Miracle-dz/UniFAR-FTUs huggingface dataset:
Ergodic Capacity Analysis of NOMA-Based Two-Way Relaying Systems

In this work, a non-orthogonal multiple access (NOMA) relying on a two-way relaying (TWR) approach is investigated to promote more efficient use of the available spectrum. Specifically, a NOMA-enabled half-duplex decode-and-forward relay node is used to simultaneously exchange data between two cellular users and a base station (BS), while assuming that the direct links between the two users and the BS are unavailable or negligible. In our analysis, the ergodic capacity is evaluated, in which high signal-to-noise ratio approximations, as well as upper bounds are derived owing to the intractability related to the exact analysis. The results reveal the superiority of the proposed TWR-NOMA scheme compared with the traditional one-way relaying (OWR-NOMA) scheme. Additionally, simulation results show that selecting a relay station (RS) closer to the BS can achieve a higher ergodic sum capacity than selecting a far RS.
A Novel Quasi-Single-Stage High Efficiency and High Power Factor AC/DC Converter

Quasi-single-stage AC/DC converter has the advantages of fewer devices, simple control strategy, and high-power density. Based on the traditional quasi-single-stage AC/DC converters, this paper proposes a new topology with a set of differential-mode coupling inductors based on magnetic integration to solve the drawbacks of the existing single-phase quasi-single-stage AC/DC converter, such as low power factor and extensive input current harmonics. Furthermore, the burst switch is added to broaden the operating range of the converter. This paper proposes the working principle and the power flow of the novel quasi-single-stage AC/DC converter. Then the working mode and equivalent circuit of the previous power factor correction (PFC) circuit are analyzed. Finally, the 400W experimental prototype is built to verify the working principle of this novel circuit. The experimental results show that the novel quasi-single-stage AC/DC converter can achieve a high power factor under the condition of cost-saving, and has effective electro-magnetic interference (EMI) suppression.
Operationalizing team commitment in a project-based learning environment

Commitment is a multi-dimensional construct that has been extensively researched in the context of organizations. Organizational and professional commitment have been positively associated with technical performance, client service, attention to detail, and degree of involvement with one’s job. However, there is a relative dearth of research in terms of team commitment, especially in educational settings. Teamwork is considered a 21stcentury skill and higher education institutions are focusing on helping students to develop teamwork skills by applied projects in the coursework. But studies have demonstrated that creating a team is not enough to help students build teamwork skills. Literature supports the use of team contracts to bolster commitment, among team members. However, the relationship between team contracts and team commitment has not been formally operationalized.This research category study presents a mixed-methods approach towards characterizing and operationalizing team commitment exhibited by students enrolled in a sophomore-level systems analysis and design course by analyzing team contracts and team retrospective reflections. The course covers concepts pertaining to information systems development and includes a semester-long team project where the students work together in four or five member teams to develop the project deliverables. The students have prior software development experiences through an introductory systems development course as well as multiple programming courses. The data for this study was collected through the team contracts signed by students belonging to one of the 23 teams of this course. The study aims to answer the following research question: How can team commitment be characterized in a sophomore-level system analysis and design course among the student teams? A rubric was developed to quantify the team commitment levels of students based on their responses on the team contracts. Students were classified as high or low commitment based on the rubric scores. The emergent themes of high and low commitment teams were also presented. The results indicated that the high commitment teams were focused on setting goals, effective communication, and having mechanisms in place for timely feedback and improvement. On the other hand, low commitment teams did not articulate the goals of the project, they demonstrated a lack of dedication for attending team meetings regularly, working as a team, and had a lack of proper coordination while working together.
Design and Performance Evaluation of Hybrid-Switched-Capacitor Voltage Regulators for 48 V to Point-of-Load Application

This paper introduces and compares the single-stage and two-stage hybrid-switched-capacitor voltage regulators for 48 V to point-of-load application. The single-stage solutions are multi-phase series-capacitor buck regulators that equally divides the input voltage to reduce the voltage stress and switching loss. The two-stage solutions utilize unregulated switched-capacitor converters to generate a intermediate bus. The intermediate bus voltage can be 1/2, 1/3, or 1/N of the input voltage depending on the circuit topology of the first stage. The second stage is a multi-phase series-capacitor buck regulator similar to the single-stage solution. The decoupling capacitors are removed and the flying capacitors of the first stage are reused as the input capacitors of the second stage to achieve higher power density. The semiconductor device rating, passive component size and efficiency of the single-stage and two-stage topologies are evaluated as a guideline of selecting the intermediate bus voltage and the stack number of the series-capacitor buck regulator. The analysis shows that the single-stage solutions have higher efficiency, lower device count, but require more capacitors. A48V to 1 V six-phase series-capacitor buck prototype is designed and tested to verify the theoretical evaluation.
Research on Upper Limb Rehabilitation Action Recognition Method of Unsupervised Contrast Learning Based on Time-domain Multi-scale Feature Fusion

