Miracle-dz/UniFAR-FTUs huggingface dataset:
Synaptic Plasticity in Novel Non-Volatile FET with Amorphous Gate Insulator Enabled by Oxygen Vacancy Related Dipoles

We experimentally demonstrate the synaptic plasticity characteristics in the non-volatile field-effect transistors (NVFET) integrated with amorphous Al<inf>2</inf>O<inf>3</inf> dielectric utilizing voltage-modulated oxygen vacancy and negative charge dipoles. Both short-term plasticity (STP) and long-term potentiation (LTP) behaviors are obtained in a single Al<inf>2</inf>O<inf>3</inf> NVFET. Sub-millisecond LTP speed allows for high-speed processing. Spike-timing-dependent plasticity (STDP) curves with 100 ns spikes are achieved for the amorphous Al<inf>2</inf>O<inf>3</inf> synaptic transistor.
Multi Software Thermal Design Optimization

System level thermal optimization of a multi-part thermal design tools is exercised, where a low heat source temperature on one part, modelled in one tool, conflicts with the low mass design objective of a heatsink, modelled in another simulation tool. A third tool is used that integrates the separate simulation models into a system model and has capabilities for automated design optimization. Use of smaller parametric surrogate models is a key enabler to limit runtime for the many simulation runs needed. Four different surrogates were tested: Quadratic and Kriging fits on 27 runs (partial DOE) or 81 runs (full DOE) design space mapping. The surrogates speed up the simulation time by about 3 orders of magnitude. Full DOE performed marginally better compared to partial DOE. Kriging surrogates performed much better than Quadratic. Pareto Front optimization turned out to be more robust against fitting errors in the surrogates than Weighted Sum optimization. The statistical capability of the optimal design was established using Monte Carlo simulation.
Design of Maximum Stopping Deceleration of Mobile Manipulator Considering the Protective Separation Distance without Tip-over

To increase the efficiency and flexibility of production operations, mobile manipulator has been developed and introduced in collaborative workspaces where robot shares the workspace with a operator. Therefore, the importance of robot safety is also increasing because of the rising possibility of the physical collision between the mobile manipulator and the operator. Keeping distance from robot and operator can be a way to prevent the collision recommended by a protective separation distance, ISO/TS 15066:2016. However, case for tip-over of mobile manipulator had not been considered although collision by tip-over could be danger to the operator. Therefore, a stability of tip-over should be considered when we apply the protective separation distance to guarantee the operator's safety. In this paper, a design method for stopping deceleration with consideration of tip-over and relationship between tip-over moment and the protective separation were presented, and simulation has been conducted to prove this method.
AC/DC Hybrid Electric Power Generation System for More Electric Aircraft by using Prototype Model

This paper declares the modeling and simulation of electric aircraft by using AC/DC hybrid electric power stage system for many electric aircraft. Compare the result of hardware & software system. There are many different voltage stock for the electrical system on ample civilian aircraft, 28 V de that can be used on large aircraft for lower power loads, and full electrical system on small aircraft. For field aircraft, 270 V dc (135 V) is used and several sub-systems are used on few big aircraft. 115 V ac at 400 Hz big loads on large military aircraft. The demonstration of the planned topology is proved with MATLAB / Simulation model.
Focal Auxiliary Classifier Generative Adversarial Network for Defective Wafer Pattern Recognition with Imbalanced Data

Defective wafer pattern recognition is important for quality control and yield enhancement in semiconductor fabrication systems. The collected wafer maps are usually imbalanced, which may degrade the performance of classifier. In this paper, a focal auxiliary classifier generative adversarial network (FAC-GAN) for defective wafer pattern recognition with imbalanced data is proposed. FAC-GAN is composed of AC-GAN modified with focal loss for imbalanced data generation and deep neural network. The performance of the proposed FAC-GAN is measured on real-world wafer map dataset “WM-811k” and it outperforms SVM and CNN.
Multi-Depth Learning with Multi-Attention for fine-grained image classification

Compared with the traditional image classification task, fine-grained image classification has the difficulty of small differences between classes and large differences within classes. In view of this difficulty, attention proposal has been widely used in fine-grained image classification. However, traditional attention proposal has to localize first and then processing. Model needs to run step by step and the attention focusing method is single. This paper proposed a model (MAMDL, Multi-Attention-Multi-Depth-Learning) which combines multiple attention mechanisms and multi network parallel learning. The advantage of MAMDL is that it can first learn end-to-end. Secondly, the multiple attention mechanisms can effectively combine four attention mechanisms to improve the network’s ability to process local features. Finally, this paper focuses on the attention found in the backbone network, Feature extraction from branch convolution neural networks with different depths enhances the classification performance of the model. The experimental results show that MAMDL outperforms mainstream fine-grained image classification methods on the fine-grained image classification dataset CUB-200, Stanford dogs and Stanford cars.
Channel Mobility Properties of β-Ga2O3 MOSFETs on Si Substrate Fabricated by Ion-cutting Process

