Miracle-dz/UniFAR-FTUs huggingface dataset:
Brief Overview on Non-Isolated DC-DC Bidirectional Power Converter Topologies

DC-DC power electronics converters that are capable of reversing power flow are playing an increasingly crucial role in various applications, such as energy storage systems, electric vehicles, and renewable energy systems. Non-isolated bidirectional DC-DC converters may be simpler and have higher power densities than isolated converters. This is because they do not require a transformer and can consist of a single power stage, making them ideal in applications that do not require a high level of safety or galvanic isolation. The fundamental topologies are the result of replacing the unidirectional semiconductors of the basic converters with bidirectional semiconductors. However, modifications, improvements and new topologies have emerged aimed at reducing power densities, power losses and costs, while increasing reliability. This paper reviews some novel and basic bidirectional topologies and discusses the features of each one along with its typical applications. The results can be valuable in determining the most suitable topology for a specific application.
Design and Selection of MMC Parameters for Improved Performance in Grid Forming Mode

In grid forming (GFM) control, the converter is synchronized to the grid without the usage of phase-locked loop (PLL). It essentially consists of two important blocks. The first one is power synchronization control (PSC) and the other one is virtual inertia emulation control. The GFM control when applied to MMC, the value of arm inductor directly influences the PSC loop while the submodule capacitance impacts the virtual inertia emulation control. This paper presents a systematic study on the values of submodule capacitance and arm inductance selection in order to improve the whole GFM control performance. Based on the analytical study, the parameters to be chosen are suggested in order to obtain better stability margins and controllable bandwidth.
Model-driven Learning for Cell-Free MIMO Beamforming with Compression Strategy

Cell-free Multiple-Input Multiple-Output (MIMO) system is an appealing solution to improve the performance of wireless communication systems. However, the system performance improvement is restricted by the capacity limitations of fronthaul links. To address this issue, a compress-and-forward scheme is introduced, and the joint optimization of precoding and fronthaul compression is considered. Existing algorithms for this joint optimization often involve high computational complexity and latency due to iterative processes, making them unsuitable for real-time implementation. In this paper, we propose a model-driven neural network-based deep learning method that leverages expert knowledge, i.e., the uplink-downlink duality, to characterize the structure of beamforming matrix using low-dimensional parameters. These parameters, referred to as "key features", are learned from the channel state information through a neural network. Simulation results show that the proposed method achieves performance close to the optimal solution while significantly reducing computational time.
Advanced 3D Printing of Patient-Specific Human Heart for Improved Surgical Planning

Pre-operative planning in surgery has become crucial, particularly in unusual cases. Modern advancements in 3D medical imaging technologies, such as computed tomography, magnetic resonance, and physical atlases, have revolutionized the preparation process. Additive manufacturing and 3D printing have enabled the creation of patient-specific physical models using medical images. These models have benefited cardiovascular surgery by providing a 3D reconstruction that allows surgeons to explore intricate details that are typically inaccessible. Resin-based polymers are designed to replicate anatomical structures' mechanical properties closely. In order to enhance surgical planning and achieve a more realistic physical model, the mechanical behavior of 3D-printed heart was compared to that of real heart tissue properties in the first-ever attempt to quantify this relationship. A literature review was conducted to obtain a general characterization of the myocardium, focusing on previous studies conducted on animal hearts. Although there was inconsistency in testing methods and sample dimensions, the evaluation metric chosen was the stress at 15% strain. The reviewed studies concurred on cardiac tissue's non-linear anisotropic viscoelastic nature, with reduced responsiveness in the cross-fiber direction compared to the main fiber direction. Synthetic materials used in the 3D printed models are closely aligned with the real specimens regarding the selected metric; however, further investigation into the biaxial properties is needed in addition to functional characterization, such as the shear stress during the cutting procedure.
Analysis and Current Stress Optimization of Novel Active Neutral Point Clamped DAB Converter with Optimal Switch Utilization

For efficient, reliable, and cost-effective power transfer using a dual active bridge (DAB) converter, the issue of soft-switching, current-stress optimization, and optimal switch utilization need to be addressed carefully. This paper presents a novel active neutral point clamped (ANPC) DAB converter, which optimally utilizes Si IGBTs and SiC MOSFETs to achieve the above-mentioned objectives. A modulation scheme is also proposed, which provides three control degrees of freedom (DoFs) to optimize converter current stress. The possible converter operating modes with a developed modulation scheme (DMS) are presented for its operation in the forward direction, and suitable modes are further investigated to optimize converter current stress ensuring soft-switching of all the Si IGBTs. A substantial simulation and analytical study are done to validate the effectiveness of the proposed converter operation with a DMS in terms of reduced current stress and semiconductor losses.
Transformer with Multi-block Encoder for Multi-turn Dialogue Translation