Vision-based action recognition technology plays a major role in human-robot interaction during the upper limb rehabilitation training of stroke patients with the aid of an upper limb rehabilitation robot. To address the problems that supervised learning requires a large amount of manual labeling, which is time-consuming and inefficient, in this paper, we present an upper limb rehabilitation action recognition method of unsupervised contrast learning based on multi-scale feature fusion and we improves SimCLR by replacing the single-scale CNN with Inception V1 which contains the multi-scale feature fusion module and increasing the temporal dimension based on the idea of the expanded convolutional neural network to fully extract the Spatio-temporal feature information of the actions. The experiment results show that unsupervised contrast learning based on time-domain multi-scale feature fusion achieves recognition accuracy of 92.59% for 8 upper limb rehabilitation actions without extensive manual labeling. It can help patients recover their motor functions through upper limb rehabilitation training.
High Voltage DC Bus Architecture for Ultium Battery Electric Vehicles

Ultium is a revolutionary electric vehicle platform built on a flexible battery architecture. It enables the production of EVs across different vehicle types - offering outstanding power, range, and performance options with a modular architecture. It facilitates cost-effective segment entries with necessary application derivatives and bandwidths in terms of vehicle size and performance. The High Voltage (HV) DC bus system architecture plays a vital role for implementing this strategy as it addresses the harmonious operation of all power converters, inverters, and energy storage systems. The objective of a robust HV bus architecture during propulsion mode is to ensure mechanization and control stability despite electrical ripple and resonance introduced by various components at various frequencies depending on their filter-sizes and control algorithms. Additionally, the HV bus architecture during charging mode should have enough flexibility to allow charging from multiple power sources depending on availability without compromising protections and industry protocols. Component and system design without considering HV DC bus robustness produces a sub-optimal design, control instability, NVH (noise vibration & harshness) malfunctions and unwanted shutdown. This paper discusses all relevant perspectives considered for a scalable, commonable HV DC bus architecture for all Electric Vehicle variants under the development of the flexible Ultium platform. Relevant system modeling, simulation results and vehicle level test results are also within the scope of this paper.
Pipeline defect detection based on feature remapping

Traditional underground drainage pipeline detection requires a lot of manpower to complete, which faces not only the challenge of time but also the challenge of efficiency. In recent years, the development of deep learning has played an amazing role in many fields, especially in pattern classification. Usually, there are some problems in real pipeline detection tasks, such as multiple sample classes which usually includes 1 normal class and N defect classes, the problem of normal class's imbalanced sample proportions, and the problem of similar features, that is, the features between partial normal class samples and defective class samples are similar. The above problems will lead to a lower precision rate of normal class. Consequently, first, for the multi-class problem, we propose to integrate 2-class classifier (normal class and defect class) and multi-class classifier (normal and specific defect classes). Second, for the problem of imbalance of normal samples, specifically, because the pipeline samples usually include a normal class and multiple defect classes, the proportion of normal samples in the multi-class classifier is small which will hurt the precision rate of normal class. Therefore, we propose to use feature remapping in the integration of 2-class classifier and multi-class classifier, that is, the trained 2-class classifier supervises the training of the multi-class classifier. At the same time, through feature remapping, the normal class features output of the multi-class classifier are approximated to the normal class features output by the 2-class classifier. These can not only alleviate the problem of normal sample imbalance, but also make full use of the gap between classes to ease the problem of similar features and improve the precision of normal classes. Third, in order to prevent information loss in the process of feature transfer along the network, we adopt the feature transfer enhancement module to improve the feature transfer capability of the network. Besides, the feature transfer enhancement module can also be beneficial to the problem of similar features. This method allows us to achieve a 9% gain in the normal category sample classification, which will be shown in the experiment.
Feedforward Deadtime Compensation Using Current Zero Crossing Detection

A feedforward deadtime compensation algorithm for inverters is proposed, utilizing current zero crossing detection (ZCD) to generate a current direction signal for the feedforward input. Conventional deadtime compensation relies on information from a standard current sensor, which may not capture current ripple behavior accurately due to latency, frequency response, and noise limitations. The ZCD and hardware logic circuits operate at extremely high speed, and the feedforward compensation algorithm has minimal computational requirements. Simulation results show a significant improvement in load current total harmonic distortion (THD). The proposed circuit was constructed, and hardware testing with a SiC inverter showed similar performance to simulation.
Factors Affecting Acceptance of Dental Appointment Application among Users in Bangkok and Metropolitan Area

