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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8138 条记录，以下是551-560 订阅

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YOLOv3 Tiny-Based Human Detection and Tracking using Vitis AI and VVAS on FPGA 5

YOLOv3 Tiny-Based Human Detection and Tracking using Vitis A...

引用

5th IEEE international conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Ngo, Hieu-Truong Phan, Manh-Khuong Le, Ngoc-Long Cao, thanh-Khiet University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350352863

Real-time human detection systems have become increasingly popular due to advancements in science and technology. Numerous studies have been conducted to enhance object recognition capabilities with higher accuracy and faster processing speeds. However, current methods still face several limitations, particularly in balancing the accuracy and processing speed of recognition algorithms. One of the current approaches is to deploy Convolutional Neural networks (CNN) on Field-Programmable Gate Arrays (FPGA) to improve performance. Previous studies have achieved certain results in using FPGAs, but they often require deep knowledge of hardware design, which presents challenges for practical implementation. To address this issue, this paper introduces the use of VVAS (Vitis Video Analytics SDK) and Vitis AI to implement CNNs on FPGA without the need for deep intervention in hardware design. We employ the YOLOv3-Tiny (You Only Look Once) model to detect humans in real-time on the Kria KV260 Kit. Experimental results show that the system can process and recognize objects with high accuracy while ensuring fast processing speeds for applications in real-world surveillance environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Fast-ECGAN: Towards More Efficient GAN Models 24

Fast-ECGAN: Towards More Efficient GAN Models

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5th international conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Zhang, Qian Li, Yingying ZhengZhou University ZhengZhou450007 China Information Engineering University ZhengZhou450007 China

ISBN: (纸本)9798400718106

In this study, we introduce a groundbreaking approach by seamlessly integrating the Edge Guided Generative Adversarial Network enhanced with contrastive learning (ECGAN) into the Jittor computational framework, which is further empowered by our innovative Edge-Aware Spatial Encoding (EASE) technique. this novel integration is designed to refine image edge clarity and enrich overall image quality significantly. By harnessing Jittor’s state-of-the-art optimization strategies, we are able to amplify the performance and efficiency of ECGAN remarkably. through rigorous experimental evaluation, our findings clearly demonstrate the superior efficacy of our integrated approach, as evidenced by substantial improvements in both the quality of the generated images and the computational speed. this research contributes profoundly to the advancement of image generation technology, illustrating the immense potential of fusing sophisticated generative adversarial network models with advanced computing frameworks to achieve unprecedented levels of image realism and processing efficiency. this synergy paves the way for new directions in the application of artificial intelligence in image processing and generation. © 2024 ACM.

关键词： Generative adversarial networks

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An Energy Efficient Node-Disjoint Path Selection Scheme for Cognitive Radio sensor networks 5

An Energy Efficient Node-Disjoint Path Selection Scheme for ...

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5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Prasad, L.V.R. Chaitanya Kamatham, Yedukondalu Sunehra, Dhiraj JNTUH Dept. of ECE India SNIST Dept. of ECE Hyderabad India CVR College of Engineering Hyderabad India JNTUH Univ. College of Engg. Jagtial Dept. of ECE Telangana Jagtial India

ISBN: (纸本)9798350338287

A new communication paradigm is necessary for efficient utilization of existing radio spectrums as bandwidths are limited and spectrum consumptions are inefficient. sensor nodes in Cognitive Radio sensor networks (CRSNs) can measure and provide accurate information at various network locations. Routing information in CRSNs are challenging issues due to their complicated topologies, sporadic connections, spectrum variations and limitations in energy. Additionally, channel stability, path dependability, and route discovery frequency should be used. Because they focus more on one feature than any other, the current routing algorithms do not satisfy all throughput, energy efficiency, and resilience criteria. Increasing the network lifetime is a goal of many routing systems, which results in the selection of optimal routing pathways. the nodes that are located along this optimal path have a greater amount of their energy consumed, which results in their premature demise. Distributing traffic among numerous paths rather than along a single path that is deemed to be best is one way that multipath routing methods extend the lifetime of WSNs. In this work, Node Grade Factor (NGF) based node-disjoint multipath routing protocol (NGFMR) is proposed for energy efficiencies during selections of disjoint node paths. When selecting a path, QoS criteria like Distance to SINK, spectrum availability, communication cost, and node latency are taken into consideration. this helps to ensure that there are no path interferences between the nodes and loads are effectively distributed amongst different paths using discontinuous path routes that assist in enhancing network's QoS. the results of this study's simulations demonstrate that the proposed NGFMR performs significantly better than other standard protocols when compared for values of network throughput, packet delivery ratio (PDR), energy consumptions, and end-to-end delays. © 2023 IEEE.

