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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia USA Dept. of Engineering Technology University of Houston Houston USA Dept. of Electrical and Computer Engineering University of Houston Houston USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.

关键词： Costs Network topology Computational modeling Qubit Topology Computational efficiency Resource management Quantum circuit Integrated circuit modeling Optimization

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Learning 3D Scene Analogies with Neural Contextual Scene Maps

arXiv

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arXiv 2025年

作者： Kim, Junho Bae, Gwangtak Lee, Eun Sun Kim, Young Min Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University Korea Republic of

Understanding scene contexts is crucial for machines to perform tasks and adapt prior knowledge in unseen or noisy 3D environments. As data-driven learning is intractable to comprehensively encapsulate diverse ranges of layouts and open spaces, we propose teaching machines to identify relational commonalities in 3D spaces. Instead of focusing on point-wise or object-wise representations, we introduce 3D scene analogies, which are smooth maps between 3D scene regions that align spatial relationships. Unlike well-studied single instance-level maps, these scene-level maps smoothly link large scene regions, potentially enabling unique applications in trajectory transfer in AR/VR, long demonstration transfer for imitation learning, and context-aware object rearrangement. To find 3D scene analogies, we propose neural contextual scene maps, which extract descriptor fields summarizing semantic and geometric contexts, and holistically align them in a coarse-to-fine manner for map estimation. This approach reduces reliance on individual feature points, making it robust to input noise or shape variations. Experiments demonstrate the effectiveness of our approach in identifying scene analogies and transferring trajectories or object placements in diverse indoor scenes, indicating its potential for robotics and AR/VR applications. © 2025, CC BY.

关键词： Adversarial machine learning

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Acoustic Device for Detecting Red Palm Weevil Using Deep Learning and IoT

Acoustic Device for Detecting Red Palm Weevil Using Deep Lea...

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International Conference on Microelectronics, ICM

作者： Maryam M. Atia Amr I. Gouda Mahmoud G. Ismail Mohammed A.-M Salem Mohamed A. Abd ElGhany Electronics Engineering Dept. German University in Cairo Egypt Computer Engineering Dept. German University in Cairo Egypt Integrated Electronic Systems Lab TU Darmstadt Germany

The Red Palm Weevil (RPW) is a major threat to the date palm industry, requiring early detection strategies. This paper presents a comprehensive exploration of two bioacoustic sensor prototypes tailored for the early detection of RPW. The first prototype employs a Raspberry Pi 4 coupled with a Convolutional Neural Network (CNN), achieving a detection accuracy of 99.02%. The second device, built with an ESP32-WROVER module, integrates a Simple Neural Network (SNN) for audio classification and attains an accuracy of 98.79%. This design’s affordability and low power consumption make it particularly suited for large-scale deployment in agricultural settings. This paper delves deeper into the bioacoustic sensor technology used, the dataset chosen for CNN and SNN models training, and provides a comparison between the two device designs in terms of cost and efficiency. Our project’s commitment to iterative improvement aims to provide an effective solution to counteract RPW’s destructive effects on date palm cultivation.

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CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function

CDFMR: A Distributed Statistical Analysis of Stock Market Da...

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IEEE Cloud Summit

作者： Devendra Dahiphale Abhijeet Wadkar Karuna Pande Joshi Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County USA Dept. of Information Systems University of Maryland Baltimore County USA

The stock market generates massive data daily on top of a deluge of historical data. Investors and traders look to stock market data analysis for assurance in their investments, a prime indicator of our global economy. This has led to immense popularity in the topic, and consequently, much research has been done on stock market predictions and future trends. However, due to the relatively slow electronic trading systems and order processing times, the velocity of data, the variety of data, and social factors, there is a need for gaining speed, control, and continuity in data processing (real-time stream processing) considering the amount of data that is being produced daily. Unfortunately, processing this massive amount of data on a single node is inefficient, time-consuming, and unsuitable for real-time processing. Recently, there have been many advancements in Big Data processing technologies such as Hadoop, Cloud MapReduce, and HBase. This paper proposes a MapReduce algorithm for statistical stock market analysis with a Cumulative Distribution Function (CDF). We also highlight the challenges we faced during this work and their solutions. We further showcase how our algorithm is spanned across multiple functions, which are run using multiple MapReduce jobs in a cascaded fashion.

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Load-Shifting Scheduling based on Manufacturing Process for Demand Response with Bidding Probability

Load-Shifting Scheduling based on Manufacturing Process for ...

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IEEE General Meeting Power& Energy Society

作者： Jounghoon Nam Nadya Noorfatima Jaesung Jung Dept. of Electrical and Computer Engineering Ajou University Suwon South Korea Dept. of Energy Systems Research Ajou University Suwon South Korea

This study develops a load-shifting scheduling algorithm for demand response to improve the profit of industrial customers. The objective function is determined to maximize the expected profit of demand response (DR) participation to reflect the market structure. For this, it converts the profit of the economic DR by using the bidding probability. Moreover, it derives the DR schedules of each manufacturing process separately and organizes constraints to improve the participation amount and minimize damage to each manufacturing machine. The proposed algorithm utilizes a genetic algorithm (GA) to derive an optimal DR solution. The actual market data and power consumption coefficient of South Korea are used to simulate the proposed algorithm.

