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Minimizing File Transfer Time in Opportunistic Spectrum Access Model

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arXiv 2022年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young Department of Electrical and Computer Engineering United States Operations Research Graduate Program North Carolina State University RaleighNC United States

We study the file transfer problem in opportunistic spectrum access (OSA) model, which has been widely studied in throughput-oriented applications for max-throughput strategies and in delay-related works that commonly assume identical channel rates and fixed file sizes. Our work explicitly considers minimizing the file transfer time for a given file in a set of heterogeneous-rate Bernoulli channels, showing that max-throughput policy doesn’t minimize file transfer time in general. We formulate a mathematical framework for static extend to dynamic policies by mapping our file transfer problem to a stochastic shortest path problem. We analyze the performance of our proposed static and dynamic optimal policies over the max-throughput policy. We propose a mixed-integer program.ing formulation as an efficient alternative way to obtain the dynamic optimal policy and show a huge reduction in computation time. Then, we propose a heuristic policy that takes into account the performance-complexity tradeoff and consider the online implementation with unknown channel parameters. Furthermore, we present numerical simulations to support our analytical results and discuss the effect of switching delay on different policies. Finally, we extend the file transfer problem to Markovian channels and demonstrate the impact of the correlation of each channel. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

Robust Fault Estimation for Wind Turbine Pitch and Drive Systems

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SSRN

SSRN 2023年

作者： Azizi, Abdesamia Youssef, Tewfik Kouadri, Abdelmalek Mansouri, Majdi Mimouni, Mohamed Fouzi Signals and Systems Laboratory Institute of Electrical and Electronic Engineering University M’Hamed Bougara of Boumerdes Boumerdes35000 Algeria Electrical and Computer Engineering Program Texas A and M University at Qatar Doha23874 Qatar " National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

In this paper, a novel fault estimator for wind turbine pitch and drive train system is proposed. The main objective is to estimate actuator and sensor faults along with the system states while mitigating the impact of process disturbances and noises. To accomplish this, an augmented state is created by combining the states of the system and faults. Subsequently, an Unknown Input Observer (UIO) is developed to estimate them simultaneously. The UIO matrices are acquired by optimizing a multi-objective function formed by transforming state and fault estimation errors into the frequency domain using a genetic algorithm. Compared with H∞, the proposed technique shows great superiority in accurately estimating various faults of actuators and sensors. © 2023, The Authors. All rights reserved.

关键词： Wind turbines

A data-stream TinyML compression algorithm for vehicular applications: a case study

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A data-stream TinyML compression algorithm for vehicular app...

Workshop on Metrology for Industry 4.0 and IoT

作者： Marianne Silva Gabriel Signoretti Thommas Flores Pedro Andrade Jordã o Silva Ivanovitch Silva Emiliano Sisinni Paolo Ferrari Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil University of Brescia Brescia Italy

The Internet of Things (IoT) has reduced the distance between one point and another and between people by connecting multiple devices to the web. However, the volume and speed of data creation and transmission have also increased. In this scenario, some challenges start to emerge, such as potentially irrelevant or redundant data transmission, that is, generating a more significant expenditure of energy and processing, in addition to the unnecessary use of the communication channel. Thus, to mitigate these, a solution for IoT devices would be data compression techniques. However, such devices available in the market today have severe limitations in terms of storage and processing power. Therefore, to overcome these limitations, the TinyML can be used to seek ways to implement machine learning models in low-power devices. In this context, this article aims to evaluate the impact of the compression algorithm (Tiny Anomaly Compress - TAC) on the performance of a microcontroller applied to the context of vehicles in a real scenario. As a result, it was found that even with the embedded algorithm, the microcontroller processing time is not affected in a meaningful way.

关键词： Performance evaluation Microcontrollers Data compression Communication channels Machine learning Real-time systems Internet of Things

Q-learning based Path Planning Method for UAVs using Priority Shifting

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Q-learning based Path Planning Method for UAVs using Priorit...

International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kevin B. de Carvalho Iure Rosa L. de Oliveira Daniel K. D. Villa Alexandre G. Caldeira Má rio Sarcinelli-Filho Alexandre S. Brandã o Department of Electrical Engineering Graduate Program in Computer Science Nucleus of Specialization in Robotics Universidade Federal de Vi&#x00E7 osa Vi&#x00E7 osa MG Brazil Department of Electrical Engineering Graduate Program in Electrical Engineering Universidade Federal do Esp&#x00ED rito Santo Vit&#x00F3 ria ES Brazil

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

Path planning is a crucial part of autonomous navigation when regarding autonomous aerial vehicles, often demanding different priorities such as the length, safety or energy consumption. Dynamic program.ing and geometric methods have been applied to solve this problem, but in recent years, more work has been developed using artificial intelligence approaches, such as reinforcement learning. In this paper we propose an offline path planning method for static environments using Q-learning. An optimal policy is found weighting three important factors: path length, safety and energy consumption. Due to a well balanced exploring/exploiting ratio, the proposed method can lead the agent to the desired destination starting from anywhere in the map. Simulations are done in different scenarios to address the performance of the proposed method and it showcased that the algorithm is able to find feasible paths in each scenario while regarding different set of priorities.