关键词： Energy utilization

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Optimised Inkjet-Printing Parameters and processing Techniques for an Exemplary Structure of an Oxygen sensor on a Polyimide Substrate 5

Optimised Inkjet-Printing Parameters and Processing Techniqu...

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5th IEEE international conference on Flexible and Printable sensors and Systems, FLEPS 2023

作者： Petanil, Lisa Schweizer, Anja Pylatiuk, Christian Stiller, Peter Reichert, Klaus-Martin Gengenbach, Ulrich Ungerer, Martin Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9781665457330

Inkjet-printing of electrochemical sensors is a rapidly developing fabrication method. Careful selection of pre-processing methods, inkjet-printing parameters, and post-processing methods is crucial for the features of the final sensor. In particular the interaction of inks and substrate is essential for setting the pre- and post-processing conditions as well as the inkjet-printing parameters. Here, commercial gold, silver, and passivation inks are printed onto a Polyimide substrate and investigated with different processing parameters. A literature search has shown that there is a lack of systematic investigation of the processing conditions for this material combination. Optimal processing parameters are presented here for this combination of materials. With the identified processing parameters, we reproducibly achieve low resistivities for gold and silver nanoparticle inks. © 2023 IEEE.

关键词： Silver nanoparticles

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A large number of optical turbidimeter designs 5

A large number of optical turbidimeter designs

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Guo, Xingchen Gao, Yingming Dalian Polytechnic University Dalian116000 China

ISBN: (纸本)9781510691667

Turbidity is an important index to evaluate water quality, and the measurement of water turbidity is of great significance to life and environmental protection. Turbidity reflects the content of suspended insoluble substances in water through the optical properties of water. Aiming at the problem of small range in existing turbidity measuring instruments, a new turbidity sensor is designed in this paper. through the combination of scattering method and transmission method, a new measuring device structure and signal circuit processing are designed to improve the measuring range of the equipment. In addition, according to the characteristics of turbidity detection, the signal transmission circuit is designed to eliminate the influence of noise. Finally, by cooperating with the single chip computer, the measurement data is calculated and presented on the computer, and the data can be observed in real time and recorded. © 2025 SPIE.

关键词： Turbidity Light sources Light emitting diodes sensors Photodiodes Liquids Infrared radiation Structural design Signal processing Water quality Water

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Survivable Virtual sensor networks Embedding Strategy for Link Failures 21

Survivable Virtual Sensor Networks Embedding Strategy for Li...

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21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Zhou, Guangxu Guo, Yue Hou, Junfeng Zhang, Hongjun Zhang, Genqin Chai, Yajie Henan Prov Tobacco Co Xuchang Branch Xuchang 461000 Henan Peoples R China

ISBN: (纸本)9781665477260

the introduction of network virtualization into wireless sensor networks can effectively improve resource utilization. However, in a virtualized environment, the failure of the physical layer will affect the availability of virtual sensor networks, resulting in network service interruption. Aiming at the virtual sensor network mapping problem under the condition of link failure in the physical network, a virtual sensor network survivability embedding model is constructed, and a differentiated service survivability virtual network embedding algorithm is proposed. For high-priority virtual sensor network requests, a protection mechanism is adopted to construct a protection path for the virtual sensor network during virtual network mapping;for low-priority virtual sensor network requests, a fault virtual line migration strategy based on link availability is proposed. Migration strategy to maximize the integrity of the virtual sensor network and the continuity of network services. Simulation results show the superiority of the proposed algorithm in the fault recovery rate and resource utilization of the virtual sensor network.

关键词： wireless sensor network network virtualization survivable embedding link failure

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ADAPTIVE WIRELESS POWER ALLOCATION WIth GRAPH NEURAL networks 47

ADAPTIVE WIRELESS POWER ALLOCATION WITH GRAPH NEURAL NETWORK...

