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Enabling LoRa energy awareness: A Multihop One-Hop Routing Protocol for Prolonging Network Lifetime 19

Enabling LoRa energy awareness: A Multihop One-Hop Routing P...

19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Katsidimas, Ioannis Manolopoulos, Alexandros-Ioannis Nikoletseas, Sotiris University of Patras Department of Computer Engineering and Informatics Patras Greece University of Athens Department of Physics National and Kapodistrian Athens Greece University of Patras and Computer Technology Institute and Press Diophantus Department of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350346497

IoT applications often rely on battery-powered nodes that may be challenging to recharge, making energy efficiency a crucial concern. In this context, routing protocols play a vital role as they dictate the paths data packets take between nodes, directly influencing the energy consumption of IoT networks. Energy-aware routing protocols have been proposed to address this issue, with the aim of optimizing energy consumption by selecting paths that minimize the energy required for data transmission and reception. These protocols employ various strategies, such as distance-based, traffic-based, and hybrid schemes, to achieve energy efficiency in IoT networks. LoRaWAnprotocol has been adopted for various IoT applications with requirements in low-power and extended coverage. Over the recent years attempts have been made to replace the typical star topology employed by LoRa with multihop solutions, in an effort to further increase its capabilities. In this paper, we present a multihop algorithm that is based on an energy-balanced routing scheme adopted by LoRa nodes to increase energy efficiency and network lifetime. Multiple one-hop routes are configured for critical nodes and a different route is selected in each transmission. Additionally, critical nodes adjust their Spreading Factor and Transmission Power according to the distance of intermediate nodes. Simulation testing results verify the improved performance of our algorithm over the direct link approach. © 2023 IEEE.

关键词： Energy efficiency

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： Azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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Lockstep Vs Microarchitecture: A Comparison 37

Lockstep Vs Microarchitecture: A Comparison

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37th IEEE International System-on-Chip Conference, SOCC 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava842 16 Slovakia Slovak University of Technology Institute of Informatics Information Systems and Software Engineering Bratislava842 16 Slovakia

ISBN: (纸本)9798350377569

Safety/mission-critical applications require high dependability of the control systems. Their state-of-the-art protection approach is a system-level lockstep. This paper compares the system-level dual and triple lockstep technique to the microarchitecture-level protection of the Hardisc RISC-V core. For a fair comparison, each system is based on the same core(s) and integrates protection against bit-flips in memory and transient faults in the bus. We propose a fault injection methodology, combining pre-synthesis simulation with synthesis data to analyse the vulnerability of a system to faults. The fault injection campaigns show that the Hardisc can withstand fault rates orders of magnitude higher than the dual-core lockstep system while preserving the same area and power consumption. It comes with a 5% frequency penalty. We have shown that system failures are more frequent once the fault rate reaches an application-specific threshold (one fault in 100 clock cycles). © 2024 IEEE.

关键词： Failure rate

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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Interoperability of Information Sources for Identification With Privacy Preservation and Early Fraud Detection 14

Interoperability of Information Sources for Identification W...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Deligiannidou, Aikaterini Rapti, Aggeliki Ioannou, Zafeiria-Marina Viennas, Emmanouil Papaloukopoulos, Christos Paschou, Mersini University of Patras Computer Engineering and Informatics Dpt Patras Greece University of Patras Computer Engineering and Informatics Dpt Greece Intraway Ltd Patras Greece Devout L.P. Patras Greece

ISBN: (纸本)9798350318067

The present work aims at the development of smart tools and techniques for the interoperability of information sources and the identification, timely detection, and suppression of forms of fraud with the assurance of data protection. The main goal is the research of smart techniques that will allow direct access to properly encoded and anonymized personal data. The innovation is in the ability to perform comparisons and actions that will allow all the necessary controls to address and prevent fraud, identifying the people that are involved in it, but not revealing personal information for other people recorded in files and registers. This is a novel approach to upgrade the operational action of public bodies and authorities and to give the possibility of dealing with fraud, and applying control to business groups and SMEs. In this work, the classification of users in terms of software use cases, the architecture of the approach and an integrated system of interoperability of information sources process is proposed. All technological developments were taken into account and the appropriate technological choices were made so that the implementing team is able to proceed with the implementation of the functional parts of the system software. Initial results are reported for a privacy-preserving process while cross-referencing files and information. © 2023 IEEE.

关键词： Interoperability

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ClipArtGAN: An Application of Pix2Pix Generative Adversarial Network for Clip Art Generation

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Multimedia Tools and Applications 2025年 1-25页

作者： Elnabawy, Reham H. Abdennadher, Slim Hellwich, Olaf Eldawlatly, Seif Technische Universität Berlin Berlin Germany Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer Science Department Faculty of Informatics and Computer Science German International University New Administrative Capital Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University 1 El-sarayat st. Abbassia Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt School of Information Technology New Giza University Giza Egypt

An image can convey a thousand words. This statement emphasizes the importance of illustrating ideas visually rather than writing them down. Although detailed image representation is typically instructive, there are several contexts where simplification of image representations may be more informative. One way to provide an abstract representation of an object is via clip art, which is a simple graphic illustration of an object that can be included in documents or presentations. Clip art representation can be utilized in graphic design for concepts expression and daily life objects illustration. Furthermore, clip art had shown remarkable advantages when compared to real images in terms of editing and manipulation since clip art geometry can be easily edited in addition to the feasibility of storing it in a resolution-independent manner. However, it can be difficult to automatically create clip arts that resemble any object. Here, we propose a novel deep learning model, named ClipArtGAN, that utilizes Generative Adversarial Networks (GANs) to convert a photo of an object to its corresponding clip art. To our knowledge, this is the first GAN model based on Pix2Pix architecture that is capable of automatically generating clip art for any arbitrary object. Three training datasets were collected to train and compare two GAN models based on Pix2Pix and CycleGAN architectures. Our results demonstrate that the mean accuracy obtained using ClipArtGAN utilizing Pix2Pix outperforms that of CycleGAN by 58% and that of Google Images-based search by 54%. These results indicate the efficacy of ClipArtGAN in generating representative clip art images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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PartSeg v2: Bioimage Segmentation Using Advanced Deep Learning Techniques 4th

