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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2953 条记录，以下是411-420 订阅

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Investigation of an algorithm for distributing panoramic image stitching, enhanced by feature point detection 3

Investigation of an algorithm for distributing panoramic ima...

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3rd international conference on signal Image processing and Communication, ICSIPC 2023

作者： Wang, Yubin North Central University No.3 College Road Shanxi Province Taiyuan China

ISBN: (纸本)9781510670945

In order to solve the problems of long matching time and high mis-matching rate in the feature point matching process of traditional panoramic image stitching algorithms, this paper proposes a panoramic image stitching algorithm based on SIFT and ***'s algorithm has been advanced to only extract features for overlapping areas (valid regions), thus lessening the computational effort of feature detection, better aligning image features, and enhancing alignment accuracy. On the ground of their corresponding degree, the RANSAC algorithm has been increasingly improved and thus allocated varying weights to distinct feature points, and the higher the matching degree, the higher the weight assigned to the points, thus more precisely discarding mismatched point pairs. A comparison of the number of mismatched points involving relevant parameters, like degree and time of matching, the SIFT and corresponding RANSAC algorithm-based panoramic image stitching algorithm are more excellent. © 2023 SPIE.

关键词： Image enhancement

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Social Media data Analysis for Malayalam YouTube Comments: Sentiment Analysis and Emotion Detection using ML and DL Models 3

Social Media Data Analysis for Malayalam YouTube Comments: S...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Abeera, V.P. Kumar, Sachin Soman, K.P. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789544520854

In this paper, we present a study on social media data analysis of Malayalam YouTube comments, specifically focusing on sentiment analysis and emotion detection. Our research aims to investigate the effectiveness of various machine learning (ML) and deep learning (DL) models in addressing these two tasks. For sentiment analysis, we collected a dataset consisting of 3064 comments, while for two-class emotion detection, we used a dataset of 817 comments. In the sentiment analysis phase, we explored multiple ML and DL models, including traditional algorithms such as Support Vector Machines (SVM), Naive Bayes, K-Nearest Neighbors (KNN), MLP Classifier, Decision Tree, and Random Forests. Additionally, we utilized DL models such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit(GRU). To enhance the performance of these models, we preprocessed the Malayalam YouTube comments by tokenizing and removing stop words. Experimental results revealed that DL models achieved higher accuracy compared to ML models, indicating their ability to capture the complex patterns and nuances in the Malayalam language. Furthermore, we extended our analysis to emotion detection, which involved dealing with limited annotated data. This task is closely related to social media data analysis. For emotion detection, we employed the same ML models used in the sentiment analysis phase. Our dataset of 817 comments was annotated with two emotions: Happy and Sad. We trained the models to classify the comments into these emotion classes and analyzed the accuracy of the different models. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language processing, RANLP 2023 - Proceedings.

关键词： Sentiment analysis

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UWB signal Generation and Transmission Technology 3

UWB Signal Generation and Transmission Technology

引用

3rd international conference on Electronic Information Engineering and Computer Science, EIECS 2023

作者： Wang, Zijian Jilin University State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Changchun China

ISBN: (纸本)9798350316674

UWB is a communication technology based on wireless signals. It does not need to use a sinusoidal carrier, but uses a non-sinusoidal signal with a narrow pulse width of nanoseconds for data transmission, so it has a large bandwidth and is suitable for high-speed communication. This paper proposes a second-order ultra-wideband signal generator with a simple structure. By constructing a microwave photon filter, the conversion from phase modulation to intensity modulation is realized, and the Gaussian pulse is first-order differentiated. By constructing a microwave photon delay line filter, the first-order ultra-wideband signal is differentiated to generate a second-order ultra-wideband pulse. The generated second-order UWB signal has a pulse width of 190ps, and the spectrum has a center frequency of about 5.4 GHz and a 10 dB bandwidth of 4.6 GHz, with a relative bandwidth of 85.2%. In order to further improve the transmission performance of ultra-wideband signals and enhance the anti-interference ability, a light-borne ultra-wideband system transmission technology is proposed, which comprehensively uses multi-pulse modulation technology, dispersion compensation technology and encoding and decoding technology. After simulation verification by Opti-system and Matlab software, when the forward error correction threshold is 3.8×10-3, the receiver sensitivity is increased by about 5.5dBm. This technology effectively reduces the bit error rate of optical UWB system transmission and further improves the performance of optical UWB transmission system. © 2023 IEEE.

