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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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Direct Shear Force Measurement by a GaN Nanopillar LED Based Tactile Sensor

Direct Shear Force Measurement by a GaN Nanopillar LED Based...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nathan Dvořák Xili Yi Nima Fazeli Pei-Cheng Ku Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA Department of Robotics University of Michigan Ann Arbor Michigan USA

The response of a GaN nanopillar based tactile sensor to a shear force is investigated. Two different materials were used to apply the force to understand how the tactile sensor responds to different slipping conditions.

ISBN: (纸本)9781957171258

关键词： Emission Gallium nitride Gallium nitride light emitting diodes Light emitting diodes Numerical simulation Optical sensing

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Effect of Environment Temperature and Relative Humidity on Thermal Emissivity: Study Case of Mango Fruit 22

Effect of Environment Temperature and Relative Humidity on T...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Sirisomboon, Panmanas Sripinyowanich Jongyingcharoen, Jiraporn Junto, Apiwat Phanomsophon, Thitima Dachoupakan Sirisomboon, Cheewanun Rerngsamran, Panan Pokhrel, Dharma Raj Sharma, Sneha Posom, Jetsada Pornchaloempong, Pimpen Terdwongwarakul, Anupun Chongstitvatana, Prabhas School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Agricultural Engineering Bangkok Thailand School of Engineering and Innovation Rajamangala University of Technology Tawan-Ok Department of Agricultural Engineering Chon Buri Thailand School of Agricultural Technology King Mongkut's Institute of Technology Ladkrabang Office of Administrative Interdisciplinary Program on Agricultural Technology Bangkok Thailand Chulalongkorn University Faculty of Science Department of Microbiology Bangkok Thailand University of Melbourne Department of Infrastructure Engineering Victoria Australia Khon Kaen University Faculty of Engineering Department of Agricultural Engineering Khon Kaen Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Food Engineering Bangkok Thailand Kasetsart University Faculty of Engineering at Kanpangsan Department of Agricultural Engineering Thailand Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350385601

This research was to study the effect of the environment condition during image captured including temperature and relative humidity in the packaging house of the mango exporting factory and in the orchard on the emissivity of mango fruit. The result showed that in the controlled environment of the packaging house in factory, the emissivity was increased (the mango emitted more energy) when the surface temperature was lower and the emissivity of mango is 0.71-0.84 and in the uncontrolled environment of open-air packaging yard in the mango orchard, the different transpiration rates of mango effected mainly by ambient temperature and relative humidity make the morning condition emissivity of 0.44-0.66 and the afternoon condition of 0.72-0.98. There was the effect of the different measured positions on the fruits where the physiology was different and the fluctuated environment in the latter condition made the wide range of emissivity of mangoes. This indicated the shortcoming of thermal imaging of horticultural product if the correct emissivity varied and difficult to set in the thermal camera setting, hence the inaccurate thermogram to be obtained. © 2024 IEEE.

关键词： Fruits

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment 8

Analyzing the Behavioral Aspects of Implementing Green IT in...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Syamsuar, Dedy Triando, Fero Ulfa, Maria Antoni, Darius Mailangkay, Adele Witarsyah, Deden School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Bina Darma Postgradute Department Palembang Indonesia Universitas Bina Darma Information System Study Program Computer Science Faculty Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350326093

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy. © 2023 IEEE.

关键词： Decision making

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Adaptive Control of Euler-Lagrange Systems under Time-varying State Constraints without a Priori Bounded Uncertainty

arXiv

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arXiv 2023年

作者： Sankaranarayanan, Viswa Narayanan Satpute, Sumeet Gajanan Roy, Spandan Nikolakopoulos, George Robotics and Artificial Intelligence Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Robotics Research Center International Institute of Information Technology Hyderabad India

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art. © 2023, CC BY-NC-ND.

关键词： Lyapunov methods

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Energy management system based on wireless sensor networks and power line communications

Energy management system based on wireless sensor networks a...

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2022 International Conference Automatics and Informatics, ICAI 2022

作者： Omar, Azhar-Husain Malekian, Reza Bogatinoska, Dijana Capeska University of Pretoria Department of Electrical Electronic and Computer Engineering Pretoria082 South Africa Malmö University Department of Computer Science and Media Technology Malmö205 06 Sweden Mach. Intell. and Robotics University of Information Science and Technology St. Paul the Apostle Faculty of Applied IT Ohrid Macedonia

ISBN: (数字)9781665476256

ISBN: (纸本)9781665476256

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement. © 2022 IEEE.

