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IEEE Symposium (IUS) Ultrasonics

作者： Takashi Buma Department of Electrical Computer and Biomedical Engineering Union College Schenectady NY USA

Many applications in photoacoustic microscopy (PAM) require laser pulses at multiple wavelengths. High repetition rate multi-wavelength lasers have been demonstrated using nonlinear fiber optics, which produce a wide wavelength range but tend to suffer from low pulse energy (e.g. 100 nJ). This paper presents our preliminary investigation into multimode fibers as a path to achieve higher output laser pulse energy for multi-spectral PAM. Laser pulses from a 532 nm pulsed Nd:YAG laser are coupled into a large mode area (LMA) fiber that supports six spatial modes at 532 nm. Phase-matched four-wave mixing (FWM) between fiber modes produces a broad continuum extending past 800 nm. The pulse energy at 550, 650, and 750 nm spectral bands is 0.84 uJ, 1.31 uJ, and 0.73 uJ, respectively. Multi-spectral imaging experiments successfully differentiate vessel phantoms containing Allura Red, methylene blue, and IR-820 dyes. These results show the potential of using intermodal FWM in a few-mode multimode fiber to achieve uJ-level pulse energies for multi-spectral PAM applications.

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers. © 2023 IEEE.

关键词： Tweets

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Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students

arXiv

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arXiv 2024年

作者： Vaswani, Namrata Selim, Mohamed Y. Gibert, Renee Serrell The Electrical and Computer Engineering department Iowa State University United States The Minority Engineering Program Purdue University United States

Signal Processing (SP) and Machine Learning (ML) rely on good math and coding knowledge, in particular, linear algebra, probability, trigonometry, and complex numbers. A good grasp of these relies on scalar algebra learned in middle school. The ability to understand and use scalar algebra well, in turn, relies on a good foundation in basic arithmetic. Because of various systemic barriers, many students are not able to build a strong foundation in arithmetic in elementary school. This leads them to struggle with algebra and everything after that. Since math learning is cumulative, the gap between those without a strong early foundation and everyone else keeps increasing over the school years and becomes difficult to fill in college. In this article we discuss how SP faculty, students, and professionals can play an important role in starting, and participating in, university-run, or other, out-of-school math support programs to supplement students’ learning. Two example programs run by the authors, CyMath at Iowa State and Algebra by 7th Grade (Ab7G) at Purdue, and one run by the Actuarial Foundation, are described. We conclude with providing some simple zero-cost suggestions for public schools that, if adopted, could benefit a much larger number of students than what out-of-school programs can reach. Copyright © 2024, The Authors. All rights reserved.

关键词： Linear algebra

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A Robust and Versatile Analog-To-Digital Converter for Space Exploration Spectroscopy on Satellites 6

A Robust and Versatile Analog-To-Digital Converter for Space...

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6th International Forum on Research and Technology for Society and Industry, RTSI 2021

作者： Grassi, Marco Malcovati, Piero University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9781665441353

A robust, versatile, on-demand incremental Analog to Digital converter for space exploration spectroscopy on satellites has been developed and characterized. The main application of the proposed circuit is the detection of X and Gamma rays in space within the THESEUS exploration mission, whose aim is a deeper investigation of high energy transient phenomena over the complete universe history. This investigation can be carried out thanks to multiple instruments on different satellites, which require a smart synchronization among the modules. The on-demand conversion of the proposed ADC avoids hysteretic behavior over different samples processing, requirement fundamental in spectroscopic applications, which also leads to power consumption reduction operating the converter only when necessary. Finally, since the ADC will be employed in a dual-mode front-To-back-end circuit for X and Gamma detection, it must exhibit maximum flexibility and re-configurability. The presented ADC is one of the key-modules of the core ASIC of the THESEUS mission called ORION, thus particular focus has been also given to reliability and robustness. © 2021 IEEE.

关键词： Space applications

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Curriculum-based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios 15

Curriculum-based Reinforcement Learning for an Effective Mul...

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Oliveira, Iure Rosa L. de Carvalho, Kevin B. Brandão, Alexandre S. Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Viçosa Viçosa Brazil

ISBN: (纸本)9798350314182

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training. © 2023 IEEE.

