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TechRxiv 2023年

作者： Xiao, Yi Deng, Shan Zhao, Zijian Faris, Zubair Xu, Yixin Huang, Tzu-Jung Narayanan, Vijaykrishnan Ni, Kai Microsystems Engineering Ph.D. program Rochester Institute of Technology RochesterNY14623 United States Department of Computer Science and Engineering Penn State University University ParkPA16802 United States Department of Electrical and Microelectronic Engineering Rochester Institute of Technology RochesterNY14623 United States

In this work, we propose a 2TnC ferroelectric random access memory (FeRAM) cell design to realize the quasi-nondestructive readout (QNRO) of ferroelectric polarization (PFE) in a capacitor, which can relax the endurance requirement of the ferroelectric thin film and exploits the benefits of both FeRAM and ferroelectric FET (FeFET). We demonstrate that: i) QNRO sensing of PFE is conducted successfully in experiment with a ON/OFF ratio (ION/IOFF)>103, ION>10 µA, and read endurance>106 cycles, which can relax the FeRAM endurance requirement by 106x;ii) optimization of the cell performance can be realized by tuning the metal-ferroelectric-metal capacitor (MFM) capacitor to read transistor area ratio and read transistor threshold voltage (VTH);iii) the proposed cell structure is 3D-compatible, enabling integration of highly dense memory solution;iv) the proposed cell structure also enables compute-in-memory (CIM) applications of FeRAM, which has not been widely explored. With this technology, storage and memory-centric computing can be enabled. © 2023, CC BY.

关键词： Cytology

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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A Portable Multiscopic Camera for Novel View and Time Synthesis in Dynamic Scenes

arXiv

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arXiv 2022年

作者： Zhang, Tianjia Lau, Yuen-Fui Chen, Qifeng The Hong Kong University of Science and Technology Hong Kong The Individualized Interdisciplinary Program The Robotics Institute Hong Kong The Department of Mathematics Hong Kong The Department of Computer Science and Engineering The Department of Electronic and Computer Engineering Hong Kong

We present a portable multiscopic camera system with a dedicated model for novel view and time synthesis in dynamic scenes. Our goal is to render high-quality images for a dynamic scene from any viewpoint at any time using our portable multiscopic camera. To achieve such novel view and time synthesis, we develop a physical multiscopic camera equipped with five cameras to train a neural radiance field (NeRF) in both time and spatial domains for dynamic scenes. Our model maps a 6D coordinate (3D spatial position, 1D temporal coordinate, and 2D viewing direction) to view-dependent and time-varying emitted radiance and volume density. Volume rendering is applied to render a photo-realistic image at a specified camera pose and time. To improve the robustness of our physical camera, we propose a camera parameter optimization module and a temporal frame interpolation module to promote information propagation across time. We conduct experiments on both real-world and synthetic datasets to evaluate our system, and the results show that our approach outperforms alternative solutions qualitatively and quantitatively. Our code and dataset are available at https://***/. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

Parallel Approximations for High-Dimensional Multivariate No...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiran Zhang Sameh Abdulah Jian Cao Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Houston Houston Texas USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of-Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n 3 ) and space complexity of O(n 2 ), mainly due to a Cholesky factorization operation for an n×n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on large-scale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificin

关键词： Accuracy Tensors Wind speed Magnetic resonance imaging Linear algebra Wind power generation Approximation algorithms

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Perovskite/Silicon Tandem Photovoltaics: Long-Term Stability through Interface engineering

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Energy & Fuels 2025年第22期39卷 10134-10149页

作者： Ahmed A. Said Xiaole Li Esma Ugur Furkan H. Isikgor Jiang Liu Randi Azmi Michele De Bastiani Erkan Aydin Shanshan Zhang Anand S. Subbiah Thomas G. Allen Gilles Lubineau Iain McCulloch Stefaan De Wolf Center for Renewable Energy and Storage Technologies (CREST) Physical Sciences and Engineering Division (PSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Mechanical Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia Department of Chemistry University of Oxford Oxford OX1 2JD United Kingdom；Andlinger Center for Energy and the Environment and Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 United States

Perovskite solar cells (PSCs) have experienced a rapid increase in power conversion efficiency (PCE) over the past decade, positioning them as strong candidates for next-generation commercial photovoltaics (PVs). Their tunable bandgap makes them ideal for tandem configurations, especially when coupled with crystalline silicon (Si) bottom cells; this combination offers the potential to exceed the PCE limits of single-junction devices and is arguably essential for successful market entry of perovskite technologies. However, commercialization of perovskite/Si tandems also demands enhanced durability and reliable integration into PV modules, withstanding long-term outdoor exposure. Besides light, temperature, and voltage bias stress, difficulties to overcome involve the mechanical stability and performance of the interfaces within tandem devices, which may degrade over time under real-world operating conditions. This review explores the critical role of interface engineering in addressing these challenges, reviewing the latest advancements in interface materials, encapsulation strategies, and novel integration techniques. By identification of the critical issues and adequate solutions, this paper provides a vision for the future of perovskite/Si tandem solar cells, emphasizing the importance of advanced manufacturing techniques and interdisciplinary research but also policy support.