Dialogue translation, typically reliant on sentence-level translation models, often struggles with accurately capturing contextual relationships and cross-sentence semantics. To address this, we took inspiration from document-level translation models and propose a Transformer architecture with a multi-block encoder, equipped with our novel context aggregation method. The applicability and effectiveness of these proposals were tested across three chat translation datasets using automated evaluation metrics. Notably, the integration of the context aggregation method improved the baseline model performance, while the Transformer with Multi-block Encoder demonstrated substantial gains in particular datasets (BLEU, METEOR). Moreover, our model and method displayed versatility, adapting effectively to various chat scenarios. These findings affirm the potential of the Transformer with Multi-block Encoder and the context aggregation method in enhancing dialogue translation by ensuring greater context sensitivity and adaptability.
63-Level Asymmetric Capacitor an Inductor Bank without Discharge Resistors and Reduced Transients

The evolution of modern electrical networks, with bidirectional power flow and interconnected renewable energy sources, presents challenges for traditional automatic capacitor banks in effectively correcting the power factor. In some scenarios, industrial installations may experience nearly zero power consumption periods due to high power generation levels. As a result, the steps in automatic banks can become disproportionately large, making it difficult to achieve proper compensation. To address this issue, a proposed 63-level asymmetric capacitor bank offers a high-resolution solution without discharge resistors and reduced transients. By significantly increasing the number of steps, the bank provides greater resolution in power factor correction, even during periods of near-zero power consumption. Its intelligent startup systems enable controlled connection and disconnection, eliminating the need for discharge resistors and minimizing voltage and current transients. This adaptive solution ensures improved power factor correction and network stability in industrial settings with fluctuating power generation and consumption. Moreover, the bank’s ability to accommodate bidirectional power flow and interconnection of renewable energy sources, such as photovoltaic panels, further enhances its suitability for modern electrical grids.
Real-Time Joint Trajectory Optimization and Predictive Safety Filtering for Telepresence Avatar Robotics

Robotic telepresence allows humanity to remotely conduct critical life-saving physical work or connect with faraway relatives. However, achieving this with an intuitive, general-purpose avatar agent demands seamless real-time motion retargeting; hence, inaccurate, sluggish robotic movement is unusable. In this paper, a new trajectory controller for the telepresence humanoid Avatar-Hubo is developed to refine manipulator accuracy, responsiveness, and safety. By using the proposed filter fusion-based trajectory generator, Avatar-Hubo's average joint motion latency decreased 90%, dropping from 1.5 $s$ to 0.15 $s$. Additionally, end-effector tracking accuracy Root Mean Square Error improved by 97 % on average. Furthermore, the risk of dangerous robot motions is remedied via the proposed predictive safety filter that identifies and prevents motions requiring excessive speeds. Ultimately, the new controller possessed superior real-time motion performance, aiding in general robotic usage and inducing the sensation of telepresence.
A Reconfigurable Partial Power Converter with Adjustable Transformer Turns Ratio for a 6.6-kW Integrated On-Board Charger

This paper proposes a novel step-up/down Type II series-connected partial power converter (S-PPC) based on a reconfigurable bridge for the dc-dc stage of a 6.6-kW integrated on-board charger (OBC). The S-PPC reconfigures its transformer primary side winding with three sets of turns ratios, which are designed to reduce the total non-active power processed. It allows the S-PPC to operate in a wide output voltage range with low conduction losses. Moreover, zero-current switching (ZCS) is achieved for the primary switches and zero-voltage switching (ZVS) for the secondary switches. Simulation results are consistent and validate the analyzes presented in this paper.
User Scheduling and Height Optimization in UAV-Assisted Maritime XL-MIMO Communications

