检索结果-内蒙古大学图书馆

ESLC-WSN: A Novel Energy Efficient Security Aware Localization and Clustering in Wireless Sensor networks 1

学校读者我要写书评

暂无评论

ESLC-WSN: A Novel Energy Efficient Security Aware Localizati...

1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020

作者： Tamtalini, Mohammed Amine Alaoui, Abdelbaki El Belrhiti El Fergougui, Abdeslam El Moulay Ismail University Computer Networks and Systems Laboratory Faculty of Sciences Meknes Morocco

ISBN: (纸本)9781728149790

Localization is one of the most essential challenges in wireless sensor networks because the location information is usually used in domains such as routing, target tracking, deployment and coverage. There be existent some localization algorithms that facilitate the sensor nodes to locate itself using the anchor/beacon nodes position. Some crucial efforts have been made in the past for optimizing the anchor node trajectory with good accuracy. This paper presents a novel algorithm for localization in wireless sensor networks. To predict unknown nodes location, we select top-3 anchor nodes using hybrid technique namely CSO-ANFIS (Chicken Swarm Optimization and Adaptive Neuro-Fuzzy System). For decision making, fusion center is used which rejects the reported location information if the distance between unknown node and anchor node beyond a threshold. On the other hand, energy consumption is a serious threat in WSN. In order to reduce the energy consumption, we proposed secure cluster based routing for data transmission by authenticating each node to sink node. Firstly, clustering is formed using Residual Energy, Node degree, and Distance between adjacent nodes. To improve the data transmission, we proposed hybrid encryption algorithms namely AES (Advanced Encryption Standard) with ECC (Elliptic Curve Cryptography) and to forward data from source to the destination, Monarch Butterfly Optimization (MBO) is presented. Finally, nodes location is verified in Sink node by its pseudo random numbers. The performance of the proposed scheme is evaluated through a series of simulations. © 2020 IEEE.

关键词： Sensor nodes

FedSTDN: A Federated Learning-Enabled Spatial-Temporal Prediction Model for Wireless Traffic Prediction

学校读者我要写书评

暂无评论

IEEE Transactions on Mobile Computing 2025年

作者： Li, Zhenyu Fu, Yuchuan Tian, Mengqiu Li, Changle Yu, F. Richard Cheng, Nan Xidian University State Key Laboratory of Integrated Services Networks Shaanxi 710071 China Xidian University Research Institute of Smart Transportation Shaanxi Xi'an710071 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Wireless Traffic Prediction (WTP) plays a significant role in achieving intelligent resource management for communication systems. However, WTP still faces challenges such as inaccurate prediction resulting from the complex spatial-temporal characteristics due to user mobility, high communication overhead caused by the complexity of the prediction model, and user privacy issues stemming from Centralized Learning (CL). To address the aforementioned issues, this paper proposes a WTP framework under the Federated Learning (FL) strategy called Federated Spatial-Temporal Dual-attention based Network (FedSTDN). Aiming at improving communication efficiency and simultaneously representing various wireless traffic patterns, a data augmentation-based clustering algorithm is adopted, which groups cells into different regions using a small augmented dataset, facilitating subsequent processing. To improve prediction performance, a local prediction model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to capture the short- and long-term dependencies of traffic. Additionally, a novel Kolmogorov-Arnold Network (KAN) layer is introduced to replace the traditional Multi-Layer Perceptron (MLP) layer, further enhancing prediction performance. Simulations on two different real-world datasets verify the effectiveness and efficiency of FedSTDN. Compared to the well-performing baseline, the proposed FedSTDN achieves up to 32.83% and 24.30% improvements in Mean Square Error (MSE) and Mean Absolute Error (MAE) on the Milan dataset, respectively. For the Trentino dataset, FedSTDN achieves up to 17.25% and 5.86% improvements in MSE and MAE, respectively. © 2002-2012 IEEE.

关键词： Prediction models

Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

关键词： Probability distributions

ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia Laboratory of Signals systems Artificial Intelligence and networks Sfax Tunisia Multimedia Information systems and Advanced Computing Laboratory Tunisia Computer Vision Center Computer Science Department Universitat Autònoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluation on three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, CC BY.

