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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Amiri, Zuhra Bash, Boulat A. Notzel, Janis Technische Universität München Emmy-Noether Group Theoretical Quantum Systems Design Lehrstuhl für Theoretische Informationstechnik Germany University of Arizona Electrical & Computer Engineering Optical Sciences TucsonAZ United States

ISBN: (纸本)9781665459754

Optical fiber transmission systems form the back-bone of today's communication networks and will be of high importance for future networks as well. Among the prominent noise effects in optical fiber is phase noise, which is induced by the Kerr effect. This effect limits the data transmission capacity of these networks and incurs high processing load on the receiver. At the same time, quantum information processing techniques offer more efficient solutions but are believed to be inefficient in terms of size, power consumption and resistance to noise. Here we investigate the concept of an all-optical joint detection receiver. We show how it contributes to enabling higher baud-rates for optical transmission systems when used as a pre-processor, even under high levels of noise induced by the Kerr effect. © 2022 IEEE.

关键词： Phase noise

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Selective Detection in Impulsive Low-Frequency Raman Imaging Using Shaped Probe Pulses

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Physical Review Applied 2023年第5期19卷 054075-054075页

作者： Siddarth Shivkumar Dekel Ranann Samuel Metais Sisira Suresh Nicolas Forget Randy Bartels Dan Oron Hervé Rigneault Aix Marseille Université CNRS Centrale Marseille Institut Fresnel Marseille France Department of Physics of Complex Systems The Weizmann Institute of Science Rehovot 76100 Israel Fastlite Antibes 06600 France Departments of Electrical and Computer Engineering and Biomedical Engineering Colorado State University Fort Collins USA

Impulsive stimulated Raman scattering (ISRS) using a single short femtosecond pump pulse to excite molecular vibrations offers an elegant pump-probe approach to perform vibrational imaging below 200cm−1. One shortcoming of ISRS is its inability to offer vibrational selectivity as all the vibrational bonds whose frequencies lie within the short pump-pulse bandwidth are excited. To date, several coherent control techniques have been explored to address this issue and selectively excite a specific molecular vibration by shaping the pump pulse. There has not been any systematic work that reports an analogous shaping of the probe pulse to implement preferential detection. In this work, we focus on vibrational imaging and report vibrational selective detection by shaping the probe pulse in time. We demonstrate numerically and experimentally two pulse-shaping strategies with one functioning as a vibrational notch filter and the other functioning as a vibrational low-pass filter. This enables fast (25μs/pixel) and selective hyperspectral imaging in the low-frequency regime (<200cm−1).

关键词： Analytical chemistry Molecular spectra Optical transient phenomena Biomolecules Time-resolved light scattering spectroscopy Ultrafast femtosecond pump probe

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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Conference on Digital Avionics systems (DASC)

作者： Joakim Lindén Giovanni Burresi Håkan Forsberg Masoud Daneshtalab Ingemar Söderquis Division of Intelligent Future Technologies School of Innovation Design and Engineering Malardalen University Vasteras Sweden Saab Aeronautics Stockholm Sweden Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample.

关键词： Training Visualization Three-dimensional displays Object detection Machine learning Data collection Neural radiance field Autonomous aerial vehicles Data models Synthetic data

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Developing Adaptive, Personalised, Autonomous Social Robots Using Physiological Signals: System Development and a Pilot Study

Developing Adaptive, Personalised, Autonomous Social Robots ...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Shruti Chandra Isha Sharma Benjamin David Schnapp Mike J. Dixon Kerstin Dautenhahn Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Department of Systems Design Engineering University of Waterloo Waterloo ON Canada Department of Mechanical and Mechatronics Engineering University of Waterloo ON Canada Department of Psychology University of Waterloo ON Canada

Maintaining physical, emotional and psychological health is vital for well-being. Social robots have been increasingly used in healthcare to support physical and mental health. Providing appropriate, adaptive and personalised feedback based on the user’s internal states is crucial for effective and engaging human-robot interaction, especially in one-to-one interaction scenarios. In this research, we developed an adaptive and autonomous system, integrating a social robot, a wearable non-intrusive Polar chest sensor and algorithms to guide people in three application scenarios to promote physical, emotional, and psychological well-being. The social robot senses users’ psychophysiological measures such as heart rate and heart-rate variability via the wearable sensor, monitors their stress responses, provides real-time feedback and guides them to perform activities. We detail the system development and a pilot study with fifteen participants to evaluate the system in the three scenarios. The findings suggest that the autonomous system could effectively guide participants through the activities by regulating their stress responses. Participants’ physiological data also support these results. Moreover, the system was well-accepted by its users.

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Fair Concurrent Training of Multiple Models in Federated Learning

arXiv

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arXiv 2024年

作者： Siew, Marie Zhang, Haoran Park, Jong-Ik Liu, Yuezhou Ruan, Yichen Su, Lili Ioannidis, Stratis Yeh, Edmund Joe-Wong, Carlee Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Northeastern University BostonMA02115 United States

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients' computing and communication resources, which we call Multiple-Model Federated Learning (MMFL). Current MMFL algorithms use naïve average-based client-task allocation schemes that can lead to unfair performance when FL tasks have heterogeneous difficulty levels, e.g., tasks with larger models may need more rounds and data to train. Just as naïvely allocating resources to generic computing jobs with heterogeneous resource needs can lead to unfair outcomes, naïve allocation of clients to FL tasks can lead to unfairness, with some tasks having excessively long training times, or lower converged accuracies. Furthermore, in the FL setting, since clients are typically not paid for their training effort, we face a further challenge that some clients may not even be willing to train some tasks, e.g., due to high computational costs, which may exacerbate unfairness in training outcomes across tasks. We address both challenges by firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks in each training round. We provide guarantees on fairness and FedFairMMFL's convergence rate. We then propose a novel auction design that incentivizes clients to train multiple tasks, so as to fairly distribute clients' training efforts across the tasks. We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets. © 2024, CC BY.

