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Rescaling of Symbol Counts for Adaptive rANS Coding 31

Rescaling of Symbol Counts for Adaptive rANS Coding

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Strutz, Tilo Coburg University Department of Electrical Engineering and Computer Science Friedrich-Streib-Str. 2 Coburg96450 Germany

ISBN: (纸本)9789464593600

The abbreviation rANS stands for a relatively new method of arithmetic coding based on asymmetric numeral systems (ANS) which combines the advantages of arithmetic coding in terms of performance and the advantages of Huffman coding in terms of speed. Compared to conventional arithmetic coding methods, the mathematical apparatus is slightly different which has the consequence that the decoding order is reversed to the encoding order, i.e. the processing follows the last-in-first-out principle. This makes it somewhat difficult to design the coding process to adapt to changing symbol statistics, and therefore rANS coding has so far only been applied in settings with fixed statistics. In particular, the frequent rescaling of statistics required to reduce the influence of old symbols becomes a problem when the order of processing is different on the encoder and decoder sides. This paper proposes a new method that allows adaptive coding within the framework of rANS coding and additionally offers the possibility of rescaling the symbols frequencies. Investigations show that this method enables the same compression performance for rANS as for conventional arithmetic coding. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Digital arithmetic

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Adaptive Multi-Link Data Allocation for LEO Satellite Networks

Adaptive Multi-Link Data Allocation for LEO Satellite Networ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zheng, Jinkai Luan, Tom H. Zhao, Jinwei Li, Guanjie Zhang, Yao Pan, Jianping Cheng, Nan Dongguan University of Technology School of Electrical Engineering and Intelligentization Guangdong China Xidian University School of Cyber Engineering Shaanxi China University of Victoria Department of Computer Science VictoriaBC Canada Northwestern Polytechnical University School of Computer Science Shaanxi China Xidian University School of Telecommunications Engineering Shaanxi China

ISBN: (纸本)9798350351255

The rapid development of Low Earth Orbit (LEO) satellite networks has provided ubiquitous Internet access to users around the world, especially in areas where there are no terrestrial networks. However, a dish can only communicate with one of the available satellites when uploading data in the current framework, resulting in low communication efficiency. As the number of satellites continues to increase, the current framework cannot make full use of the user-satellite link resources. In this paper, we first conduct a measurement of Starlink's network performance and report some unique features. Then, we propose an adaptive multi-link data allocation framework for LEO satellite networks where a dish can communicate with multiple satellites at the same time to improve data transmission efficiency. With this framework, data can be split into chunks and uploaded simultaneously over multiple links. Our goal is to determine the data allocation strategies to jointly optimize the transmission latency and data processing costs. To this end, we propose a deep reinforcement learning-based algorithm integrated with the traffic prediction module to determine the optimal data allocation strategies in a dynamic network environment. Through extensive simulations, we demonstrate the effectiveness of our approach compared with baselines. © 2024 IEEE.

关键词： Satellite links

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Enhancing Grid Stability through Grid-Interactive Efficient Buildings with Deep Reinforcement Learning: Innovations and Challenges 50

Enhancing Grid Stability through Grid-Interactive Efficient ...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Amer, Aya Bayhan, Sertac Abu-Rub, Haitham Ehsani, Mehrdad Massoud, Ahmed Electrical and Computer Engineering Department Texas A&M University College Station United States Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Electrical and Computer Engineering Department Texas A&M University Doha Qatar College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Electrical Engineering Department Qatar University Doha Qatar

ISBN: (纸本)9781665464543

Integrating Deep Reinforcement Learning (DRL) into building energy management systems presents a transformative approach to enhancing grid stability and efficiency. Grid-Interactive Efficient Buildings (GEBs), equipped with advanced DRL algorithms, can dynamically optimize their energy consumption and production in response to real-time grid conditions. This paper explores the innovative applications of DRL in GEBs, highlighting its potential to autonomously optimize energy decisions, accommodate the stochastic nature of renewable energy sources, and effectively respond to variable building energy demands. Through a comprehensive analysis, this study not only sheds light on the successes to date but also maps out the significant challenges that must be overcome. By addressing these challenges, DRL for building energy management can fully realize its potential, leading to a more sustainable and efficient energy future. © 2024 IEEE.

