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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Nurcahyo, Aldian Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Compuer Science Binus Graduate Program Jakarta Indonesia Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia Doctor of Computer Science Bina Nusantara University Binus Graduate Program Compuer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350394559

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices. © 2023 IEEE.

关键词： Forecasting

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A Survey of Camouflaged Object Detection and Beyond

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CAAI Artificial Intelligence Research 2024年第1期3卷 1-30页

作者： Fengyang Xiao Sujie Hu Yuqi Shen Chengyu Fang Jinfa Huang Longxiang Tang Ziyun Yang Xiu Li Chunming He Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China Department of Biomedical Engineering Duke UniversityDurhamNC 27708USA School of Electrical and Computer Engineering Peking UniversityShenzhen 518055China

Camouflaged object detection(COD)refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings,posing a significant challenge for computer vision *** recent years,COD has garnered widespread attention due to its potential applications in surveillance,wildlife conservation,autonomous systems,and *** several surveys on COD exist,they often have limitations in terms of the number and scope of papers covered,particularly regarding the rapid advancements made in the field since *** address this void,we present the most comprehensive review of COD to date,encompassing both theoretical frameworks and practical contributions to the *** paper explores various COD methods across four domains,including both image-level and video-level solutions,from the perspectives of traditional and deep learning *** thoroughly investigate the correlations between COD and other camouflaged scenario methods,thereby laying the theoretical foundation for subsequent ***,we delve into novel tasks such as referring-based COD and collaborative COD,which have not been fully addressed in previous *** object-level detection,we also summarize extended methods for instance-level tasks,including camouflaged instance segmentation,counting,and ***,we provide an overview of commonly used benchmarks and evaluation metrics in COD tasks,conducting a comprehensive evaluation of deep learning-based techniques in both image and video domains,considering both qualitative and quantitative ***,we discuss the limitations of current COD models and propose 9 promising directions for future research,focusing on addressing inherent challenges and exploring novel,meaningful *** comprehensive examination aims to deepen the understanding of COD models and related methods in camouflaged *** those interested,a curated list of CODrelated techniques,datasets,and addit

关键词： camouflaged object detection camouflaged scenario understanding deep learning artificial intelligence

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Pilot study of using transcranial temporal interfering theta-burst stimulation for modulating motor excitability in rat

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Journal of Neuroengineering and Rehabilitation 2024年第1期21卷 147页

作者： Wu, Chun-Wei Lin, Bor-Shing Zhang, Zhao Hsieh, Tsung-Hsun Liou, Jian-Chiun Lo, Wei-Lun Li, Yu-Ting Chiu, Shao-Chu Peng, Chih-Wei School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City 237303 Taiwan School of Mechanical and Electrical Engineering Wuyi University Fujian Province Wuyishan City China School of Physical Therapy and Graduate Institute of Rehabilitation Science College of Medicine Chang Gung University Taoyuan Taiwan Neuroscience Research Center Chang Gung Memorial Hospital Linkou Taiwan Department of Surgery Division of Neurosurgery Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taiwan Instrument Research Institute National Applied Research Laboratories Hsinchu Taiwan School of Gerontology and Long-Term Care College of Nursing Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan

Transcranial temporal interference stimulation (tTIS) is a promising brain stimulation method that can target deep brain regions by delivering an interfering current from surface electrodes. Most instances of tTIS stimulate the brain with a single-frequency sinusoidal waveform generated by wave interference. Theta burst stimulation is an effective stimulation scheme that can modulate neuroplasticity by generating long-term potentiation- or depression-like effects. To broaden tTIS application, we developed a theta burst protocol using tTIS technique to modulate neuroplasticity in rats. Two cannula electrodes were unilaterally implanted into the intact skull over the primary motor cortex. electrical field of temporal interference envelopes generated by tTIS through cannula electrodes were recorded from primary motor cortex. Theta burst schemes were characterized, and motor activation induced by the stimulation was also evaluated simultaneously by observing electromyographic signals from the corresponding brachioradialis muscle. After validating the stimulation scheme, we further tested the modulatory effects of theta burst stimulation delivered by tTIS and by conventional transcranial electrical stimulation on primary motor cortex excitability. Changes in the amplitude of motor evoked potentials, elicited when the primary motor cortex was activated by electrical pulses, were measured before and after theta burst stimulation by both techniques. Significant potentiation and suppression were found at 15 to 30 min after the intermittent and continuous theta burst stimulation delivered using tTIS, respectively. However, comparing to theta burst stimulations delivered using conventional form of transcranial electrical stimulation, using tTIS expressed no significant difference in modulating motor evoked potential amplitudes. Sham treatment from both methods had no effect on changing the motor evoked potential amplitude. The present study demonstrated the feasibility of usin

