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Joint Optimization Offloading and Resource Allocation in Vehicular Edge Cloud Computing Networks with Delay Constraints

Joint Optimization Offloading and Resource Allocation in Veh...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xueying Liu Guanglin Zhang College of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

The development of 5G technology has led to the rapid construction of the Internet of Vehicles (IOV). The emergences of various IOV applications like autonomous driving require unlimited low-latency computation resources, which can be provided by Mobile edge computing (MEC) technique. In this paper, we study a framework of IOV with a cloud computing center that supports edge computing, aiming at improving the quality of service (QoS) for vehicles. We consider delay-sensitive tasks and non-delay-sensitive tasks, which are classified according to their task processing delay constraints. We formulate the optimization problem as a system utility maximization problem, searching for the optimal QoS. Based on the distribution method and the ant colony algorithm (ACO), we propose an optimization approach with joint offloading selection strategy and computing resource distribution strategy. Lastly, we show the simulation results, which demonstrates the effectiveness and high-efficiency of the proposed algorithm.

关键词： Cloud computing computational modeling Quality of service Delays Resource management Task analysis Optimization

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An Efficient Seizure Prediction Method Based on Multi-scale Feature Fusion with Reduced Channels

An Efficient Seizure Prediction Method Based on Multi-scale ...

引用

symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Shunxian Gu Xinning Song College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9781665426565

Epilepsy is one of the most common neurological diseases worldwide as a common mental disorder. Seizure prediction plays a vital role in improving a patient’s quality of life. This paper proposes a patient-specific seizure prediction method based on multi-scale feature fusion. This study aims at developing an efficient and automatic seizure prediction technique by raw scalp EEG signals with reduced channels. The proposed approach utilizes the deep convolutional neural network in noise handling and the recurrent neural network in establishing contextual correlation. Not any manual feature engineering is performed on the raw EEG data. A multi-scale fusion approach based on the downsampling technique is introduced to compensate for the performance degradation problem caused by reduced channels. 2 is proven to be the best view number. Our proposed C-Bi-LSTM model with multi-views provides the highest overall accuracy of 99.597% and the lowest false positive rate of 0.004 per hour by comparing the classification results.

关键词： Degradation Correlation Sensitivity Recurrent neural networks computational modeling Scalp Predictive models

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Improving Neural Network Architecture Compression by Multi-Grain Pruning

Improving Neural Network Architecture Compression by Multi-G...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Kevin Kollek Miguel Angel Aguilar Marco Braun Anton Kummert School of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany Aptiv Services Deutschland GmbH Wuppertal Germany

ISBN: (纸本)9781665426565

Pruning techniques for neural networks are applied to achieve superior model compression while maintaining accuracy. Common pruning approaches rely on single granularity (e.g., weights, channels, or layers) compression techniques and miss valuable optimization potential. This major limitation results in a sequence of obsolete layers with a small number of channels or highly sparse weights. In this paper, we present a novel pruning approach to address this issue. More precisely, in this work, a Multi-Grain Pruning (MGP) framework is proposed to optimize neural network architectures from coarse to fine in up to four different granularities. Besides the traditional pruning granularities, a new granularity is introduced on so-called blocks, which consist of multiple layers. By combining multiple pruning granularities, models can be optimized even further. We evaluated the proposed framework with VGG-19 on CIFAR-10 and CIFAR-100 as well as ResNet-56 on CIFAR-10 and ResNet-50 on imageNet. The results show that our technique achieves from 31.9x up to 185.3x model compression rates with an accuracy drop from 0.08% up to 1.73% with VGG-19 on CIFAR-10.

关键词： image coding computational modeling Conferences Neural networks Computer architecture Informatics Optimization

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Robust RGB-T tracking via Bimodal Constrained Correlation Filtering

Robust RGB-T tracking via Bimodal Constrained Correlation Fi...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xin Li Jun Cai Wan Ding Zhi Fang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China School of Artificial Intelligence and Information Technology Nanjing University Of Chinese Medicine Nanjing China School of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

With the rapid development of Internet of Things, thermal camera has been widely applied in the intelligence surveillance. Thus, RGB-T tracker has become a hot topic, because it can effectively use the complement of RGB and thermal target information to achieve full-time visual tracking. To effectively enhance the RGB-T tracking speed, we propose a bimodal constrained correlation filter based method. The key point of our method is to integrate bimodal regularization into the traditional correlation filter tracking framework. In addition, to enhance the robustness of tracking performance, we mine the multi-source oriented correlation filter coefficients to make the regression parameters of RGB and thermal images avoid negative effect when obtaining complementary advantages. The experiment results demonstrate that the proposed method can improve the tracking speed without decreasing the tracking accuracy.

