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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Analysis and Research of image Processing Optimization technology based on Big Data Processing 2

Analysis and Research of Image Processing Optimization Techn...

引用

2nd international conference on Data Science and Information System, ICDSIS 2024

作者： Chen, Yelei Lan, Ruixue Hubei University Of Technology Hubei Wuhan China

ISBN: (纸本)9798350381665

Convolutional neural network algorithm is a kind of deep neural network algorithm, which has good performance of image feature extraction. It can reduce the large data amount of images to a small data amount without affecting the feature expression, and effectively solve the problem of low image recognition accuracy. In this paper, the structure of convolutional neural network algorithm is studied deeply, and a 10-layer convolutional neural network model is designed according to the requirements of computer image recognition. Through image transformation and backpropagation algorithm training, the overfitting problem in model training is effectively controlled. The experimental results show that the algorithm has smaller peak signal-to-noise ratio, structural similarity coefficient and mean square error, and the running time is below 0.13s. © 2024 ieee.

关键词： Big data

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Water Quality Monitoring and Prediction Using IOT and Analytics 2

Water Quality Monitoring and Prediction Using IOT and Analyt...

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

作者： Suthiv, Diya Kanojia, Aditi Sengupta, Shritama Jain, Rajashree Saini, Jatinderkumar R. Symbiosis International Deemed University Symbiosis Institute of Computer Studies and Research Maharashtra Pune India

ISBN: (纸本)9798350366570

Water is indispensable resource. Due to increased industrialization, urbanization water consumption is increasing day by day. At the same time global warming and adverse environment effects are posing challenges on the water as natural resource in terms of its availability and quality. It has become necessary to detect and continuously monitor water pollutants and toxins for its drinking and agriculture purposes. Technological advancements like sensors, internet of things and machine learning techniques are useful in water quality monitoring and prediction. The present paper proposes a cost effective method of calculating Water Quality Index using sensors, microcontroller and a cloud database. based on the historical data, the web application was capable of predicting the Water quality. © 2024 ieee.

关键词： analytics Cloud data base internet of Things Microcontroller Water Quality Index

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Distortion Analyzer based on the internet of Things 3

Distortion Analyzer Based on the Internet of Things

引用

3rd international conference on Artificial Intelligence, internet of Things and Cloud Computing technology, AIoTC 2024

作者： Ruan, Hongyu Zhou, Keyu Zhang, Qisheng Zhang, Jingwen School of Geophysics and Information Technology CUGB Beijing China

ISBN: (纸本)9798331530280

With the rapid development of the industrial sector, the application scenarios of distortion analyzers have also become more extensive. However, most distortion analyzers currently available on the market still have some drawbacks, such as large size, high cost, limited data collection capabilities, and the inability to perform remote monitoring via the internet. To address these issues, this paper applies FFT to analyze the spectral components of signals through specific algorithms and calculates the Total Harmonic Distortion (THD). We have designed and developed a novel distortion analysis system based on wireless communication technology. This system is compact, portable, and low-power, featuring a 100 mm × 60 mm, 480 × 320 resistive touch serial screen to display the total harmonic distortion (THD), and waveform of the measured signal with high accuracy. Additionally, it supports remote data monitoring. The hard ware employs a modular design, utilizing the MSP432E401Y microcontroller as the main control chip, which offers high sampling rates, good compatibility, and abundant hardware resources. The system integrates IoT technology and uses the Android Studio software development platform to create an application for the host computer, providing a remote real-time monitoring system based on IoT. © 2024 ieee.

关键词： Harmonic distortion

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Research on image Recognition System based on Computer Artificial Intelligence technology 4

Research on Image Recognition System Based on Computer Artif...

引用

4th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

This paper uses mobile network mode to develop and model Python crawler based on Ubuntu16.04 and CUDA9.0. The visualization of this pattern was achieved using Tensor Board software. The hardware design of the system consists of three modules: linear array CCD sensor, DSP processor and signal processing circuit. Through the reconstruction and learning of such models, the trained models are stored, so as to obtain higher precision image recognition. A method of classifying objects using Fourier description operators is proposed. The experimental results show that the accuracy of the image is 96.8% and the recognition time is 0.55 seconds. Motion tracking is based on the positioning and capture of the specific position of the human hand in each screen, and the captured frame rate is 28 frames per second. The system can quickly acquire images of 1024∗1500 pixels at a rate of 5 photos/second. Through the static identification of posture and tracking of motion trajectory, the interaction between human and machine is better. © 2024 ieee.

