Miracle-dz/UniFAR-FTUs huggingface dataset:
A switched dynamic model for pointing tasks with a computer mouse

In this paper, we study one of the most fundamental tasks in human‐computer interaction, the pointing task. It can be described simply as reaching a target with a cursor starting from an initial position (e.g. executing a movement using a computer mouse to select an icon). In this paper, a switched dynamic model is proposed to handle cursor movements in indirect pointing tasks. The model contains a ballistic movement phase governed by a nonlinear model in Lurie form and a corrective movement phase described by a linear visual‐feedback system. The stability of the model is first established and the derived model is then validated with experimental data acquired in a pointing task with a mouse. It is established that the measured data of pointing movements of different types can be fitted within the proposed model. Numerical comparison against pointing models available in the literature is also provided.
A Novel Binary Classification Algorithm for Carpal Tunnel Syndrome Detection Using LSTM

Carpal tunnel syndrome (CTS) is one of the common neurological disorders caused by prolonged compression of the median nerve. Thus, CTS patients’ daily tasks are significantly affected. Traditional diagnostic methods are invasive or subjective, causing pain or inaccuracy. Therefore, a more accurate machine/deep learning classifier is needed to provide an accessible assessment approach that can help screen out early-stage patients to prevent further deterioration. Behavioral biomechanics has shown great potential to be used for CTS and its severity classification. The biomechanical parameters are collected when identified patients and healthy individuals perform daily life activities, such as grasping and lifting in a controlled manner. Facing the challenges of time series biomechanical data with small sample sizes and high dimensions, we propose a novel classification algorithm to create an ensemble model for CTS detection using Long Short-Term Memory (LSTM). The proposed algorithm achieves 93% accuracy on average for CTS detection using biomechanical data of daily life activities.
Bottleneck Transformer model with Channel Self-Attention for skin lesion classification

Early diagnosis of skin diseases is an important and challenge task for proper treatment, and even the deadliest skin cancer: the malignant melanoma can be cured for increasing the survival rate with less than 5-year life expectancy. The manual diagnosis of skin lesions by specialists not only is time-consuming but also usually causes great variation of the diagnosis results. Recently, deep learning networks with the main convolution operations have been widely employed for vision recognition including medical image analysis and classification, and demonstrated the great effectiveness. However, the convolution operation extracts the feature in the limited receptive field, and cannot capture long-range dependence for modeling global contexts. Therefore, transformer as an alternative for global feature modeling with self-attention module has become the prevalent network architecture for lifting performance in various vision tasks. This study aims to construct a hybrid skin lesion recognition model by incorporating the convolution operations and self-attention structures. Specifically, we firstly employ a backbone CNN to extract the high-level feature maps, and then leverage a transformer block to capture the global correlation. Due to the diverse contexts in channel domain and the reduced information in spatial domain of the high-level features, we alternatively incorporate a self-attention to model long-range dependencies in the channel direction instead of spatial self-attention in the conventional transformer block, and then follow spatial relation modeling with the depth-wise convolution block in the feature feed-forward module. To demonstrate the effectiveness of the proposed method, we conduct experiments on the HAM10000 and ISIC2019 skin lesion datasets, and verify the superior performance over the baseline model and the state-of-the-art methods.
Input Structure Selection for Soft-Sensor Design: Does It Pay Off?

This contribution seeks to compare the efficiency of several soft-sensor design methods using the datasets from three different case studies, including two industrial examples. The selected set of design methods compared considers different principles to design soft sensors, consisting of three consequent stages: (a) data preprocessing with input domain analysis, (b) input structure selection, and (c) model training. The input domain analysis explores the potential of various nonlinear structures within the input dataset. The results obtained indicate that multivariate feature selection approaches have the highest efficiency. Moreover, the nonlinear soft sensors achieve higher accuracy compared to linear ones.
Phase-Shifted Full-Bridge Converter with an Improved Coupled Inductor Rectifier

A phase-shifted full-bridge (PSFB) converter with coupled inductor rectifier (CIR) structure has the advantage that eliminate freewheeling current in the primary side of the PSFB converter. However, during freewheeling period, the output current flows through high-voltage-rated diodes, resulting in large conduction loss. Also, the converter should be designed with a small magnetizing inductance to achieve zero-voltage switching (ZVS) for the lagging-leg switches, resulting in increased transformer loss. This article proposes a modified PSFB converter to mitigate the drawbacks of the conventional CIR PSFB converter. By employing two diodes in the secondary side, the output current is transferred to the primary side during the freewheeling period, facilitating ZVS for the lagging-leg switches. Moreover, the output current during the freewheeling period flows through the low-voltage-rated diodes. Therefore, the proposed converter achieves high efficiency. The performance of the proposed converter was verified using prototypes with 320–400-V input and 56-V/700-W output specifications.
Double-Side Direct Oil-Cooling Automotive Power Module: from Material Compatibility to Thermal Management

