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arXiv 2021年

作者： Niyazi, Lama B. Kammoun, Abla Dahrouj, Hayssam Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for NEOM Research at KAUST King Abdullah University of Science and Technology Thuwal Saudi Arabia

Unlike its intercept, a linear classifier’s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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Opto-thermoelectric pulling of light-absorbing particles

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Light(Science & Applications) 2020年第1期9卷 1686-1696页

作者： Linhan Lin Pavana Siddhartha Kollipara Abhay Kotnala Taizhi Jiang Yaoran Liu Xiaolei Peng Brian A.Korgel Yuebing Zheng Walker Department of Mechanical Engineering The University of Texas at AustinAustinTX 78712USA Materials Science&Engineering Program and Texas Materials Institute The University of Texas at AustinAustinTX 78712USA State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084People’s Republic of China McKetta Department of Chemical Engineering The University of Texas at AustinAustinTX 78712USA Department of Electrical and Computer Engineering The University of Texas at AustinAustinTX 78712USA

Optomechanics arises from the photon momentum and its exchange with low-dimensional *** is well known that optical radiation exerts pressure on objects,pushing them along the light ***,optical pulling of an object against the light path is still a counter-intuitive ***,we present a general concept of optical pulling-opto-thermoelectric pulling(OTEP)—where the optical heating of a light-absorbing particle using a simple plane wave can pull the particle itself against the light *** irradiation orientation-directed pulling force imparts self-restoring behaviour to the particles,and three-dimensional(3D)trapping of single particles is achieved at an extremely low optical intensity of 10^(−2)mWμm^(−2).Moreover,the OTEP force can overcome the short trapping range of conventional optical tweezers and optically drive the particle flow up to a macroscopic *** concept of selfinduced opto-thermomechanical coupling is paving the way towards freeform optofluidic technology and lab-on-achip devices.

关键词： absorbing light trapping

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Mode-locked laser in nanophotonic lithium niobate

arXiv

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arXiv 2023年

作者： Guo, Qiushi Sekine, Ryoto Williams, James A. Gutierrez, Benjamin K. Gray, Robert M. Ledezma, Luis Costa, Luis Roy, Arkadev Zhou, Selina Liu, Mingchen Marandi, Alireza Department of Electrical Engineering California Institute of Technology PasadenaCA United States Photonics Initiative Advanced Science Research Center City University of New York NY United States Physics Program Graduate Center City University of New York New YorkNY United States Department of Applied Physics California Institute of Technology PasadenaCA United States Jet Propulsion Laboratory PasadenaCA United States

Mode-locked lasers (MLLs) have enabled ultrafast sciences and technologies by generating ultrashort pulses with peak powers substantially exceeding their average powers. Recently, tremendous efforts have been focused on realizing integrated MLLs not only to address the challenges associated with their size and power demand, but also to enable transforming the ultrafast technologies into nanophotonic chips, and ultimately to unlock their potential for a plethora of applications. However, till now the prospect of integrated MLLs driving ultrafast nanophotonic circuits has remained elusive because of their typically low peak powers, lack of controllability, and challenges with integration with appropriate nanophotonic platforms. Here, we overcome these limitations by demonstrating an electrically-pumped actively MLL in nanophotonic lithium niobate based on its hybrid integration with a III-V semiconductor optical amplifier. Our MLL generates ∼4.8 ps optical pulses around 1065 nm at a repetition rate of ∼10 GHz, with pulse energy exceeding 2.6 pJ and a high peak power beyond 0.5 W. We show that both the repetition rate and the carrier-envelope-offset of the resulting frequency comb can be flexibly controlled in a wide range using the RF driving frequency and the pump current, paving the way for fully-stabilized on-chip frequency combs in nanophotonics. Our work marks an important step toward fully-integrated nonlinear and ultrafast photonic systems in nanophotonic lithium niobate. Copyright © 2023, The Authors. All rights reserved.