The mobility properties of MOSFETs on the heterogeneous <tex>$\beta$</tex> -Ga203-Al<inf>2</inf>O<inf>3</inf>-Si (GaOISi) substrate are investigated. Depletion (D)-mode GaOISi MOSFET achieves a peak effective mobility <tex>$(\mu_{\text{eff}})$</tex> of <tex>$95\text{cm}^{2}/\mathrm{V}\cdot \mathrm{s}$</tex> As <tex>$t_{\text{ch}}$</tex> is reduced to 40 nm, the enhancement (E) operation is demonstrated, obtaining a peak <tex>$\mu_{\text{eff}}$</tex> of 13.7 Cm<sup>2</sup>/ <tex>$\mathrm{V}\cdot \mathrm{s}$</tex>. D-mode device achieves a <tex>$\mu_{\text{eff}}$</tex> comparable to those for epitaxial and bulk Ga<inf>2</inf>O<inf>3</inf> indicating that defects induced by the ion-cutting can be effectively eliminated by a high-temperature thermal annealing.
The Effect of Sound in VR Exergame to Adult Player: A Primary Investigation

A serious game is a new concept of game development that is a digital game created to entertain and achieve an objective or learning at least one other goal. Ascribable, the digital game has affected of player motivation, and users gain emotional level. Like the reward system, sound is typically embedded as a part of game mechanics and affecting player‘s motivation. The research aims to explore how the game components affect the motivation in virtual reality (VR) exergames. We evaluated the impact of the immersive rehabilitation system on motivation and affect. We conducted a study with 5 participants to compare the motivation in VR exergame with enabling and disable the sound system because We preliminary employ the formative evaluation to evaluate the in-depth detail of sounds effect to the player experience. Participants reported significant improvements in motivation and engagement in the experiment to enable system conditions than questionnaires and performance players. In conclusion, the sound system is an inclination key feature to increase motivation in exergame. We derive a design experiment for future research: (1) Increasing participants in virtual reality exergame experiments. (2) Addition decibel sound chat to compare with a sound system. (3) Addition a new method player performance with motivation.
PVT and Aging Degradation Invariant Automated Optimization Approach for CMOS Low-Power High-Performance VLSI Circuits

In this paper, we aim at optimizing the leakage power and propagation delays using optimization algorithms like Glowworm Swarm optimization and Neighbourhood Cultivation Genetic Algorithm, subjected to variations in Process, Voltage, Temperature and Aging degradation (PVTA) targeting low power or high performance applications. For high performance applications, we synthesize transistor sizes, at which the critical path delay in worst case PVT conditions with 3 years of NBTI aging degradation is optimized below the critical path delay (of initial sizing) at nominal conditions keeping power budget in bound. On the other hand, for low power applications, we obtain transistor sizing where leakage is reduced by more than 50%, keeping a bound on critical path delay. We have also proposed a step by step optimization method for optimizing complex cells. All the pre and post stress simulations are performed using HSPICE for 22nm Metal Gate High-K dielectric model parameters. The temperature range and the supply voltage ranges are -55°C to 125°C and 0.72V to 0.88V respectively. The process parameters are considered at $\pm 3\sigma$ variation. The ingenuous working of the circuits for the obtained sizing is ensured by Monte Carlo analysis evaluating over entire range of process variations and operating conditions for the intended life time.
Deep Neural Networks for Sound Synthesis of Thai Duct F1ute, Khlui

This paper introduces Thai musical instrument sound synthesis with an intelligent technique which is a combination of deep neural networks and conventional signal processing techniques. The instrument focused on in this paper is the Khlui, which is a unique Thai reedless wind instrument. Khlui sound can be synthesized by performing a combination of additive and subtractive syntheses. The synthesis system takes the pitch and loudness of any monophonic input signal. Then, multilayer perceptron and recurrent neural networks are used together in order to generate 3 parameters which are harmonic distribution, filter magnitude response, and the signal envelope. Finally, the harmonic characteristic of the Khlui along with the blowing noises are highly synthesized. The results are natural-sounding and realistic when compared with the recorded Khlui audio. This research will contribute to the more convenient natural synthesis of the Khlui sounds.
Automate Traditional Interviewing Process Using Natural Language Processing and Machine Learning