This paper presents the concept design, material reliability, and thermal management of the direct oil-cooling automotive power module we developed. This type of module faces several significant challenges compared to the conventional water-cooling power module, such as copper lead corrosion, rubber swelling, and high thermal resistance. We therefore prototyped a double-side direct oil-cooling power module to investigate the reliability and thermal resistance performance. Our findings showed that mineral oil-based coolant and insulating ester oil have outstanding reliability and compatibility to power module materials. A 31% reduction of thermal resistance at 10L/min 25°C was obtained, which is much lower than even referenced double-side water-cooling power modules, thus demonstrating the feasibility of direct oil-cooling automotive power electronics.
Deep Autoencoder With Orthogonal Features for Process Monitoring

With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.
Introducing a group-based remote laboratory for embedded education

Teaching in the area of embedded systems is challenging because both software and hardware development must be considered in an integrated manner. The experience gained through the practical hardware-related implementation of prototypes cannot simply be replaced by simulations. The question is, however, how can students gain as much practical experience as possible while taking into account real laboratory constraints? We present an approach that allows students to gain practical experience via virtual access to laboratory setups, which allows them to work in groups remotely and then continue their work in the labs, providing a similar experience as under normal lab conditions. For educators, we introduce a flexible environment that can be adopted to specialized embedded courses due to the used components and methodology of the ADDIE teaching model.
Performance Analysis of Virtual Synchronous Generator based Grid Forming Inverter under Different Load Types

Virtual synchronous generator (VSG) based grid-forming inverter (GFMI) control architecture has gained high popularity, since it is capable of closely emulating the inherent characteristics of a synchronous generator (SG). However, a detailed performance analysis of the VSG based controller under the different types of loads has not been thoroughly investigated yet. In order to address this, a cascaded VSG based controller is proposed and implemented in MATLAB/Simulink platform to evaluate the performance of the VSG converter under different load conditions. This will allow system planners to have an understanding on how the VSG based GFMI behave under the presence of different load categories and the required performance improvements that needs to be done when deploying the VSG based GFMI in the power system.
Underwater Wall-Climbing Inspection ROV Scheme Design and Flow Resistance Simulation Based on BlueROV2 Modification

The condition monitoring of underwater components of ships is an important means to ensure the safe navigation of ships. To address the problems of the current portable small underwater ROVs (Remotely Operated Vehicle) such as weak resistance to flow and difficult operation, based on the BlueROV2 HEAVY, an underwater ROV, this paper adds a tracked wall-climbing and barrier-crossing chassis to give it the ability to crawl on the underwater parts of the hull of ship. Simulation of the flow resistance of the modified wall-climbing ROV by ANSYS CFD software showed that: The modified underwater wall-climbing ROV not only has all the motion characteristics of the original BlueROV2 HEAVY, but also has good resistance to flow and can crawl steadily on the surface of the ship's submerged hull to achieve rapid underwater inspection of the submerged parts of the hull of ship.
Research on Intelligent Segmentation Processing and Navigation Area Optimization methods Based on ENC

As marine resources are being exploited, intelligent navigation is becoming more important, and intelligent optimization of navigation areas is the key to intelligent navigation. In this paper, we propose a framework for intelligent segmentation processing and optimization of navigation areas based on electronic nautical charts (ENC), addressing the problem of ignoring the depth of the sea and the state of the vessel in traditional chart processing and route selection. Combined with navigation-related standards, ship data, marine environment, etc., we build a model for the relationship between the permissible water depth of navigation and the ship’s draft depth, and optimize the navigation area through the intelligent segmentation algorithm of electronic nautical charts, which helps to improve the safety of ship navigation. Finally, the effectiveness of the proposed ship navigation area optimization framework is verified through experiments.
A New Control Strategy for Multilevel Inverter to Reduce THD

The paper deals with a new control strategy for single-phase multilevel inverter with asymmetrical input dc sources to reduce total harmonics distortion (THD) of output voltages. Multilevel inverter circuits have been designed and simulated using PSIM software to generate 3 to 9-level output voltages. The switching angles at different voltage levels has been changed in accordance with the value of sinusoidal wave. Unequal independent dc sources have been connected to each Hbridge unit. The driving signals at the gate of MOSFETs of the H-bridge units have been applied using gating blocks with proper switching angles. The THD of output voltage was estimated without any filter circuit at the output. This study shows that THD of the output voltage of proposed inverter has been decreased significantly when the number of voltage steps at the output of inverter with changing of switching angle in sinusoidal steps compared to THD obtained in case of switching angle changed in equal steps.
Study on Impedance Measurement Device for Grid-Connected Converters Based on Model Predictive Control

