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共 1333 条记录，以下是221-230 订阅

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2nd IFAC international conference on Intelligent Control Systems and signal processing, ICONS 2009 - proceedings

2nd IFAC International Conference on Intelligent Control Sys...

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2nd IFAC international conference on Intelligent Control Systems and signal processing, ICONS 2009

ISBN: (纸本)9783902661661

The proceedings contain 99 papers. The topics discussed include: on the multivariable iterative learning control base on the gradient method;harmonic elimination control of an inverter based on an artificial neural network strategy;adaline-based approaches for time-varying frequency estimation in power systems;experimental modeling of propulsion transients of a brushless DC motor and propeller pair under limited power conditions: a neural network based approach;motion detection and tracking of classified objects with intelligent systems;real-time global optimization using multiple units;state estimation based optimal control and NARMA-L2 controllers of a scaled-model helicopter;constrained suboptimal dual control algorithms for discrete-time stochastic systems;the continuous system equivalent to the system with sliding mode control;and the air-jet texturing and twisting machine's and model predictive control based on state-space.

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The research and education application of distributed virtual reality platform based on Web3D

The research and education application of distributed virtua...

引用

2010 2nd international conference on signal processing Systems, ICSPS 2010

作者： Yang, Ruobing College of Communication LinYi Normal University LinYi China

ISBN: (纸本)9781424468911

At present, many virtual learning communities are just simple two-dimensional replicas of former teaching materials, most virtual learning environments are not capable of realizing multi-person cooperation, and lots of educational researchers lack basics about computer network server programming. In addressing to these problems, first a Web3D-based distributed virtual reality platform is introduced and analyzed, then a general Web3D-based distributed virtual reality platform ABNet is introduced, finally its education application is elaborated, hoping to provide basis and references for the design of distributed virtual learning environments and the education application. © 2010 IEEE.

关键词： E-learning

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Generative Object Detection: Erasing the Boundary via Adversarial learning with Mask 2

Generative Object Detection: Erasing the Boundary via Advers...

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2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jang, Heeoh Kim, Dongkyu Ahn, Wonhyuk Lee, Heung-Kyu Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Samsung Elect Suwon South Korea

ISBN: (纸本)9781728151021

Recently, object detection has presented superior performance using the deep convolutional neural network (CNN). However, most CNN-based models need the bounding box information of the input image in pairs. To overcome this limitation, we propose the Generative Object Detection which learns with only cropped images that are not in pairs. Our model based on Generative Adversarial Networks (GAN) creates cropped images by making a mask that represents the object region. To achieve this goal, we devise a novel mask mean loss (MML) that helps the GAN be able to estimate the distribution of training data and uses dilated convolution for a wider reception field in the generator. The experimental results show that Generative Object Detection improves the mIoU and accuracy.

关键词： generative adversarial network object detection deep-learning

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Lagrangian support vector regression based image watermarking in wavelet domain 2

Lagrangian support vector regression based image watermarkin...

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2nd international conference on signal processing and Integrated Networks (SPIN)

作者： Mehta, Rajesh Vishwakarma, Virendra P. Rajpal, Navin Amity Sch Engn & Technol Dept Comp Sci & Engn New Delhi India Gtrru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C New Delhi India Gtrru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Dwarka Sect 16C New Delhi India

ISBN: (纸本)9781479959914

To enhance the imperceptibility and robustness against image processing operations, the advantage of artificial neural network (ANN) and machine learning algorithms such as support vector regression (SVR), extreme learning machine (ELM) etc. are employed into watermarking applications. In this paper, Lagrangian support vector regression (LSVR) based blind image watermarking scheme in wavelet domain is proposed. The good learning capability, high generalization property against noisy datasets and less computational cost of LSVR compared to traditional SVR and ANN based algorithms makes the proposed scheme more imperceptible and robustness. Firstly, four sub images of host image are obtained using sub sampling. Each sub image is decomposed using discrete wavelet transform (DWT) to obtain the low frequency subband. Low frequency coefficients of each sub image are used to form the dataset act as input to LSVR. The output obtained by trained LSVR is used to embed the binary watermark. The security of the watermark is enhanced by applying Arnold transformation. Experimental results show the imperceptibility and robustness of the proposed scheme against several image processing attacks. The visual quality of watermarked image is quantified by the peak-signal-to noise ratio (PSNR) and the similarity between the original and extracted watermark is evaluated using bit error rate (BER). Performance of the proposed scheme is verified by comparing with the state-of-art techniques.