Nowadays, almost everything is equipped with technology. People can save time by using modern day technological applications in the most convenient way. Smart Interviewing System is one such software/tool which automates the traditional interviewing process using modern Natural Language Processing techniques and deep learning applications. The system will be mainly beneficial for interviewers and HR management employees working for different organizations who conduct technology related interviews. The system works with human voice and writing patterns. The system converts human language into system understandable text-based inputs, and these are used as inputs in the automated interviewing process. The system then checks the accuracy of the answers which candidates provided on the both oral interviews/ technical interviews and written tests. Later, the system automatically predicts scores for each answer using concepts of the deep learning. Interviewers can reduce the effort that they have to put in for selecting the most suitable candidates who are qualified enough to work with their organization. SIS is developed based on modern DL and NLP concepts using Python programming language alongside with ReactJS Framework. This system checking and evaluating candidate more accurately in every stage of the interview using advance evaluation parameters than human oriented evaluations. Above process lead system to find more human errors which critically can be affected to future of the organizations. Because of that, it can be led organizations to find best human resources comparing to the traditional interviewing process by sacrificing less time and effort.
PERCEIVED STRESS AND COPING STRATEGIES AMONG UNIVERSITY STUDENTS

The purpose of the study is to assess both perceived stress and coping strategies among university students in a specific cultural context. A sample of 223 undergraduate students from Qatar University responded to self-rating questionnaires, Perceived Stress Scale (PSS) and Brief Cope Inventory adapted forms. Based on the Perceived Stress Scale, the results show that a majority of participants evinced a moderate stress level (76 %) followed by a high stress level (12 %) and that girls are significantly more stressed than boys are. In addition, a high academic level is correlated with more perceived stress. Based on the Brief Cope Inventory, most of the participants used religion and a positive coping strategy. These results reveal that the effect of stress depends on the way it is perceived. Coping strategies are influenced by social and cultural characteristics. Culture affects our stress perception and individual choice of coping strategies. Assessing students’ problems may help prevent the bad effects of stress on health and academic performance. Results also invite further study, particularly in the form of a longitudinal followup.
Design for Interpretability: Meeting the Certification Challenge for Surgical Robots

This paper presents a perspective on some issues related to safety in the context of autonomous surgical robots. To meet the challenge of safety certification and bring about acceptance of the technology by the public, we propose principles for a design paradigm that goes in the direction of safety by construction: design with certification in mind, clearly distinguish the notion of safety from that of responsibility, view the human component as scaffolding in the progressive transfer of decision-making to the machine, preserve interpretability by renouncing black-box approaches, leverage interpretability to assign responsibility, and take corrective action only when the semantic of the human-machine interface is violated.
A novel prescribed performance control model with ESO to optimize trajectory tracking for quadrotor UAV

In the study of trajectory tracking of the quadrotor UAV, there exists two major problems: external interference and tracking estimation error. Based on the extended state observer (ESO), a unilateral no-overshoot fast convergence model with Novel Prescribed Performance Control (NPPC) mechanism is proposed in this paper. Firstly, the quadrotor UAV is transformed into an external loop position and an inner loop attitude control system. A novel error transform function is proposed for the limitation that the former prescribed performance control scheme cannot achieve rapid convergence of the system state. Secondly, the controllers are designed by the position subsystem and the attitude subsystem, according to the backstepping method and the ESO are designed to estimate external interference. Finally, it is combined with dynamic surface control to realize the trajectory tracking of the UAV under the external interference. Several simulations based on this proposed model have been performed and the results show that the designed controllers can make the quadrotor from any initial state reach the steady one according to the specified convergence time and have better convergence than Traditional Prescribed Performance Control (TPPC).
Effect of Size of Preceding Vehicle on Behavior of Following Vehicle

In this study, we demonstrated that the size of a preceding vehicle affects the behavior of the following driver. Intelligent assistance systems, including autonomous driving, have been considered in the prevention of traffic accidents. If forecasting future actions of surrounding vehicles is possible, driving safety can be significantly improved. Therefore, our research group developed a prediction system. Previous studies reported on the effect of the size of the preceding vehicle on the driving behaviors of the following vehicle. However, the effect on the lane changes of the following vehicle has not yet been discussed. This paper analyzes the effect of the size of the preceding vehicle, particularly focusing on lane changes. It is demonstrated that drivers frequently change lanes when a large vehicle is ahead compared with when a normal-sized vehicle is ahead. In addition, the model of lane-change detection considering the size of the preceding vehicle was constructed using actual traffic data. Based on a comparison with a conventional model, our approach proved to be capable of improving early detection performance.
Influence of conducting filament dimension on the performance of ReRAM device in the SET state