The dynamic stability problems caused by the broadband interfering characteristics of power electronics grid-connected systems have received wide attention due to the large-scale installation of renewable energy and intelligent distribution equipment. Measuring the port impedance of grid-connected converters and determining the system stability by using Nyquist stability criterion is the most commonly used method in broadband stability studies. This paper presents an impedance measurement method by generating series disturbance voltage, where the broadband disturbance is generated by means of model predictive control (MPC) with a frequency domain segmentation model. A comparison with theoretical calculations shows that the method enables accurate measurement of the port impedance of grid-connected converters over broadband frequency range, providing a basis for system stability analysis.
A forest fire detection method based on adaptive feature fusion module

Image-based flame detection technology is an important research direction in the field of forest fire detection. Flame is a dynamically changing, luminescent chemical process, RGB images can only reflect two-dimensional information about the flames, which is not sufficient for distinguishing flames in any scene. With the development of infrared technology, the thermal properties of flames can be effectively visualized, and the use of IR features can well complement the characteristic information of the flame, which helps to improve the accuracy of flame detection. Therefore, we design a flame detection method based on adaptive feature fusion module, which aims to accomplish more accurate prediction using the two-dimensional features and the infrared features of the flame. The feature fusion module combines the channel attention mechanism and spatial attention mechanism of the image, which can selectively combine the RGB and infrared features of the flame and suppress the invalid feature information in the image. Experiments have demonstrated that the method can stably identify flames in forest scenes. In addition, we find that the method proposed in this paper also has good robustness for detecting obscured flames and small flames.
The IGBP-DIS global 1 km land cover data set

Apparatus for reproducing and amplifying the sound emitted by a stringed musical instrument in which the individual strings of the instrument are connected to separate microphones and the microphones are connected through an amplifier to sound distributing means having a soundboard with similar tonal characteristics as the musical instrument so that the individual tones are reproduced and amplified.
Individualized Clustered Cooperative Communication Units in Automated Electrical Routing in 3D CAD

Many industries are faced with the dilemma of increasing the amount of wires and cables in their products. The process of mapping the path of these cables is tedious, iterative, and prone to errors. Instead of manually specifying all the waypoints for a diverse set of cables, automation can provide globally optimized and proven pathways accelerating the product development process. To implement automated electrical routing, an industry-appropriate method is seamlessly integrated into existing 3D computer-aided design (CAD) workflows. The aim of this research is to evaluate the effectiveness and practicality of multi-agent reinforcement learning in determining the most efficient paths in three-dimensional space. To achieve this goal, information is extracted directly from 3D CAD and the results are immediately fed back into CAD. This paper proposes a novel approach that involves clustering the cables based on example paths prior to the actual learning process. Then, a communicating multi-agent proximal policy optimization (PPO) algorithm learns the routing process. To solve the shortest path problem in three-dimensional space while considering cable-and environment-specific constraints and minimizing the total cable length, the agents’ accessible space is restricted to a maximum distance from the initial 3D CAD geometry. The developed approach is explained in this paper and compared to established techniques in electrical routing such as the A* algorithm.
A Broadband Millimeter-Wave 5G Low Noise Amplifier Design in 22 nm Fully-Depleted Silicon-on-Insulator (FD-SOI) CMOS

This paper presents a design of a broadband millimeter-wave (mm-Wave) low noise amplifier (LNA) designed in a 22 nm fully-depleted silicon-on-insulator (FD-SOI) CMOS technology. The post-layout parasitic extracted (PEX) simulations suggest the LNA has a 3-dB bandwidth (BW) from 16.9 – 41.8 GHz and a fractional bandwidth (FBW) of 84.8 %, covering the key frequency bands within the mm-Wave 5G FR2 band, with its noise figure (NF) ranging from 2.9 – 4.1 dB, and its input-referred 1-dB compression point (IP1dB) of −19.4 dBm at 28 GHz with 15.8 mW DC power consumption. Using the FOM (figure-of-merit) developed from Ref. [1] for broadband LNAs (FOM $\equiv\mathbf{2 0}\times\log((Gain[V / V]\times BW[GHz])/(P_{DC}[mW]\times(F-1))$, this LNA achieves a competitive FOM among reported mm-Wave LNAs in literature [1–7].
EAU-Net++: An Efficient Segmentation Network for Ultrasound Vessel Images

