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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Charlotte Cathcart María Santos Shinkyu Park Naomi Ehrich Leonard Department of Mechanical and Aerospace Engineering Princeton University Princeton NJ USA Park is with the Electrical and Computer Engineering program King Abdullah University of Science and Technology Thuwal Saudi Arabia

We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's “opinion” for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.

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The Effect of Major Depressive Disorder on Producing Intelligible Speech

SSRN

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SSRN 2024年

作者： Seneviratne, Nadee Li, Leslie Espy-Wilson, Carol Department of Electrical and Computer Engineering University of Maryland College ParkMD20740 United States Program in Neuroscience and Cognitive Science Department of Linguistics University of Maryland College ParkMD20740 United States

Understanding the characteristics of depressed speech can provide insights on aspects to consider when developing biomarkers for automatic depression detection systems. We present the results of a speech intelligibility study that was conducted to assess the intelligibility of the speech of a female and a male subject when they were severely depressed and when they were in remission. Our analysis shows that, in general, the depressed speech is at least as intelligible as in-remission speech. This is further supported by the underlying "simpler" articulatory coordination of depressed speech as derived from the Articulatory Coordination Features. Acoustically, the simpler articulatory coordination means that there is less variability due to coarticulation in the speech signal. © 2024, The Authors. All rights reserved.

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A Machine Learning Approach to Predicting Dry Eye-Related Signs, Symptoms and Diagnoses from Meibography Images

SSRN

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SSRN 2024年

作者： Graham, Andrew D. Kothapalli, Tejasvi Wang, Jiayun Ding, Jennifer Tse, Vivien Asbell, Penny A. Yu, Stella X. Lin, Meng C. Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Vision Science Group University of California Berkeley United States Clinical Research Center School of Optometry University of California Berkeley United States Department of Bioengineering University of Memphis United States International Computer Science Institute Berkeley United States

Purpose: To use artificial intelligence to identify relationships between morphological characteristics of the Meibomian glands (MGs), subject factors, clinical outcomes, and subjective symptoms of dry eye. Methods: A total of 562 infrared meibography images were collected from 363 subjects (170 contact lens wearers, 193 non-wearers). Subjects were 67.2% female and were 54.8% Caucasian. Subjects were 18 years of age or older. A deep learning model was trained to take meibography as input, segment the individual MG in the images, and learn their detailed morphological features. Morphological characteristics were then combined with clinical and symptom data in prediction models of MG function, tear film stability, ocular surface health, and subjective discomfort and dryness. The models were analyzed to identify the most heavily weighted features used by the algorithm for predictions. Results: MG morphological characteristics were heavily weighted predictors for eyelid notching and vascularization, MG expressate quality and quantity, tear film stability, corneal staining, and comfort and dryness ratings, with accuracies ranging from 65% to 99%. Number of visible MG, along with other clinical parameters, were able to predict MG dysfunction, aqueous deficiency and blepharitis with accuracies ranging from 74% to 85%. Conclusions: Machine learning-derived MG morphological characteristics were found to be important in predicting multiple signs, symptoms, and diagnoses related to MG dysfunction and dry eye. This deep learning method illustrates the rich clinical information that detailed morphological analysis of the MGs can provide, and shows promise in advancing our understanding of the role of MG morphology in ocular surface health. © 2024, The Authors. All rights reserved.

关键词： Morphology

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and Information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-Sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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Entanglement-Enhanced Neyman-Pearson Target Detection

arXiv

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arXiv 2024年

作者： Ward, William Hariri, Abdulkarim Zhang, Zheshen Applied Physics Program University of Michigan Ann ArborMI48104 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48104 United States

Quantum illumination (QI) provides entanglement-enabled target-detection enhancement, despite operating in an entanglement-breaking environment. Existing experimental studies of QI have utilized a Bayesian approach, assuming that the target is equally likely to be present or absent before detection, to demonstrate an advantage over classical target detection. However, such a premise breaks down in practical operational scenarios in which the prior probability is unknown, thereby hindering QI's applicability to real-world target-detection scenarios. In this work, we adopt the Neyman-Pearson criterion in lieu of the error probability for equally likely target absence or presence as our figure of merit for QI. We demonstrate an unconditional quantum advantage over the optimal classical-illumination protocol as benchmarked by the receiver operating characteristic, which examines detection probability versus false-alarm probability without resorting to known prior probabilities. Our work represents a critical advancement in adapting quantum-enhanced sensing to practical operational settings. © 2024, CC BY.

关键词： Quantum entanglement

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Textual Training for the Hassle-Free Removal of Unwanted Visual Data: Case Studies on OOD and Hateful Image Detection

arXiv

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arXiv 2024年

作者： Lee, Saehyung Mok, Jisoo Park, Sangha Shin, Yongho Jung, Dahuin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm Korea YH Seoul Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT © 2024, CC BY.

关键词： Data assimilation

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