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Reduction of interface defects in gate-recessed GaN HEMTs by neutral beam etching

Materials Today Advances 2024年 23卷

作者： Yu, Chia Hao Chiang, Wei Hsiang Chen, Yi-Ho Samukawa, Seiji Wuu, Dong Sing Chung, Chin-Han Hsiao, Ching-Lien Horng, Ray Hua Institute of Electronics National Yang Ming Chiao Tung University Department of Materials Science and Engineering National Chung Hsing University Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Institute of Comunications Engineering National Yang Ming Chiao Tung University Department of Applied Materials and Optoelectronic Engineering National Chi Nan University International College of Semiconductor Technology National Yang Ming Chiao Tung University Thin Film Physics Division Department of Physics Chemistry and Biology (IFM) Linköping University

This study investigates and compares the impact of different etching techniques on the fabrication of GaN high electron mobility transistors (HEMTs) between the inductively coupled plasma reactive ion etching (ICP-RIE) and the neutral beam etching (NBE) for the gate recess. By conducting direct current analysis, it was found that devices manufactured using the NBE exhibited superior electrical performance as compared with those produced using the ICP-RIE. These enhanced electrical characteristics include a transconductance of up to 100.4 mS/mm, a threshold voltage (V_th) of −2.3 V, an on/off current ratio of 1.1 × 10⁹, a subthreshold swing (S.S.) of 99.63 mV/dec, and a remarkably low gate leakage current. Additionally, we noted varying degrees of hysteresis in the I–V characteristics were related to process disparities possibly leading to interface defects. Multi-frequency capacitance-voltage (C–V) measurements were used to identify the interface defects at the oxide/AlGaN interface of the gate. The results revealed that devices fabricated using the NBE exhibited a lower interface defect density as compared with those fabricated using the ICP-RIE, thereby elucidating the reduced hysteresis observed in the I–V characteristics. These findings indicated the significant advantages of the NBE process in the fabrication of GaN HEMTs. © 2024 The Authors

关键词： GaN HEMTs Gate recess Interface defects Multi-frequency C–V Neutral beam etching

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Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Chengchi University Taipei11529 Taiwan Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Institute of Information Science Academia Sinica Taipei11529 Taiwan

The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is difficult to distinguish from genuine content. Currently, machine learning techniques have been extensively studied for automatically detecting deepfakes. However, human perception has been less explored. Malicious deepfakes could ultimately cause public and social problems. Can we humans correctly perceive the authenticity of the content of the videos we watch? The answer is obviously uncertain;therefore, this paper aims to evaluate the human ability to discern deepfake videos through a subjective study. We present our findings by comparing human observers to five state-of-the-art deepfake detection models. To this end, we used gamification concepts to provide 110 participants (55 native English speakers and 55 non-native English speakers) with a web-based platform where they could access a series of 40 videos (20 real and 20 fake) to determine their authenticity. Each participant performed the experiment twice with the same 40 videos in different random orders. The videos are manually selected from the FakeAVCeleb dataset. We found that all AI models performed significantly better than humans when evaluated on the same 40 videos. The study also reveals that while deception is not impossible, humans tend to overestimate their detection capabilities. Our experimental results may help benchmark human versus machine performance, advance forensics analysis, and enable adaptive countermeasures. © 2024, CC BY.

关键词： Audiovisual

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Understanding Audiovisual Deepfake Detection: Techniques, Challenges, Human Factors and Perceptual Insights

arXiv

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arXiv 2024年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan The Department of Computer Science National Chengchi University Taipei11529 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deepfakes through joint analysis of audio and visual streams remains relatively unexplored. To fill this gap, this survey first provides an overview of audiovisual deepfake generation techniques, applications, and their consequences, and then provides a comprehensive review of state-of-the-art methods that combine audio and visual modalities to enhance detection accuracy, summarizing and critically analyzing their strengths and limitations. Furthermore, we discuss existing open source datasets for a deeper understanding, which can contribute to the research community and provide necessary information to beginners who want to analyze deep learning-based audiovisual methods for video forensics. By bridging the gap between unimodal and multimodal approaches, this paper aims to improve the effectiveness of deepfake detection strategies and guide future research in cybersecurity and media integrity. © 2024, CC BY.

