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arXiv 2024年

作者： Liao, Guiqiu Jogan, Matjaz Koushik, Sai Eaton, Eric Hashimoto, Daniel A. Penn Computer Assisted Surgery and Outcomes Laboratory Department of Surgery University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. Weakly supervised semantic segmentation of objects in surgical videos is however more challenging due to a complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing Video Spatio-Temporal Disentanglement Networks (VDST-Net), a framework to disentangle complex spatiotemporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Lightweight Disaster Semantic Segmentation for UAV On-Device Intelligence

Lightweight Disaster Semantic Segmentation for UAV On-Device...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hanbin Lee Gihwan Kim Sangho Ha Hyungshin Kim Department of Computer Science and Engineering Embedded Systems Laboratory (ESLAB) Chungnam National University Daejeon Republic of Korea

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.

关键词： Accuracy Quantization (signal) Disasters Computational modeling Semantic segmentation Autonomous aerial vehicles Real-time systems

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Communication and Control Co-design in Non-cooperative Games

arXiv

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arXiv 2025年

作者： Aggarwal, Shubham Başar, Tamer Maity, Dipankar UrbanaIL61801 United States Department of Electrical & Computer Engineering Coordinated Science Laboratory UIUC United States Department of Electrical and Computer Engineering University of North Carolina at Charlotte CharlotteNC28223 United States

In this article, we revisit a communication-control co-design problem for a class of two-player stochastic differential games on an infinite horizon. Each ‘player’ represents two active decision makers, namely a scheduler and a remote controller, which cooperate to optimize over a global objective while competing with the other player. Each player’s scheduler can only intermittently relay state information to its respective controller due to associated cost/constraint to communication. The scheduler’s policy determines the information structure at the controller, thereby affecting the quality of the control inputs. Consequently, it leads to the classical communication-control trade-off problem. A high communication frequency improves the control performance of the player on account of a higher communication cost, and vice versa. Under suitable information structures of the players, we first compute the Nash controller policies for both players in terms of the conditional estimate of the state. Consequently, we reformulate the problem of computing Nash scheduler policies (within a class of parametrized randomized policies) into solving for the steady-state solution of a generalized Sylvester equation. Since the above-mentioned reformulation involves infinite sum of powers of the policy parameters, we provide a projected gradient descent-based algorithm to numerically compute a Nash equilibrium using a truncated polynomial approximation. Finally, we demonstrate the performance of the Nash control and scheduler policies using extensive numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Inferring Quantum Network Topology Using Local Measurements

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PRX Quantum 2023年第4期4卷 040347-040347页

作者： Daniel T. Chen Brian Doolittle Jeffrey Larson Zain H. Saleem Eric Chitambar Mathematics and Computer Science Division Argonne National Laboratory USA Department of Physics University of Illinois at Urbana-Champaign USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign USA

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities—namely, the von Neumann entropy and the measured mutual information—as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

关键词： Entanglement detection Quantum communication Quantum computation

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Blockchain-Based Secure Crowdsourcing in Wireless IoT

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Journal of Communications and Information Networks 2022年第1期7卷 23-36页

作者： Daquan Feng Long Zhang Shengli Zhang Qihui Wu Xianggen Xia Guangdong Province Engineering Laboratory for Digital Creative Technology Shenzhen UniversityShenzhen 518060China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu 611731China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

With the explosive growth of interconnected smart devices and sensors,the Internet has been entering the Internet of things(IoT)era and revolutionizing many aspects of our daily life. Meanwhile, crowdsourcing has been considered as a promising technology to realize collaborative intelligence. Therefore, more and more IoT-based crowdsourcing applications are emerged to take advantages of the widely distributed IoT devices to sense, collect, and analyze data with the aim to solve complex and nontrivial tasks. However,there exist many technical challenges to be addressed in the IoT-based crowdsourcing, such as security, privacy, and incentive provision. In this paper, we propose a blockchain-based architecture as an integrated solution to realize the secure and trustworthy crowdsourcing in wireless IoT. We first overview the challenges in the traditional crowdsourcing system. Then, we briefly introduce the background of the blockchain and smart contract, and propose a blockchain-based crowdsourcing architecture. In particular, we elaborate the utilization of smart contract on the specific phases of crowdsourcing. By deploying the smart contract instance, we confirm the proposed blockchain-based architecture is feasible.