关键词： Deep reinforcement learning

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Multi-task Neural Networks for Pain Intensity Estimation Using Electrocardiogram and Demographic Factors 7th

Multi-task Neural Networks for Pain Intensity Estimation Us...

引用

7th and 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2021 and ICT4AWE 2022

作者： Gkikas, Stefanos Chatzaki, Chariklia Tsiknakis, Manolis Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Heraklion71410 Greece Institute of Computer Science Foundation for Research and Technology-Hellas Estavromenos Heraklion70013 Greece

ISBN: (纸本)9783031374951

Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to attain a reliable and unbiased healthcare system. In this work, we elaborate electrocardiography signals revealing the existence of variations in pain perception among different demographic groups. We exploit this insight by introducing a novel multi-task neural network for automatic pain estimation utilizing the age and the gender information of each individual, and show its advantages compared to other approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Enhanced Neural SLAM with Semantic Segmentation in Dynamic Environments 8

Enhanced Neural SLAM with Semantic Segmentation in Dynamic E...

引用

8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Shen, Zhengcheng Mao, Minzhe Kastner, Linh Lambrecht, Jens Chair Industry Grade Networks and Clouds Berlin Institute of Technology Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350300178

Mobile robots face a significant challenge when navigating in dynamic environments such as human crowds. Existing research works typically employ separate data or simulators for dynamic object detection and navigation tasks. This paper combines the photo-realistic simulator Habitat with embedded dynamic objects to create a playground for optimizing vision-based navigation algorithms. To validate our system, we implement and train three approaches - zero masks, image memory inpainting, and semantic map filter - using semantically segmented information. We then implement and evaluate an adapted reinforcement-learning-based SLAM algorithm using the validation Gibson dataset. The results indicate that moderate localization bias occurs when the environment is small and the navigation process is short. However, the error will accumulate over time. Additionally, a dynamic environment leads to less accurate localization. All three approaches can reduce the localization error, while the semantic map filter approach shows the best overall performance. © 2023 IEEE.

关键词： Object detection

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Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ANN

Enhancing Energy Efficiency in WSNs with Hybrid LEACH-D and ...

引用

2024 Multimedia University engineering Conference, MECON 2024

作者： Qamar, Muhammad Salman Haq, Ihsan Ul Munir, Muhammad Fahad Roslee, Mardeni Bin Awan, Adnan Anwar Waseem, Athar International Islamic University Department of Electrical and Computer Engineering Islamabad Pakistan Multimedia University Centre for Wireless Technology Faculty of Engineering Cyberjaya63100 Malaysia School of Engineering California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798331530747

This paper explores strategies to improve the energy efficiency and operational longevity of Wireless Sensor Networks (WSNs) through the optimization of clustering techniques and energy efficient routing protocols. The proposed methodology is precisely crafted to optimize energy consumption (EC) while preserving the overall effectiveness of WSNs. LEACH (Low Energy Adaptive Cluster Hierarchy) is a cluster-based protocol where non-cluster head nodes deactivate their RF until their allocated time slots. Nevertheless, a drawback of traditional LEACH is its random rotation of local Cluster Heads (CH), leading to unequal cluster distribution. To overcome this issue, we proposed a novel hybrid technique called LEACH-D protocol and Artificial Neural Network (ANN). The LEACH-D algorithm enhances the duration of transmission tasks while ensuring uniform battery energy consumption. On the other hand, the ANN plays a pivotal role in coordinating optimal routing decisions and CH placement, refining data aggregation and transmission mechanisms to restrain idle listening. The findings indicate that the suggested approach manifests as substantial reductions in energy consumption and prolonged network lifespan of WSN. © 2024 IEEE.

