance for both convolution and transposed convolution.
Effect of Temperature on Main Partial Discharges Phenomena Under DC Voltage Stress

This work is aimed at characterizing the main partial discharges phenomena under HVDC stress and with the presence of thermal gradient. Specifically, the increase in discharge activity without changes in applied voltage is investigated. Even the supply and disconnection phases are excluded from the analyzed acquisition range. In this way, any variation in discharge activity can be attributed only to thermal effects. A setup consisting of a pair of specimens connected in series and supplied with a voltage of 20 kV DC has been used for the tests. One specimen is defect-free and immersed in a tank containing silicone oil. The other is designed to give rise to certain discharge phenomenon. Three tests were carried out using specimens for internal, surface, and corona discharges. For each test, two acquisitions, lasting 30 minutes, have been made. One with both specimens at room temperature and another with the healthy specimen heated through a resistive element in order to obtain a discrete thermal gradient. Thus, a total of 6 acquisitions have been made and analyzed. The results show that for all three specimens the discharge activity undergoes a significant increase in the transition from the room-temperature condition to the thermal gradient condition. The increase in the amount of detected discharge is greater in tests with corona and surface specimens. In general, an increase in average discharge amplitude is also observed. The observed changes can be attributed to a different distribution of the applied voltage between the two specimens due to changes in the conductivity of the defect-free specimen caused by heating. This behavior is also found in variations in the electric field profile in HVDC applications due to the presence of heat sources that generate thermal gradients. For example, load currents for cables.
Visual-Based Localization for Autonomous Vehicles in Simulated Environments

Autonomous vehicles require constant information from the environment to safely navigate in urban environments. Multiple systems could be used to increase the certainty and precision of the vehicle position, like satellite, inertial and visual systems. Understanding where the vehicle is at any instance supports the autonomous navigation process. This research uses a simulator to estimate the vehicle trajectory by studying the visual odometry with images taken from a camera installed on the simulated vehicle.
Adaptive Droop Control to Reduce Steady-State Power Imbalances in DC Microgrids

DC Microgrids (DCMGs) are gaining significant attention in the field of power electronics due to their superior reliability, efficiency, and adaptability in comparison to AC microgrids. Several types of control are employed in DCMGs, with droop control emerging as the predominant choice. However, the mismatch of line impedances leads to power-sharing errors that are not fixed by the conventional droop approach. This paper presents a novel adaptive droop control to address the steady-state power-sharing imbalance among grid-forming converters in DCMGs. The DCMG under examination comprises two battery banks that form the energy storage system (ESS) serving as grid-forming units, alongside distributed generation (DG) units, and loads. These systems are connected to a common bus through DC-DC converters. Based on the proposed technique, an operational procedure is delineated to acquire real-time data based on the existing line impedance mismatches. The proposed technique considers only the output power of the elements to adjust the droop coefficient, thus mitigating the steady-state power imbalances among the ESS units. A section conducting an in-depth analysis of the method under different droop gain adjustments is also presented. Simulation results are presented to showcase the system's operation under different operation scenarios. The findings indicate the feasibility and effectiveness of the proposed approach.
Active Loss Balancing Scheme with Decision Chart for 3L-ANPC-VSCs with IGBTs for Bidirectional Energy Flow Operation

An active loss balancing (ALB) scheme for the three-level active neutral point clamped voltage source converter (3L-ANPC-VSC) with insulated gate bipolar transistors (IGBTs) is presented in this work. This scheme is based on a decision chart which selects one of the four redundant zero states in terms of the estimated junction temperatures of the power semiconductors, which allows to actively select the most suited state in terms of losses. The decision chart is simplified by using a temperature multiplexer which defines the temperatures to be compared in terms of the voltage and current sign, and two decision charts were compared, one of them based on previous works, and the other one as an improvement. The simulation results show that, in generation operation (voltage and currents in counterphase), the improved proposal has a better performance in terms of maximum junction temperatures, while in motor operation (voltage and currents in phase), both charts have the same performance.
Feasibility and Accuracy of a Dry and Wireless EEG Helmet for Upper Limb Motor Imagery-Based Brain-Computer Interfaces

