Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative analysis of Bohman Windowing Performance for UFMC with Higher QAM Modulation

This paper presents a study on the performance of different windowing techniques for high-level quadrature amplitude modulation (QAM) in universal filtered multicarrier (UFMC) systems. The focus is on comparing the impact of different windowing types on bit error rate (BER) performance, peak-to-average power ratio (PAPR), and out-of-band emission (OOBE). The present study assesses the performance of the Bohman window in the context of QAM levels, namely 256QAM, 512QAM, and 1024QAM, under a signal-to-noise ratio (SNR) of 15 dB. The findings indicate that utilization of the Bohman window yields a 3.21% enhancement in BER performance, a 1.01% reduction in PAPR, and a decrease in OOBE by 62.46 dBW/Hz for the 1024QAM in contrast to conventional UFMC. At 20 dB SNR, the BER is still lower than the traditional by 1.16%, but PAPR remains unchanged.
A Wide IF Baseband-Noise-Cancelling CMOS Analog Receiver with Current-Mirror TIA

by using a baseband noise cancellation, a CMOS mixer-first analog receiver with power reduction is proposed. Based on a current-mirror TIA structure, positive capacitive feedback is applied to manipulate poles/zeros location, enabling a wide baseband bandwidth and out-of-band second-order filtering profile. The additional radio frequency N-path filtering and baseband 40dB/dec roll-off supress out-of-band interferences. The presented frontend is designed in 65 nm CMOS. Simulations demonstrate a minimal noise figure of 2.0 dB, and a voltage gain of 32.5 dB across the 220 MHz intermediate frequency range. The in-band (IB) and out-of-band (OOB) IIP3 manifests −12.5/15 dBm. The receiver circuit draws 27.4 mW at a typical 1 GHz LO stimulus.
Laser Vision for Low-Coupling Indoor Positioning

We proposed a low-coupling indoor localization algorithm for mobile robot based on laser and vision sensors with better robustness and real-time performance. Firstly, mobile robot kidnap problem was decomposed into pre-kidnap state judgment and post-kidnap localization recovery. Mobile robot was jointly judged by a probabilistic model and CLIFF to determine whether abduction occurs or not. The post-kidnap localization recovery problem was solved by using KLD resampling based on coarse localization. The ORB feature descriptors were extracted from the images of different frames and the corresponding pose information was recorded. Then the VP DBOW was constructed, followed by filling the particles with the pose information of coarse localization during resampling or the first global localization. Coarse localization was implemented to match the current frame in V-PDBOW to get the pose information corresponding to the a posteriori observation frame, which could reduce the iteration time of the algorithm and solve the recovery of localization after abduction. Finally, the likelihood domain model was optimized and a high-weight laser beam was adopted as the calculation basis for evaluating the particle weights to avoid the influence of noisy data on the evaluation, thus improving the robustness of the localization algorithm and more rapidly correcting the positional distribution to the true distribution.
Design and Stability Analysis of Control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.
A Hybrid Training Framework for Speeding up the Inference Process of Spiking Neural Networks

Spiking Neural Networks (SNNs) have recently attracted enormous research interest as their spike-based computing paradigm brings high energy efficiency. However, there is a trade-off between the accuracy and the inference speed of SNNs. The existing works on SNNs achieve high accuracy at the cost of low speed. In this paper, we first analyze the reason why high-accuracy SNNs require a large number of time steps. Then we propose a hybrid training framework to speed up the inference process and reduce the accuracy loss of SNNs. Experimental results show that the converted SNN with our proposed technique can improve the inference speed by 7X on CNET and 2.67X on VGG-16.
A LiDAR Inertial Odometry Based on Adaptive Voxel Grid Filter for Indoor and Outdoor Mixed Environments

Simultaneous localization and mapping (SLAM) is developing rapidly and has received increasing attention for its performance in challenging environments. However, the challenging indoor and outdoor mixed environments are unexpectedly ignored, which are the main application scenarios for robots and low speed vehicles. In this paper, we present a LiDAR inertial odometry based on an adaptive voxel grid filter to cope with point density variations in indoor and outdoor mixed environments. Benefiting from the adaptive voxel grid filter we proposed, our system can adjust the voxel resolution automatically according to indoors or outdoors, improving the system performance in mixed environments. Moreover, we design a dynamic initialization for the adaptive voxel grid filter. The initial voxel size is set from coarse to fine with computational efficiency as feedback. Extensive experiments were performed on the latest public dataset and in the real world, the results demonstrate that our method is effective, achieving higher efficiency of the system while ensuring the same level of accuracy.
Anti-Interference Algorithm of Environment-Aware Millimeter Wave Radar

