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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

共 761 条记录，以下是171-180 订阅

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Face detection technology based on skin color features in MATLAB 3

Face detection technology based on skin color features in MA...

引用

3rd international conference on Machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Dang, Longfei Yang, Miao Jing, Xiaodong Xu, Yizhi Xiao, Qicheng He, Wanyu Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China Department of Computer Science and Technology Sichuan Police College LuZhou China Department of Crimina Technology Sichuan Police College LuZhou China

ISBN: (纸本)9781510681880

Face detection is the most basic processing in the face recognition process. If the detection method is not appropriate, it can cause the recognition process to collapse. Face detection is very important in face recognition. The facial pattern is very complex and susceptible to external interference. Common facial detection algorithms generally have weaknesses such as high computational complexity, slow speed, and high false alarm rates. In this paper, a skin color model detection method based on the YCbCr color space is used for facial detection, which solves the problem of high computational complexity. Morphological theory is also used for subsequent image processing. © 2024 SPIE.

关键词： MATLAB

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Hardware and Software Infrastructure Driven by Big Data: Challenges and Prospects 3

Hardware and Software Infrastructure Driven by Big Data: Cha...

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2023 3rd international conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2023

作者： Chen, Kangyelin Wang, Fang Wei, Zhe School of Science Civil Aviation Flight University of China Sichuan Deyang618000 China School of Computer Science Civil Aviation Flight University of China Sichuan Deyang618000 China

ISBN: (纸本)9798400716478

This article provides a comprehensive definition of big data and focuses on the current status and development trends of big data hardware and software infrastructure by comprehensively summarizing relevant research at home and abroad. Explored potential challenges in practice and proposed corresponding solutions. In terms of hardware, the impact of storage technology, computing resources, and networks on big data processing performance and scalability was discussed. In terms of software, the focus was on the functions and characteristics of big data processing frameworks, as well as comparing their advantages and limitations in practical applications. By comprehensively summarizing the hardware and software infrastructure of big data and exploring solutions to challenges, a solid foundation can be laid for the development of the big data field, achieving more efficient, secure, and intelligent big data processing and applications. © 2023 ACM.

关键词： Big data

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Real-time detection network of contraband based on YOLOX 3

Real-time detection network of contraband based on YOLOX

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3rd international conference on computer science and Communication Technology, ICCSCT 2022

作者： Jiang, Shifeng Pan, Chunrong Gan, Yufeng Chen, Junjie Gao, Xiangdong School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China

ISBN: (纸本)9781510661240

Today, the increasingly complex external environment and the goal of building a harmonious society have put forward higher requirements for security check work. Traditional security inspection methods are facing enormous challenges. With the maturity of intelligent security technology, Intelligent security inspection has become a trend. Security check that requires both reasoning speed and accuracy, this paper proposes a lightweight target detection network, which takes YOLOX as the overall framework and Mobilenet-V3 as the backbone network. The effectiveness of the network is proved by the simulation experiment on the public data set OPIXray and can achieve a real-time computing speed of 87.7FPS. © 2022 SPIE.

关键词： signal detection

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Motor Imagery EEG signal Classification Using Deep Neural Networks 1

引用

3rd international conference on Computing science, Communication and Security (COMS)

作者： Nakra, Abhilasha Duhan, Manoj DCRUST Sonepat India

ISBN: (数字)9783031105517

ISBN: (纸本)9783031105517;9783031105500

EEG signals can be considered a secure and safe way to capture human brain activity. EEG signals can play a vital role in motor imagery applications to control hands, feet, etc. Brain computer interface (BCI) is method of connecting human brain to laptop or computer. To control the movement of hands and feet, a BCI system is required. The generated EEG signal is read via computer and, as per EEG signal command, is set for motory movement of the hand and feet. Therefore, in motor imagery EEG applications, proper classification of the EEG signal is desired to distinguish hand and foot movements. In this work, an EEG signal classification is done using Deep Neural Networks (DNN) method and the results are compared with recent notable methods.

关键词： BCI EEG DNN

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A Modular Deep Denoising Autoencoder for speech enhancement 3

A Modular Deep Denoising Autoencoder for speech enhancement

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3rd international conference for Emerging Technology, INCET 2022

作者： Al-Taai, Raghad Yaseen Lazim Wu, Xiaojun School of Computer Science Ministry of Education Shaanxi Normal University Snnu Key Laboratory of Modern Teaching Technology Xi'an China

ISBN: (纸本)9781665494991

Recent researches have proven that deep denoising autoencoder is an effective method for noise reduction and speech enhancement, and can provide better performance than several existing methods. However, training deep denoising autoencoder has proven to be difficult computationally. The goal of this study is to develop a modular approach for training deep denoising autoencoders as a set of classifiers based on collaborative learning. Each classifier is a multilayer deep denoising autoencoder network and specialized for a particular enhancement task and handles a subset of the complete training dataset. The approach performance was assessed using a perceptual evaluation of speech quality and the segmental signal-to-noise ratio. We have trained two individual DDAEs with three and five hidden layers respectively for comparison purposes. We have also compared our proposed model with the traditional spectral subtraction and, log MMSE methods. The results showed that DDAE with three and five hidden layers was sufficient (deeper is better), while the proposed approach provided higher intelligibility results and was more suitable for high-quality cases. © 2022 IEEE.

