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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xiuguang Zheng Xiaorui Li Zhaolin Yuan Xiaojuan Ban School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In the process of copper flotation, the traditional detection method has some defects in accurately detecting the depth of foam layer in the flotation cell, especially for the identification of foam information of highly reflective and attenuation materials in the flotation foam system. We aim to introduce a new synthetic dataset, specifically for the flotation foam system, and pay special attention to the foam information of high reflection and high attenuation materials, so as to promote it to the actual flotation foam system and improve the accuracy of liquid level detection. We combine the newly synthesized dataset with binocular ranging methods and propose a soft strategy method for depth estimation of flotation cell thickness using binocular stereo matching based on IoT technology. The experimental results show that our method has achieved remarkable results in the flotation foam system, can accurately measure the depth of the flotation cell, and provides reliable technical support for the control and optimization of the flotation process. Therefore, this binocular stereo matching depth estimation method based on IoT technology provides an effective way to solve the limitations of traditional flotation depth detection methods. It not only effectively monitors the current efficiency of the flotation cell but also automatically identifies instances of flotation cell froth overflowing, offering automated inspection and diagnostics.

关键词： Visualization Liquids Depth measurement Transportation Attenuation Real-time systems Reflection Reliability Synthetic data Thickness measurement

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A Conversational Human-computer Interface for Smart Energy System Simulation Environments 23

A Conversational Human-Computer Interface for Smart Energy S...

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Proceedings of the Winter Simulation Conference

作者： Gabriel Dengler Peter Bazan Reinhard German Pooia Lalbakhsh Ariel Liebmann Laboratory of Computer Networks and Communication Systems Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr Erlangen Germany Department of Data Science and AI Faculty of Information Technology Monash University Clayton Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily.

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Demo: Secure Inventorying and Lifecycle Management of IoT Devices with DLT

Demo: Secure Inventorying and Lifecycle Management of IoT De...

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Conference on Local computer Networks (LCN)

作者： Armin R. Veres Daria Schumm Thomas Grübl Katharina O. E. Müller Bruno Rodrigues Burkhard Stiller Department of Informatics IfI Communication Systems Group CSG University of Zürich UZH Zürich Switzerland Institute of Computer Science in Vorarlberg ICV University St.Gallen HSG Dornbirn Austria

ISBN: (数字)9798350388008

ISBN: (纸本)9798350388015

Security management of Internet-of-Things (IoT) infrastructures encompassing the entire lifecycle of products and their continuous certification are fundamental functions to guarantee a high level of security. This paper demonstrates a DLT-based platform enabling secure inventorying and management of IoT devices and sensors distributed across multiple stakeholders. This work makes use of the Hyperledger framework, Decentralized Identifiers (DID), and Manufacturer Usage Description (MUD) files for IoT device inventorying and the identification of critical software updates.

关键词： Multiuser detection distributed ledger Security management Software computer networks Sensors Internet of Things

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Enabling eBPF-based packet duplication for robust volumetric video streaming

Enabling eBPF-based packet duplication for robust volumetric...

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IEEE Symposium on computers and communications (ISCC)

作者： Peng Qian Ning Wang Foh Chuan Heng Jia Zhang Carl Udora Rahim Tafazolli 5GIC&6GIC Institute for Communication Systems (ICS) University of Surrey Guildford Surrey U.K. Department of Computer Science and Technology Zhongguancun Laboratory Tsinghua University Beijing China

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Volumetric video streaming, an innovative media application, facilitates the real-time and immersive teleportation of individuals or objects into the virtual environment of the audience. Unlike conventional video streaming applications, volumetric content is particularly vulnerable to network fluctuations, which can lead to performance degradation such as reduced FPS, delayed frame delivery. In this research, we introduce an eBPF-based network function that duplicates packets along the pathways between network nodes, ensuring timely packet delivery amid network instability. Furthermore, we propose a path elimination algorithm to discard paths incapable of delivering frames within the target latency. Our implementation and evaluation validate the rapid and robust performance achieved across various resolution levels.

关键词： Degradation Fluctuations Virtual environments Streaming media Teleportation Media Throughput Real-time systems Quality of experience Kernel

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PupilRec: Leveraging Pupil Morphology for Recommending on Smartphones

PupilRec: Leveraging Pupil Morphology for Recommending on Sm...

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作者： Shen, Xiangyu Jiang, Hongbo Liu, Daibo Yang, Kehua Deng, Feiyang Lui, John C. S. Liu, Jiangchuan Dustdar, Schahram Luo, Jun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China The City University of Hong Kong School of Graduate Studies Hong Kong The Chinese University of Hong Kong Computer Science and Engineering Department Hong Kong Simon Fraser University School of Computing Science BurnabyBCV5A 1S6 Canada Vienna University of Technology Distributed Systems Group Vienna1040 Austria Nanyang Technological University School of Computer Engineering 639798 Singapore

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users' inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users' inner feelings, we consider using the change of pupil size when browsing to model users' preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users' pupil size change information through the front camera of smartphones;it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user-product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users' preference and can recommend products users interested in. © 2014 IEEE.

关键词： Smartphones

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Big Brother is Watching You: Non-Intrusive ZigBee User Profiling

Big Brother is Watching You: Non-Intrusive ZigBee User Profi...