Environment-aware millimeter wave radar (EMWR) is one of the most important sensors in autonomous driving system of car. With the number of cars installed EMWR increased rapidly in recent year, the problem of interference between EMWRs on different cars becomes a mounting crisis, which can seriously affect the safety of drivers on that car, especially driving on the highway. In order to solve the above problem, this paper conducts an anti-interference algorithm for 77G EMWR by dividing the interference among EWMRs into two types: the same frequency interference (SFI) and different frequency interference (DFI). In the SFI, the random initial frequency and random starting time are used to reduce the probability of signal collision to inhibit the interference. In DFI, the Hilbert transformation is used to extract the envelope and gets the location of the interference signal. Furthermore, Lagrange interpolation is used to improve the accuracy of the location in DFI to reduce the effective of different frequency interference on EMWRs.
Improvement of Sige Relaxation by a New Clamping Film Deposition Process Method

SiGe has been widely used as stressors in source/drain (S/D) regions of Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) to enhance the carrier mobility of channel. However, relaxation of SiGe films led to less strain and the increase of hole mobility can not achieve expectations. In this paper, a method to prevent SiGe relaxation is provided. The method, which we call clamping film deposition process (CFD), consists of two parts: the common germanium concentration gradient down step is used to eliminate the lattice constant mismatch between films, while the low temperature silicon film deposition is used to eliminate the thermal mismatch between SiGe and silicon cap. The CFD can completely solve the problem of SiGe relaxation. Compared to common SiGe film deposition, the device performance is greatly improved ($\sim 8$%) by this process, pattern wafer yield is baseline level. And this method is compatible with more advanced GAA process.
Averaged Model of Single-Ended Primary Inductor Converter in Discontinuous Inductor Current Mode

This paper newly proposes an averaged model of pulse width modulated (PWM) single-ended primary inductor converter (SEPIC) operating discontinuous inductor current mode (DICM). The model is based on a new duty ratio constraint which is more accurate than the traditional voltage-second balance in high frequencies. Experimental results verified that the proposed model is accurate up to one third of switching frequency.
Comparative Evaluation of DC-link Capacitor RMS Current Stress for Conventional and Reduced Common Mode Voltage SVPWM based Inverters

This work investigates the comparative assessment of DC-link current ripples in perspective of reduced common mode voltage (RCMV) PWM schemes. The anticipated simultaneous reduction of ripple current on DC-link capacitor and common mode voltage compared to traditional PWM approach is seen to exist in certain regions of operation. This may result in reduced thermal stress and consequently in enhanced reliability and operational lifetime. Another increasingly important consideration is for DC microgrids where the inverters are connected directly to the dc-bus and ripple reduction and RCMV for one or more of these inverters is critical for reliable operation. This work utilizes the piece-wise sinusoidal form of DC-link current computing a closed form RMS current expression that is further analyzed for varying load and mod-indices. The RMS DC-link current of conventional space vector PWM has also been analyzed as base case. It would be seen that these modified PWMs mostly put higher ripple stresses on capacitor. However, there are specific regions where one PWM scheme can be employed for reducing DC-link stress.
High-Performance Discontinuous Pulse Width Modulation Strategy for 3-level Asymmetric T-NPC Inverter

Discontinuous mode pulse width modulation (DPWM) is commonly used to improve switching losses for three-phase converters. This paper studies the DPWM strategies for the 3-level asymmetrical T-NPC (AT-NPC) inverter. First, a carrier-based DPWM technique with an offset function is introduced. Secondly, a space vector DPWM strategy is proposed based on the three nearest voltage vectors principle. The PWM control performance of the proposed modulation methods is then verified by simulation and experimental results. A detailed comparison between the two methods is also conducted.
Quantitative Assessment of Head Impulse Test Using Stewart Platform

The Head impulse Test (HIT) has been widely used in the assessment of patients with vestibular deficits. However, the objectively quantified head impulse device has not been used in HIT yet. This study evaluates the Stewart platform through two test impulse commands that satisfied the angular velocity criteria of horizontal head impulse motion and measure the angular velocity response of platform by using IMU. The results showed the proposed Stewart platform satisfies the minimum peak velocity criteria (50 °/s) of manual head impulse motion. The evaluations prove that the 6-DOF Stewart platform has the potential to be an objective head impulse device in HIT.
Designing an Improved ML Task Scheduling Mechanism on Kubernetes

