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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lucine L. Oganesian Omid G. Sani Maryam M. Shanechi Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA

ISBN: (纸本)9798331314385

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.

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Polymethyl Methacrylate-based Light Collector for Enhanced Indoor Visible Light Communication

Polymethyl Methacrylate-based Light Collector for Enhanced I...

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Optics and Photonics Conference (SBFoton IOPC), SBFoton International

作者： David Esteban Farfán-Guillén João Pedro Duarte Jankosz Neri Volpato Alexandre de Almeida Prado Pohl Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University Technology-Parana Curitiba Brazil Graduate Program in Mechanical Engineering and Material Science Federal University Technology-Parana. Curitiba Brazil

A new polymethyl methacrylate-based light collector has been designed and fabricated with additive manufacturing to optimize the efficiency of VLC systems. It enhances the area of detection, leading to a ~ 1.9x increa... 详细信息

ISBN: (数字)9798350388176

ISBN: (纸本)9798350388183

关键词： Optical device fabrication Optical variables measurement Three-dimensional printing Optics Optical receivers Optical sensors Reliability Visible light communication Testing Photonics

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A Low Power Digitizer Array with Adaptive Split Current Source for 3-D-Stacked 100 MP High Dynamic Range Imager

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IEEE Sensors Journal 2025年第12期25卷 22609-22617页

作者： Lee, Youngwoo Kim, Seyeon Kim, Jinha Kim, Suhwan Jun, Jaehoon Seoul08826 Korea Republic of Samsung Electronics Hwaseong18448 Korea Republic of Inha University Department of Electrical and Computer Engineering Program in Semiconductor Convergence Incheon22212 Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Gwanak Analog Company Ltd Seoul08826 Korea Republic of

The increasing demand for high dynamic range (HDR), power efficiency, and high resolution has driven the adoption of single-exposure dual conversion gain (DCG) techniques. This paper proposes an adaptive split current source to optimize power consumption in single-exposure DCG readouts. By splitting the comparator bias current, the power consumption of the digitizer array can be adaptively optimized based on the conversion gain (CG) of pixel in single-exposure DCG operation. Additional power-saving features including decision-feedback and auto-zeroing (AZ) power-down techniques are also implemented to further improve power efficiency. The proposed digitizer chip was fabricated in a 28 nm CMOS process, achieving a power consumption reduction of 44.5% in comparator. The integral nonlinearity (INL) was measured as +2.67/–2.34 LSB in high conversion gain (HCG) and +2.95/–1.92 LSB in low conversion gain (LCG). The input-referred random noise (RN) values of 2.17 LSB (HCG) and 2.41 LSB (LCG) were measured at an analog gain of 16, corresponding to 93 μVrms and 103 μVrms, respectively. The prototype chip shows a highly competitive figure of merit (FoM) of 2.41 mV∙pJ/pixel/frame. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Adaptive Neuro-Fuzzy Inference System As New Real-Time Approach for Parkinson Seizures Prediction 4

Adaptive Neuro-Fuzzy Inference System As New Real-Time Appro...

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4th International Conference on Bio-engineering for Smart Technologies, BioSMART 2021

作者： Daher, Alaa Yassin, Sally Alsamra, Hadi Ali, Hassan Abou Beirut Arab University Biomedical Engineering Program Faculty of Engineering Electrical and Computer Engineering Department Debbieh Lebanon

ISBN: (纸本)9781665408103

Parkinson's disease, as a definition, is a neurological condition that affects the brain and causes tremors, stiffness, and difficulties walking, balancing, and coordinating. Symptoms of Parkinson's disease normally appear gradually and worsen with time. People with Parkinson's disease may have difficulties walking and speaking as the condition develops. Numerous recent studies have shown a direct association between Parkinson's disease and the incident of having epileptic seizures, which is defined to be a burst of the uncontrollable electrical activity of the brain cells, that is associated with an increased risk of sudden unexplained deaths. This project aims to obtain a real-time seizure prediction system for Parkinson's disease patients based on the electroencephalogram (EEG) signals, enabling the detection of a seizure before it happens. This will hopefully save them from risky situations or sudden death, as they will be alerted and have the time enabling them to be prepared and take the needed precautions and steps to prevent the seizure from happening. For this project, we've used the Neural Network and the ANFIS ('udaptive neuro-fuzzy inference system '') to process and analyze the electroencephalogram (EEG) data signals of the Parkinson patients to detect seizures starting point. © 2021 IEEE.

关键词： Brain

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Design of Motorcycle Speed Limiter through Global Positioning System 13

Design of Motorcycle Speed Limiter through Global Positionin...