EEG-based brain-computer interfaces (BCIs) are a common tool in neurorehabilitation. However, the benefits of EEG-based BCIs can be limited by the use of wet electrodes, which are often uncomfortable and time-consuming to arrange. Here, we investigated the efficacy of an 8-channel wireless and dry EEG system (Helmate, ab medica s.p.a.) in the context of motor imagery-based BCIs, depending on the positioning of the channels. We collected EEG signals from ten healthy subjects and identified the best electrode configuration to discriminate imagined left and right arm movements based on characteristic event-related desynchronization patterns. This study remarks on and refines the use of the Helmate as a tool for motor imagery-based BCI applications.
Comparative Study of Iterative Methods for Inverse Kinematics of Redundant Serial Robots with Increasing Degrees of Freedom

This paper presents a comparative study of five state-of-the-art iterative methods - Particle Swarm Optimization, Quantum Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Damped Least Squares (DLS), and Forward and Backward Reaching Inverse Kinematics (FABRIK) - used to solve the challenging inverse kinematics problem in robots with increasing degrees of freedom in their kinematic chains. The analysis includes 7, 9, 11, 13, and 15 degrees of freedom redundant serial robots with hinge and pivot joints. Performance indicators, such as execution time, iteration count, and final error, were evaluated for each method across 500 randomly generated target poses for the five robotic chains, providing valuable insights into the algorithms' behavior as the degrees of freedom in the kinematic chains increase.
WeAIR: Wearable Swarm Sensors for Air Quality Monitoring to Foster Citizens' Awareness of Climate Change

The present study proposes the implementation of an air quality measurement tool through the use of a swarm of wearable devices, named WeAIR, consisting of wearable sensors for measuring NOx, CO2, CO, temperature, humidity, and barometric pressure. Data will be stored and processed in a secure cloud environment. Artificial Intelligence algorithms will be used to visualize and make available real-time predictions of future states of air quality, by reconstructing spatio-temporal dynamical maps collected through the habitual movement of citizens in space and time. Then a pre-processing phase will allow us to appropriately homogenize the collected data, by means of the application of innovative artificial intelligence methods. All the data collected will be embedded into an integrated system with multiple functions. Among them, an important goal will also be to raise citizens' awareness of climate change.
Joint Blind Channel Estimation, Activity Detection, and Decoding for Coded Massive Random Access

A signal scrambling based joint blind channel estimation, activity detection, and data decoding (SS-JCAD) scheme is proposed for coded massive random access systems. The signal scrambling, which is a symbol-wise phase rotation to each user’s modulated data, has no bandwidth expansion and energy consumption. Yet, with it, each user’s channel state information (CSI), including both the user’s active status and channel coefficient, is blindly detected by a low-complexity single-user maximum a posteriori estimation without the help of pilots. With the estimated CSI as a prior, a soft parallel interference cancellation based multi-user decoding is performed. A soft information iteration algorithm between the channel estimation and multi-user decoding is developed to improve the reliability of both. Simulation shows that for massive random access systems with system load less than 1.5, SS-JCAD achieves almost the same bit error rate as the ideal case that perfect CSI is available, implying SS-JCAD as a near-optimal solution to the random access problem.
Research on Algorithm of Land Engineering Survey Technology Based on Computer Big Data Mining

The improvement of land engineering survey technology based on computer big data is closely related to the technical development. With the rapid development of computer information technology, the application of computer big data technology in land engineering survey technology has become a norm. The dependence of land engineering survey technology on data and computer information technology determines that it must keep pace with the times in the wave of computer information technology information age, constantly innovate its own technical level, rely on advanced technology for data collection and effective application, so as to make it play a greater role in China's land resource management and development. At the national level, the development of computer information technology and big data has become a strategic choice to enhance competitiveness; At the economic level, the development of big data has become a key factor in building new momentum. Based on the specific practice of applying computer big data technology to land engineering measurement technology, this paper will help to further improve the accuracy and efficiency of land engineering measurement technology, and is more conducive to the significant reduction of measurement cost, so that the land engineering measurement technology under the big data of computer information technology will enter a new stage.
Control Delay and Load Based Controller Placement in LEO Satellite Network

The Software-Defined Network (SDN) architecture offers flexible management for Low Earth Orbit (LEO) satellite network, enhancing scalability and efficiency in the integrated satellite-terrestrial network. However, the dynamic topology induces constant changes in communication links between satellites, and the uneven distribution of ground traffic results in varying flow requests. Consequently, dynamic adjustments in controller placement become essential to accommodate these network changes. In this paper, a control delay and load based controller placement (DLCP) mechanism is proposed to address this issue. DLCP assesses the impact of flow requests on control delay, formulates the initial and dynamic controller placement problems (CPP) to reduce the control delay and flow setup time, and subsequently solves them using an improved heuristic algorithm. Simulation results demonstrate that the dynamic controller placement adjustment of DLCP yields superior performance in terms of average flow setup time and packet drop rate.
High Voltage Stress-Lifetime Study of Insulated Printed Circuit Boards