As the fields related to machine learning (ML)/deep learning (DL) continue to mature, the MLOps machine learning automation process is also gradually emerging. Then, many open-source MLOps frameworks based on Kubernetes have begun to be proposed. Currently, most Kubernetes-based MLOps frameworks aim to establish a common and easy-to-use ML pipeline environment for users to use based on ML containerized tasks. However, Kubernetes’ default container scheduler only considers the resource conditions of individual containerized tasks, rather than considering the scheduling of the entire containerized ML task composition. Such a situation may lead to the system resources not being utilized properly. Therefore, this study designs an improved ML task mechanism based on the Kubernetes-based platform to replace the Kubernetes default scheduler. The scheduling strategy in Kubernetes can be modified to better suit the needs of the machine learning development environment.
A Motion Classification Algorithm of Lower-Limb Rehabilitation Robot

When patients with lower limb motor dysfunction do active rehabilitation training, the rehabilitation robot has to predict the patient’s posture accurately. A human motion posture recognition algorithm based on a Naive Bayes classifier is proposed for improving the motion recognition accuracy of the rehabilitation robot. Firstly, a multi-sensor information acquisition system is built on the rehabilitation robot to collect the shoulder displacement data and the forces magnitude on both sides of sagittal plane during the rehabilitation process of the patient. Then, the information acquisition system sends the acquisition data to the upper computer through wireless communication for generating the training data set. Finally, the data set is processed by Naive Bayes classifier to calculate the current human action and predict the next moment action. Simulation and experimental analysis of the Naive Bayes algorithm show that the classifier can constantly update the posterior probability based on the conditional probability and achieve a high recognition rate.
Large space Wireless Power Transfer System that Meets Human Electromagnetic Safety Limits

This paper presents a wireless power transfer system that allows for the free reception of energy throughout a room while meeting human electromagnetic safety standards. We optimized the working frequency, coil size, and excitation current of the energy transmission coil to reduce the potential harm of electromagnetic fields on the human body. Finally, a prototype of a dual circular energy emission system with a working frequency of 1.53 MHz, a radius of 2 m, and a distance of 2 m was built, and a receiver was tuned and matched. In a space of approximately 25 m³, a single receiver can achieve wireless power transmission of about 40 W, with a transmission efficiency of 30%. This research demonstrates the possibility of humans breaking free from cable constraints in medium and low-power application scenarios, having significant theoretical significance and practical value.
EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning

The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization.
Assistive Robot Manipulator Pose Prediction based on Object Orientation using CNN

Robot, as dictated by its assistive function for human, frequently needed to perform object manipulation (i.e. robotic manipulation). A successful robotic manipulation not only determined by the robot actuator, it is also strongly affected by the robot perception in capturing the world and the workpieces. Robotics has made extensive use of camera-based visual perception, especially robotic manipulation. This perception will enable the robot to perform object(workpieces) detection and localization and generate suitable control policy to move the robot via a series of robot poses. This paper proposed an assistive robot manipulator pose prediction based on object orientation using CNN. The manipulator pose prediction will enable further action for a successful robotic manipulation by providing a relative pose between the object and manipulator. Experiment result shown that the presented method was able to predict the robot pose, especially on the fifth joint of the robot accurately based on the object orientation images.
A Novel Foreign Object Detection and Classification Algorithm for Capacitive Wireless Charging Systems

Wireless power transfer (WPT) systems have gained widespread adoption in recent years owing to their wide application range and high performance. However, these resonant systems are designed to operate at a fixed operating point and the presence of foreign objects may degrade their performance considerably, even potentially damaging the charger. This paper proposes a simple foreign object detection (FOD) algorithm for capacitive WPT systems using a machine learning-based classifier. This FOD technique does not require any additional sensing circuitry and uses the inherent WPT system components for sensing. A Machine Learning classifier is used to systematically categorize the foreign objects into categories. The proposed algorithm is tested with a wide set of foreign objects and is shown to have a good detection accuracy of 89%.
A Non-Resonant Multi-Output Half-Bridge Inverter for Flexible Cooking Surfaces