关键词： Visualization Correlation Target tracking Surveillance Information filters Robustness Internet of Things

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Video Saurus system: movement evaluation by a genetic algorithm

Video Saurus system: movement evaluation by a genetic algori...

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ieee International Conference on computational intelligence for Measurement Systems and Applications (CIMSA)

作者： G. Mastronardi V. Bevilacqua POLITECNICO di Ban DEE Bani Italy

In this paper we have addressed the problem of adopting in a combined way a genetic algorithm and the Hough transform for implementing an auto tracking method in a video conference system. By applying this method we have been able to track an object that moves slowly following quite parallel trajectories. The proposed algorithm considers just the shape of the object to be tracked and a priori known as a template, without taking into account other several characteristics of images like colour or texture. Because the implementation of Hough transform is a problem of maximising a function with particular constraints, and each run of evaluating Hough transform is time consuming, in this paper we have adopted a particular genetic algorithm to evaluate the rectangular region in which evaluate the Hough transform. The genetic algorithm used elitistic strategy, fitness sharing, mating restriction, adaptive rate of mutation and adaptive rate of cross-over with a double cross point. Moreover, in this paper, since the evaluation of Hough transform is very time consuming, we have addressed the strategy of dividing the whole scene in different shorter window in order to partition the evaluating load on parallel DSP.

关键词： Genetic algorithms Shape image edge detection Filters Body sensor networks Videoconference Genetic mutations Layout Digital signal processing image segmentation

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A new multilevel codebook searching algorithm for vector quantization

A new multilevel codebook searching algorithm for vector qua...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： H.Q. Cao W. Li Department of Electrical Engineering and Computer Science Lehigh University Bethlehem PA USA

A new multilevel codebook searching (MCS) algorithm for vector quantization is presented. Although it belongs to the category of the fast nearest neighbor searching (FNNS) algorithms for vector quantization, the new MCS algorithm is not a variation of any existing FNNS algorithms (such as k-d tree searching algorithm, partial distance searching algorithm, triangle inequality searching algorithm...). The searching strategy involves several search levels. Each level stores a certain size codebook. Searching starts from the stage containing the smallest size (lowest bitrate) codebook to the level containing largest size (highest bitrate) codebook. The searching paths between any two adjacent levels are built by using training sets. The simulation result of applying MCS algorithm to image VQ shows that the MCS algorithm can reduce searching complexity to less than 3% of an exhaustive searching VQ (ESVQ) (codebook size of 4096) while introducing negligible error (0.064 db degradation from ESVQ). A comparison between the MCS algorithm and several k-d binary tree searching algorithms is presented too. The MCS algorithm fits very well into multilevel codebook VQ in the vector transform and vector sub-band domains.

关键词： Vector quantization signal processing algorithms Decision trees Bit rate Binary trees computational complexity Algorithm design and analysis Statistical distributions Partitioning algorithms Electronic mail

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Unite and conquer with bagging-boosting “UC2B” for enhanced anomaly detection

Unite and conquer with bagging-boosting “UC2B” for enhance...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Zineb Ziani Nahid Emad Ahmed Bouaziz Saclay MDLS/ LI-PaRAD/ Numeryx University of Paris Paris France MDLS/ LI-PaRAD University of Paris Saclay Paris France Numeryx Paris France

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

The increasing importance of cybersecurity de-mands effective methods for detecting anomalous behavior in computer systems and networks. This paper presents an approach for anomaly detection that combines Unite and Conquer techniques with Bagging and Boosting methods based on machine learning and statistical models. The proposed approach was evaluated using the UNSW-NB15 dataset, which is a bench-mark for cybersecurity evaluation. The results demonstrate the effectiveness of the combination of Unite and Conquer approaches with Bagging and Boosting in detecting anomalies in cybersecurity. This research provides a valuable contribution to anomaly detection, achieving an accuracy of up to 99.5 % on unseen data. This suggests that the proposed approach may have promising potential for practical applications, especially in real-world scenarios where high computing power is not a prerequisite.