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2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Bajenting, Edmund B. Ompoc, Ralph Eimerson B. Palmero, Reynaldo G. Adtoon, Jetron J. Linsangan, Noel B. University of Mindanao Computer Engineering Program College of Engineering Education Davao Philippines Mapua University School of Electrical Electronics and Computer Engineering Manila Philippines

ISBN: (纸本)9781665401678

This study focused on designing a motorcycle speed limiter using GPS with throttle modification. The study was brought about the increase in road accidents involving motorcycle vehicles tagged to be overspeeding and some not wearing helmets. The designed system focused on controlling the throttle of the motorcycle through a microcontroller and a GPS. Also, communication from the helmet to the motorcycle was established to complete the system functionality. The system does not allow the use of the said motorcycle once the rider does not wear the helmet prescribed for the said motorcycle. Speed control of the designed system relies solely upon the GPS, making it different from typical sensory for speed and PID algorithm applied in servo motor to control the throttle acceleration. The selected road stretch was assigned 30 kph,40kph, and 60 kph, respectively. Initial tests showed a responsive rate of all parameters of the helmet and LCD design. © 2021 IEEE.

关键词： Microcontrollers

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STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS 10

STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORK...

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10th International Conference on Learning Representations, ICLR 2022

作者： Hwang, Uiwon Kim, Heeseung Jung, Dahuin Jang, Hyemi Lee, Hyungyu Yoon, Sungroh Department of Electrical and Computer Engineering AIIS ASRI INMC ISRC NSI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Generative adversarial networks

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MathReader : Text-to-Speech for Mathematical Documents

MathReader : Text-to-Speech for Mathematical Documents

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sieun Hyeon Kyudan Jung Nam-Joon Kim Hyun Gon Ryu Jaeyoung Do Department of Electrical and Computer Engineering Seoul National University Department of Mathematics Chung-Ang University NVIDIA Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://***/hyeonsieun/MathReader.

关键词： Text recognition Error analysis Pipelines Optical character recognition Graphics processing units Signal processing Real-time systems Mathematical models Text to speech Speech processing

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Impact of In flux on self-assembled InGaN/GaN superlattice grown on GaN template by plasma-assisted molecular beam epitaxy

Impact of In flux on self-assembled InGaN/GaN superlattice g...

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2022 Compound Semiconductor Week, CSW 2022

作者： Khan, Kamruzzaman Sun, Kai Wurm, Christian Ahmadi, Elaheh University of Michigan Department of Materials Science and Engineering Ann ArborMI United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI United States University of Michigan Applied Physics Program Ann ArborMI United States

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

We have previously reported spontanous formation of InGaN/GaN superlattice structure on nominal InGaN films grown by plasma-assisted molecular beam epitaxy (PAMBE). In this work, we report on the impact of In flux on the formation and periodicity of self-assembled InxGa1-xN/InyGa1-yN superlattice structure (SASL). We show that the thickness and In composition in the InGaN layer varies by changing the In flux. These films were stucturally characterized by X-ray diffraction and simulated by globalfit software to get the thickness and In composition. The superlattice structures were confimred by scanning transmission electron microscopy and the thickness of InGaN layer and In composition were determined by energy dispersive X-ray spectroscopy. This work can provide a method for using the SASL with better control for their optoelectronics application. © 2022 IEEE.

关键词： III-V semiconductors

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An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

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IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

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MAZENET: AN ACCURATE, FAST, & SCALABLE DEEP LEARNING SOLUTION FOR STEINER MINIMUM TREES

arXiv

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arXiv 2024年

作者： Ramos, Gabriel Díaz Arikan, Toros Baraniuk, Richard G. Rice University United States Department of Electrical and Computer Engineering Smalley-Curl Institute Applied Physics Program United States

The Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem, which seeks the shortest interconnection of a given number of terminals in a rectilinear plane while avoiding obstacles, is a critical task in integrated circuit design, network optimization, and robot path planning. Since OARSMT is NP-hard, exact algorithms scale poorly with the number of terminals, and so practical solvers sacrifice accuracy for large problems. We propose and study MazeNet, a deep learning-based method that learns to solve the OARSMT from data. MazeNet re-frames OARSMT as a maze-solving task that can be addresssed with a recurrent convolutional neural network (RCNN). A key hallmark of MazeNet is its scalability: we only need to train the RCNN blocks on mazes with a small number of terminals;mazes with a larger number of terminals can be solved simply by replicating the same pre-trained blocks to create a larger network. Across a wide range of experiments, MazeNet achieves perfect OARSMT-solving accuracy, with significantly reduced runtime compared to classical exact algorithms, and with the ability to handle larger numbers of terminals than state-of-the-art approximate algorithms. © 2024, CC BY.

关键词： Convolutional neural networks

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