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International Conference on computer and Information Technology (ICCIT)

作者： Al Zadid Sultan Bin Habib Md. Ekramul Islam Md Asif Bin Syed Md Younus Ahamed Tanpia Tasnim Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown USA Department of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Industrial and Management Systems Engineering West Virginia University Morgantown USA Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child’s growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach’s efficacy in pediatric endocrine disease diagnosis.

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Efficient Coordination and Synchronization of Multi-Robot systems Under Recurring Linear Temporal Logic

arXiv

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arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

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Advances in cost-effective integrated spectrometers

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Light(Science & Applications) 2022年第7期11卷 1379-1396页

作者： Ang Li Chunhui Yao Junfei Xia Huijie Wang Qixiang Cheng Richard Penty Yeshaiahu Fainman Shilong Pan Key Laboratory of Radar Imaging and Microwave Photonics Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 210016China Litin Technology XuzhouJiangsuChina Department of Engineering University of Cambridge9 JJ Thomson AvenueCambridge CB30FAUK Colllege of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Electrical and Computer Engineering University of California at San DiegoLa JollaCAUSA

The proliferation of Internet-of-Things has promoted a wide variety of emerging applications that require compact,lightweight,and low-cost optical *** substantial progresses have been made in the miniaturization of spectrometers,most of them are with a major focus on the technical side but tend to feature a lower technology readiness level for *** importantly,in spite of the advancement in miniaturized spectrometers,their performance and the metrics of real-life applications have seldomly been connected but are highly *** review paper shows the market trend for chip-scale spectrometers and analyzes the key metrics that are required to adopt miniaturized spectrometers in real-life *** progress addressing the challenges of miniaturization of spectrometers is summarized,paying a special attention to the CMOS-compatible fabrication platform that shows a clear pathway to massive *** for ways forward are also presented.

关键词： cost summarized spite

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Fundamental limits of distributed covariance matrix estimation under communication constraints 24

Fundamental limits of distributed covariance matrix estimati...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mohammad Reza Rahmani Mohammad Hossein Yassaee Mohammad Ali Maddah-Ali Mohammad Reza Aref Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran and Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings.

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Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC systems Using Neural Networks

TechRxiv

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TechRxiv 2023年

作者： Seitz, Steffen Lindenberg, Christopher Tetzlaff, Ronald Götz, Thomas Schlegel, Stephan The Department of Electrical and Computer Engineering Technische Universität Dresden Dresden01062 Germany The Department Electrical Power Systems and High Voltage Engineering Technische Universität Dresden Germany

Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location. © 2023, CC BY-SA.

关键词： Partial discharges

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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Efficient Estimation of the Human Circadian Phase via Kalman Filtering

Efficient Estimation of the Human Circadian Phase via Kalman...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Chukwuemeka O. Ike John T. Wen Meeko M.K. Oishi Lee K. Brown A. Agung Julius Lighting Enabled Systems and Applications (LESA) Engineering Research Center Troy NY USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Department of Internal Medicine University of New Mexico Albuquerque NM USA

Circadian rhythms play a vital role in maintaining a person’s well-being but remain difficult to quantify accurately. Numerous approaches exist to measure these rhythms, but they often suffer from performance issues on the individual level. This work implements a Steady-State Kalman Filter as a method for estimating the circadian phase shifts from biometric signals. Our framework can automatically fit the filter’s parameters to biometric data obtained for each individual, and we were able to consistently estimate the phase shift within 1 hour of melatonin estimates on 100% of all subjects in this study. The estimation method opens up the possibility of real-time control and assessment of the circadian system, as well as chronotherapeutic *** relevance— This establishes a near real-time alternative to melatonin measurements for the estimation of circadian phase shifts, with potential applications in feedback circadian control and chronotherapeutics

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Beam Scanning for Integrated Sensing and Communication in IRS-aided mmWave systems 24

Beam Scanning for Integrated Sensing and Communication in IR...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Li, Renwang Shao, Xiaodan Sun, Shu Tao, Meixia Zhang, Rui Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Shanghai Jiao Tong University Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9781665496261