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Customer Segmentation and Churn Prediction via Customer Metrics

Customer Segmentation and Churn Prediction via Customer Metr...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Tunahan Bozkan Tuna Ç akar Alperen Sayar Seyit Ertuğ rul MEF University Information Technology Graduate Program Istanbul Turkey Computer Engineering Department MEF University Istanbul Turkey Department of Information Technology MEF University Istanbul Turkey

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

In this study, it is aimed to predict whether customers operating in the factoring sector will continue to trade in the next three months after the last transaction date, using data- driven machine learning models, based on their past transaction movements and their risk, limit and company data. As a result of the models established, Loss Analysis (Churn) of two different customer groups (Real and Legal factory) wascarried out. It was estimated by the XGBoost model with anF1 Score of 74% and 77%. Thanks to this modeling, it was aimed to increase the retention rate of customers through special promotions and campaigns to be made to these customer groups, together with the prediction of the customerswho will leave. Thanks to the increase in retention rates, a direct contribution to the transaction volume on a company basis was ensured.

关键词： Measurement Analytical models Law Companies Machine learning Predictive models Signal processing

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Morphological Investigation and Ph Sensing Properties of β-Ga2O3 Egfet-Ph Sensor

SSRN

引用

SSRN 2023年

作者： Wuu, Dong-Sing Yadlapalli, Bharath Kumar Chou, Hsin-Yu Chiang, Jung-Lung Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In our study, β-Ga2O3 particles of various morphologies deposited on FTO glass substrates through hydrothermal method under different pH conditions and pre-stirring temperatures. The particles examined using XRD, FESEM, FTIR and Raman spectroscopy instruments. The results showed that the β-Ga2O3 microrods prepared with pH=3.15 solution at different pre-stirring temperatures followed by hydrothermal reaction had several crystalline orientations ([−401], [002], [403]). The FTIR and Raman spectra measurements confirmed the existence of Ga−O−Ga and Ga−OH bending modes. In the range of pH=3-11, the EGFET-pH sensor prepared with β-Ga2O3 microrods shown a high current-pH sensitivity of 0.541 (mA)1/2 /pH with a linearity of 97.83 % and a high voltage-pH sensitivity of 73.99 mV/pH with linearity of 96.85 %. These results suggest that β-Ga2O3 microrods have pH sensitivity higher than the ideal Nernstian response. This confirms the use of β-Ga2O3 microrods as a sensing membrane in high-performance pH sensors or biosensors. © 2023, The Authors. All rights reserved.

关键词： Gallium compounds

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Ga2O3 nanorods synthesized by hydrothermal method for dual-functional sensing of pH value and glucose

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Sensors and Actuators A: Physical 2023年 363卷

作者： Chou, Hsin-Yu Yadlapalli, Bharath Kumar Yen, Chao-Chun Singh, Anoop Kumar Chiang, Jung-Lung Wuu, Dong-Sing Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In this work, hydrothermal (HT)-synthesized gallium oxide (Ga2O3) nanorods were applied on indium tin oxide (ITO) glass substrates to create a dual-functional pH-glucose sensing device. Extended-gate field-effect transistors (EGFETs) were designed for pH and glucose sensing, and the response to pH levels ranging from 1 to 11 was tested using both chemical bath deposition (CBD) and high-pressure hydrothermal methods. Two sensor configurations, Ga2O3(CBD)/ITO/glass and Ga2O3(HT)/ITO/glass, were immersed in solution pH1 to 11 and cultured medium for hydrogen ion and glucose sensing. The results demonstrated that, for pH sensing in different samples, both IDS-VDS analysis and IDS-VGS analysis showed superior linearity and sensitivity with the HT method. For glucose detection, the HT method was selected for further experiments due to its maintained concentration-dependent relationship and selectivity towards various common sugars. When using culture medium DMEM as the sample (4.5 mg/ml glucose concentration), the accuracy of glucose estimation reached 99%. Stability and biocompatibility tests were also conducted, and the results indicated consistent performance. In summary, the HT-synthesized Ga2O3 nanorods exhibited high sensitivity, selectivity, and stability without affecting cell growth. Therefore, they hold great potential for applications not only in general blood glucose sensing but also in studying cell microenvironment metabolism. © 2023 Elsevier B.V.

关键词： Nanorods

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On the Impact of Knowledge Distillation for Model Interpretability

arXiv

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arXiv 2023年

作者： Han, Hyeongrok Kim, Siwon Choi, Hyun-Soo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of ZIOVISION Inc. Chuncheon Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improving model performance. In this study, we have attempted to show that KD enhances the interpretability as well as the accuracy of models. We measured the number of concept detectors identified in network dissection for a quantitative comparison of model interpretability. We attributed the improvement in interpretability to the class-similarity information transferred from the teacher to student models. First, we confirmed the transfer of class-similarity information from the teacher to student model via logit distillation. Then, we analyzed how class-similarity information affects model interpretability in terms of its presence or absence and degree of similarity information. We conducted various quantitative and qualitative experiments and examined the results on different datasets, different KD methods, and according to different measures of interpretability. Our research showed that KD models by large models could be used more reliably in various fields. © 2023, CC BY-NC-SA.

关键词： Distillation

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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International Seminar on Research of Information technology and Intelligent Systems (ISRITI)

作者： Kus Andriadi Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large.

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