关键词： Curriculum Learning Mobile Robotics Multi-Agent System Reinforcement Learning Transfer Learning

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State-Space Estimation Framework for Acoustic Radiation Force Sequence Optimization

State-Space Estimation Framework for Acoustic Radiation Forc...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Richardson, Joseph B. Gallippi, Caterina M. North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill Chapel HillNC United States North Carolina State University RaleighNC United States

ISBN: (纸本)9798350346459

Sequence design for acoustic radiation force (ARF)-based material characterization methods involves adjusting ultrasound excitation and tracking configurations to extract maximal material response information and optimize material model fits. For example, Viscoelastic Response (VisR) ultrasound uses a double-push ARF excitation sequence, and the resulting displacements are fit to a 1D mass spring damper (MSD) model. The fit procedure minimizes the point-wise error between measured VisR displacements and a Laplace domain analytic solution to the MSD model for a double-push excitation. In this work, we develop a new state-space material model fit procedure that allows arbitrary discrete time excitation/displacement signal pairs to be fit to material models, enabling facile evaluation of excitation and tracking configuration performance. © 2023 IEEE.

关键词： Viscoelasticity

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Multidimensional Similarity Fusion for Speech Quality Assessment

Multidimensional Similarity Fusion for Speech Quality Assess...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Fan Huang Xiongkuo Min Yuqin Cao Xiao-Ping Zhang Guangtao Zhai Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Since the perceptual quality of audio signals is easily to be affected by compression, transmission, noise adding, etc, it is of great significance to develop an effective audio quality assessment (AQA) method to measure end-user’s quality of experience. In this paper, we propose a full reference AQA model named Multidimensional Similarity Fusion for Audio Quality Assessment (MSF-AQA). We generalize the similarity-based image quality assessment methods for audio, then extract audio similarity features from multiple dimensions, and finally regress the multidimensional similarity features into the final quality score. The experimental results across three databases indicate that our MSF-AQA model outperforms the state-of-the-art AQA methods.

关键词： Image quality Databases Noise Transforms Feature extraction Quality assessment Quality of experience

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SensoPatch: A Reconfigurable Haptic Feedback with High-Density Tactile Sensing Glove

SensoPatch: A Reconfigurable Haptic Feedback with High-Densi...

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IEEE biomedical Circuits and Systems (BIOCAS)

作者： Yanisa Angkanapiwat Ariel Slepyan Rebecca J. Greene Nitish Thakor Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Haptic feedback is integral to the improved experience of prosthetic users and the reduction in prosthesis rejection. Unfortunately, existing haptic feedback systems often face challenges that impede their transition to clinical use including lack of intuitive control, reproducibility, and personalized customization. Prior studies have explored various methods to encode tactile information and deliver vibration feedback. However, a comprehensive study comparing performance across different stimulation locations and feedback modalities for wearable devices is absent and there is no robust test platform. This paper proposes an open-source reconfigurable haptic feedback system which features 25 sensors and wireless communication to allow customized number of vibration motors, adjustable motor placement, and programmable encoding of tactile data to change feedback modalities. To demonstrate the potential studies that can be investigated using the proposed system, we conducted two preliminary experiments: 1) to assess the vibration discrimination accuracy on three body parts (upper arm, shoulder, and lower back) 2) to evaluate the effect of six methods of mapping tactile data to varying number of motors on object manipulation. This proposed tool utilizes low-cost off-the-shelf components, enabling large-scale comparative studies of feedback modalities and stimulation sites to optimize vibrotactile feedback and facilitate its deployment in upper limb prostheses.

关键词： Vibrations Wireless communication Shoulder Sensor phenomena and characterization Motors Sensor systems Reproducibility of results Haptic interfaces Wearable devices Prosthetics

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Overview of object detection in low light using deep learning and polarimetric three-dimensional integral imaging

Overview of object detection in low light using deep learnin...

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3D Image Acquisition and Display: Technology, Perception and Applications, 3D 2022

作者： Usmani, Kashif O’Connor, Timothy Krishnan, Gokul Javidi, Bahram Electrical and Computer Engineering Department University of Connecticut 371 Fairfield Road StorrsCT06269 United States Biomedical Engineering Department University of Connecticut StorrsCT06269 United States

We overview the previously published work for object detection in low light and occlusion using deep learning and polarimetric 3D integral imaging. 3D polarimetric integral imaging outperformed in object detection and... 详细信息

ISBN: (纸本)9781957171098

关键词： Object detection

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ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

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IEEE Transactions on Image Processing 2023年 32卷 2843-2856页

作者： Chiou, Chien-Yu Lee, Kuang-Ting Huang, Chun-Rong Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan 701 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung 402 Taiwan National Cheng Kung University Cross College Elite Program and Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan 701 Taiwan

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://***/nchucvml/ADMM-SRNet. © 1992-2012 IEEE.

关键词： contrastive learning deep learning dictionary learning One-class classification sparse representation

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