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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

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An intricate spectrogram processing in speech enhancement using machine learning with cloud computing 7

An intricate spectrogram processing in speech enhancement us...

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7th international conference on image Information processing, ICIIP 2023

作者： Reddy, Yenna Geetha Mahendran, G. Al-Khaykan, Ameer Alkawak, Omar A. Kalaiselvan, S.A. Priya, A. Department of Computer Science And Engineering Kompally Telangana Secunderabad India R.M.K Engineering College Department of Mechancal Engineering Chennai Tamil Nadu Kavaraipettai India University of Kufa Department of Electronic And Communications Engineering Najaf Iraq Al-Mustaqbal University College Department of Air Conditioning And Refrigeration Technical Engineering Babil Hilla Iraq Department of Computer Science And Engineering Tamil Nadu Chennai India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9798350371406

The spectrogram is defined as the visual representation of the signal strength with a particular interval of time regarding frequencies in the appropriate waveform. It is demonstrated using the Fourier transform. It is illustrated in a three-dimensional plot of signal composed with amplitude versus frequency and time. They are classified as long signal sections and short signal sections with filters. It demonstrates the consecutive spectacle of the sound signal at a particular interval of time. The time-frequency representation of audio signals is obtained through spectrograms. This helps in the understanding of the speech data. Machine learning plays an important role in the identification of the relevant features in the spectrogram. The intricate patterns in the time and frequency domains are represented through CNN and RNN. These models are trained and tested through a large dataset which includes both clean and noisy speech samples. This helps to learn complex interrelationships and patterns associated with varied environmental conditions and interference systems. The computational demands of these systems are handled through the cloud computing techniques. They provide a platform for effective solutions for performing real-time interference. These cloud services provide various collaborations to numerous models to achieve desired outcomes. The proposed system provides higher adaptability in varied environmental and noisy profiles. Thus the integration of cloud computing with machine learning provides real-time processing with a natural seamless user experience. © 2023 ieee.

关键词： Cloud computing

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international conference on Electromagnetics in Advanced applications and ieee-APS Topical conference on Antennas and Propagation in Wireless Communications, ICEAA-APWC 2023

International Conference on Electromagnetics in Advanced App...

引用

12th ieee-APS Topical conference on Antennas and Propagation in Wireless Communications, APWC 2023

ISBN: (纸本)9798350320602

The proceedings contain 55 papers. The topics discussed include: advanced web tool for the optimization of antenna positioning based on evolutionary algorithms;hybrid wireless rf-perovskite photovoltaic energy harvester design consideration for low-power Internet of Things;a study on propagation of frequency diverse array in multipath environments;robust array signal processing using L1-kernel-based principal component analysis;SSW-2D: some open-source propagation software introducing split-step wavelet and wavelet-to-wavelet propagation technique;and measurement of near-field electromagnetic distribution radiated from slotted circuit-shape waveguides to melt snow with microwave.

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Using Active Learning Techniques for Analysis of Photomicroscopy images 12

Using Active Learning Techniques for Analysis of Photomicros...

引用

12th ieee international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Blahova, Linda Kostolny, Jozef University of Zilina Faculty of Management Science and Informatics Zilina Slovakia

ISBN: (纸本)9798350358056

processing biomedical image data, specifically microscopic data from a blood sample, where it is necessary to identify and classify leukocytes, is a complex issue. Manual processing of such data is time-consuming and can be subjective and prone to human error. Therefore, this work aims to analyze current processing techniques and available tools to simplify and automate this process. The paper includes an overview of the biomedical data used, and describes the analysis, the selection of the relevant data, a proposal for a solution using the general methods and overall automation of the process, an implementation of the solution, and an evaluation of its benefits in further research. Logistic regression was used to predict the leukocyte type, in combination with the active learning approach. The work results in a solution that simplifies and mainly speeds up the processing of microscopic data for leukocyte identification and classification by removing the human error and subjectivity. The proposed solution achieved classification accuracy of 92% in both training and testing dataset (Raabin-WBC) and 84% on vakidation dataset (BCCD), without any changes in the model parameters. This solution may find application in diagnosing various diseases such as blood cancers, infections, autoimmune diseases, or allergies. © 2023 ieee.