关键词： Energy consumption Q-learning Atmospheric modeling Autonomous aerial vehicles Path planning Safety Dynamic program.ing

Adaptive and Fair Deployment Approach to Balance Offload Traffic in Multi-UAV Cellular Networks

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arXiv 2022年

作者： Lai, Chuan-Chi Bhola Tsai, Ang-Hsun Wang, Li-Chun The Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan The Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu30010 Taiwan The Department of Communications Engineering Feng Chia University Taichung40724 Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Unmanned aerial vehicle-aided communication (UAB-BS) is a promising solution to establish rapid wireless connectivity in sudden/temporary crowded events because of its more flexibility and mobility features than conventional ground base station (GBS). Because of these benefits, UAV-BSs can easily be deployed at high altitudes to provide more line of sight (LoS) links than GBS. Therefore, users on the ground can obtain more reliable wireless channels. In practice, the mobile nature of the ground user can create uneven user density at different times and spaces. This phenomenon leads to unbalanced user associations among UAV-BSs and may cause frequent UAV-BS overload. We propose a three-dimensional adaptive and fair deployment approach to solve this problem. The proposed approach can jointly optimize the altitude and transmission power of UAV-BS to offload the traffic from overloaded UAV-BSs. The simulation results show that the network performance improves by 37.71% in total capacity, 37.48% in total energy efficiency and 16.12% in the Jain fairness index compared to the straightforward greedy approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

Sensing beyond the exceptional point for high detectivity

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arXiv 2024年

作者： Li, Zhichao Prasad, Ciril S. Wang, Xielin Zhang, Ding Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Program Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity due to their acute sensitivity to perturbations such as noise. When the optical budget is limited as in applications on mobile platforms, high detectivity might be equally important as high sensitivity. In such scenarios, off-EP sensing is advantageous where a slight loss in sensitivity can lead to a significant gain in detectivity. Here, we show that a passive parity-time symmetric plasmonic-photonic hybrid shows a peak in its detectivity off EP where the sensitivity is still very high – surpassing the sensitivities of equivalent fully-plasmonic and fully-photonic systems. We demonstrate the high detectivity of off-EP sensing of anti-mouse IgG protein using this plasmonic-photonic hybrid system. We report a sensitivity of 1.2 nm/nM while requiring a minimum optical budget of 1.3 nJ. Thus, non-Hermitian plasmonic-photonic hybrids could be the best class of nanophotonic sensors for real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety

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arXiv 2023年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina at Charlotte United States Public Policy Program University of North Carolina at Charlotte 9201 University City Blvd CharlotteNC28223 United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird’s Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system’s robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

Manhattan Nonnegative matrix factorization using the alternating direction method of multipliers

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Manhattan Nonnegative matrix factorization using the alterna...

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Chan Cao Shuyu Tang Nian Zhang Xiangguang Dai Wei Zhang Yuming Feng Jiang Xiong Jinkui Liu Lara Thompson Chongqing Three Gorges University Wanzhou China Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA

Nonnegative matrix factorization (NMF) was a classic model for dimensional reduction. Manhattan NMF is a variant version of NMF that uses a L 1 -norm cost function as the objective function instead of the L 2 -norm cost function. Manhattan NMF can be formulated as a nonconvex nonsmooth optimization problem. An algorithm framework for solving the Manhattan NMF problem based on the alternating direction method of multiplication is presented to us. Compared with the existed algorithm, our proposed algorithm is more effective by experiments on synthetic and real data sets.

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Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion

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arXiv 2024年

作者： Kim, Eunji Kim, Siwon Park, Minjun Entezari, Rahim Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Stability AI United Kingdom AIIS ASRI INMC ISRC Seoul National University Korea Republic of

Recent advancements in text-to-image models, such as Stable Diffusion, show significant demographic biases. Existing debiasing techniques rely heavily on additional training, which imposes high computational costs and risks of compromising core image generation functionality. This hinders them from being widely adopted to real-world applications. In this paper, we explore Stable Diffusion’s overlooked potential to reduce bias without requiring additional training. Through our analysis, we uncover that initial noises associated with minority attributes form "minority regions" rather than scattered. We view these "minority regions" as opportunities in SD to reduce bias. To unlock the potential, we propose a novel de-biasing method called ‘weak guidance,’ carefully designed to guide a random noise to the minority regions without compromising semantic integrity. Through analysis and experiments on various versions of SD, we demonstrate that our proposed approach effectively reduces bias without additional training, achieving both efficiency and preservation of core image generation functionality. © 2024, CC BY-NC-SA.

关键词： Digital elevation model