As an important part of the space-air-ground-sea integrated communication networks, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) have recently garnered considerable attention. In this paper, we consider the UAV-assisted user scheduling and height optimization in the maritime extremely large-scale multiple-input-multiple-output system. To improve the achievable sum-rate, we focus on reducing inter-channel interference by optimizing the USV positions and the UAV-base station (BS) height. Specifically, we derive a set of orthogonal positions in the near-field region of the uniform rectangular array such that the channels of the USVs located at that positions are orthogonal to each other. Based on the orthogonal positions, we propose a joint optimization algorithm that provides a suboptimal orthogonal position assignment scheme for the USVs and optimizes the UAV-BS height. Simulation results verify the effectiveness of the proposed orthogonal position and the joint optimization algorithm.
Multimodal Contrastive Learning for Unpaired and Depth-privileged Semantic Segmentation

Depth privileged semantic segmentation assumes that RGB and depth information are provided for training but depth is not available during testing. In this work, we propose a multimodal contrastive learning approach to learn a depth privileged segmentation model from unpaired color and depth images. We first construct a two-stream network, in which each stream performs semantic segmentation based on either RGB or depth modality. Meanwhile, features of each modality are projected into a common embedding space, and the embedded features are attracted or repelled under the supervision of an inter-modal contrastive learning (CL) loss. Additionally, we integrate a tunable self-attention module to make the sparse CL supervision more effectively. By mining modality-invariant features, the proposed inter-modal contrastive learning approach enables our model to gain robustness against illumination changes even if no depth information is used during test time. Experiments on two datasets show that our approach performs competitively with previous RGB-D semantic segmentation methods that require color and depth images to be paired and well-aligned for training and testing.
Compressed Sensing and Kalman Filter Based Channel Tracking for mmWave Massive MIMO Systems

Millimeter wave (mmWave) communication system requires efficient yet accurate channel tracking to achieve high effective spectral efficiency. In this paper, we pay special attention to mmWave massive multiple-input multiple-output (MIMO) systems and propose a channel tracking approach based on compressed sensing and Kalman filter. To be specific, the proposed approach first utilizes compressed sensing technique to estimate the angular parameters, which tackles the grid mismatch and angle crossing. Subsequently, the angle prediction, beam training, and angle update are jointly designed by exploiting the temporal channel correlation, including different levels of channel variations. Simulation results show that the proposed channel tracking has higher spectral efficiency and lower tracking error than traditional methods for mmWave massive MIMO systems.
Modeling and Probabilistic Security Assessment of a Synthetic Urban Grid with PV and EV Integration

This paper first develops a synthetic urban power grid model with solar photovoltaic (PV) and electric vehicles (EV) using public data from utilities, regulators, open-source datasets, and relevant publications. Then, a probabilistic security assessment approach is proposed to evaluate the impact of the PV and EV integration under different penetration and uniformity levels, deployment scenarios, and uncertain daily curves. The security indexes, including maximum and violation rates, quantify the bus voltage magnitude and branch power flow. Simulation results show that the integration of PV and EV can substantially affect the voltage and branch power flow, which may cause security risks to distribution networks. Moreover, integrating PV and EV reshapes the netload daily profiles and may generate the reverse active power in substations and branches. Although the power grid model is synthetic, the modeling process and security assessment method can be a reference for similar studies of realistic distribution grid systems.
Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy.
An Analog-to-Information Architecture for Single-Chip Sensor-Processor Inference System

Compressed sensing (CS) establishes that signals fulfilling certain conditions can be sampled at a much lower rate than that defined by the Nyquist-Shannon sampling theorem while still keeping most information. Therefore, the monolithic implementation of CS is notably interesting for different applications. By its very nature, CS encodes global information of the sampled signal and hence compressed samples can be used for inference purposes. Thus, a particular application of CS is the implementation of inference in the compressed domain, i.e., without reconstructing the compressed signal. In this paper, we present an analog-to-information CS-based architecture for on-chip inference. The performance of the proposed architecture has been evaluated using facial recognition as a case study for different scenarios with varying lighting, expression, and occlusion conditions. Different mixed-signal hardware building blocks are also presented. These building blocks have been designed in the 110 nm LFoundry technology.
Adap CDP-ML: Concentrated Differentially Private machine learning with Adaptive Noise