Vessel segmentation on ultrasound images is an important tool for surgical planning and execution, but proves to be a challenging task due to noise and speckle. In order to achieve accurate surgical treatment and diagnosis, 386 ultrasound images from 10 subjects were manually marked. The collected data was used for data expansion through rotation, flipping, histogram equalization and least squares image transformation methods to alleviate the adverse effect of small data set. For the segmentation of ultrasound blood vessel images, this paper proposes the EAU-Net++ network. On the basis of U-Net++, EfficientNet is employed as an encoder. An attention mechanism is introduced to furtherly improve the segmentation accuracy. On the unified dataset, ablation experiments and comparative experiments are carried out to verify the proposed model. Experimental results show that EAU-Net++ can effectively achieve blood vessel image segmentation.
Parameter Design Scheme for the embedded Multiport Flexible ac Interconnector

The softly open point (SOP) is a burgeoning technology to deal with the potential voltage violation and feeder congestion issues yielded by the increasing integration of distributed generations (DGs) and various novel loads. Compared with the traditional solution with the back-to-back modular multilevel converters (BTB-MMC), a novel embedded multiport flexible ac interconnector (eM-FACI) is proposed to serve as the SOP for the feeders’ power regulation with light weight, small volume, and low cost. Flexible multi-feeder interconnection can be achieved by using half-bridge submodules instead of full-power MMC stations. However, due to the feeder integration, the eM-FACI’s unique configuration makes the parameter design more complicated than the conventional BTB-MMC. This paper proposes a parameter design scheme for the eM-FACI’s according to the extreme fluctuate power interaction condition. And the extreme case is designed by the current decoupling and the power decoupling between different feeders. The feasibility and effectiveness of the proposed method are verified by MATLAB/Simulink.
MOEA/D with Adaptive Weights for Multi-objective Optimization Problems

Multiobjective optimization is always one of the most popular problems in evolutionary computation due to its wide existence in various practical applications. This paper proposes a new improved version of multiobjective evolutionary algorithms based on decomposition (MOEA/D) for solving multiobjective optimization problems, which is named MOEA/D-ADW including three significant strategies. Specifically, firstly, weight vector initialization mechanism is designed to generate high quality weights, which includes UR and WS-transformation strategies. Secondly, an external population is designed for storing the obtained individuals based on the neighborhoods of the weight vector and the search population. Finally, population agents stored in external set are reused for updating the weight adaptively during the evolutionary process. To evaluate the performance of the proposed algorithm, experimental results carried on a set of benchmark functions with various characteristics, demonstrate that the MOEA/D-ADW is competitive with respect to some existing multiobjective optimization algorithms.
A Small Signal Rotating Frame Model of a Self-synchronizing Single-phase Grid-tied Inverter

A self-synchronizing grid-tied inverter uses the internal dynamic states of the current regulator to estimate the grid voltage for synchronization. The stability analysis of a single-phase grid-tied inverter is complex because of the lack of an orthogonal channel and non-linearities present due to the phase-locked loop (PLL). This paper introduces fictitious orthogonal quantities for a single-phase grid-tied inverter, creating a model in the synchronous reference frame. This approach enables Jacobian linearization to develop a small-signal inverter model. The stability analysis of the small-signal model using Floquet theory then allows for stability boundaries to be determined across a wide range of operating conditions. The model has been validated using detailed switched simulations of a single-phase grid-tied inverter with matching experimental results.
Design and Control of Optimized Switched Capacitor for EV Wireless Power Transfer Systems Considering Wide Coupling Coefficient Range

This paper proposes an output voltage control method of an inductive power transfer (IPT) system for electric vehicles (EVs) considering the coupling coefficient variation as a result of magnetic pad misalignment. Since the output voltage of the IPT converter corresponds to the input voltage of the DC-DC converter, an excessively wide output voltage range increases the burden on the DC-DC converter. Therefore, a method of limiting the output voltage range of the IPT converter by applying a switched capacitor method is proposed. A 7.2kW class IPT system is designed, and the compensation topology characteristics determined by the switched capacitor are analyzed. Finally, the feasibility of the output voltage control method proposed in this paper was verified by experiment.
Photonic integrated building-blocks for rubidium cold atom systems

We report on integrated silicon nitride photonics at 780 nm with resonators over 100M Q for laser linewidth narrowing and mm-sized, intersecting beam emitters for rubidium atom cooling and trapping. We discuss prospects for integrating these components and controllable locked lasers into cold atom systems-on-a-chip.