关键词： Image enhancement

SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xu, Rongtao Wang, Changwei Zhang, Jiguang Xu, Shibiao Meng, Weiliang Zhang, Xiaopeng The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China CASIA Beijing China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence Beijing University of Posts and Telecommunications China

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer’s global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future. Our code is available at https://***/RongtaoXu/SkinFormer. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

St-Keys: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Jemni, Sana Khamekhem Ammar, Sourour Souibgui, Mohamed Ali Kessentini, Yousri Cheddad, Abbas Digital Research Center of Sfax B.P. 275 Sakiet Ezzit Sfax3021 Tunisia SM@RTS: Laboratory of Signals systeMs aRtificial Intelligence and neTworkS MIR@CL: Multimedia InfoRmation systems and Advanced Computing Laboratory Computer Vision Center Computer Science Department Universitat Aut`onoma de Barcelona Spain Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden

Keyword spotting (KWS) in historical documents is an important tool for the initial exploration of digitized collections. Nowadays, the most efficient KWS methods are relying on machine learning techniques that require a large amount of annotated training data. However, in the case of historical manuscripts, there is a lack of annotated corpus for training. To handle the data scarcity issue, we investigate the merits of the self-supervised learning to extract useful representations of the input data without relying on human annotations and then using these representations in the downstream task. We propose ST-KeyS, a masked auto-encoder model based on vision transformers where the pretraining stage is based on the mask-and-predict paradigm, without the need of labeled data. In the fine-tuning stage, the pre-trained encoder is integrated into a siamese neural network model that is fine-tuned to improve feature embedding from the input images. We further improve the image representation using pyramidal histogram of characters (PHOC) embedding to create and exploit an intermediate representation of images based on text attributes. In an exhaustive experimental evaluationon three widely used benchmark datasets (Botany, Alvermann Konzilsprotokolle and George Washington), the proposed approach outperforms state-of-the-art methods trained on the same datasets. © 2023, The Authors. All rights reserved.

关键词： Embeddings

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

学校读者我要写书评

暂无评论

eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

Brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading Channels

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Papazafeiropoulos, Anastasios Kourtessis, Pandelis Kaklamani, Dimitra I. Venieris, Iakovos S. Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Microwave and Fiber Optics Laboratory Greece Intelligent Communications and Broadband Networks Laboratory School of Electrical and Computer Engineering National Technical University of Athens Zografou Athens15780 Greece

The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than the base station (BS) antennas, the acquisition of the channel state information (CSI) in SIM-aided multi-user systems is challenging, especially when a line-of-sight (LoS) component is present. Thus, in this letter, we address the channel procedure under conditions of Rician fading by proposing a protocol in terms of a minimum mean square error (MMSE) estimator for wave-based design in a single phase. Moreover, we derive the normalized mean square error (NMSE) of the suggested estimator, and provide the optimal phase shifts minimising the NMSE. Numerical results illustrate the performance of the new channel estimation protocol. © 2025, CC BY.

关键词： Mean square error

Designing a Human-Machine Interface Based on the Cognitive Model of Information systems Users

学校读者我要写书评

暂无评论

Designing a Human-Machine Interface Based on the Cognitive M...

2022 International Russian Automation Conference, RusAutoCon 2022

作者： Sineshchuk, Yury Vostrykh, Aleksey Saenko, Igor Petersburg University of State Fire Service of Emercom of Russia Department of Information Systems St Saint-Petersburg Russia Petersburg University of the Ministry of Internal Affairs of the Russian Federation Department of Communicatiom Networks St Saint-Petersburg Russia Laboratory of Computer Security Problems St Saint-Petersburg Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Currently, new information systems appear almost daily. Their main task is to meet the society information needs in various activity fields. An analysis of existing information systems has shown that the efficiency of such systems is significantly increased with the improvement of human-machine interaction. The paper offers a cognitive model of information systems users, containing a set of psycho-physiological parameters and limitations, as well as environmental factors. These factors have a direct impact on the working conditions for specialists. In addition, the model makes it possible to take into account these factors at the stage of designing graphical user interfaces of information systems. Experiments have shown that the implementation of the proposed model reduces the human-machine interface redundancy and improves the human-machine interaction quality. © 2022 IEEE.

关键词： Information systems