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LiDAR Spoofing Attack Detection in Autonomous Vehicles

LiDAR Spoofing Attack Detection in Autonomous Vehicles

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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Methodology for Evaluating Equivalent Models for the Dynamic Analysis of Power systems

Methodology for Evaluating Equivalent Models for the Dynamic...

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Power Tech Conference

作者： Georgios A. Barzegkar-Ntovom Eleftherios O. Kontis Theofilos A. Papadopoulos Panagiotis N. Papadopoulos Dept. of Electrical and Computer Engineering Power Systems Laboratory Democritus University of Thrace Xanthi Greece Power Systems Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Dept. of Electronic and Electrical Engineering Institute for Energy and Environment University of Strathclyde Glasgow Scotland U.K.

The increasing penetration of distributed renewable energy sources drastically alters the dynamic characteristics of distribution networks (DNs). Therefore, several equivalent models have been recently proposed, to analyze more accurately the complex behavior of modern DNs. However, relatively simple models are still commonly used in practice for dynamic power system studies. In addition, dynamic equivalent models for DNs are sensitive to different operating conditions and there is lack of systematic understanding of their performance. Scope of this paper is to propose a methodology for identifying the applicability range in terms of accuracy and generalization capability of several conventional and newly developed equivalent models for the dynamic analysis of modern DNs. A set of metrics is used for the modelling accuracy assessment and a sensitivity analysis framework is introduced to fully quantify the generalization capabilities of DN equivalent models. Based on the above, guidelines and recommendations for the development of robust equivalent models for DN analysis are proposed.

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AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework

AirWave: Enhancing UAV Connectivity in Cellular Networks thr...

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Big Data & Machine Learning (ICBDML), IEEE International Conference on

作者： Emirhan Zor Yusuf Taskiran Fares A. Dael Ibraheem Shayea Leila Rzayeva Zuleikha Syzdykova Electronic and Communication Engineering Istanbul Technical University Istanbul Turkey Management Information Systems İzmir Bakırçay University İzmir Turkey Dept. of Intelligent Systems and Cybersecurity Astana IT University Astana Kazakhstan Dept. of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-dept. investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications.

关键词： Cellular networks Reinforcement learning Propulsion Parallel processing Autonomous aerial vehicles Vehicle dynamics Physics

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Design and Evaluation of a Low-profile Haptic Interface Based on Surface Electrical Nerve Stimulation

Design and Evaluation of a Low-profile Haptic Interface Base...

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Telepresence, IEEE Conference on

作者： Rachel S. Jakes Luis Mesias Veronica J. Santos Michael J. Fu Dustin J. Tyler Human Fusions Institute (HFI) Case Western Reserve University (CWRU) Dept. of Biomedical Engineering CWRU Dept. of Electrical Computer and Systems Engineering CWRU Dept. of Mechanical and Aerospace Engineering University of California Los Angeles (UCLA) Dept. of Physical Medicine & Rehabilitation The MetroHealth System Dept. of VA Northeast Ohio Healthcare System Advanced Platform Technology Center

ISBN: (数字)9798331528249

ISBN: (纸本)9798331528256

Haptic feedback in telepresence applications is vital for remote task performance and object manipulation. To maximize telepresence capabilities, haptic systems need to be integrated with immersive telepresence interfaces such as virtual reality, which offer benefits such as improved spatial awareness and operator mobility. Compatibility with the mobility of these systems and their built-in optical hand tracking require low-profile wearable interfaces. Further, for full functional benefits, haptic systems must be able to elicit spatially congruent sensation without impeding operator movement and physical object interactions. In this paper, we describe the design of a lightweight, compact, and adaptable surface electrical nerve stimulation-based haptic interface. We demonstrate its capacity to consistently generate spatially congruent sensation on the all five fingertips without occlusion, as well as its compatibility with headset-based optical hand tracking.

关键词： Hands Telepresence Tracking Stimulated emission Optical feedback Virtual reality Electrical stimulation Haptic interfaces Optical sensors

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Near-Optimal Battery Swapping Algorithm in Dockless Electric Bike Sharing systems

Near-Optimal Battery Swapping Algorithm in Dockless Electric...

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IEEE International Conference on Communications (ICC)

作者： Chun-An Yang Shih-Chieh Chen Yi-Hsuan Peng Yu-Wen Chen Jian-Jhih Kuo Ming-Jer Tsai Dept. of Computer Science National Tsing Hua University Hsinchu Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Computer Systems Technology New York City College of Technology Brooklyn New York USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Dockless electric bike (E-bike) sharing has become a new urban modality of green transportation to offer convenient services. Typically, the service provider arranges a truck starting from the depot to visit multiple parking locations to replace low-energy batteries. However, visiting many parking locations may cause a considerable tour cost. One efficient way is to aggregate low-energy E-bikes together. Some incentive mechanisms are thus adopted to encourage E-bike users to move their bikes to suitable parking locations, but leading to an incentive cost. The service provider would like to balance the tour cost of the truck and the incentive cost of E-bike users. To address this problem, the paper formulates an optimization problem and then proposes an approximation algorithm. The simulation results with the real dataset show that our algorithm outperforms the other baselines.

关键词： Costs Shared transport Simulation Aggregates Approximation algorithms Batteries Green transportation

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