关键词： Sensor nodes

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Towards Collaborative Aerial Swarming Architecture for Military Applications 23

Towards Collaborative Aerial Swarming Architecture for Milit...

引用

23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Hughes, Jason Kim, Dongbin Henderson, Steve Manjunath, Pratheek United States Military Academy Department of Electrical Engineering & Computer Science West PointNY10996 United States

ISBN: (纸本)9788993215274

The United States Department of Defense (DoD) has long been interested in aerial swarms for military intelligence, surveillance, and reconnaissance (ISR) missions. Through work with DoD partners, we found a unique need for a dynamic task allocation (TA) algorithm for a swarm of unmanned aerial systems (UAS). Capable control algorithms have already been developed but deploying such algorithms in an actual swarm, reliably, requires information exchange and data organization among the agents. To this end, we introduce a collaborative aerial swarming architecture (CASA), capable of dynamic task allocation and decentralized control. We highlight, through CASA, how this algorithm can be applied to ISR tasks. Finally, we present the results of deploying CASA on a simulated swarm of UAS for a notional ISR task. © 2023 ICROS.

关键词： Antennas

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Open-Source Architecture for Multi-Party Update Verification for Data Acquisition Devices

Open-Source Architecture for Multi-Party Update Verification...

引用

2022 IEEE Power and Energy Conference at Illinois, PECI 2022

作者： Newberg, Benjamin Grijalva, Santiago Mooney, Vincent Georgia Institute of Technology School of Electrical and Computer Engineering School of Computer Science AtlantaGA United States

ISBN: (纸本)9781665402293

Power grids are integral parts of modern daily life and are increasingly under cyberattack. Common software update processes for grid control devices rely on only one organization to provide verification. This paper proposes a multi-signature software update process to help better secure data acquisition devices from malicious actions by using standard cryptosystems such as TLS. A prototype system build on Linux and off-the-shelf hardware has shown successful update and attack prevention with our customized multi-party update software. © 2022 IEEE.

关键词： Verification

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Validation of Machine Learning-Based Lane Lines Detection Methods Using Different Datasets

Validation of Machine Learning-Based Lane Lines Detection Me...

引用

2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Jukic, Grgur Vranjes, Mario Vajak, Denis Vranjes, Denis TTTech Auto CEE Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350347722

Advanced Driver Assistance System (ADAS) uses various sensors that exist in the vehicle to collect information about the vehicle and its surroundings. One of the most commonly used ADASs is the one designed to recognize driving lane lines. In this paper, analysis, and validation of freely available state-of-The-Art deep learning-based lane lines detection (LLD) methods are done using three different well-known datasets: CULane, TuSimple, and LLAMAS. To perform the validation, as part of this research, two converters of lane line label formats were made. Converters allow testing models that have been previously trained on a dataset that uses different lane line label formats so that their performances can be verified on other datasets with different lane line format types. The experimental results show that the adaptability of the model to the change of the dataset depends on the architecture used as the basis of the model for the LLD, and the similarity of the training set of the initially used dataset and the new, *** dataset. All tested models showed shortcomings when tested on images from datasets on which they were not trained, which confirms that there is still no model that we can confidently say achieves high performance in all possible scenarios. © 2023 IEEE.

关键词： Advanced driver assistance systems

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Neural Wavefront Shaping in the Photon-Starved Regime

Neural Wavefront Shaping in the Photon-Starved Regime

引用

2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Feng, Brandon Y. Guo, Haiyun Xie, Mingyang Boominathan, Vivek Sharma, Manoj K. Veeraraghavan, Ashok Metzler, Christopher A. Department of Computer Science University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Neural Wavefront Shaping (NeuWS) is a recently developed guidestar-free wavefront shaping technique which enables imaging through scattering media at the diffraction-limit. We show NeuWS’s performance degrades gradua... 详细信息

ISBN: (纸本)9781957171289

关键词： Photons

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