关键词： Audiovisual

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Visuotactile Sensor Enabled Pneumatic Device Towards Compliant Oropharyngeal Swab Sampling

arXiv

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arXiv 2023年

作者： Li, Shoujie He, Mingshan Ding, Wenbo Ye, Linqi Wang, Xueqian Tan, Junbo Yuan, Jinqiu Zhang, Xiao-Ping Tsinghua Shenzhen International Graduate School Shenzhen518055 China The Department of Mechanical Engineering Seoul National University Seoul Korea Republic of The Department of Intelligent Control Qianyuan Institute of Science Hangzhou310024 China Clinical Research Center The Seventh Affiliated Hospital Sun Yat-sen University Guangdong Shenzhen518107 China The Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada

Manual oropharyngeal (OP) swab sampling is an intensive and risky task. In this article, a novel OP swab sampling device of low cost and high compliance is designed by combining the visuo-tactile sensor and the pneumatic actuator-based gripper. Here, a concave visuo-tactile sensor called CoTac is first proposed to address the problems of high cost and poor reliability of traditional multi-axis force sensors. Besides, by imitating the doctor’s fingers, a soft pneumatic actuator with a rigid skeleton structure is designed, which is demonstrated to be reliable and safe via finite element modeling and experiments. Furthermore, we propose a sampling method that adopts a compliant control algorithm based on the adaptive virtual force to enhance the safety and compliance of the swab sampling process. The effectiveness of the device has been verified through sampling experiments as well as in vivo tests, indicating great application potential. The cost of the device is around 30 US dollars and the total weight of the functional part is less than 0.1 kg, allowing the device to be rapidly deployed on various robotic arms. Videos, hardware, and source code are available at: https://***/view/swab-sampling/. Copyright © 2023, The Authors. All rights reserved.

关键词： Pneumatic actuators

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Biophysics of amplitude-modulated giga-hertz electromagnetic waves stimulation

Biophysics of amplitude-modulated giga-hertz electromagnetic...

引用

Asilomar Conference on Signals, Systems & computers

作者： Fatima Ahsan Aravind C. Govindaraju Robert M. Raphael Taiyun Chi Sameer A. Sheth Wayne Goodman Behnaam Aazhang Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curly Institute Rice University Houston TX Department of Bioengineering Rice University Houston TX USA Department of Neurosurgery Baylor College of Medicine Houston TX USA Menninger Department of Psychiatry and Behavioral Sciences Baylor College of Medicine Houston TX USA

This paper presents the biophysical modeling for neuronal stimulation caused by amplitude-modulated giga-hertz (GHz) electromagnetic (EM) waves. We model a 1D cable equation for electrical signal propagation in a vestibular ganglion neuronal fiber and use Hodgkin-Huxley formalism to model voltage-dependent channel conductances. We find out that for a given stimulation signal strength, the GHz waveform amplitude-modulated at a low frequency of Δf causes neuronal firing with the frequency of Δf whereas the continuous wave GHz did not elicit any neuronal firing. Moreover, the voltage-dependent channel gating dynamics of the Δf amplitude-modulated GHz stimulation matches that of a simple low frequency stimulation at Δf. Additionally, we find that the modulation depth controls the firing rate of the neurons. The significance of this property is that the amplitude modulation caused by the interference of two similar high frequency signals could be leveraged to focally excite neurons at depth without stimulating overlying cortical regions. These theoretical predictions based on the Hodgkin-Huxley model can later be tested experimentally.

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Time-Distributed Feature Learning for Internet of Things Network Traffic Classification

arXiv

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arXiv 2024年

作者： Manjunath, Yoga Suhas Kuruba Zhao, Sihao Zhang, Xiao-Ping Zhao, Lian Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada Shenzhen Key Laboratory of Ubiquitous Data Enabling Tsinghua Shenzhen International Graduate School Tsinghua University China NovAtel Autonomy Positioning division of Hexagon CalgaryABT3K 2L5 Canada

Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper;therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers. © 2024, CC BY.

关键词： Quality of service

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Snapshot hyperspectral imaging using a random phase mask and spectral filter array

Snapshot hyperspectral imaging using a random phase mask and...

引用

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Monakhova, Kristina Yanny, Kyrollos Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States

We introduce a snapshot hyperspectral imager that uses a random phase mask, repeated spectral filter array, and compressive recovery to achieve high spatial and spectral resolution in a small form factor. © 2020 ... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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A weighted multivariate fuzzy time series method for multiple output high-dimensional time series forecasting in IoT applications

A weighted multivariate fuzzy time series method for multipl...