引用

47th IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： NaderiAlizadeh, Navid Eisen, Mark Ribeiro, Alejandro Univ Penn Philadelphia PA 19104 USA Intel Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781665405409

We consider the problem of power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating with each other over a single shared wireless medium. To achieve both a high total rate and a level of fairness across users, we formulate a policy optimization problem with constraints on the minimum per-user rate across network configurations with an adaptive slack parameter. To apply unsupervised learning algorithms in the dual domain, we parameterize the power control policy, slack variable, and dual parameters using graph neural networks (GNNs), which leverage the network topology to create a scalable and network-invariant processing architecture. We use a primal-dual algorithm to learn the optimal GNN parameters and demonstrate via numerical simulations the resulting GNNs' success in achieving the right balance between sum- and 5th percentile rates throughout a range of network configurations.

关键词： Wireless power allocation graph neural networks primal-dual learning unsupervised learning

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Multi-level Feature Extraction and Edge Reconstruction Fused Generative Adversarial networks for Image Super Resolution 5

Multi-level Feature Extraction and Edge Reconstruction Fused...

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5th international conference on Natural Language processing, ICNLP 2023

作者： Li, Yinghua Liu, Yue Liu, Ying Qiao, Yangge He, Jinglu Xi'an University of Posts and Telecommunications Center for Image and Information Processing Xi'an China

ISBN: (纸本)9798350302219

At present, the image super-resolution method based on convolutional neural network has achieved a very high PSNR, but the high-frequency information obtained by using the mean square error as the loss function is not sufficient, and when the scale factor is large, the detail texture of the restored image is blurred, and it is not completely consistent with the human visual perception. therefore, this paper proposes an image super-resolution algorithm based on GAN. We modify the residual block of the original SRGAN generator network into three modules: Edge-Reconstruction network, Low-Frequency feature (LF-feature) extraction module and Residual network. the Edge-Reconstruction network reconstructs the edge of SR image, and the LF-feature extraction module extracts the low-frequency information of the image. After that, the two parts of information are fused and transmitted to Residual network to extract the high-frequency information of the image, and then the SR image is reconstructed and enlarged. And use skip connection in the network to increase the network depth. the training results show that our network has better performance in both objective evaluation indicators and subjective vision. Even with a large-scale factor, our network can recover fine texture information. © 2023 IEEE.

关键词： Feature extraction

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Musical Genre Classification Using Support Vector Machines and Convolutional Neural networks 5

Musical Genre Classification Using Support Vector Machines a...

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5th IEEE international conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Ragav, tharun R Divya, B. Kavitha, A. College of Engineering Guindy Anna University Department of Electronics and Communication Engineering Chennai India Sri Sivasubramaniya Nadar College of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350338287

Music is an escape to another dimension where everything remains infinitely better. It plays a vital role towards the establishment of cultural characteristics of a human society across the globe and the power of music can touch human lives in unimaginable ways. the creation of music is performed by composing the musical notes, which are the smallest and most basic elements of music. the significance of music has made it crucial to understand how musical notation works. Music signal processing is related to speech signal processing, because music can be closely correlated with speech as a lot of techniques used for the former can also be applied to the latter, with appropriate results. the genres of various audio files have been grouped into various classes through multiple training models. Starting with the extraction of spectrograms to its sub-windowing which would help us gain information more accurately. the training accuracy of MFCC images and spectrograms has been verified using Support Vector Machines (SVMs) and Transfer Learning Models in this process. © 2023 IEEE.

关键词： Music

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the Target Detection Method of Pulse-Doppler Radar Based on Graph Attention networks 5

The Target Detection Method of Pulse-Doppler Radar Based on ...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Yang, Yang Zhao, Yang Wang, Haibo Cao, Dong Liu, Linyan Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China

ISBN: (纸本)9781665485081

Target detection under strong clutter is a relevant issue in many civilian and military application scenarios. Structured data based Deep Neural networks (DNNs), such as Convolutional Neural networks (CNNs), can automate feature extraction of radar time series and achieve better detection performance. We present in this work a target detection method to exploit spatial-temporal joint information of signal samples between Pulse Doppler (PD) radar range cells. the key point of proposed solution relies on the transformation of traditional signal time-series to graph-structured data, and application of Graph Attention networks (GAT) to classify graph-structured data nodes. the presented results, achieved by processing PD radar data, demonstrate the validity of proposed method. © 2022 IEEE.

关键词： Convolution

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