关键词： artificial intelligence digital signal processing fourier image identification technology mobile network

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

引用

2024 conference on Visual Communications and image Processing

作者： Khosroshahi, Hamed Razavi Sancho, Jaime Bonatto, Daniele Fachada, Sarah Bang, Gun Lafruit, Gauthier Juarez, Eduardo Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium Univ Politecn Madrid Res Ctr Software Technol & Multimedia Syst Madrid Spain Telecommun Res Inst Elect Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model's ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Neural Radiance Fields NeRF View synthesis Data augmentation Depth map

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An optimal nonlinear predictive control based on a decoupled ARX-Laguerre multimodel

An optimal nonlinear predictive control based on a decoupled...

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2024 international conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Adaily, Sameh Mbarek, Abdelkader Garna, Tarek University of Monastir Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir 5019 Tunisia

ISBN: (纸本)9798350361025

This paper deals with the representation of invariant, causal and stable systems on orthogonal Laguerre bases by combining the multimodel approach with the output and the input filtering principle. This representation allows the modeling of a large class of nonlinear systems and guarantees a significant parametric reduction. The resulting model of this representation is entitled ARX-Laguerre multimodel where the output is written as a linear combination of Laguerre filters and the Fourier coefficients. Thus, a new control technique of nonlinear systems, is proposed based on decoupled ARX-Laguerre multimodel. © 2024 ieee.

关键词： Nonlinear systems

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Methods of Frequency Estimation Applied in internet of Things Technologies

Methods of Frequency Estimation Applied in Internet of Thing...

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2024 international conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Kolosova, Zoya A. Abdullin, Vildan V. Shnayder, Dmitry A. South Ural State University Dept. Of Autimatics And Control Chelyabinsk Russia South Ural State University The Industrial Automation Systems And IoT Laboratory Chelyabinsk Russia

ISBN: (纸本)9798350395013

In the rapidly evolving realm of technology, the Industrial internet of Things (IIoT) has gained prominence, including vibrodiagnostics and industrial equipment health monitoring. Integrating wireless networks within IIoT introduces challenges, notably in a balance between the desired amount of data transmitted and the technical limitations dictated by the issue of autonomy of IIoT devices. When implementing vibration signal-based equipment monitoring, a critical decision arises: transmitting the entire signal for cloud computation or conducting local computations on the sensor for selective data transmission. This paper explores three frequency estimation methods - parabolic interpolation, demodulation, and the harmonic series - in the context of IIoT technologies. Demonstrating their efficacy on synthetic signals, we highlight their suitability for vibrodiagnostics, considering the computational constraints of decentralized IIoT systems. Parabolic interpolation offers efficiency, demodulation excels in real-time processing, and the harmonic series method proves robust in scenarios with limited resolution. The comparative analysis showcases their strengths and limitations, emphasizing the importance of informed decision-making in choosing a frequency estimation method tailored to the specific constraints of the evolving IIoT environment. This study empowers practitioners to enhance the reliability and performance of IIoT-enabled devices and systems through efficient frequency estimation methods. © 2024 ieee.

关键词： Frequency estimation

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Research on Parallel Computation and Memory Optimization of Multi-Core Embedded DSP technology in High Precision Missile Terminal Guidance image Tracking Algorithm 6

Research on Parallel Computation and Memory Optimization of ...

