检索结果-内蒙古大学图书馆

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Huang, Jie Li, Fengyong Zhang, Lei Qin, Chuan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Computer Science and Technology Shanghai University of Electric Power Shanghai200090 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China Computer Science Department University of Victoria VictoriaBC Canada

ISBN: (纸本)9783031067631

This paper proposes a two-stage lossless robust watermarking scheme based on deep neural network. In the first stage, the encoder, noise layer and decoder network are combined to design an end-to-end network framework, which is used to train the input watermark information and cover image. In this framework, the encoder outputs a robust watermarked image whose visual quality is similar to the cover image, and the decoder can extract the embedded watermark information to evaluate robustness. In the second stage, by using reversible information hiding algorithm with extended prediction error, the auxiliary information that can recover the cover image is embedded into the robust watermarked image to ensure reversibility. If the watermarked image does not undergo attack processing, the hidden watermark information can not only be extracted, but also the cover image can be completely recovered. Even if the watermarked image does undergo attack processing, the embedded watermark information can also be extracted correctly because of the strong robustness in the first stage. Experimental results demonstrate the robustness of proposed scheme in terms of JPEG compression, gaussian filtering noise, rotation and shearing operations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading Rate Prediction Using Deep Neural Networks

Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading R...

引用

International KES Conference on Human Centred Intelligent Systems, KES HCIS 2023

作者： Akoulih, Meryem Tigani, Smail Wakrim, Asmaa Chehri, Abdellah Saadane, Rachid Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College KingstonONK7K 7B4 Canada

ISBN: (纸本)9789819934232

This research paper presents a deep learning based predictive model for predicting the azo dye fading rate during electrocoagulation coupled with ultrasound. Electrocoagulation is a commonly used method for the treatment of wastewater contaminated with azo dyes, but the efficiency of this process can be improved by incorporating ultrasound. The proposed deep learning model is trained on a dataset of electrocoagulation experiments with varying parameters such as current density, pH, and treatment time, as well as the presence or absence of ultrasound. The model is able to accurately predict the azo dye removal rate for new sets of electrocoagulation parameters, providing a valuable tool for optimizing the treatment process. The model’s performance is evaluated using standard metrics, and the results demonstrate its effectiveness in predicting azo dye removal rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Azo dyes

来源：评论

学校读者我要写书评

暂无评论

Advancing Proactive Producer Mobility Management in Named Data Networking: A Conceptual Model 7

Advancing Proactive Producer Mobility Management in Named Da...

引用

7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Zukri, Nurul Hidayah Ahmad Arif, Ahmad Suki Che Mohamed Alsamman, Mohammed Abrar, Ahmad Fazea, Yousef Universiti Teknologi MARA Cawangan Perlis Kampus Arau Perlis Malaysia School of Computing Universiti Utara Malaysia Kedah Malaysia InterNetWorks Research Lab Universiti Utara Malaysia Kedah Malaysia Department of Computer Science and Electrical Engineering Marshall University One John Marshall Dr. HuntingtonWV25755 United States

ISBN: (纸本)9798331519513

In recent years, the Named Data Networking (NDN) paradigm has emerged as a promising candidate for future Internet architecture. Underpinned by the notion of a data-centric forwarding philosophy, NDN is purported to offer seamless network communication for mobile users. However, while it provides inherent support for consumer mobility, viable solutions for producer mobility remain insufficient. Therefore, this study aims to highlight the mobility issues in NDN, particularly concerning high latency, Interest packet loss, and Interest retransmission, with a focus on producer mobility. In this paper, we critically review previous works based on proactive and reactive approaches. Furthermore, we propose a mobility management model that adopts a proactive approach, estimating the producer's handoff time from the connected access point. The proposed model leverages this information to notify consumers about the moving producer, aiming to prevent consumers from sending Interest to the mobile producer during handoff and thereby overcome the associated challenges. © 2024 IEEE.