关键词： signal generation signal transmission UWB over optical fiber

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG 3

Evaluation of Deep Training and Adapting Algorithms in Medic...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Abdulwahid, Ali Hadi Karetla, Girija Rani Ganesh, K.V.B. Kumar, A Suresh Radha, K. Shivakumar, K. Southern Technical University Engineering Technical College Control and Automation Engineering Department Basra Iraq Western Sydney University School of Computer Data and Mathematical Sciences Australia Koneru Lakshmaiah Education Foundation KL Business School Guntur Andhra Pradesh India Graphic Era Deemed to Be University Department of CSE India St. Martin's Engineering College Dept. of IT Secunderabad Telangana India SR University School of Computer Science and Artificial Intelligence Warangal Telangana India

ISBN: (纸本)9798350399264

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean Square (LMS) technique, the Connected Neural Network, and also the Recursive Least-square (RLS) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMSE and signal-to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods. © 2023 IEEE.

关键词： Electroencephalography

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Research and Application of YOLOv3-Based Intelligent Module for Remote Sensing Image Recognition on Satellites 3

Research and Application of YOLOv3-Based Intelligent Module ...

引用

3rd international conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Tang, Hua Ye, Bingxin Shi, Huajun Wang, He Yu, Tao China Electronics Technology Group Corporation China The 32nd Research Institute of China Electronics Technology Group Corporation China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

With the continuous development of satellite technology, the acquisition and processing of satellite remote sensing data have become increasingly common. However, at present, satellites still use a mode of transmitting data back to the ground and utilizing ground CPU/GPU platforms for intelligent processing, which is inefficient due to the process of data transmission between the satellite and the ground. This article studies the application method and implementation of real-time remote sensing image recognition by integrating the Yulong810 on-board intelligent module on a satellite based on the YOLOv3 object detection *** response to the actual needs of satellite remote sensing image recognition, the algorithm is optimized based on the Yulong810 on-board intelligent module, which improves the recognition efficiency and reduces power consumption while ensuring the accuracy of remote sensing image recognition. By comparing with ground CPU/GPU platforms, the results show that the Yulong810 on-board intelligent module has lower power consumption and higher efficiency, and the recognition accuracy is comparable to that of ground platforms, which can completely replace ground remote sensing image detection equipment when integrated into *** integrating the module into the satellite, real-time intelligent processing can be achieved in orbit, improving the efficiency of satellite remote sensing image recognition. © 2023 SPIE.

关键词： Image recognition

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data augmentation by using gans and image transformation in facial emotion classification 3

Data augmentation by using gans and image transformation in ...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Huang, Hongpei Weatherhead School of Management Case Western Reserve University 10900 Euclid Avenue ClevelandOH44106-7235 United States

We propose different approach combinations including GANs and image transformation in facial emotion classification especially for FER-2013 dataset. Our goal is not to reach the highest accuracy for this dataset, but to compare and verify the data augmentation effects for several main GAN methods such as Vanilla GAN, CGAN, DCGAN and BiGAN. We used these GAN models to generate fake images and put them to our train dataset. And the data together was through image transformation to train in the CNN model that we designed to perform facial emotion classification. Base on the result when we used some GANs (BiGAN/DCGAN) and image transformation, the accuracy of the model indeed has been improved. The best combination data augmentation approaches in this paper are using image transformation and DCGAN. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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Balise uplink signal IQ collection 3

Balise uplink signal IQ collection

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3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Li, Liang Xu, Qing School of Automation & Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China CRSC Research & Design Institue Group Co Ltd Beijing100000 China

ISBN: (纸本)9781510661363

Balise uplink signal IQ (I: Inphase;Q: Quadrature) acquisition is an important basis for signal demodulation and uplink signal parameter estimation. Relying on traditional acquisition methods, it is difficult to achieve the requirements of automation, accuracy and real-time online analysis. In order to solve the problems existing in the traditional collection method and improve the collection efficiency of IQ signal. Based on LabVIEW as the development platform, the PXI (PCI extensions for instrumentation) system of NI is used to build a small balise test bench. Combined with the Hilbert transform principle and the characteristics of the uplink signal IQ, the uplink signal IQ acquisition system is designed. Through the actual project debugging of LabVIEW acquisition system, combined with the online analysis of the collected data, the results show that the uplink signal IQ acquisition system based on LabVIEW can effectively solve the problems existing in the traditional acquisition method and can expand the acquisition program according to the actual data processing requirements. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data handling