引用

Latin America Congress on Computational Intelligence (LA-CCI)

作者： Hugo Vinicius Bitencourt Patrícia de Oliveira Lucas Omid Orang Petrônio Silva Frederico Gadelha Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Salinas MG Brazil Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Instituto Federal do Norte de Minas Gerais Januária MG Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

In Internet of Things (IoT) applications, data flows are continuous streams of high-dimensional time series that aggregate various data sources. In this context, decision-making processes frequently encompass multiple factors and criteria that demand forecasting these time series. This paper introduces MO-WMVFTS, a novel multiple-input multiple-output (MIMO) fuzzy time series (FTS) method for tackling this complex scenario. MO-WMVFTS is a hybrid forecasting method that fuses weighted multivariate FTS (WMVFTS) with embedding transformations, designed for IoT applications. To assess the performance of the proposed method, it was applied to the prediction of energy consumption in smart homes and air quality in smart cities. Here, three real-world datasets are used to assess the validity of our proposed approach, and the results obtained are highly competitive when compared to other existing methods.

关键词： Smart buildings Smart cities Soft sensors Time series analysis Smart homes Predictive models Internet of Things Forecasting Streams MIMO communication

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An exploration of neural dynamics of motor imagery for people with amyotrophic lateral sclerosis

An exploration of neural dynamics of motor imagery for peopl...

引用

作者： Hosni, Sarah M. Deligani, R.J. Zisk, A. McLinden, J. Borgheai, S.B. Shahriari, Y. Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States Interdisciplinary Neuroscience Program University of Rhode Island KingstonRI United States

Objective. Studies of the neuropathological effects of amyotrophic lateral sclerosis (ALS) on the underlying motor system have investigated abnormalities in the magnitude and timing of the event-related desynchronization (ERD) and synchronization (ERS) during motor execution (ME). However, the spatio-spectral-temporal dynamics of these sensorimotor oscillations during motor imagery (MI) have not been fully explored for these patients. This study explores the neural dynamics of sensorimotor oscillations for ALS patients during MI by quantifying ERD/ERS features in frequency, time, and space. Approach. Electroencephalogram (EEG) data were recorded from six patients with ALS and 11 age-matched healthy controls (HC) while performing a MI task. ERD/ERS features were extracted using wavelet-based time-frequency analysis and compared between the two groups to quantify the abnormal neural dynamics of ALS in terms of both time and frequency. Topographic correlation analysis was conducted to compare the localization of MI activity between groups and to identify subject-specific frequencies in the µ and β frequency bands. Main results. Overall, reduced and delayed ERD was observed for ALS patients, particularly during right-hand MI. ERD features were also correlated with ALS clinical scores, specifically disease duration, bulbar, and cognitive functions. Significance. The analyses in this study quantify abnormalities in the magnitude and timing of sensorimotor oscillations for ALS patients during MI tasks. Our findings reveal notable differences between MI and existing results on ME in ALS. The observed alterations are speculated to reflect disruptions in the underlying cortical networks involved in MI functions. Quantifying the neural dynamics of MI plays an important role in the study of EEG-based cortical markers for ALS. © 2019 IOP Publishing Ltd.

关键词： Electroencephalography

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The behavior of internet traffic for internet services during COVID-19 pandemic scenario

arXiv

引用

arXiv 2021年

作者： da Silva, Carlos Alexandre Gouvea Ferrari, Allan Christian Krainski Osinski, Cristiano Pelacini, Douglas Antonio Firmino Computer Science Department State University of Parana Midwest Unicentro Guarapuava Brazil Centro Universitário Unicuritiba Curitiba Brazil Department of Electrical Engineering Electrical Engineering Graduate Program Federal University of Paraná Curitiba Brazil

Since the end of 2019, the SARS-CoV-2 virus known as COVID-19 has spread rapidly around the world, forcing many governments to impose restrictive blocking or lockdown to combat the pandemic. With locomotion restriction of people in almost of countries of the world, workers and students needed to keep their activities at home. As a result, people's behavior, habits, and the way they started using the Internet changed significantly. Like professionals of offices, the younger played an important role in this behavior, especially in the type of resources used by them. As result, the characterization and traffic of communication networks were affected in some way. In this perspective article, we join from many available studies about the COVID-19 effect at networks and investigate the effects on the Internet traffic of using services such as video streaming, video conferencing, and gaming during 2020's months of the pandemic. © 2021, CC BY.

关键词： Diseases

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