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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Padmakala, S. Alkawak, Omar A. Reddy, G. Vidya Sagar Narendranathan, S.K. Muthuraman, M.S. Sendilvelan, S. Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Al-Mustaqbal University College Department of Air Conditioning and Refrigeration Technical Engineering Babil Hilla Iraq School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Agni College of Technology Department of Mechanical Engineering Tamil Nadu Chennai India Psna College of Engineering and Technology Department of Mathematics Tamil Nadu Dindigul India Dr. M.G.R Educational and Research Institute Department of Mechanical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665474511

The railway industry is one of the most sustainable modes of transport. To reduce the carbon footprints, renewable energy sources are initiated. The proposal of attaching solar panels to railway sleepers, smart garbage bin, lighting, automatic closing and opening of doors that are accompanied with real time monitoring system with multiple technologies. Among the multiple technology the Internet of Things (IoT) plays a significant role by sending information and monitoring the overall functions. The solar energy obtained is used to generate electricity and further they are stored in battery for future usage. © 2023 IEEE.

关键词： Carbon footprint

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Xdn: towards efficient inference of residual neural networks on cambricon chips 2nd

Xdn: towards efficient inference of residual neural networks...

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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Li, Guangli Wang, Xueying Ma, Xiu Liu, Lei Feng, Xiaobing State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Technology Jilin University Changchun China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

ISBN: (纸本)9783030495558

In this paper, we present XDN, an optimization and inference engine for accelerating residual neural networks on Cambricon chips. We leverage a channel pruning method to compress the weights of ResNet-50. By exploring the optimization opportunities in computational graphs, we propose a layer fusion strategy, which dramatically decreases the number of scalar computation layers, such as Batch Normalization, Scale. Furthermore, we design an efficient implementation of XDN, including data preprocessing, hyper-parameter auto-tuning, etc. The experimental results show that the ResNet-50 model can achieve significant speedup without accuracy loss by using our XDN engine. © Springer Nature Switzerland AG 2020.

关键词： Engines

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Multi-modal Data based Semi-Supervised Learning for Vehicle Positioning

arXiv

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arXiv 2024年

作者： Huan, Ouwen Yang, Yang Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

In this paper, a multi-modal data based semisupervised learning (SSL) framework that jointly use channel state information (CSI) data and RGB images for vehicle positioning is designed. In particular, an outdoor positioning system where the vehicle locations are determined by a base station (BS) is considered. The BS equipped with several cameras can collect a large amount of unlabeled CSI data and a small number of labeled CSI data of vehicles, and the images taken by cameras. Although the collected images contain partial information of vehicles (i.e. azimuth angles of vehicles), the relationship between the unlabeled CSI data and its azimuth angle, and the distances between the BS and the vehicles captured by images are both unknown. Therefore, the images cannot be directly used as the labels of unlabeled CSI data to train a positioning model. To exploit unlabeled CSI data and images, a SSL framework that consists of a pretraining stage and a downstream training stage is proposed. In the pretraining stage, the azimuth angles obtained from the images are considered as the labels of unlabeled CSI data to pretrain the positioning model. In the downstream training stage, a small sized labeled dataset in which the accurate vehicle positions are considered as labels is used to retrain the model. Simulation results show that the proposed method can reduce the positioning error by up to 30% compared to a baseline where the model is not pretrained. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Explainable Asymmetric Auto-Encoder for End-to-End Learning of IoBNT Communications

Explainable Asymmetric Auto-Encoder for End-to-End Learning ...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Roya Khanzadeh Stefan Angerbauer Jorge Torres Gomez Pit Hofmann Falko Dressler Frank H. P. Fitzek Andreas Springer Werner Haselmayr Institute for Communications Engineering and RF-Systems JKU LIT SAL eSPML Lab Johannes Kepler University Linz Linz Austria School of Electrical Engineering and Computer Science TU Berlin Berlin Germany Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Dresden Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Dresden Germany Institute for Communications Engineering and RF-Systems Johannes Kepler University Linz Linz Austria

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

The Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of artificial and natural units that are connected to the Internet. Hence, it extends the connectivity and control to unconventional domains, such as the human body. A potential use case for IoBNT is the communication from the outside to the inside of the human body. In this scenario, typically the Receiver (RX) inside the human body has limited computational complexity, while the Transmitter (TX) outside has large computational resources. In this paper, we address this scenario and propose a novel Asymmetric Auto-Encoder (AAEC) architecture for end-to-end learning of a Molecular Communication (MC) system. It applies a Neural Network (NN) at the TX and a low-complexity slope detector at the RX. We discuss the different layers of the NN-based TX and the corresponding training approach. Moreover, we investigate the explainability of the NN-based TX and show through the use of meta modeling that it can be approximated by a linear model. In addition, we demonstrate that the proposed AAEC resembles an MC system with Zero Forcing (ZF) precoding for low and moderate Inter Symbol Interference (ISI). Finally, through numerical results, we confirmed the aforementioned findings and showed that the proposed AAEC outperforms MC systems with and without ZF precoding, especially in high ISI scenarios.