关键词： Wireless sensor networks

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Predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep Learning Techniques 1st

Predictive Models for the Early Diagnosis and Prognosis of K...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Malathi, S.Y. Bharamagoudar, Geeta Shiragudikar, Sharada K. G. Totad, Shashikumar CSE Department KLEIT Karnataka Hubballi India School of Computer Science and Engineering KLE Technological University Karnataka Hubballi India Department of Automation and Robotics KLE Technological University Karnataka Hubballi India

ISBN: (纸本)9783031756047

Uncovering hidden patterns and relationships in knee OA data using machine learning algorithms can be incredibly useful for predicting illness progression and developing more effective treatments. By analyzing large amounts of data, machine learning algorithms can identify patterns and relationships that might not be immediately apparent to human researchers. This can help us better understand the mechanisms behind knee OA and identify potential targets for treatment. By developing more potent treatments, we may be able to slow the progression of the disease and improve patient outcomes. The knee osteoarthritis dataset information was initially acquired from Kaggle and the information on knee osteoarthritis is processed before model training. The study aims to develop and apply advanced machine learning methods, such as deep learning techniques like CNN-SVM, Mobilenet v2, Resnet152v2, Resnet 50, and EfficientnetB5, to analyze knee OA data and develop accurate prediction models for early diagnosis and prognosis. The study evaluates the performance of these models on a dataset of individuals with knee OA to determine their efficacy and accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN EfficientNetB5 Knee osteoarthritis mobilenetV2 ResNet50

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Spectrum from Defocus: Fast Spectral Imaging with Chromatic Focal Stack

arXiv

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arXiv 2025年

作者： Kerem Aydin, M. Hung, Yi-Chun Pytlarz, Jaclyn Guo, Qi Alexander, Emma Department of Computer Science McCormick School of Engineering Northwestern University United States Dolby Laboratories Inc. Elmore Family School of Electrical and Computer Engineering Purdue University United States Center for Robotics and Biosystems McCormick School of Engineering Northwestern University United States

Hyperspectral cameras face harsh trade-offs between spatial, spectral, and temporal resolution in an inherently low-photon regime. Computational imaging systems break through these trade-offs with compressive sensing, but require complex optics and/or extensive compute. We present Spectrum from Defocus (SfD), a chromatic focal sweep method that recovers state-of-the-art hyperspectral images with a small system of off-the-shelf optics and © 2025, CC BY-NC-ND.

关键词： Hyperspectral imaging

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Pain Classification Using Statistical Feature Extraction Using Machine Learning Approach: A Pilot Study

Pain Classification Using Statistical Feature Extraction Usi...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Sudimanto Agung Trisetyarso Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Computer Engineering Department Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta Indonesia

Electrodermal activity (EDA) is a general term for all electrical phenomena occurring on the skin, both passive and active. EDA measurements are used by researchers to measure levels of stress, emotion, mental strain, and so on. Measuring human stress levels, emotions, and mental strain are generally associated with the skin conductance response. The function GSR sensor is not only used to read people’s psychology but also can be used as a pain sensor used to read the degree of pain in the skin. This pilot study uses sample data from ***. The *** data is galvanic skin response sensor data. The output of this sensor is the conductivity value that occurs in the skin. The data obtained from *** will be extracted using the mean, standard deviation, maximum, minimum, RMS, skewness, and peak-to-peak characteristics. The extracted functions are selected using the forward selection method. The results of the feature selection are three features with an accuracy percentage greater than 50%, namely the mean feature, the RMS feature, and the skewness feature. The machine learning models used are bagged tree, SVM, and K-NN models. Of the three models used, the bagged tree model has the highest accuracy rate, at 98.05%, with an F1 score is 0.9807. The KNN model with k=10 has the lowest level of accuracy compared to other models, at 96.75%.

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NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification

arXiv

引用

arXiv 2023年

作者： Zhang, Chuhan Pan, Wei Della Santina, Cosimo Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Department of Computer Science The University of Manchester Manchester United Kingdom

Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a Non-iterative Leaky Integrate-and-Fire (NiLIF) neuron model, overcoming the gradient issues in traditional Spiking Neural Networks (SNNs) that use Iterative LIF neurons for long time steps. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed Non-iterative SNN with Attention (NiSNN-A) model on two motor imagery EEG datasets, OpenBMI and BCIC IV 2a. Experiment results demonstrate that 1) our model outperforms other SNN models by achieving higher accuracy, 2) our model increases energy efficiency compared to the counterpart CNN models (i.e., by 2.13 times) while maintaining comparable accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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