This paper examines the main factors affecting dental-related users” acceptance of dental appointment technology as a means for receiving dental appointment services. A questionnaire was developed based on the Technology Acceptance Model (TAM) and incorporated perceived useful features to understand user characteristics, acceptance, and usage behavior of a dental appointment application. A proposed research model and hypotheses were tested with a sample of 555 customers of a dental clinic in Bangkok and Metropolitan area using descriptive analysis, factor analysis, and multiple regression. The findings show that perceived ease of use, perceived usefulness and perceived value have significant effects on the acceptance of a dental appointment application. However, the application feature has no direct effect on the intention to use. Results can be used as a reference to develop dental appointment services that align with the needs of the users.
The Variable Reduction Method for Nonlinear Programming

A first-order method for solving the problem: minimize f(x) subject to Ax - b \geqq 0 is presented. The method contains ideas based on variable reduction with anti-zig-zagging and acceleration devices based on the Variable Metric Method. Proof of convergence to a Kuhn-Tucker Point, and statement of the rate of convergence when the strict second order sufficiency conditions hold are given.
Rural School Students’ Attitudes and Perceptions toward the Engineering Education and the Engineering Profession

As the growth of science and technology accelerates, the prospect of engineering as a field of study and profession gains importance. Many school students prefer to study engineering in the future and work as engineers. Examining their views on engineering will help correct frivolous attitudes and proactively revamp engineering education. However, only a few studies have been conducted on secondary school students’ attitudes and perceptions toward engineering, especially from the schools located on rural outskirts. Against this backdrop, this study critically examines rural secondary school students’ attitudes and perceptions of engineering education and the engineering profession. The students were part of the Rural Student Technical Enhancement Program (R-STEP), conducted by the IEEE Education Society. The program was designed as a free-of-cost advanced technical training and skilling session for rural higher secondary students in Kerala, a southern state in India. The data collection for the study was performed by administering a questionnaire containing ten questions, among which five were concerning engineering education, and the other five were on the engineering profession. The questionnaire was prepared in the regional language (Malayalam), considering the respondents’ age group and background. A total of 225 responses were collected from students from various districts of Kerala. The questionnaire responses were then critically analyzed to interpret students’ opinions on engineering education and profession. The research findings show that nearly 60% of rural higher secondary school students sincerely aspire to become engineers. It is also observed that about 50% of students acknowledge that engineers should exhibit problem-solving skills. Another noticeable finding is that about 80% of students accept the significance of practical laboratory-oriented learning in engineering education. On the other hand, more than 30% of students are confused about the significance of mathematics in engineering. Another 30% of students even observed that mathematical aptitude is not obligatory for engineering education. More insights from other questions identified students’ attitudes and perceptions toward the engineering profession. The study concludes with a few curative suggestions and recommendations to enhance rural school students’ awareness of engineering education and profession.
Identification of atomic-scale defect structure involved in the negative bias temperature instability in plasma-nitrided devices

We utilize a very sensitive electron spin resonance technique called spin-dependent tunneling to identify defect centers involved in the negative bias temperature instability in plasma-nitrided p-channel metal-oxide-silicon field-effect transistors. The defect’s Si29 hyperfine spectrum identifies it as a K center which we refer to as KN. The generation of KN centers provides an explanation for the instability’s enhancement in nitrided devices.
The Construction and Analysis of the LTE Network Model with Joint Service of Traffic of LTE and NB-IoT Devices

The active implementation of the Internet of Things (IoT) concept has led to an impressive increase in both the number of connected devices and data traffic during past decade. The ever-increasing data traffic, mass connectivity, and network densification introduced by the IoT concept is driving the need for a new resource sharing strategy between traditional cellular mobile devices and IoT devices. The need for resource allocation between traditional cellular subscribers and IoT devices was recently recognized by 3GPP with the ratification of the special “In-band” deployment mode in LTE Rel. 13, which allows to share radio resources between LTE and NB-IoT devices. However, 3GPP does not provide clear or specific guidance on resource allocation. The purpose of this work is to develop and analyze a model for evaluating the efficiency of resource sharing for an LTE network serving jointly traffic generating by LTE and NB-IoT devices. In the article the general principles of the Internet of Things concepts are analyzed. The mobile communication technologies recommended by 3GPP for the implementation of the concepts of the Internet of things are studied. The mathematical model of the LTE network with NB-IoT functions enabled is constructed. The algorithm for calculating quality indicators of the model is evolved.
Power Semiconductor Lifetime Extension Technique using Turn-on Energy as a Variable to Maintain Constant Loss