关键词： Named Data Network Proactive Producer Mobility

来源：评论

学校读者我要写书评

暂无评论

Unfolding for Joint Channel Estimation and Symbol Detection in MIMO Communication Systems

arXiv

引用

arXiv 2023年

作者： Bhattacharya, Swati Hari, K.V.S. Eldar, Yonina C. Statistical Signal Processing Lab Department of Electrical Communication Engineering Indian Institute of Science Bangalore India Faculty of Math and Computer Science Weizmann Institute of Science Israel

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for Multiple-Input Multiple-Output (MIMO) wireless communication systems. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) and its model-based neural network version JED using Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna configurations. Both proposed algorithms exploit the non-smooth constraint, that occurs as a result of the Quadrature Amplitude Modulation (QAM) data symbols, to effectively improve the performance using the ADMM iterations. The proposed unfolded network JED-U-ADMM consists of a few trainable parameters and requires a small training set. We show the efficacy of the proposed methods for both uncorrelated and correlated MIMO channels. For certain configurations, the gain in SNR for a desired BER of 10−2 for the proposed JED-ADMM and JED-U-ADMM is upto 4 dB and is also accompanied by a significant reduction in computational complexity of upto 75%, depending on the MIMO configuration, as compared to the complexity of JED-AM. © 2023, CC BY.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Mean-field games among teams

arXiv

引用

arXiv 2023年

作者： Subramanian, Jayakumar Kumar, Akshat Mahajan, Aditya Media and Data Science Research Lab Digital Experience Cloud Adobe Inc. Uttar Pradesh Noida India School of Computing and Information Systems The Singapore Management University Singapore Department of Electrical and Computer Engineering McGill University Montreal Canada

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with other teams. The dynamics and the costs are coupled through the empirical distribution (or the mean field) of the state of agents in each team. This mean-field is assumed to be observed by all agents. Agents have asymmetric information (also called a non-classical information structure). We propose a mean-field based refinement of the Team-Nash equilibrium of the game, which we call mean-field Markov perfect equilibrium (MF-MPE). We identify a dynamic programming decomposition to characterize MF-MPE. We then consider the case where each team has a large number of players and present a mean-field approximation which approximates the game among large-population teams as a game among infinite-population teams. We show that MF-MPE of the game among teams of infinite population is easier to compute and is an Ε-approximate MF-MPE of the game among teams of finite population. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Facial Feature Priors Guided Blind Face Inpainting

Facial Feature Priors Guided Blind Face Inpainting

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Ruhao Zhao Wenxin Huang Wenxuan Liu Wei Li Xian Zhong Hubei Key Lab of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

While face image inpainting has advanced significantly in recent years thanks to deep learning, its practical use is often hindered by the manual requirement of specifying corrupted regions through masks. Blind face inpainting seeks to automate the prediction of these masks instead of relying on manual labeling. However, existing methods overlook the significance of facial priors in detecting corrupted regions and performing face image inpainting, leading to decreased accuracy and artifacts. In this work, we propose a blind face inpainting network that leverages clear facial feature priors to guide the model in identifying corrupted features during mask prediction and completing facial features during inpainting. To achieve this, we initially extract a Facial Feature Dictionary (FFD) from clear face images, followed by the introduction of Facial Feature Filter (FFF) to facilitate the localization and restoration of features in corrupted regions. Experimental results demonstrate the robustness and efficacy of our approach across various filling patterns.