Machine learning and big data, foundational pillars of artificial intelligence, are propelling the societal development, garnering significant attention. Recent studies reveal that malevolent actors can exploit vulnerabilities, leading to privacy breaches. These breaches occur through orchestrated privacy attacks on machine learning models, enabling the extraction of sensitive information from data via model parameters. Moreover, prevailing privacy-preserving machine learning approaches consume substantial privacy budgets, presenting a considerable challenge in balancing privacy guarantees and model utility. To tackle this challenge, our paper explores an adaptive differential privacy learning framework, integrating zero-concentration differential privacy techniques into machine learning to eliminate privacy threats and yield clearer quantitative results. Additionally, we propose a dynamic privacy budget allocation strategy to mitigate the high accumulation of the total privacy budget, providing enhanced privacy guarantees without compromising model utility. Theoretical analysis and extensive experiments on benchmark datasets validate that our approach effectively enhances model performance while reducing privacy loss.
Design and Evaluation of a Wearable Single-Lead ECG for Continuous Monitoring

One of the most used techniques to track the heart activity is the electrocardiogram (ECG). Wearables represent an opportunity in this field because they can be easily integrated in telemedicine platforms. Several wearables able to perform the ECG are present on the market, however they present limitations. Two main limitations are the difficulty of integration into existing platforms and scarce reliability of wearables during dynamic activities. The work consisted in realizing a wearable single-lead ECG that could be seamlessly integrated into a previously existing telemedicine platform. The aim of the work was to test the device in different body locations and during different static and dynamic activities to identify preferred placements and modes of use. The device has been tested on a cohort of 10 people (6 men). Traces were evaluated in terms of the following parameters: heart rate (HR), P wave, QRS complex, ST segment, signal continuity, artifacts, and overall quality. Evaluations were performed by an expert cardiologist, who could express an evaluation between 0 and 5 for each trace (one per activity in each body location), with a sufficiency threshold equal to 3. In the case of the best body location, all parameters received evaluations around 3 except for signal continuity and P wave quality. In conclusion, the new device can detect the ECG signal with an acceptable quality for a subset of the parameters considered when placed in a defined body location.
System-level Security Solution for Heterogeneous D2D-underlaid Cellular Network

This paper focuses on the problem of secure communications in a D2D-underlaid cellular network, where device-to-device (D2D) communication operates in a hybrid manner using both millimeter-wave (mmWave) and microwave frequency bands. To combat vigilant adversaries, we employ the technique of covert communication to conceal the presence of legitimate D2D transmissions. The D2D transmitters employ power control, communication-mode switching, and leverage the cellular signals to evade detection by adversaries. The conflict situation between the D2D transmitters and the adversaries is modeled as a Stackelberg game. To mathematically characterize the network’s spatial configuration, we utilize the stochastic geometry modeling and consider a large-scale D2D-underlaid network, enabling a system-level analysis of communication strategies. We analyze the game equilibrium and thereafter search for equilibrium by using a bi-level algorithm. Insightful findings are drawn from numerical simulation results.
A Comprehensive Review in DC microgrids: Topologies, Controls and Future Trends

Microgrids are an emerging technology that maximizes the use of renewable energy sources (RES). Unlike AC microgrids, a DC microgrids do not need to consider the reactive power, frequency, etc. In addition, most RESs and energy storage system (ESS) have DC nature, which can be linked to the DC microgrid without energy conversion process, thereby reducing energy losses. Hence, the DC microgrids are receiving more attentions. This article presents a comprehensive review on the control methods and topologies for the DC microgrids. First, five topologies and equivalent structure diagrams are presented and discussed. Then, a hierarchical control encompassing primary, secondary and tertiary control is discussed and studied in detail. Finally, a future trends and challenges of DC microgrids are presented based on the literature review.
Properties and Perspectives of Digital Holographic Microscopy for Bioaerosol Detection

Bioaerosols are an important component of air quality and their detection has emerged as a central concern. Current bio-detection tools do not meet the need to be fast, autonomous and ready for use in the field. Artificial intelligence-powered digital holographic microscopy (DHM) offers a possible solution for real-time, cost-effective and field-deployable bioaerosol analysis. This study investigates the characteristics of DHM using a simulation-based approach to define the theoretical limit of detection for submicron objects under different experimental conditions. Spherical particles with different diameters were simulated according to Mie scattering theory, optimising their size and refractive index to be comparable to bacteria. Statistical analysis was used to analyse particles' holograms, both individually and across a range of concentrations. The evidence from this study indicates that air rather than water medium allows more reliable identification of smaller particles. In addition, basic statistical indices enable the differentiation between particles and the background, as well as discrimination among particles based on their diameter, even with higher particle concentrations. Overall, this study suggests that DHM allows for detection and distinction of airborne micro- and even nano-particles. The theoretical detection limit with a lensless setup is around 500 nm, while with a lens-based setup it can reach at least 30 nm under ideal conditions.