关键词： crowdsourcing blockchain wireless IoT security privacy incentive

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Analysis of Partial Discharge Characteristics of MVDC Power Cables Optimally Designed for All-Electric Wide Body Aircraft Under Varying Pressure During Takeoff and Descent

Analysis of Partial Discharge Characteristics of MVDC Power ...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Md Asifur Rahman Anoy Saha Saikat Chowdhury Mona Ghassemi Jane Lehr Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque UM USA

ISBN: (数字)9798350366679

ISBN: (纸本)9798350366686

Introducing all-electric aircraft (AEA) is a viable option to reduce greenhouse gas (GHG) emissions from the aviation sector. The wide-body AEA's electric power system needs higher voltage levels (a few kVs) than the voltage levels of existing aircraft cables. Consequently, the aircraft cable insulation needs to withstand higher voltage, maintaining low mass since the EPS contributes about 30% of the total mass of aircraft. Aircraft cables face several challenges: arcing, partial discharge (PD), and electromagnetic interference due to low-pressure environments at high altitudes. Several multilayer insulation designs for aircraft cables were proposed to overcome these challenges, and the optimized fabrication process of these cables was presented in our previous works. This paper analyzes the PD characteristics of fabricated flat insulation samples for the cables under different air pressures as the aircraft operates from 1 atm at the time of takeoff to 18.8 kPa at the typical cruising altitude (12.2 km).

关键词： Partial discharges Insulation Power cables Voltage Nonhomogeneous media Power systems Aircraft Greenhouse gases Faces Cables

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Generating Multichannel Colored Noise for Underwater Acoustic Simulations

Generating Multichannel Colored Noise for Underwater Acousti...

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Underwater Communications and Networking (UComms)

作者： Mandar Chitre Acoustic Research Laboratory Tropical Marine Science Institute Department of Electrical and Computer Engineering National University of Singapore

ISBN: (数字)9798331542511

ISBN: (纸本)9798331542528

Underwater acoustic communication algorithms typically undergo numerical testing within simulated environments prior to real-world application. These simulations require noise samples that accurately reflect the ambient noise in the underwater environment. The common practice of employing a white Gaussian noise model for additive noise in simulations fails to capture the true nature of under-water noise. Given the wideband nature of most underwater communication systems and the frequency-dependence of ambient noise, the assumption of white noise becomes untenable. Furthermore, communication systems equipped with multiple hydrophones for spatial diversity experience correlated noise across hy-drophones. This paper presents a simple technique for generating colored noise that is consistent with the spatial and temporal correlation characteristics of ambient noise measured at sea.

关键词： Underwater communication Spatial diversity Noise Sea measurements White noise Numerical models Underwater acoustics Colored noise Wideband Testing

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An Optimal Approach to Fabricate MVDC Multilayer Insulation systems as Flat Samples for Wide-Body All-Electric Aircraft

An Optimal Approach to Fabricate MVDC Multilayer Insulation ...

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Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

作者： Saikat Chowdhury Arian Azizi Anoy Saha Md Asifur Rahman Mona Ghassemi Jane Lehr Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque UM USA

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

To accomplish the net zero emission target, the current fuel-based transportation sector, which is the largest greenhouse gas emitter, needs to be decarbonized. Moving towards electrified ones is a highly promising solution to reduce carbon emissions. Aviation sectors, which rely on fossil fuels and emit harmful pollutants, are also being challenged by electric and hybrid-electric propulsion systems. Fully electrifying aviation or all-electric aircraft (AEA) requires high-power density and low-system mass electric power systems (EPS) and MVDC emerges as a viable solution to address this critical need. Multilayer Multifunctional electrical Insulation (MMEI) systems can be a solution to address challenges towards higher voltages and higher density designs under aviation harsh environments. Many MMEI systems have been designed in our earlier work. However, only experimental testing can ascertain the intended MMEI systems' electrical insulation performance. This study presents an optimal approach to fabricating the flat samples of down-selected MMEI designs by a compression-molding process and is discussed in detail. For evaluating different fabrication approaches to determine the optimal one, partial discharge levels of built samples are measured and compared.

关键词： Fabrication Insulation Power cables Transportation Prototypes Nonhomogeneous media Power systems

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Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data

Implicit Neural Representations for Simultaneous Reduction a...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Alif Bin Abdul Qayyum Xihaier Luo Nathan M. Urban Xiaoning Qiari Byung-Iun Yoon Department of Electrical and Computer Engineering Texas A&M University Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce green-house gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency.

关键词： Dimensionality reduction Deep learning Wind energy generation Extrapolation Wind energy Wind speed Superresolution Wind farms Data models Testing Climate change Renewable energy sources

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