To achieve the current-day demand for data storage, resistive random access memory (ReRAM) is an emerging candidate in non-volatile memory (NVM) segment. The switching of ReRAM device deals with SET and RESET states where formation and dissolution of conducting filament (CF) decide the resistive state of the device. In SET state, the current flows through CF which induces Joule heat inside ReRAM. An electro-thermal numerical model has been designed to demonstrate the Joule heat induction and the temperature distribution caused by this. Following this model, the temperature distribution in two directions, such as along radial and longitudinal directions are executed numerically. The results obtained from numerical models are validated with an analytical model with the help of Weidemann-Franz-Lorenz law. In SET sate, there are many factors to influence the width of CF such as concentration of defect in oxide layer, oxygen vacancies, presence of interface layer, applied voltage. Going further, to investigate the CF dimension influence on Joule heat, devices with different CF sizes have been modeled and then we have tracked the maximum temperature (Tmax) for each case. It has been found that with an increase in radius of cylindrical CF, Tmax increases linearly. To demonstrate the influence of CF dimension on device performances like device resistance, current, power dissipation; we further implemented an analytical framework considering an isothermal switching of the ReRAM device. With CF radius increment, current follows an exponential growth where resistance and power dissipation show an exponential decrement in the designed ReRAM devices.
Chiplet-based System-on-Chip for Edge Artificial Intelligence

As one of the breakthroughs in the semiconductor industry, chiplet-based design technology has changed the industry ecosystem by integrating multiple chiplets with different functions in the same package. In this paper, a chiplet-based system-on-chip (SoC) is introduced. This SoC can be widely applicable to different edge artificial intelligence (AI) applications. Compared to traditional systems with discrete components, it is much smaller, energy-efficient and supports shorter time to market.
Deep Learning-Based Object Detection Improvement for Fine-Grained Birds

When the object detection algorithm is applied to the bird protection project, there are many problems like large model parameters, high similarity between bird species and single sample scene. In order to further improve the detection accuracy and stability of the object detection model, a multi-object detection algorithm for fine-grained birds is proposed. Firstly, the algorithm introduces Depthwise separable convolution into the feature extraction layer of YOLOv3 algorithm. The convolution process is divided into two parts: deep convolution and point-by-point convolution. The separation between intra-channel convolution and inter-channel convolution is realized. On the basis of high detection accuracy, the number of algorithm model parameters and calculation amount are greatly reduced. Finally, Focal loss was added to the loss function to solve the serious imbalance of positive and negative samples. By reducing the weight of the large number of simple background classes, the algorithm was more focused on detecting foreground classes. The experimental results show that, in the bird data set, the average precision mean (mAP) index of this algorithm is 2.71% higher than YOLOv3 algorithm, the number of parameters is 79.88% lower than YOLOv3 basic model, and the number of frames per second (FPS) is 19.98% higher than YOLOv3 algorithm. This algorithm not only greatly reduces the number of model parameters and computation, but also improves the detection speed and mAP.
In-advance Deployment of Shared Content Replicas over Hybrid Peer-to-Peer Network Using Linear Popularity Prediction

This paper proposes an efficient content deployment method over a Peer-to-Peer network in advance, before issuing content retrieval requests from the users, according to the users' location and whose content preferences. In that situation, the proposed method predicts the temporal alteration of each content items' popularity using simple linear approximation and deploys each item to its appropriate place. As a result, the method can be expected that the network load, content loss, and storage cost required for redundant replica deployment will be reduced. On top of that, the proposal is evaluated by computer simulations from the viewpoint of those three costs mentioned above, and from the results, the effectiveness of the proposed method is finally discussed.
Piezoelectric Micromachined Ultrasonic Transducers for Range-Finding Applications

This paper presents the basic design equations and equivalent circuit model for piezoelectric micromachined ultrasonic transducers (PMUTs) used in time-of-flight range-finding applications. A model for acoustic propagation loss is shown and compared to experimental results for a commercially-available PMUT-based time-of-flight sensor operating to a maximum pulse-echo range of 5 m.
International Journal of Engineering

Visual vehicle tracking is an important topic in intelligent transportation systems. Intersections are challenging locations for visual systems to track vehicles which are simultaneously moving in different directions. In addition, normal traffic flow may change at intersections due to accidents. Congestion, occlusion and undetermined motion flows are the nominated challenging issues of vehicle tracking at intersections. In this paper, a method for tracking multiple vehicles is proposed considering the vehicle motion directions to overcome undetermined motion flows. For this purpose, a multilayer model is presented, which assigns each motion flows to distinct layers. Moreover, we introduce different neighborhoods for various layers considering the regular motion flows in a layer. Hence, vehicles entering from the same side of intersection with the same motion direction are assigned to the same layer. Then the tracking is performed on different layers separately. In special cases such as vehicles crossing each other, misdetections or occlusion, the proposed tracking method can predict the vehicles tracks by using the stored tracking history and considering neighborhoods in that layer. Experimental results show consistency between proposed tracking method results and ground truth, also outperformance of other tracking methods in tracking vehicles crossing the intersection.