关键词： Niobium compounds

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory

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Wearable strain sensor utilizing the synergistic effect of Ti3C2Tx MXene/AgNW nanohybrid for point-of-care respiratory monitoring

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Materials Today Chemistry 2024年 37卷

作者： Kumar, Amit Kumar, R.K. Rakesh Shaikh, Muhammad Omar Yang, Jia-Yu Bharti, Aditya Manu Huang, Bo-Yan Chang, Hsu-Liang Lee, Da-Huei Chuang, Cheng-Hsin Institute of Medical Science and Technology National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Chemical Engineering National Taiwan University Taipei City10617 Taiwan Institute of Biomedical Engineering and Nanomedicine National Healthcare Research Institutes 350 Miaoli County Taiwan National Sun Yat-sen University Kaohsiung80424 Taiwan Sustainability Science and Engineering Program Tunghai University Taichung407224 Taiwan Department of Internal Medicine Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Electrical Engineering National Chung-Kung University Tainan Taiwan

Respiratory signals are significant indicators for detecting changes in physiological conditions and early diagnosis of numerous respiratory illnesses. Herein, we have integrated a high-performance strain sensor and portable circuit board (PCB) with personalized interface to develop point-of-care respiratory monitoring device. The ultrasensitive strain sensor utilizes a conductive polymeric nanocomposite that harnesses the synergistic effect of Ti3C2Tx (MXene)/AgNW nanohybrid to develop a wearable device. The wearable device is capable of analysing pulmonary volumes, such as forced volume capacity (FVC) and forced expiratory volume (FEV1), while accurately recognizing various breathing patterns in a resting state (such as normal, forced, and obstructive), as well as during different physical activities. It shows excellent correlation (>93%) with commercial spirometer for measurement of pulmonary parameters. In addition, we present a wireless device for lab rat respiratory monitoring in anaesthetic state. The device is implemented for real-time respiratory monitoring under mild, normal and high anaesthesia doses. The levels of anaesthesia doses including a critical limit can be significantly discriminated by means of breathing frequency and amplitude, which ultimately results in saving their lives. These results demonstrate the practical feasibility of the strain sensing device as wearable electronics for point-of-care respiratory monitoring in humans and other species. © 2024 Elsevier Ltd

关键词： Diagnosis

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Development of high on/off current ratio in p-type copper oxide thin-film transistors with a back-end-of-line compatible process

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Journal of Vacuum Science and technology A: Vacuum, Surfaces and Films 2024年第5期42卷

作者： Kuo, Chi-Yuan Lin, Wei-Chen Lo, Tsung-Tien Shen, Ching-Hsuan Shen, Ming-Yu Lee, Chia-Chan Lin, Chi-Ping Lin, Yuang-Ming Huang, Haw-Tyng Yeh, Po-Chun Chen, Hsin-Chu Wu, Chih-I Graduate Institute of Photonics and Optoelectronic Department of Electrical Engineering National Taiwan University Taipei106 Taiwan Program of Quantum Technology and Advanced Material College of Science National Tsing Hua University Hsinchu300 Taiwan Institute of Innovative Semiconductor Manufacturing National Sun Yat-sen University Kaohsiung804 Taiwan Institute of Advanced Semiconductor Packaging and Testing National Sun Yat-sen University Kaohsiung804 Taiwan Department of Materials Science and Engineering National Taiwan University Taipei106 Taiwan Electronic and Optoelectronic System Research Laboratories Industrial Technology Research Institute Hsinchu300 Taiwan

The objective of this study is to develop an efficient process to synthesize wafer scale p-type cuprous oxide (Cu2O) with the back-end-of-line (BEOL) compatible process (2/Si substrates followed by rapid thermal oxidation to oxidize it into large-area, uniform Cu2O. Bottom-gate Cu2O thin-film transistors (TFTs) were fabricated as gate dielectric on 100 nm thermal oxide. The results of the ID-VG curve demonstrate that we have successfully fabricated BEOL-compatible p-type Cu2O TFTs. The drain-off current can be achieved to 0.1 pA, with the highest on/off ratio reaching up to 6 orders. Significantly, the TFT with an on/off ratio of 106 is sufficient to meet the requirements for digital circuit applications, including logic circuits, and the BEOL-compatible process (2O TFTs. © 2024 Author(s).

关键词： Thin film transistors

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Characterization of Rectangular Plates using Complex Natural Resonance

Characterization of Rectangular Plates using Complex Natural...