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A Steganographic Method Based on Pixel Block Differences in Grayscale Images 3

A Steganographic Method Based on Pixel Block Differences in ...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Rahman, Hilmy Syarif, Hisyam Wahono, Dimas De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia African Centre of Excellence in Internet of Things University of Rwanda Kigali Rwanda

ISBN: (纸本)9798350319125

Nowadays, protecting shared data from interception on public networks is critical. Steganography, an approach to concealing data, emerges as a practical remedy to confront this challenge. However, hiding confidential information within a digital medium, such as an image, often results in the degradation of visual quality. This decline can elicit suspicions among unauthorized users regarding the potential presence of concealed information. Over time, researchers have devised various steganographic algorithms to alleviate the substantial image distortion resulting from the data embedding process. Despite these endeavors, achieving a stego image of elevated quality that carries concealed data remains an enduring challenge. This study introduces a steganographic methodology grounded in the logic of difference expansion (DE), intending to augment the quality of the stego image for a specified payload size. The experimental outcomes showcase a Peak signal-to-noise ratio (PSNR) reaching 62.7512 decibels (dB), underscoring the superior efficacy of the proposed method. © 2023 IEEE.

关键词： data hiding digital images information security national security network infrastructure

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Characteristic Analysis of Power Grid Disaster Early Warning signal: A Case Study 3

Characteristic Analysis of Power Grid Disaster Early Warning...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Zhang, Wei Li, Shan Zhou, Yangjun Ouyang, Jianna Huang, Zhidu Electric Power Research Institute of Guangxi Power Grid Co. Ltd Guangxi China

ISBN: (纸本)9798350322842

This research study completes the construction of a disaster monitoring and early warning system for smart grids, based on the actual needs of disaster prevention and planning work, in order to effectively compensate for the deficiencies in the existing grid disaster prevention, mitigation and relief work. Through the review, it was noticed that in terms of early warning analysis of natural disasters in power grids, there is currently a lack of application of dynamic early warning analysis based on the evolution of disaster, and to deal with this, several innovation ideas are proposed, such as: (1) The different disaster types are defined and their connection patterns are re-defined to better estimate the risk level. (2) The comprehensive non-stop rate of regional transmission lines and its fuzzification are defined to better estimate the status. (3) Linear matrix inequalities are applied to provide an accurate estimate of the stability. The warning performance is validated by the experiment with the public data set and the efficiency of the warning performance has been proven. © 2023 IEEE.

关键词： Disasters

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Early Detection and Prognosis of Brain Tumor from Micro Array Gene data using Machine Learning Classifiers 3

Early Detection and Prognosis of Brain Tumor from Micro Arra...

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3rd international conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Chandradass, Keerthivasan Nair, Ajin R Rajaguru, Harikumar Bannari Amman Institute of Technology Ece Tamilnadu India Bannari Amman Institute of Technology Tamilnadu India

ISBN: (纸本)9798350370867

Brain tumour is one of the most lethal illnesses affecting the brain. It typically begins as little benign cells that form on the interior of brain cells and progresses to tumour. Brain tumours are caused by the spread of somatic mutations that change gene expression. The DNA microarray method offers a standardized format for evaluating the levels of gene expression in thousands of different genes. Microarray technology's patterns of gene expression can be utilized to distinguish between cancers from various anatomical regions. A two level classification strategy is suggested because processing the microarray data requires processing very large amounts of data due to the problem of dimensionality. The genes or characteristics are dimensionally condensed in the first level using Particle Swarm Optimization and Cuckoo Search Algorithm technique. Five classifiers namely GMM, KNN, BLDC, FDC and SVM (RBF) are used to detect brain tumor. Classifiers performance are validated from the parameters such as F1 Score, ER and MCC. When comparing all classifiers, SVM (RBF) settled at the accuracy (92%). SVM (RBF) appears to be the most effective classifier for distinguishing brain tumor cells from normal cells based on gene expression patterns. © 2023 IEEE.

关键词： Gene expression

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