关键词： Training Transmitters Precoding Symbols Molecular communication Artificial neural networks Receivers

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery

Exploring the Needle Tip Interaction Force with Retinal Tiss...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Simon Pannek Shervin Dehghani Michael Sommersperger Peiyao Zhang Peter Gehlbach M. Ali Nasseri Iulian Iordachita Nassir Navab Department of Computer Science Technische Universität München München Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Wilmer Eye Institute Johns Hopkins Hospital Baltimore MD USA Augenklinik und Poliklinik Klinikum Rechts der Isar der Technische Universität München München Germany Professor and the Chair for Computer Aided Medical Procedures & Augmented Reality Technical University of Munich Munich Germany Adjunct Professor at the Whiting School of Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch’s membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical *** has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force *** introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset’s applicability.

关键词： Force Estimation Pigments Robot sensing systems Retina Needles Robot learning

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Anomaly detection in beehives using deep recurrent autoencoders

arXiv

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arXiv 2020年

作者： Davidson, Padraig Steininger, Michael Lautenschlager, Florian Kobs, Konstantin Krause, Anna Hotho, Andreas Institute of Computer Science Chair of Computer Science X University of Würzburg Am Hubland Würzburg Germany

Precision beekeeping allows to monitor bees’ living conditions by equipping beehives with sensors. The data recorded by these hives can be analyzed by machine learning models to learn behavioral patterns of or search for unusual events in bee colonies. One typical target is the early detection of bee swarming as apiarists want to avoid this due to economical reasons. Advanced methods should be able to detect any other unusual or abnormal behavior arising from illness of bees or from technical reasons, e.g. sensor failure. In this position paper we present an autoencoder, a deep learning model, which detects any type of anomaly in data independent of its origin. Our model is able to reveal the same swarms as a simple rule-based swarm detection algorithm but is also triggered by any other anomaly. We evaluated our model on real world data sets that were collected on different hives and with different sensor setups. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Cacheap:Portable and Collaborative I/O Optimization for Graph Processing

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Journal of computer science & Technology 2019年第3期34卷 690-706页

作者： Peng Zhao Chen Ding Lei Liu Jiping Yu Wentao Han Xiao-Bing Feng State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Department of Computer Science University of RochesterRochester 14623U.S.A. Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Increasingly there is a need to process graphs that are larger than the available memory on today's *** systems have been developed with grapli representations that are efficient and compact for out-of-core processing.A necessary task in these systems is memory *** paper presents a system called Cacheap which automatically and efficiently manages the available memory to maximize the speed of grapli processing,minimize the amount of disk access,and maximize the utilization of memory for graph *** has a simple interface that can be easily adopted by existing graph *** paper describes the new system,uses it in recent graph engines,and demonstrates its integer factor improvements in the speed of large-scale grapli processing.

关键词： out-of-core graph processing system I/O optimization memory cache graph analytics locality

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Investigation of Circular Patch Antenna for Wi-Fi and Bluetooth Applications

Investigation of Circular Patch Antenna for Wi-Fi and Blueto...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Maryam Ghassan Majeed Haitham Bashar Qasim Shakhlokhon Kosimova Hussein Alaa Diame Alaa M. Lafta Haider Alchilibi Computer Engineer Department Al-Kunooze University College Basra Iraq Forensic sciences department Al-Esraa University College Baghdad Iraq Department of Architecture Fergana Polytechnic Institute Fergana Uzbekistan National University of Science and Technology Dhi Qar Iraq Medical Laboratory Techniques Department Al-Farahidi University Baghdad Iraq

The purpose of this paper is to examine and contrast the Novel Circular Patch Antenna employing FR4 and RT Droid (CPA). Resonant frequency of 2.5 GHz in a static medium. To create the antenna, we use the HFSS and two different sample sets. Ten samples are taken from each antenna at 80% G-Power. Assume that the members of Group 1 are all CPAs that use the FR4 substrate. The Novel circular patch antenna (CPA) on RT roger's substrate is in the second group under consideration. This antenna operates at 2.5 GHz to measure reflectance at both ends. A VSWR of 2.1786 dB was measured at 2.4 GHz for a circular CPA designed for RT rogers, while the measured value for a CPA with FR4 was 0.7088 dB. The significance level in SPSS was 0.038 (p 0.05). The findings of this study show that RT rogers is approximately as effective as the FR4 substrate.

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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