关键词： Brain stimulation Motor evoked potential Neuroplasticity Theta burst stimulation Transcranial electrical stimulation Transcranial temporal interference stimulation

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Xu, Zefeng Chen, Chun-Kuei Lin, Hong-Lin Gao, Yuan Ke, Wei Xu, Baochang Dmitriev, Pavel Arbiz, Carlan Zamburg, Evgeny Touzard, Steven Cai, Xinlun Lee, James Venkatesan, Suresh Danner, Aaron Thean, Aaron Voon-Yew Department of Electrical and Computer Engineering Singapore Nus Graduate School Integrative Sciences and Engineering Program Singapore 117583 Singapore School of Electronics and Information Technology 117583 Singapore Centre for Quantum Technologies Nus Singapore Poet Technologies Canada Sun Yat-Sen University 510275 China

ISBN: (纸本)9798350327670

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf0.5Zr0.5O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 109 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies' 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device. © 2023 IEEE.

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Improving the Cooling Time of Twisted Coiled Actuators in Soft Robotics

Improving the Cooling Time of Twisted Coiled Actuators in So...

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IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： Alex Lizotte Parisa Daemi Brendan DiFabio Ana Luisa Trejos School of Biomedical Engineering (BME) Western University BME Western University Department of Electrical and Computer Engineering (ECE) Department of Mechanical and Materials Engineering Mechatronics Program Western University ECE and BME Western University

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Fabric cooling channels for twisted coiled actuators (TCAs) were recently proposed to achieve the required response times for motion assistance in a manner suitable for soft wearable robotic devices. While previous work demonstrated that the fabric channel reduced the cooling time by 42% in comparison to the same TCA without the cooling channel, the TCAs were still not cooled quickly enough to support human motion. Therefore, in this paper, two variations to the channel are proposed to further reduce the cooling time of the TCAs. The variations include unsealing the inlet and adding vents along the length of the channel to take advantage of air entrainment and natural convection. While both variations reduced the cooling time on their own, when they were employed together there was a 34% reduction in cooling time compared to the original channel design ( $19.1 \pm 2.4\ \mathrm{s}$ vs. $13.5 \pm 0.9\ \mathrm{s}, \mathrm{p} < 0.001$ ). This decrease occurred without any significant differences in the stroke or heating time of the TCA. The modified channel was then compared to the TCA without the cooling apparatus and the cooling time was reduced by 57% ( $25.1 \pm 1.7\ \mathrm{s}$ vs. $14.0 \pm 1.2\ \mathrm{s}, \mathrm{p} < 0.001)$ . This work advances the development of a cooling system for TCAs, making it suitable for soft wearable robotic devices by improving portability, and thereby enabling their use in wearable devices for rehabilitation applications.

关键词： Heating systems Actuators Cooling Vents Soft robotics Fabrics Time factors Wearable devices Convection Testing

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Spectral DefocusCam: hyperspectral imaging using defocus and a spectral filter array

Spectral DefocusCam: hyperspectral imaging using defocus and...

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Computational Optical Sensing and Imaging, COSI 2022

作者： Foley, Christian 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 propose a compact hyperspectral camera based on a focus-tunable lens and spectral filter array. The camera captures then fuses multiple defocused measurements to obtain a high-resolution hyperspectral volume. ©... 详细信息

ISBN: (纸本)9781557528209

关键词： Hyperspectral imaging

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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Monitoring AI-modified content at scale: a case study on the impact of ChatGPT on AI conference peer reviews 24

Monitoring AI-modified content at scale: a case study on the...

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Proceedings of the 41st International Conference on Machine Learning

作者： Weixin Liang Zachary Izzo Yaohui Zhang Haley Lepp Hancheng Cao Xuandong Zhao Lingjiao Chen Haotian Ye Sheng Liu Zhi Huang Daniel A. McFarland James Y. Zou Department of Computer Science Stanford University Machine Learning Department NEC Labs America Department of Electrical Engineering Stanford University Graduate School of Education Stanford University Department of Computer Science and Department of Management Science and Engineering Stanford University Department of Computer Science UC Santa Barbara Department of Biomedical Data Science Stanford University Graduate School of Education and Department of Sociology and Graduate School of Business Stanford University Department of Computer Science and Department of Electrical Engineering and Department of Biomedical Data Science Stanford University

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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Enhanced Light-Matter Interactions for a Single T Center in a Silicon Nanocavity

Enhanced Light-Matter Interactions for a Single T Center in ...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Wong, Yu-En Johnston, Adam Felix-Rendon, Ulises Chen, Songtao Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States

We investigate light-matter interactions for a single T center coupled to a silicon photonic crystal cavity. By solving Lindblad master equation, we extract the cavity-QED parameters for the coupled system. © Opt... 详细信息

ISBN: (纸本)9781957171395

关键词： Silicon photonics

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