引用

6th ieee international conference on Power, Intelligent Computing and systems, ICPICS 2024

作者： Xia, Fuming Jing, Bo Huo, Mingge Jiao, Xiaoxuan Wang, Shenglong Pan, Jinxin School of Aeronautical Engineering Air Force Engineering University Xi'an710038 China Beijing Electro-Mechanical Engineering Institute Beijing100074 China

ISBN: (纸本)9798350374315

This paper mainly discusses how to effectively use multi-core embedded digital signal processor (DSP) technology to realize parallel computation of image tracking algorithm and memory optimization in high precision missile terminal guidance system. With the increasing requirement of missile strike accuracy in modern war, the vision-based terminal guidance technology has been widely concerned because of its strong autonomy and anti-jamming ability. However, such image processing algorithms are usually highly computation-intensive and data dependent, which requires high real-time and hardware resources. In this paper, the key steps of image tracking algorithm are analyzed and reconstructed deeply, and the architecture suitable for parallel processing of multi-core DSP is designed by modularization. The parallel computing advantages of multi-core DSP are given full play through task division and scheduling strategies to improve the execution efficiency and real-time performance of the algorithm. Secondly, the research also focuses on memory optimization, including the design of data cache strategy, reducing data redundancy, improving memory access locality, etc., and strives to maximize the data reading speed and reduce the memory bandwidth pressure under the condition of limited embedded system resources. To sum up, this research is committed to building an efficient and stable multi-core embedded DSP environment, parallel computing model and memory optimization scheme for high-precision missile terminal guidance image tracking algorithm, which has important theoretical value and practical significance for improving the intelligence level and combat efficiency of missile terminal guidance system. © 2024 ieee.

关键词： Guided missiles

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FHRDiff: Leveraging Diffusion Models for Conditional Fetal Heart Rate signal Generation

FHRDiff: Leveraging Diffusion Models for Conditional Fetal H...

引用

2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaoqing Lu, Yu Ngiam, Kee Yuan Yu, Zichang Furqan, Mohammad Shaheryar Shenzhen Technology University College of Big Data and Internet Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China National University of Singapore Yong Loo Lin School of Medicine Department of Surgery Singapore

ISBN: (纸本)9798350386226

Accurate analysis of Fetal Heart Rate (FHR) signal is often impeded by challenges such as data scarcity and label imbalance, which affect the reliability and robustness of deep learning models. To address these challenges, this study introduces FHRDiff, a novel diffusion model conditioned on Phase-Rectified signal Averaging (PRSA) spectrograms for the creation of synthetic FHR signals. Our model integrates time encoding, condition generation from PRSA spectrograms, and residual blocks with dilated convolutions to effectively manage temporal dynamics and long-range dependencies. Extensive qualitative and quantitative experiments on FHR signal synthesis demonstrate the feasibility and effectiveness of FHRDiff. Compared to Generative Adversarial Networks (GANs) and image-based diffusion model, our method achieves the highest signal fidelity and distribution similarity, with key measures including 0.067 maximum mean deviation (MMD), 0.492 percent root mean square difference (PRD), 1.763 relative entropy (RE), and 0.160 Frechet distance (FD). Expert validation confirms the model's capacity to accurately generate data for normal and abnormal FHR signals based on the paired condition. In addition, an ablation study was conducted to highlight that the spectrogram paired condition can guide the diffusion model to produce synthetics FHR signals with greater diversity compared to unconditional models. The results emphasize diffusion models' potential for broad application in biomedical time series analysis, such as generation, imputation and noise removal. © 2024 ieee.

关键词： Heart

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KLMN: Knowledge distillation based lightweight multi-clue image forgery detection and localization

KLMN: Knowledge distillation based lightweight multi-clue im...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Huang, Heng Liu, Yaqi Jin, Xin Xiao, Song Liu, Bin University of Science and Technology of China Anhui China Beijing Electronic Science and Technology of Institute Beijing China

ISBN: (纸本)9798350368741

Current image forensics methods often utilize image features from various frequency domains. However, the effective use of these features frequently depends on complex network architectures and a large number of parameters. In this paper, we introduce a lightweight Multi-Clue image forgery detection and localization network (KLMN) along with a novel extractor-adapter framework based on multi-target knowledge distillation. Our extractor-adapter framework effectively integrates RGB features with low-level features, enhancing resilience against various forms of forgery while reducing the number of parameters. We also propose a feature-aggregated decoder that reconstructs the predicted mask by remixing features. The multi-target knowledge distillation process includes intermediate feature distillation, predicted map distillation from the teacher networks, and supervised training using ground truths. Experimental results on multiple datasets demonstrate that our approach can accurately and efficiently detect and localize manipulated regions while meeting the requirements for fewer parameters and reduced memory usage. © 2025 ieee.

关键词： image forgery detection and localization knowledge distillation low-level features

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