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IEEE International Symposium on Antennas and Propagation

作者： Siyuan Li Chad Owen Hargrave Hoi-Shun Lui School of Information Technology and Electrical Engineering (ITEE) The University of Queensland St. Lucia Australia Sustainable Mining Research Program Commonwealth Scientific and Industrial Research Organisation (CSIRO) Pullenvale Australia Global Big Data Technologies Centre School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia

ISBN: (纸本)9781728146713

The practicality of extracting natural resonant modes from the late-time target responses of conducting rectangular plates with various length-to-width ratios is studied using the matrix pencil method. The extracted resonant modes follow a similar behavior as the results obtained from a theoretical analysis reported in the literature. Our results show that it is feasible for these modes to be excited using a practical system, which opens the opportunities for automated target recognition.

关键词： Damping Target recognition Meetings Radar antennas Radar applications

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Accelerating adoption of species-agnostic plant sensors for precision farming

Nature Reviews Electrical Engineering

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Nature Reviews electrical engineering 2025年 58-70页

作者： Abdullah Bukhamsin Khaled Nabil Salama Jürgen Kosel Ikram Blilou Bioengineering Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Center of Excellence — Sustainable Food Security (SFS) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Sensor Systems Division Silicon Austria Labs Villach Austria Plant Science Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Precision farming is an optimized management farming scheme that seeks to link the real-time needs of crops with the nutrients to be administered. Sensing platforms that can monitor the physiological status of crops in situ are key to enabling timely and localized interventions. However, the underdevelopment of plant sensing strategies limits the potential of precision farming. In this Review, we discuss the challenges and advancements in phyto-monitoring, focusing on strategies that are applicable to a wide range of plant species and suitable for field deployment. We explore species-agnostic sensors, including optical and electrochemical sensors, whose operation is based on principles that are widely applicable to all plant species. These platforms enable real-time monitoring of the physiological state of crops by assessing key biomarkers, such as plant hormones, and metabolites such as salicylic acid and reactive oxygen species. Evaluating these systems, we conclude that an integrative sensing approach is necessary to compensate for the limitations of the individual methods and can provide a holistic view of crop health. Cost-effective species-agnostic sensors are thus needed to provide information that can be used to minimize the resource footprint of farming and meet the growing global demand.

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of Computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning Computer science Image recognition Gesture recognition Computer architecture Auditory system Assistive technologies

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Groundwater quality assessment using few-shot learning with prototypical, Siamese, and matching networks

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Journal of Water Process engineering 2025年 75卷

作者： Derdour, Abdessamed Baz, Mohammed Alzaed, Ali Bojer, Amanuel Kumsa Ghoneim, Sherif S.M. Artificial Intelligence Laboratory for Mechanical and Civil Structures and Soil University Center of Naama P.O. Box 66 Naama 45000 Algeria Laboratory for the Sustainable Management of Natural Resources in Arid and Semi-Arid Zones University Center of Naama Naama 45000 Algeria Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Ethiopian Artificial Intelligence Institute PO Box 40782 Addis Ababa Ethiopia Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia

Groundwater quality assessment is critical for sustainable water resource management, especially in hyperarid regions like Adrar, Algeria, where data scarcity and environmental challenges hinder traditional monitoring methods. This study explores the application of three Few-Shot Learning (FSL) algorithms: Prototypical Networks, Siamese Networks, and Matching Networks, for groundwater quality classification using limited datasets. The dataset comprises 166 groundwater samples from the Adrar region, characterized by five quality classes: “Excellent,” “Very Good,” “Good,” “Satisfactory,” and “Unsatisfactory.” Results demonstrate that Prototypical Networks outperform other FSL algorithms, achieving 93 % accuracy with 10 support samples per class, while Siamese and Matching Networks achieve 90 % and 88 % accuracy, respectively. The study highlights the potential of FSL in addressing data scarcity, offering a cost-effective and efficient approach for groundwater quality assessment in data-scarce regions. The findings underscore the importance of FSL in complementing traditional methods, particularly in hyper arid areas where data collection is challenging. © 2025 The Authors

关键词： Accuracy evaluation Classification algorithms Data scarcity Hyperarid regions Water resource management

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