In domestic induction heating (IH) appliances most commonly the series resonant half-bridge (HB) inverter is used. The number of required components can be further reduced by using non-resonant inverter topologies. Within this contribution, a novel non-resonant multi-output HB inverter for use in flexible cooking surfaces is presented. The proposed inverter topology is described and simulation as well as measurement results are shown. Measurement results are presented for operation with up to two inductors. The hardware prototype is equipped with gallium nitride semiconductors and operates at switching frequencies up to 400 kHz. Measurement results show that an output power of 522W is reached for use of a single inductor. When using two inductors, a maximum output power of 828W is reached. The presented results demonstrate the feasibility of the concept, while the efficiency of the system needs to be investigated in future work.
Movement Classification for Hand Telerobot Based on Electromyography Signal Using Convolutional Neural Networks

A telerobot or teleoperated robot is a device that is remotely controlled by a human operator as opposed to following a predetermined sequence of movements, but which exhibits semi-autonomous behavior [1] Combining the major subfields of teleoperation and telepresence. In the scientific and technical community, teleoperation is the most common term for remote operation. Telepresence, on the other hand, is a subset of telerobotic systems equipped with an immersive interface that allows the operator to feel physically present in a remote environment by conveying his presence through the remote robot [2]. In a teleoperation system, there are two main components that must be prepared. Namely the controller or robot operator (local site) and the controlled robot (remote site) [3]. Telerobot requires a transmission medium to communicate. WebSocket is very appropriate to be used as a telerobot communication protocol [4]. There are many types of telerobot, including hand robot. In this study, a telerobot uses the convolutional neural network (CNN) algorithm to classify hand movements based on electromyography signal. Model testing has a training process and model tuning to get the best hyperparameter value. These results are tested on five subjects and call an average accuracy of 0.996600, f1 score of 0.996634, and precision of 0.996309 with a data composition of 80% for training data and 20% for testing data. To test the telerobot time delay by sending several motion codes from the local site to the gateway server and forwarded to the remote site with a local site bandwidth of 7.79 Mbps download and 1.56 Mbps upload and a remote site bandwidth of 11.6 Mbps for download and 5.96 Mbps for upload, the resulting average values average of 0.395 seconds or 395 ms.
A Full-Bridge Boost LCL-Type DC/AC/DC Converter

This paper presents a full-bridge boost LCL-type dc/ac/dc converter with a capacitive output filter and operating with a constant output voltage. When the input voltage has changed, the frequency changes with a constant turn-off time of transistors to keep the output voltage constant. Based on this idea, a 50 to 100V dc input, 100-120kHz, 400-W, 200-V dc output converter has been built, and a fourier’s mathematical description is exploited to help the design. Finally, a report on the implementation of a laboratory prototype with 400-W power is presented.
Cut Interruption Detection in the Laser Cutting Process Using ROCKET on Audio Signals

Laser cutting is one of the classic methods used in metal processing. With increasing automation, it is important to ensure that large volumes can be produced reliably. This includes avoiding re-welding, known as cut interruption. In the presented work, audio signals are used to detect cut interruptions during laser cutting. The audio signal is classified into two classes: good cuts and cut interruptions. To solve this classification problem, the time series classifier RandOm Convolutional KErnel Transform (ROCKET) is used. The influence of the window size, the number of kernels and the repeatability of the training is investigated. With the presented work it is shown that a cut interruption detection with a microphone is possible. For a real world application there is a trade-off between accuracy and window size.
Security-Reliability Analysis of FSO Communications Over Generalized F Channel

This research paper investigates the security and reliability aspects of a multiuser free space optical (MU-FSO) system when exposed to eavesdropper attacks. The system experiences atmospheric turbulence, specifically generalized Fisher-Snedecor F turbulence, which leads to significant signal fading. To enhance the security and reliability of the MU-FSO system, the study employs three scheduling schemes: optimal scheduling (OS), round-robin scheduling (RS), and cumulative distribution function scheduling (CS). The paper derives precise expressions for the intercept probability (IP) and outage probability (OP) of the MU-FSO system to compare the performance of these opportunistic scheduling schemes considering the impact of atmospheric turbulence and path loss. Additionally, the paper presents simplified expressions for the asymptotic behavior of IP and OP with acceptable accuracy. Numerical results demonstrate how the performance of the MU-FSO system in terms of secrecy and reliability is influenced by factors such as the conditions of the main and wiretap channels, distances between legitimate users, the number of active users, and the power levels of the legitimate and eavesdropping links. Monte Carlo simulations are used to verify the correctness of the numerical results.