关键词： Learning systems

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Real-Time Demonstration of Low-Latency Video Delivery via Hybrid Unicast-Broadcast Networks

Real-Time Demonstration of Low-Latency Video Delivery via Hy...

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International Conference on Network and Service Management

作者： Casper Haems Jeroen van der Hooft Hannes Mareen Peter Steenkiste Glenn Van Wallendael Tim Wauters Filip De Turck Department of Information Technology Ghent University - Imec Ghent Belgium Department of Electronics and Information Systems Ghent University - Imec Ghent Belgium Departments of Computer Science and Electrical and Computer Engineering Carnegie Mellon University Pittsburg United States of America

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

In response to the growing demand for low-latency video streaming, this paper presents a demonstration of a hybrid unicast-broadcast video delivery system that combines 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. The demonstration features a scalable setup with an interactive dashboard, allowing users to experiment with various configurations and observe key metrics such as bandwidth usage, packet loss, buffer size, and live latency in real-time. Key techniques include Low-Latency DASH (LL-DASH) for HTTP Adaptive Streaming (HAS), packet recovery (PR) and Forward Error Correction (FEC) for reliability, Temporal Layer Injection (TLI) for enhanced quality, and Common Media Application Format (CMAF) with Chunked Transfer Encoding (CTE) for reduced latency. The demonstration shows that this scalable hybrid approach can effectively reduce unicast bandwidth to nearly 0 Mb/s in scenarios without packet loss on the broadcast network, and achieve similar bandwidth reductions in lossy broadcast networks with appropriate Forward Error Correction (FEC) settings, while maintaining a live latency lower than 1 second. These results demonstrate the system's potential for optimizing multimedia delivery, significantly reducing unicast bandwidth while maintaining low-latency streaming.

关键词： Over-the-top media services Unicast 5G mobile communication Packet loss Bandwidth Forward error correction Real-time systems Multimedia communication Low latency communication Video recording

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IncluSim: An Accessible Educational Electronic Circuit Simulator for Blind and Low-Vision Learners 25

IncluSim: An Accessible Educational Electronic Circuit Simul...

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Mouallem, Aya Mendez Pons, Mirelys Malik, Ali Rogando, Trini Kim, Gene S-H Kulkarni, Trisha Chong, Charlene Fan, Danyang Patel, Shloke Nirav Shluzas, Lauren Aquino Chen, Helen L. Sheppard, Sheri D. Electrical Engineering Stanford University Stanford CA United States Design Stanford University Stanford CA United States Computer Science Stanford University Stanford CA United States Symbolic Systems Stanford University Stanford CA United States Office of Accessible Education Stanford University Stanford CA United States Mechanical Engineering Stanford University Stanford CA United States Center for Design Research Stanford University Stanford CA United States

ISBN: (纸本)9798400713941

Electronic circuit simulation is a core skill for electronics-based education. However, conventional introductory simulators often rely on visual tasks and features and are inherently inaccessible to learners who are blind or have low vision (BLV). In this work, we present IncluSim, a novel, open-source BLV-accessible circuit simulator tool, incorporating tactile elements and a digital interface. We relied on extensive needfinding to identify barriers faced by BLV learners in electronics-based education. Next, with the larger BLV community, and as a team of BLV and sighted researchers, we adopted the co-design method over 2.5 years to design and develop the simulator tool. Over two studies, BLV participants completed different circuit design and simulation tasks using the IncluSim tool. Our findings indicate that IncluSim, via its hardware-digital medium, enables BLV learners to successfully design, simulate and debug circuits, overcoming the accessibility barriers of conventional simulators. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Accessibility Circuit simulation Co-design Electronics engineering Education Tangible user interface

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Impedance Estimation at Multi-Antenna Receivers

Impedance Estimation at Multi-Antenna Receivers

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Annual Conference on Information Sciences and systems (CISS)

作者： Shaohan Wu Brian L. Hughes Department of Communication Systems Design MediaTek USA Inc. Irvine CA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC

This paper considers antenna impedance matrix estimation using training sequences at multi-antenna receivers. We formulate this problem in a classical estimation framework, treating the unknown channel matrix as a nuisance parameter. The maximum-likelihood impedance estimator is derived as a function of the top block eigenvector of the sample covariance matrix in i.i.d. Rayleigh fading channels. Fundamental lower bounds, e.g., Cramér-Rao bounds and Miller-Chang bounds, on these estimators are derived, the latter of which better predicts the ML estimator's behavior using numerical simulations. Another finding is that transmit diversity may lead to diminishing returns if the total transmitted power is the constraint.

关键词： Training Measurement Maximum likelihood estimation Impedance matching Channel estimation Receiving antennas Rayleigh channels

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Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Zhang, Hongliang Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electronics Peking University Beijing100871 China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision approximate large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pre-trained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce "cached model-as-a-resource" for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources. We design "age of thought" (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize network operators, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and free from adverse selection. © 2024, CC BY.

关键词： Deep learning

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