关键词： Wavelet transform Lagrangian support vector regression Peak signal to noise ratio Bit error rate

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Image processing Techniques for Identification of Fish Disease 2

Image Processing Techniques for Identification of Fish Disea...

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2nd IEEE international conference on signal and Image processing (ICSIP)

作者： Malik, Shaveta Kumar, Tapas Sahoo, A. K. Lingayas Univ Nachauli India Reverie Ecol & Fisheries Div Biodivers Lab Barakpur India

ISBN: (纸本)9781538609699

Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not met any extra ordinary success. Identification of diseased fish at early stage is necessary step to prevent from spreading disease. This paper recognizes and identifies the EUS (Epizootic Ulcerative syndrome) disease which is caused by bn Aphanomyces invadans, a fungal pathogen. It is a red spot disease and looks like ulcer hence it is generally misidentified by the people. The paper is divided into two parts, in the first part segmentation has been applied to enhance the image, various edge detection techniques have applied to get the useful information and Morphological operations have been applied on the EUS diseased fish image. In second part features are extracted from the EUS infected fish image through different feature Descriptors i.e. HOG (Histogram of Gradient), FAST (Features from Accelerated Segment Test) and classify the EUS infected and Non-EUS infected fish image through machine learning Algorithms and find the classification accuracy through Classifier. If PCA applied after feature extraction then it increases the accuracy as it is a dimensional reduction. The proposed combination of techniques gives better accuracy as compared to the others. The Experimentation has been done on MATLAB environment on real images of EUS infected fish database.

关键词： HOG (histogram of gradient) FAST (features from accelerated segment test) PCA (principle component analysis) the EUS (epizootic ulcerative syndrome)

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Efficient Cardiovascular Health Monitoring System for Heart Disease Detection Based on machine learning Technique 2

Efficient Cardiovascular Health Monitoring System for Heart ...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kumar, Kottu Santosh Sahoo, Ramesh Chandra Rout, Saroja Kumar Mohammad, Manzoor Jit, Biswa Ranjan Reddy, S. Eswar Kar, Bhabesh Ranjan Department of Information Technology Hyderabad India Manav Rachna International Institute of Research and Studies Faridabad India Hyderabad India School of Professional Studies DRIEMS UNIVERSITY Odisha Cuttack India Bhubaneswar India Cast and Crew LLC Hyderabad India

ISBN: (纸本)9798350366570

Heart diseases are a global leading cause of death, affecting nations universally. Early detection and machine learning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucial to prevent heart attacks. This study aims to identify a machine-learning method that strikes a balance between computational efficiency and prediction accuracy when it comes to heart disease prognosis. This work proposes a model integrating various methods for effective heart disease prediction. Employing efficient data collection, pre-processing, and transformation, our model uses an ensemble approach that includes Six classification machine learning algorithms- SVM, DT, RF, LR, KNN, ANN, and PCA. The study identifies significant heart disease features, predicting the highest risk through machine learning. Experimental outcomes indicate prediction accuracy obtained by SVM, DT, RF, LR, KNN, ANN, and PCA. as a feature selection process are 91%, 90%, 86%, 91%, 88%, and 91%. Collaboration is vital for enhancing algorithm precision in clinical settings. The application of machine learning to cardiovascular health has the potential to transform continuous management, personalized treatment, and early diagnosis. Robust clinical tools for better patient outcomes and public health are ensured by addressing issues like data privacy and model interpretability and by fostering interdisciplinary collaboration. © 2024 IEEE.