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International Conference on Network and Service Management

作者： Katharina O. E. Müller Delia Datsomor Daria Schumm Bruno Rodrigues Burkhard Stiller Department of Informatics IfI Communication Systems Group CSG University of Zurich UZH Switzerland Embedded Sensing Group ESG Institute of Computer Science in Vorarlberg ICV University of St. Gallen HSG Switzerland

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

The rise of the Internet-of-Things (IoT) and smart homes has resulted in the increased use of ZigBee as the communication protocol of choice in home networks, giving ample opportunity for network monitoring and user profiling, as a consequence, raising a major privacy concern. Yet, there has been little exploration of the extractable information solely from network packets, particularly Philips Hue *** as, to the authors’ knowledge, there have been no studies examining whether a single network key provides enough generalization to extract data from other unknown ZigBee networks. To address this gap, this paper proposes StealthProfiler, a passive and real-time Proof-of-Concept (PoC) tool designed to identify, classify, and extract devices and events within a Philips Hue *** a result, the tool was successfully used to extract network events from encrypted Zigbee networks, achieving an accuracy of approximately 94% in identifying devices and events within the network without decrypting network traffic.

关键词： Knowledge engineering Privacy Protocols Zigbee Telecommunication traffic Smart homes Real-time systems Object recognition Data mining Monitoring

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SatCooper: Enhancing Cooperative Inference Analytics for Satellite Service via Multi-exit DNNs

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Qiyang Wang, Shangguang Guan, Jinglong Donta, Praveen Kumar Ma, Xiao Prasad, RangaRao Venkatesha Dustdar, Schahram Liu, Xuanzhe Peking University School of Computer Science Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Stockholm University Department of Computer and Systems Sciences Kista16455 Sweden Beiyou Shenzhen Institute Shenzhen China Netherlands TU Wien Distributed System Group Wien1040 Austria UPF Barcelona Spain

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges-e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models-in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks. © 2002-2012 IEEE.

关键词： Dynamic programming

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A Novel Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer for Satellite Image Classification with Google Earth Engine

A Novel Dual Temporal Channel Convolutional Network with Exp...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： S D Prabu Ragavendiran NR Gowthami Gayathri C D. Ramya D. Padmapriya S.V. Suji Aparna Department of Computer Science and Engineering Builders Engineering College Tirupur Tamil Nadu India Department of Civil Engineering Annamacharya University Rajampet Andhra Pradesh India Computer Science and Engineering Mahendra Institute of Technology Namakkal Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Hyderabad Telangana India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Classification of satellite images using Google Earth Engine entails identifying features on the satellite images to classify the land cover in a particular region through the use of deep learning algorithms. Such a process contributes to monitoring the environment and geographical distribution of resources, planning of cities, and management of resources. However, in many existing approaches, some challenges are worth noting. To address these challenges, this study introduces a new approach, the Dual Temporal Channel Convolutional Network with Exponential Distribution Optimizer (ANDualTCCNet+EDO) for satellite image classification using Google Earth Engine. This approach uses satellite imagery from the Landsat 8 Surface Reflectance dataset, with the input images preprocessed through the Adaptive Tri-Plateau Limit Tri-Histogram Equalization Algorithm (ATri-PLTri-HEA) technique. Following preprocessing, features are extracted from the images using the DenseNet201 method. Then, classification is carried out by A Novel Dual Temporal Channel Convolutional Network (ANDualTCCNet), optimized by the Exponential Distribution Optimizer (EDO) for precise satellite image classification in Google Earth Engine (GEE). The ANDualTCCNet+EDO method is implemented in Python. The proposed method was evaluated using the Landsat 8 Surface Reflectance dataset, achieving a high accuracy of 99.8%, surpassing existing methods. The results presented in this paper show that the use of this method allows for the accurate classification of satellite images in Google Earth Engine. The outcomes of the proposed method improve the method of satellite image classification in Google Earth Engine. This model captures both short- and long-duration temporal characteristics, therefore enhancing spatial-temporal feature acquisition compared to the previous method used in the classification of land cover, especially for temporal dynamics of land covers with different seasonal changes.

关键词： Earth Reflectivity Accuracy Exponential distribution Land surface Landsat Satellite images Internet Convolutional neural networks Engines

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Optimal Resource Utilization in Hyperledger Fabric: A Comprehensive SPN-Based Performance Evaluation Paradigm

Optimal Resource Utilization in Hyperledger Fabric: A Compre...

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IEEE Symposium on Network Operations and Management

作者： Carlos Melo Glauber Gonçalves Francisco A. Silva Leonel Feitosa Iure Fé André Soares Eunmi Choi Tuan Anh Nguyen Dugki Min Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil School of Software College of Computer Science Kookmin University Seoul South Korea Konkuk Aerospace Design-Airworlhiness Research Inslilule (KADA) Konkuk Universily Seoul Soulh Korea Department of Computer Science and Engineering Konkuk University Seoul South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

关键词： Performance evaluation distributed ledger Computational modeling Data security Petri nets Stochastic processes Fabrics

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Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller

Random suppression of ECG artifacts using a NLSRLMMN-based c...

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Asilomar Conference on Signals, systems & computers

作者： Mohammed Mujahid Ulla Faiz Azzedine Zerguine Izzet Kale Department of Electrical and Computer Engineering Presidency University Bengaluru Karnataka India Electrical Engineering Department Interdisciplinary Research Center for Communication Systems and Sensing KFUPM Dhahran Saudi Arabia Department of Computer Science and Engineering Applied DSP and VLSI Research Group (ADVRG) University of Westminster London United Kingdom

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages.

关键词： computers Adaptation models Adaptive systems Mean square error methods Interference Electrocardiography Muscles Noise cancellation Signal to noise ratio Motion artifacts

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