PartSeg v2: Bioimage Segmentation Using Advanced Deep Learni...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Bokota, Grzegorz Basu, Subhadip Sroka, Jacek Insititute of Informatics University of Warsaw Warsaw Poland Department of Computer Science and Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789819726134

In this paper, we present a new version of our bioimaging tool PartSeg. It allows integration of deep learning models from the Bioimage Model Zoo, which is a community-driven AI model repository. We also show how PartSeg’s power users can train such models, for example, to increase automatization of their data processing workflows. Then, we present further new features of PartSeg that make it easier to publish datasets in a durable and referencable way with version tracking. We also describe good practices related to data publishing and how adding metadata can allow for easier data discovery. Availability of such published datasets enables development of even better machine learning models. Publishing of the data is also a requirement for verifiable and repeatable science, leads to greater visibility of the results and enables cooperation and cross-domain exchange of ideas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bioimaging

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Factors influencing the adoption of awareness-raising activities in SMEs 10

Factors influencing the adoption of awareness-raising activi...

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10th International Conference on Socio-Technical Perspectives in Information Systems, STPIS 2024

作者： Bergström, Erik Kävrestad, Joakim Gustafsson, Johannes Hou Jonsson, Hannes Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden

Information security incidents are most commonly caused by user behaviour, placing the user in focus. In order to mitigate information security threats and thereby protect the organisation, more and more are adopting a socio-technical viewpoint, which implies adopting the belief that information security cannot be solved with technology alone. A common way to address the user is by adopting awareness-raising activities. All types of organisations struggle to raise awareness. Several studies have pointed out small and medium-sized enterprises (SMEs) as being extra vulnerable and, in addition, having more issues adopting awareness-raising activities. There are few studies investigating factors influencing the adoption of awareness-raising activities in general, and the body of literature is even more scarce when focusing on the factors from an SME perspective. This study targets the gap by investigating what factors influence the adoption of awareness-raising activities in SMEs. We did this by conducting a semi-structured interview study in 10 organisations. Five factors with a total of seven sub-factors were found: Resources (with the sub-factors time and cost), implementation, content (with the sub-factors quality, adaptability, and comprehensibility), compliance, management (with the sub-factors management support and motivation of the employees). © 2024 Copyright for this paper by its authors.

关键词： Information hiding

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Transient events during photic driving in single-trial EEG within the second harmonics 46

Transient events during photic driving in single-trial EEG w...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Oppermann, Hannes Haueisen, Jens Technische Universität Ilmenau Insititute of Biomedical Engineering and Informatics Department of Computer Science and Automation Ilmenau Germany

ISBN: (纸本)9798350371499

Intermittent photic stimulation (IPS) is a commonly used activation method in clinical applications, e.g. epilepsy, but also used in research for investigating excitability states in the brain. Effects in the brain followed by IPS are also known as photic driving response. These effects are usually characterized using averaged data. Here, we used single-trial analysis to determine photic driving effects at the second harmonics of the stimulation *** rest and auditory-visual stimulation, 64-channel EEG data were recorded with eyes closed. The visual stimulation included eight stimulation frequencies within the alpha and beta bands. A complex Morlet wavelet was used for time-frequency (TF) analysis. Bursts within the TF representations were then detected and characterized by number, frequency, and power near the second *** observed bursts close to the second harmonic of each stimulation frequency. The number of these events increased during stimulation and the main frequencies were shifted towards the second harmonics. Similarly, the power increased at all but one *** new single-trial analysis during IPS could lead to a better understanding of the photic driving effects in the brain and, in particular, the generation mechanisms with regard to the occurrence of effects in the harmonics and sub-harmonics. © 2024 IEEE.

关键词： Harmonic analysis

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Adaptive Frequency Enhancement Network for Single Image Deraining

Adaptive Frequency Enhancement Network for Single Image Dera...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yan, Fei He, Yuhong Chen, Keyu Cheng, En Ma, Jikang School of Informatics Xiamen University Xiamen China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665410205

Image deraining aims to improve the visibility of images damaged by rainy conditions, targeting the removal of degradation elements such as rain streaks, raindrops, and rain accumulation. While numerous single image deraining methods have shown promising results in image enhancement within the spatial domain, real-world rain degradation often causes uneven damage across an image's entire frequency spectrum, posing challenges for these methods in enhancing different frequency components. In this paper, we introduce a novel end-to-end Adaptive Frequency Enhancement Network (AFENet) specifically for single image deraining that adaptively enhances images across various frequencies. We employ convolutions of different scales to adaptively decompose image frequency bands, introduce a feature enhancement module to boost the features of different frequency components and present a novel interaction module for interchanging and merging information from various frequency branches. Simultaneously, we propose a feature aggregation module that efficiently and adaptively fuses features from different frequency bands, facilitating enhancements across the entire frequency spectrum. This approach empowers the deraining network to eliminate diverse and complex rainy patterns and to reconstruct image details accurately. Extensive experiments on both real and synthetic scenes demonstrate that our method not only achieves visually appealing enhancement results but also surpasses existing methods in performance. The source code is available at https://***/yanfefei/AFENet. © 2024 IEEE.

关键词： HTTP

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