关键词： Industries computational modeling Scalability Transportation Finance Medical services Boosting Data models Computer security Anomaly detection

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Transforming Speech Translation: An End-to-End Transformer-LAS-CTC System for Chinese Dialects to Minor Languages

Transforming Speech Translation: An End-to-End Transformer-L...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Jing Wang Wenhao Fang College of Intelligent Manufacturing City College of Huizhou Huizhou China Department of Computer Science and Technology Hohai University Nanjing China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Speech translation involves converting spoken language from one language to text in another. However, traditional speech translation systems use a cascaded structure that has several shortcomings and defects. As global trade and economic development continue to grow, there is an increasing demand for speech translation tasks from Chinese dialects to minor languages. To tackle these issues, this paper proposes an end-to-end transformer- Las-ctcspeech translation system for converting Chinese dialects to minor language text. Traditional systems have limitations, and with globalization, there is an increasing demand for speech translation. The proposed system combines the attention mechanism with a joint LAS-CTC decoder and outperforms traditional models in convergence speed and results. It has the potential to become a useful tool for bridging language barriers and facilitating communication.

关键词： Translation Biological system modeling computational modeling Globalization Transformers Encoding Decoding Informatics Long short term memory Convergence

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Tourism Local Knowledge Acquisition via Pre-Trained Language Model

Tourism Local Knowledge Acquisition via Pre-Trained Language...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Junyang Pan Hang Lin Yimin Wen Yate Feng Guangxi Key Laboratory of Culture and Tourism Smart Technology Guilin Tourism University Guilin China Guilin Tourism University Guilin China Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Local knowledge which is rich in travelogue provides differentiation, competitiveness and sustainability for tourism entrepreneurs and tourist, that can be well expressed by knowl-edge graph. Traditional tourism knowledge graph construction method constructed automatically by structured data facing the difficulties in extracting entity and complex relationship in travelogue from UGC data. To address these limitations, an tourism local knowledge information extraction framework for constructing a knowledge graph via pre-trained language model is proposed in this paper. A fine-tuned smaller language model (6B) is employed to extract tourism local knowledge entity, property and relationship from the travelogue generated by user on the online tourism agency platform, enabling automated and universal analysis of textualized data.

关键词： Training Annotations Large language models computational modeling Knowledge graphs Named entity recognition Manuals Data models Data mining Sustainable development

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Double-Sided Auction Mechanism for Peer-to-Peer Energy Trading Markets

Double-Sided Auction Mechanism for Peer-to-Peer Energy Tradi...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Jema Sharin Pankiraj Abdulsalam Yassine Salimur Choudhury Lakehead University Thunder Bay Canada

ISBN: (纸本)9781665426565

The emerging smart grid uniquely combines two-way communication and energy flow, allowing consumers to become active participants in market-based energy supply and demand strategies. In such a market, Peer-to-Peer (P2P) energy trading paradigm allows local communities and individuals who generate electricity to freely decide how and with whom they are going to trade it. The greatest challenge of P2P energy trading is how to design efficient mechanisms among rational participants that maximize their monetary benefits. Furthermore, since utility companies own the transmission lines, a key question that yet to be addressed in P2P markets is: how to match between different energy buyers and sellers while taking into account the physical constraints of the underlying grid infrastructure, e.g., capacity, congestion, and line transmission costs. This paper proposes a novel double-sided auction mechanism with a matching algorithm that addresses the aforementioned challenges. In this paper, the social welfare of the participants is modeled as an optimization problem with cost constraints incurred due to energy generation, operating and maintenance, capacity, and line transmission costs. The study provides theoretical analysis of the P2P auction model including mechanism design properties such as individual rationality, computational efficiency, and truthfulness. The results of the experiments indicate that the proposed auction model outperform existing systems and yields better economic incentives for participants.

关键词： Costs Supply and demand Power transmission lines computational modeling Maintenance engineering Production facilities Peer-to-peer computing

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