关键词： Patient treatment

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Recent Advancements in Applications of Artificial Intelligence and machine learning for 5G Technology: A Review 2

Recent Advancements in Applications of Artificial Intelligen...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Nyalapelli, Alekya Sharma, Shubham Phadnis, Pranjal Patil, Maithili Tandle, Avinash MPSTME - NMIMS Mumbai India

ISBN: (纸本)9798350310719

As the fifth generation (5G) of wireless communication rolls out worldwide, conceptualized use cases and disruptive industry solutions are being deployed to offer smooth, frictionless, and secure connectivity. The landscape of Artificial Intelligence (AI) and machine learning (ML) can be seen as potential drivers in the automation and optimization of network performances and management complexities. The shifting network behaviors and complicated modern applications present diverse network performance traffic, which can be exploited by service providers to deal with network demands and provide superior user experiences. The existing research can be divided into the following 5G research areas, which include network traffic, resource allocation, network slicing, mobility management, physical layer security, etc., to name a few. The primary objective of this paper is to provide a comprehensive perspective on the expanding diversity of viable ML-assisted solutions for tackling various 5G network-level issues. The paper concludes with an indepth investigation of the challenges and unexplored directions of future research pertaining to making 5G applications more reliable for future use cases. © 2023 IEEE.

关键词： machine learning

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Neural Synchronization with Queries

Neural Synchronization with Queries

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2nd international conference on signal Acquisition and processing

作者： Revankar, Pravin Gandhare, W. Z. Rathod, Dilip Govt Coll Engn Aurangabad Maharashtra India PES Coll Engn Dept Informat Technol Aurangabad Maharashtra India

ISBN: (纸本)9780769539607

The neural key exchange algorithm for choosing the relevant inputs is sufficient to achieve a more or less secure key-exchange protocol, however A and B could improve it by taking more information into account, including queries in the training process of the neural networks. Alternatively A and B are generating an input which is correlated with its state and A or B is asking the partner for the corresponding output bit[7]. The overlap between input and weight vector is so low that the additional information does not reveal much about the internal states. But queries introduce a mutual influence between A and B which is not available to an attacking network E. In this work query incorporated to the case of the Hebbian training rule. The probability of a successful attack is calculated for different model parameters using numerical simulations. The results show that queries restore the security against cooperating attackers.

关键词： Neural cryptography query mutual learning tree parity machine neural synchronization encryption/decryption neural network

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Enhancing UAV Pitch Angle Measurement Through Radar Cross-Section Data and machine learning 2

Enhancing UAV Pitch Angle Measurement Through Radar Cross-Se...

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2nd IEEE international conference on Image processing and Computer Applications, ICIPCA 2024

作者： Liu, Yuchen Gao, Siqi Wang, Bo School of Information and Electronics Beijing Institute of Technology Beijing102401 China School of Physics and Electronic Information Henan Polytechnic University Jiaozuo454000 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100088 China

ISBN: (纸本)9798350360240

This study introduces a novel methodology for measuring the pitch angle of Unmanned Aerial Vehicles (UAVs) by leveraging radar cross-section (RCS) data combined with machine learning algorithms. Traditional pitch angle measurement techniques face challenges in accuracy and reliability. Our approach utilizes RCS data as a unique identifier of UAVs' interaction with electromagnetic waves, enhancing the measurement precision of pitch angles. By applying Fourier series for signal analysis and employing machine learning models for pattern recognition, this paper significantly improves the predictability and accuracy of pitch angle measurements under dynamic flight conditions. The methodology's effectiveness is validated through extensive simulations and numerical experiments, demonstrating its superiority over conventional methods. © 2024 IEEE.

关键词： Angle measurement

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Deep Multimodal Network: a multi-signal input neural network for Ad Click Predictions 19

Deep Multimodal Network: a multi-signal input neural network...

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2nd international conference on Big Data Technologies (ICBDT) / 3rd international conference on Business Information Systems Workshop (ICBIS)

作者： Chen, Yang Du, Guanglei Wang, Xu Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9781450371926

Online advertisement is an important source of revenue for internet companies, so increasing click-through rates (CTR) on ads is crucial. the traditional CTR prediction model only extracts the classification features and numerical features of the advertisement, but ignores the title, description and other text features of the advertisement. However, we know that such information is very important for an advertisement. Therefore, we propose a deep multimodal network (DMN) to solve this problem. on the basis of the traditional deep model, DMN add the text features of cyclic neural network learning, so as to improve the performance of the model. we also did a lot of visual data analysis on the dataset, Finally, we conducted a comparative experiment on the real-world dataset.

关键词： Multimodal Deep learning CTR

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