关键词： Speech enhancement

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EEG-driven GAN for Alpha Rhythm Generation in Passive BCI 3

EEG-driven GAN for Alpha Rhythm Generation in Passive BCI

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3rd IEEE international conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Cinquetti, Ettore Zanni, Guglielmo Menegaz, Gloria Storti, Silvia F. University of Verona Dept. of Computer Science Italy University of Verona Dept. of Engineering for Innovation Medicine Italy

ISBN: (纸本)9798350378009

Passive brain-computer interface (BCI) based on electroencephalographic (EEG) signals show promise for monitoring human vigilance in critical settings that continuously strain human attention. However, the lack of extensive public datasets limits progress in artificial intelligence research. To overcome this problem, we implemented generative adversarial networks (GANs) to augment existing EEG datasets. We focused on two datasets: a publicly available resting-state dataset and a custom one simulating industrial activities. After extracting time-variant alpha indices using continuous wavelet transform, we compared the generated data with real data using the L2 distance metric and autocorrelation function. We additionally assessed two forecasting models trained on original and augmented datasets, comparing their predictions. The integration of synthetic data led to an improvement in signal behavior prediction, as evidenced by a 32% reduction in mean absolute error for the resting-state dataset. Furthermore, a metric inspired by the Frechét Inception Distance was computed using the forecasting model to discern the distributions of embeddings for the real and generated data, with results showing a strong resemblance between the two. This study challenges the limitation of acquiring domain-specific EEG data and aims to develop a robust signal generation framework. © 2024 IEEE.

关键词： Wavelet transforms

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Identification of Skin Diseases using a Novel Deep CNN 3

Identification of Skin Diseases using a Novel Deep CNN

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Nagaraj, P. Muneeswaran, V. Krishna, K. Jaya Reddy, K. Yerriswamy Morries, J. Rock Kumar, G. Pavan Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Germany Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Germany

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Skin disorders are the most frequent health problems that people face around the world, and they come with a hefty price tag. Chickenpox, impetigo, infectious erythema, scabies, and skin warts are chronic and incurable skin disorders that cause severe morbidity in the form of physical discomfort. The technique of image processing is used in our procedure of identifying skin problems. Because this approach is based on Deep Convolutional Neural Network Learning, extremely precise results can be obtained by employing these techniques. The patient submits a photo of the affected area of the skin to the prototype as an input. In this photograph, the Deep Learning technique is employed, and as a result, the discovered skin disease is displayed as the output. © 2022 IEEE.

关键词： Convolutional neural networks

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Speech Enhancement Algorithms for Wind Noise Reduction 3

Speech Enhancement Algorithms for Wind Noise Reduction

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Rakshitha, A.H. Rs Chikkamath, Satish Desai, Shrinivas D Kle Technological University School of Electronics and Communication Hubballi580031 India Kle Technological University School of Computer Science Hubballi580031 India

ISBN: (纸本)9781665494991

Speech is the most convenient communication mode. Speech signal, while transmission degrades due to various factors such as white noise addition, noise from the surrounding environment, etc. Wind noise gets added in mobile phones and corrupts speech signals leading to listener's fatigue. Speech enhancement is a research area focusing on improving the quality and intelligibility of degraded speech signals. Wind noise, being non-stationary, is difficult to remove. This work focuses on reducing the effect of wind noise using various speech enhancement algorithms such as Multi-band Spectral Subtraction, Minimum Mean Square Error Short-Time Spectral Amplitude (MMSE-STSA), MMSE log STSA and Wiener filtering. The experiments are conducted considering wind noise alone and in combination with white noise. The performance of these speech enhancement algorithms on wind noise is evaluated using measures such as overall SNR, segmental SNR, Perceptual Evaluation of Speech Quality (PESQ), Itakura Saito Spectral distance and Short-Time Objective Intelligibility (STOI). © 2022 IEEE.

关键词： Speech enhancement

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Research on GPU-Based Parallel processing for the Paillier Algorithm 3

Research on GPU-Based Parallel Processing for the Paillier A...

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3rd international conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Teng, Yifei Liu, Chunyuan School of Computer and Information Engineering Heilongjiang University of Science and Technology Harbin China

ISBN: (纸本)9798350340402

The homomorphic encryption algorithm Paillier is a cryptographic method that can be applied in areas such as privacy-preserving computation and federated learning. However" due to the inherent complexity and computational overhead of the algorithm, it may affect real-time requirements when dealing with a large volume of encrypted data. In this paper, we propose a method to improve the efficiency of the encryption algorithm by combining GPU parallel processing technology with homomorphic encryption. Through experiments on encrypting and decrypting plaintexts of different sizes, the results show that the introduction of GPU significantly improves the encryption and decryption speed Compared with other parallel processing methods" the acceleration effect of our proposed method is more significant. © 2023 IEEE.

关键词： Graphics processing unit

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Systematic Literature Review on Application of Artificial Intelligence in Cancer Detection Using Image processing 3

Systematic Literature Review on Application of Artificial In...

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3rd international conference on Artificial Intelligence and Data sciences, AiDAS 2022

作者： Chandra, Giovanni Irisha, Kevina Diva Vica, Vivianit Indyca Suri, Puti Andam Syahputra, Muhammad Edo School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Having a low survival rate, cancer has become one of the deadliest diseases with complicated prognosis. In this matter, Artificial Intelligence (AI) is frequently used to aid humans due to the difficulties of cancer identification. Specifically, the use of AI in cancer detection and treatment uses two of its subclasses, machine learning and deep learning. Because those two subclasses can handle large amounts of data, their algorithms can support humans in making decisions that yield the best possible results with the least amount of risk. The purpose of this research is to find out the effectiveness of the Artificial Intelligence algorithm that is used in image processing to diagnose cancer and what is the most effective image processing algorithm for cancer diagnosis. The methods that are used in this research is Systematic Literature Review (SLR). As a result, Artificial Intelligence algorithms are sufficiently effective to be used in diagnosing cancer with image processing. Meanwhile, the most effective algorithm is CatBoost with AUC of 0.939 and Support Vector Machine (SVM) with AUC of 0.934. © 2022 IEEE.

关键词： Image processing

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