The junction temperature swing in a power semiconductor causes wear-out due to mismatch in the coefficient of thermal expansion in between die and substrate. This work proposes an active thermal control technique that attempts to mitigate the junction temperature swing in a power semiconductor by keeping the semiconductor losses constant with respect to loading level. This is done by using an active driver to control the turn-on energies. In this paper, the device losses are first characterised experimentally and the data is then used to adjust the turn-on energy with respect to loading. During operation, the driver's resistance varies to minimise variation in the semiconductor loss and consequently, junction temperature swing. Extension of lifetime is assessed through simulation. The proposed open-loop technique is benchmarked against a closed-loop technique. In addition, the proposed technique is simulated for two topologies with varying load profiles to assess the benefit and trade-off in terms of life extension and increased losses. Results suggest that significant gains in device life in the order of 10 times or more can already be gained without the need for closed-loop temperature feedback. This comes at the expense of increased losses of about 15%.
Deep Reinforcement Learning For Autonomous Control Of Low Voltage Grids With Focus On Grid Stability In Future Power Grids

The effort to achieve a low-emission electrical grid in Germany poses challenges for distribution system operators. These include the expansion of volatile and decentralised re-newable energy resources (DER), such as photovoltaics, and the steady expansion of electromobility with high charging capacities in the low voltage (LV) grid. To enhance the grid stability and to avoid costly grid expansion, a autonomous control systems must be implemented. For this purpose a double deep Q-learning (DDQN) approach with multiple agents are being investigated. The reinforcement learning (RL) agents are being trained and validated on a LV grid with grid instabilities such as voltage drops, transformer overload and reverse power flow to the medium voltage level. The grid consists of 13 nodes, 1 string with 11 households, 11 PV systems with battery storage systems (BSS) and 11 electrical vehicle (EV). As results the voltage level drops are decrease from 0.89 p.u. to 0.95 p.u. compared between controlled and uncontrolled cases. Also, a reverse power flow can be fully prevented. The transformer utilisation can be decreased by a maximum of 1 p.u. Moreover, the batteries are more often utilised for global grid stability purposes then optimise local self consumption.
Strategy Generation Based on DDPG with Prioritized Experience Replay for UCAV

Unmanned combat aerial vehicles are playing an increasingly important role in the future military field, while the optimal control strategy remains a great challenge due to the high dynamics of the aerial vehicles themselves as well as the environmental uncertainties in air-combat. Based on a deep deterministic policy gradient algorithm framework, an air combat decision-making strategy is designed and implemented, and further a prioritized experience replay method is proposed for the proposed algorithm to further improve the efficiency in the training process. Simulation experiments show that, at much reduced training cost, the proposed approach achieves superior air combat performance with fast convergence.
Thermal analysis of an indirectly heat pulsed non-volatile phase change material microwave switch

We show the finite element simulation of the melt/quench process in a phase change material (GeTe, germanium telluride) used for a radio frequency switch. The device is thermally activated by an independent NiCrSi (nickel chrome silicon) thin film heating element beneath a dielectric separating it electrically from the phase change layer. A comparison is made between the predicted and experimental minimum power to amorphize (MPA) for various thermal pulse powers and pulse time lengths. By including both the specific heat and latent heat of fusion for GeTe, we find that the MPA and the minimum power to crystallize follow the form of a hyperbola on the power time effect plot. We also find that the simulated time at which the entire center GeTe layer achieves melting accurately matches the MPA curve for pulse durations ranging from 75–1500 ns and pulse powers from 1.6–4 W.
CALNet: LiDAR-Camera Online Calibration With Channel Attention and Liquid Time-Constant Network

3D LiDAR and 2D camera are widely used in environment perception tasks in autonomous driving and robot navigation. The prerequisite for multi-sensor data fusion is to calibrate between sensors. In the calibration work of 3D LiDAR and 2D camera, most of the existing calibration techniques require a lot of manual work and complex calibration environment settings. How to improve the automation of calibration tasks and the applicability of dynamic environments is a valuable research topic. In this paper, we proposed a novel online calibration network (CALNet) to Automaticlly infer the 6 degrees of freedom (DOF) rigid body transformation between 3D LiDAR and 2D camera. CALNet not only adopts the attention mechanism to selectively extract features from RGB image and point cloud depth map, but also achieves high precision and robustness of the calibration network through a hybrid spatial pyramid pooling (HSPP) and a liquid time constant network (LTC). In the training process, besides using the L2 loss of the network to predict extrinsic calibration parameters as a supervised signal, we also considered the geometric transformation distance loss of the 3D point cloud. Extensive experiments have demonstrated that the performance of CALNet is better than the state-of-the-art DL-based methods. The code will be publicly available at https://github.com/XD319328/CALNet.