关键词： Location awareness Dictionaries Manuals Predictive models Field-flow fractionation Feature extraction Robustness

来源：评论

学校读者我要写书评

暂无评论

Characteristic Modes for Orthogonal Beamsteering Using ESPAR

Characteristic Modes for Orthogonal Beamsteering Using ESPAR

引用

Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Zixiang Han Yujie Zhang Shanpu Shen Tianrui Qiao Junhui Rao Jing Jin Qixing Wang Ross Murch Future Research Lab China Mobile Research Institute Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical Engineering and Electronics University of Liverpool U.K. Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A method based on the Theory of Characteristic Modes (TCM) for finding orthogonal beamsteering radiation patterns of electronically steerable parasitic array radiator (ESPAR) is proposed. This method can be used to design single-radio-frequency (RF) multiple-input multiple-output (MIMO) systems for Internet-of-Things (IoT) applications in next-generation wireless communication. It can directly generate a set of symmetrical and orthogonal pattern basis for beamsteering purpose and demodulated reference signal transmission. In addition, the input impedances of ESPAR are the same for all pattern basis so that impedance matching can be achieved by a fixed matching network. Simulation example of a 5-element circular dipole ESPAR is provided to demonstrate the effectiveness of the proposed method.

关键词： Wireless communication Radio frequency Simulation Impedance matching Scattering parameters Internet of Things MIMO communication

来源：评论

学校读者我要写书评

暂无评论

Developing a computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

Developing a Computer-Aided Diagnostic System for Breast Can...

引用

2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Taha, Radwa Afifi, Shereen Abd-Elghany, Mohamed A. Salem, Mohammed A.-M German University in Cairo The Faculty of Media Engineering and Technology New Cairo Egypt German University in Cairo Faculty of Media Engineering and Technology Computer Science Engineering New Cairo Egypt German University in Cairo Faculty of Information Engineering and Technology Electrical Engineering New Cairo Egypt Tu Darmstadt Integrated Electronics Systems Lab Germany German University in Cairo Faculty of Media Engineering and Technology Digital Media Engineering New Cairo Egypt

ISBN: (纸本)9798350383386

In this research we aim to develop a computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall *** Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Learning-Based Biharmonic Augmentation for Point cloud Classification

arXiv

引用

arXiv 2023年

作者： Wei, Jiacheng Lin, Guosheng Ding, Henghui Hu, Jie Yap, Kim-Hui The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore State Key Lab of Computer Science ISCAS University of Chinese Academy of Sciences China

Point cloud datasets often suffer from inadequate sample sizes in comparison to image datasets, making data augmentation challenging. While traditional methods, like rigid transformations and scaling, have limited potential in increasing dataset diversity due to their constraints on altering individual sample shapes, we introduce the Biharmonic Augmentation (BA) method. BA is a novel and efficient data augmentation technique that diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. This approach calculates biharmonic coordinates for the deformation function and learns diverse deformation prototypes. Utilizing a CoefNet, our method predicts coefficients to amalgamate these prototypes, ensuring comprehensive deformation. Moreover, we present AdvTune, an advanced online augmentation system that integrates adversarial training. This system synergistically refines the CoefNet and the classification network, facilitating the automated creation of adaptive shape deformations contingent on the learner status. Comprehensive experimental analysis validates the superiority of Biharmonic Augmentation, showcasing notable performance improvements over prevailing point cloud augmentation techniques across varied network designs. © 2023, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jung Uk Kim Yong Man Ro Image and Video Systems Lab School of Electrical Engineering KAIST South Korea Department of Computer Science and Engineering Kyung Hee University South Korea

Although multispectral pedestrian detection studies have shown remarkable detection performances, they are still vulnerable to adversarial attacks. We see the similarity relations between object candidates were not maintained because of the adversarial attacks, resulting in performance degradation. In this paper, we introduce a new method that can preserve the similarity relation between candidates against adversarial attacks using multispectral knowledge. First, we propose Similarity Relation Generation (SRG) module to generate the optimal similarity relation between clean candidates by referring to the two modalities (color and thermal). Second, we propose Adversarial Similarity Relation Preserving (ASRP) module to guide the similarity relation between adversarial candidates to be similar to that of the clean candidates. By maintaining the relationship between candidates, our multispectral detector can distinguish between pedestrian/background classes even in adversarial attacks. Comprehensive experimental results show that our method conspicuously improves the adversarial robustness.

关键词： Degradation Detectors Color Signal processing Robustness Acoustics Speech processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：