High voltage technology is a niche in spacecraft business and is very important since it enables highly-efficient radio frequency amplification with travelling wave tube amplifiers, a significant payload element for a telecom spacecraft, as well as for thrust by electrical propulsion. Furthermore, numerous scientific instruments rely on high voltage. Pending on the equipment usage, in a space environment (and on the way there), voltages above 200V must be considered as high voltage. Standards for high voltage (HV) on printed circuit board (PCB) designs either do not exist yet or differ in the distance and material thickness rules. Furthermore, the parameters and sources, demonstrating how the rules (if existing) were determined, is not available. Heritage and current designs either avoid high voltage on PCBs, or are based on qualified heritage designs, where the true margin for lifetime until electrical breakdown is not known. Overall, there is a need for research into this field, to refine design rules for HV PCBs for different space environment compatible coating, potting, and PCB substrate materials, based on lifetime measurements under known parameters, to enable better prediction of design durability. In summary, typical single- and double-layer PCB design variants and typical isolation systems have been tested. The results find the electrical breakdown level is strongly depending on the insulation material and method. In comparison to bare PCBs, Parylene almost doubles the isolated voltage, filled epoxy is ∼8 times better. Furthermore, the variance of time until breakdown for a given high voltage is very high. Therefore, large design margins for high voltage isolation are recommended.
Facial Expression Control Method for Humanoid Expression Robot Based on Multi-Layer Gate Recurrent Unit Network

Humanoid expression robots can perform facial expression that are highly similar to humans, which is of great significance for improving the experience of human-computer interaction. In this paper, a novel electromechanical system of humanoid expression robot based on the coupling drive of flexible skin and rigid body mechanism is developed. 22 effective control points of the flexible skin of humanoid expression robot are designed. The facial feature vector of the robot based on the facial feature point extraction method is constructed. In order to obtain the nonlinear mapping relationship between the facial feature vector and the motor control vector of the rigid-flexible coupled humanoid expression robot, and realize the mapping relationship in the whole expression formation process, an inverse kinematics mapping model of humanoid expression robot based on multi-layer gate recurrent unit (GRU) network was proposed. The effect of the multi-layer GRU network on the formation of robot facial expressions is verified by experiments, and the average temporal similarity and spatial similarity of the five basic facial expressions are 91.3% and 84.3%.
Comparative analysis of stained normalization in H&E histopathological images of breast cancer for nuclei segmentation improvement

According to the World Health Organization, breast cancer is defined as the abnormal and disorganized growth of cells in breast tissue. It is currently one of the biggest challenges facing health systems worldwide. Making a precise diagnosis is essential to offer the most appropriate treatment adapted to each patient's condition. Computer vision-based tools could help the specialist in the diagnosis task. One major challenge in digital histopathology is the variation from the staining in the tissue. This work assesses the performance of color normalization under different approaches, measuring its impact on the structures of interest related to the graduation of tissue samples. It also analyzed how color normalization could improve relevant structure segmentation, particularly nuclei, an essential element in the Nottingham Graduation System followed by pathologists. Four methods of color normalization were implemented, and their respective structural and color-associated metrics (SSIM, PSNR, and colorfulness) were calculated. With the Reinhard method, the benign group got the highest SSIM 0.9744 ± 0.1417. For the normal group, the highest value in SSIM was obtained from the Stain-Net method 0.9403 ± 0.0109. Finally, the highest SSIM value we obtained for the invasive group was 0.9388 ± 0.0090 from the Stain-Net method. After the color normalization methodology, we got a segmentation using the StarDist tool. We can generally observe better segmentation using the color normalization methods proposed by Reinhard and Stain-Net.
Control Chart Pattern Recognition Based on MDWOP and Ensemble Classifier