关键词： classification leukocytes machine learning microscopic image data python segmentation

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BIRD-PCC: Bi-Directional Range image-Based Deep Lidar Point Cloud Compression

BIRD-PCC: Bi-Directional Range Image-Based Deep Lidar Point ...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Liu, Chia-Sheng Yeh, Jia-Fong Hsu, Hao Su, Hung-Ting Lee, Ming-Sui Hsu, Winston H. National Taiwan University Taiwan Mobile Drive Technology

ISBN: (纸本)9781728163277

The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments. © 2023 ieee.

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REPNP: PLUG-AND-PLAY WITH DEEP REINFORCEMENT LEARNING PRIOR FOR ROBUST image RESTORATION 29

REPNP: PLUG-AND-PLAY WITH DEEP REINFORCEMENT LEARNING PRIOR ...

引用

ieee international conference on image processing (ICIP)

作者： Wang, Chong Zhang, Rongkai Ravishankar, Saiprasad Wen, Bihan Nanyang Technol Univ Sch Elect Elect Engn Singapore Singapore Michigan State Univ Dept Computat Math E Lansing MI USA Michigan State Univ Dept Sci & Engn E Lansing MI USA Michigan State Univ Dept Biomed Engn E Lansing MI USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, the Plug-and-Play (PnP) framework is a popular and powerful tool that can integrate an off-the-shelf deep denoiser for different image restoration tasks with known observation models. However, obtaining the observation model that exactly matches the actual one can be challenging in practice. Thus, the PnP schemes with conventional deep denoisers may fail to generate satisfying results in some real-world image restoration tasks. We argue that the robustness of the PnP framework is largely limited by using the off-the-shelf deep denoisers that are trained by deterministic optimization. To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks. Experimental results demonstrate that the proposed RePNP is robust to the observation model used in the PnP scheme deviating from the actual one. Thus, RePNP can generate more reliable restoration results for image deblurring and super resolution tasks. Compared with several state-of-the-art deep image restoration baselines, RePNP achieves better results subjective to model deviation with fewer model parameters.

关键词： image restoration Plug-and-play Deep reinforcement learning Denoiser prior

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GCN-BASED MULTI-MODAL MULTI-LABEL ATTRIBUTE CLASSIFICATION IN ANIME ILLUSTRATION USING DOMAIN-SPECIFIC SEMANTIC FEATURES 29

GCN-BASED MULTI-MODAL MULTI-LABEL ATTRIBUTE CLASSIFICATION I...

引用

ieee international conference on image processing (ICIP)

作者： Lan, Ziwen Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

This paper presents a multi-modal multi-label attribute classification model in anime illustration based on Graph Convolutional Networks (GCN) using domain-specific semantic features. In animation production, since creators often intentionally highlight the subtle characteristics of the characters and objects when creating anime illustrations, we focus on the task of multi-label attribute classification. To capture the relationship between attributes, we construct a multimodal GCN model that can adopt semantic features specific to anime illustration. To generate the domain-specific semantic features that represent the semantic contents of anime illustrations, we construct a new captioning framework for anime illustration by combining real images and their style transformation. The contributions of the proposed method are two-folds. 1) More comprehensive relationships between attributes are captured by introducing GCN with semantic features into the multi-label attribute classification task of anime illustrations. 2) More accurate image captioning of anime illustrations can be generated by a trainable model by using only real-world images. To our best knowledge, this is the first work dealing with multilabel attribute classification in anime illustration. The experimental results show the effectiveness of the proposed method by comparing it with some existing methods including the state-of-the-art methods.

关键词： Anime illustration graph convolutional networks semantic feature multi-modal classification image captioning

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Research on Real-Time Data Transmission and signal processing System of CIM Practical Training Teaching Platform Based on 5G Network

Research on Real-Time Data Transmission and Signal Processin...