This paper investigates an intelligent reflecting surface (IRS) aided millimeter-wave integrated sensing and communication (ISAC) system. Specifically, based on the passive beam scanning in the downlink, the IRS finds the optimal beam for reflecting the signals from the base station to a communication user. Meanwhile, the IRS estimates the angle of a nearby target based on its echo signal received by the sensing elements mounted on the IRS (i.e., semi-passive IRS). We propose an ISAC protocol for achieving the above objective via simultaneous (beam) training and sensing (STAS). Then, we derive the achievable rate of the communication user and the Cramer-Rao bound (CRB) of the angle estimation for the sensing target in closed-form. The achievable rate and CRB exhibit different performance against the duration of beam scanning. Specifically, the average achievable rate initially rises and subsequently declines, while the CRB monotonically decreases. Consequently, the duration of beam scanning should be carefully selected to balance communication and sensing performance. Simulation results have verified our analytical findings and shown that, thanks to the efficient use of downlink beam scanning signal for simultaneous communication and target sensing, the STAS protocol outperforms the benchmark protocol with orthogonal beam training and sensing. © 2023 IEEE.

关键词： Millimeter waves

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Proposed Flight Recorder Recommendations for Unmanned Aircraft systems Integration into the National Airspace System

Proposed Flight Recorder Recommendations for Unmanned Aircra...

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Conference on Digital Avionics systems (DASC)

作者： Olayemi Ogirimah Waleed Raza Gurvir Bawa Justus Renkhoff Houbing Song Richard S. Stansbury Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach USA Department of Information Systems University of Maryland - Baltimore County Baltimore USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The increasing integration of Unmanned Aircraft systems (UAS) into the National Airspace System necessitates the development of flight data recorder (FDR) recommendations tailored specifically for these systems. Traditional FDRs used in manned aviation are inadequate for capturing the flight data required for UAS operations. This study involved a detailed review of existing guidelines and regulations for manned aircraft FDRs, adapting and extending them to meet the unique needs of UAS operations. By drawing upon industry best practices and aca-demic research, a holistic framework for UAS FDRrecommendations is proposed. This framework out-lines the essential data parameters and recommended recording intervals required to facilitate detailed in-cident/accident investigations and continuous safety enhancements. Implementing UAS-specific FDR stan-dards is crucial for realizing the full potential of unmanned aviation while upholding stringent safety and operational standards.

关键词： Industries Regulators Reviews System integration Aerospace electronics Regulation Safety Aircraft Standards Guidelines

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Harnessing Inherent Noises for Privacy Preservation in Quantum Machine Learning

Harnessing Inherent Noises for Privacy Preservation in Quant...

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IEEE International Conference on Communications (ICC)

作者： Keyi Ju Xiaoqi Qin Hui Zhong Xinyue Zhang Miao Pan Baoling Liu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX Department of Computer Science Kennesaw State University Marietta GA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Quantum computing revolutionizes the way of solving complex problems and handling vast datasets, which shows great potential to accelerate the machine learning process. However, data leakage in quantum machine learning (QML) may present privacy risks. Although differential privacy (DP), which protects privacy through the injection of artificial noise, is a well-established approach, its application in the QML domain remains under-explored. In this paper, we propose to harness inherent quantum noises to protect data privacy in QML. Especially, considering the Noisy Intermediate-Scale Quantum (NISQ) devices, we leverage the unavoidable shot noise and incoherent noise in quantum computing to preserve the privacy of QML models for binary classification. We mathematically analyze that the gradient of quantum circuit parameters in QML satisfies a Gaussian distribution, and derive the upper and lower bounds on its variance, which can potentially provide the DP guarantee. Through simulations, we show that a target privacy protection level can be achieved by running the quantum circuit a different number of times.

关键词： Privacy Computational modeling Noise Machine learning Gaussian distribution Mathematical models Noise measurement

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