The anomalies in product manufacturing process are related to product defects, and the accurate detection of these anomalies is conducive to improving product quality. The feature-based control chart pattern recognition (CCPR) method has been widely applied to this problem. However, most of the existing feature methods only focus on the amplitude characteristics of the data, ignoring the structural characteristics and sequence relations of the data. A novel feature extraction and recognition method of control chart pattern (CCP) based on multi-delay weighted ordinal pattern (MDWOP) is proposed. MDWOP features integrate the amplitude and sequence structure characteristics of the data, and comprehensively characterize the complexity of CCP from different scales based on time delay parameters. An ensemble classifier recognition method based on multi-delay features is proposed to improve model recognition accuracy. Simulation results show that the average accuracy of the proposed method for eight small fluctuation CCPs is 95.44%, and is better than that of the single classifier method.
An optimal charging and Vehicle-to-Grid management system for the integration of EVs into low voltage distribution networks

The increasing adoption of electric vehicles (EVs) brings about the need for effective integration into energy distribution networks. However, this integration poses challenges such as increased demand for electricity, grid congestion, and power quality issues. To address these challenges, this paper presents a distributed EVs’ energy management system for low-voltage energy distribution networks. The system leverages multi-agent systems (MAS) and fuzzy cognitive maps (FCMs) to manage EV charging and power supply using vehicle-to-grid (V2G) technology. The objectives of the system that are examined is the minimization of the cost burden on EV owners caused by battery degradation from V2G usage and the minimization of EVs charging time. By optimizing the weights of the energy management system, by using the Fuzzy - particle swarm optimization (PSO) algorithm, the proposed approach aims to achieve these objectives. The system’s significance lies in cost reduction, battery life preservation, efficient charging, and grid congestion management. The IEEE European Low Voltage test feeder is enabled in order to evaluate the proposed energy management system. The results showed that V2G technology in combination with energy management systems can be a promising solution for distribution system operators (DSOs).
Blocklength Allocation for Security-Enhanced MEC with Dependency-Aware Tasks

Providing information security while fulfilling high-reliability and low-latency transmission is crucial for mobile edge computing (MEC). In this work, we investigate a security-enhanced MEC network operating with finite blocklength (FBL) codes, while the interdependence between tasks is considered. Particularly, we characterize the joint security-reliability performance of system by leveraging the novel metric, leakage-failure probability (LFP). Aiming to minimize the overall LFP while fulfilling the latency constraint, a blocklength allocation design is proposed. In particular, we establish the convexity property of the problem, based on which a block descend method based approach is proposed, which efficiently achieves nearly the optimal solution.
Integration of Battery Energy Storage Dispatch Using Model Predictive Control for a Hybrid Microgrid Energy Management System

This paper presents an optimal energy management system (EMS) with the aim to minimize the daily operational cost of a grid-connected hybrid microgrid (HMG) composed of wind turbines, solar photovoltaic (PV), and a battery energy storage system (BESS). A modified IEEE-33 distribution system is considered to show the effectiveness of the proposed approach. An incremental model predictive controller (MPC) using mixed integer quadratic programing, is proposed to optimally schedule the wind, PV and BESS. While maximizing self-energy consumption and satisfying equality/inequality constraints. Finally, an economic analysis is conducted showing the overall costs considering a variable (dynamic) price tariff. All the simulations and optimization modeling were carried out using MATLAB/Simulink.
Principal Component Regression and Partial Least Squares Regression Evaluation of Electrode Performance for Non-Invasive Multimodal Measurement of Arm Muscle Activity

Surface electromyography (sEMG) is the de-facto biopotential medical instrumentation solution for non-invasive detection of limb motion EMG activation signals, which in turn provides important information regarding intentional and reactive muscle activity. However, sEMG delivers information of electrical activity mainly due to superficial muscles over a wide sensing field. Additional physiological processes may be elucidated by combining sEMG data with other measurements, such as Electrical Impedance Tomography (EIT) imaging, to assess muscular activity due to increased vascularization. In any case, the choice of the electrode material and shape influences the quality of measured data; "wet" electrodes (Ag/AgCl) are typically used for non-invasive sEMG measurements, whereas "dry" electrodes (i.e. stainless steel, copper-nickel) are preferred for electrical impedance measurements. Here, the authors explore the use of different types of electrode materials to measure sEMG upper limb motion signals, in comparison with Ag/AgCl electrodes, to determine a suitable electrode array that can accommodate both sEMG and EIT measurements. sEMG data was obtained from 10 healthy volunteers and processed using Short Time Fourier Transform (STFT) and Principal Component Spectral Analysis (PCSA) for Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR). The results suggest that steel electrodes perform closer to commonly used Ag/AgCl electrodes, offering the potential for a developing a multimodal sEMG/EIT wearable measurement system for upper limb motion evaluation.