引用

image processing and Computer applications (ICIPCA), ieee international conference on

作者： Xuemei Cheng Hao Zhang Shandong University of Engineering and Vocational Technology Jinan China Poly Shandong Real Estate Group Corporation Limited Jinan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In order to solve the problems of “digital signal processing”, such as many common knowledge points, difficult teaching, difficult visual teaching, and so on, a software platform for “signal processing” open teaching is built based on Visual C++6.0. This system is to cooperate with the teaching content of CIM, but also to improve the practical ability of students, so that they can more easily and quickly understand some of the more complex digital signal theory. The timing of the beam position signal is optimized based on the existing down sampling method, and the digital AGC and slow application signal processing modules are completed. We need to get turn-by-turn, quick application, and track closing data simultaneously. The experiment shows that the system can obtain the multi-frequency position information including the actual running state well. The advantages of this system are easy to control parameters, easy to use, and strong expansibility. This project plans to further improve the system in the future research work so as to achieve better teaching and practical application purposes.

关键词： Training Human computer interaction Visualization image processing Education signal processing Sampling methods Software Real-time systems Timing

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Animal Species image Classification 3

Animal Species Image Classification

引用

3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Prudhivi, Likhith Narayana, M. Subrahmanyam, Ch Gopi Krishna, M. Chavan, Salim Infosys Ltd Hyderabad India Anurag University Electronics & Communication Engineering Hyderabad India Vignan Institute of Technology and Science. Electronics & Communication Engineering Telangana India Vardhaman College of Engineering Electronics & Communication Engineering Shamshabad India Govindrai Wanjari College of Engineering and Technology Nagpur India

ISBN: (纸本)9798350320749

In forests, in order to protect wild animals, real-time animal species identification is done via picture recognition. Many computer vision approaches were first presented in the past, however they couldn't meet the demands since accuracy decreased as technology evolved. Nonetheless, accuracy in the categorization, identification, and segmentation of pictures was significantly improved as a consequence of the inclusion of multiple approaches. In order to achieve a high degree of accuracy, this study intends to provide an effective approach for animal species picture categorization. Convolutional neural networks have been developed specifically for the categorization of images. To achieve high accuracy, final layer features are learned and synchronised to the pre-trained model. With the dataset, a variety of architectures for deep learning are evaluated. © 2023 ieee.

关键词： image recognition

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A Comparative Study of image Classification Models for Terrain Recognition 3

A Comparative Study of Image Classification Models for Terra...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna Kumar, V. Karthika, V. Mohana, T. Mugebur Rahman, A. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

Terrain recognition is vital for various applications such as autonomous navigation, environmental monitoring, military operations, and urban planning. It enables safe navigation for autonomous vehicles, aids in environmental surveillance, assists in military reconnaissance, and supports urban infrastructure development. Recent years have witnessed the augmentation of machine learning models with online feature extraction techniques and self-supervised learning frameworks. However, a notable challenge lies in managing the complexity inherent in processing extensive datasets or numerous features. In this study, this problem is addressed by employing transfer learning for classifying terrains using images. The effectiveness of three widely used pre-trained convolutional neural network models - ResNet50, VGG16, and InceptionV3 - was compared. The findings demonstrate the strong performance of the models, emphasizing the effectiveness of transfer learning in tasks focused on terrain recognition. © 2024 ieee.

关键词： Self-supervised learning

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Enhancing Dehazing Performance of Single Optical Satellite images using Gamma Correction and Improved DCP

Enhancing Dehazing Performance of Single Optical Satellite I...

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2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Regan, D. Priyan, S. Vishnu Siddartha Institute of Science and Technology Dept. of ECE Puttur India Kings Engineering College Dept. of Bio-Medical Engineering Chennai India

ISBN: (纸本)9798350319200

Dehazing is an essential preprocessing step for improving the quality of satellite imagery. Hazy conditions can significantly impact the visibility and clarity of satellite images, making it difficult to extract useful information from them. In recent years, several dehazing techniques have been proposed for satellite imagery, including contrast stretching, dark channel prior, and adaptive gamma correction. In this research work, we propose a novel haze removal approach that combines gamma correction with modified dark channel prior-based haze removal approach in the YCbCr color space. Our approach enhances the luminance component of the image by applying gamma correction to the Y channel, and reduces computational complexity by subsampling and combining the chrominance channels. The performance of the proposed system is assessed using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and compared with the state-of-the-art methods. Experimental results demonstrate that the proposed our approach outperforms other methods in terms of dehazing and contrast improvement, suggesting its potential for various applications that require high-quality satellite image processing. © 2023 ieee.

关键词： DCP Gamma correction Satellite image dehazing YCbCr Color space

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