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4th International conference on Signal processing and Computer Science, SPCS 2023

作者： Wang, Ruifeng Gao, Weixin Deng, Guohao xiao, Yang Li, Yuqing School of Electronic Engineering Xi'an Shiyou University Shaanxi Xi'an710065 China

ISBN: (纸本)9781510672505

The automatic assessment of X-ray weld image quality is an important basis for the automatic assessment of weld image defects. For ring weld X-ray welds, inspectors judge the image quality through human vision and physical blackness meter, which can easily lead to staff fatigue and thus misjudgment, to achieve an automatic assessment of X-ray weld image quality, this paper combines the subjective evaluation results with objective evaluation algorithms to propose a digital blackness meter model suitable for automatic assessment calculations, and gives the ring weld quality assessment algorithm The results show that the sensitivity of the proposed method can reach 98%. The results show that the proposed method has a sensitivity of 98. 5% and a specificity of 100%. © 2023 SPIE.

关键词： Densitometers

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Application of Lossless Compression Method in image Compression 7

Application of Lossless Compression Method in Image Compress...

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7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Ni, Weifeng Pu, Tianqiu Wang, Yuxi College of Information Science and Engineering Hohai University Changzhou China

ISBN: (纸本)9798350350760

—This paper is based on the application of lossless compression algorithms in image compression, aiming to solve problems such as insufficient storage space, low transmission efficiency, and heavy data processing burden. In order to optimize the image lossless compression algorithm, a combination of Huffman encoding and LZW compression algorithm is proposed. The implementation process and advantages and disadvantages of the two are compared, and the compression ratio and encoding efficiency of image lossless compression technology are discussed. We need to optimize decoding speed, image quality, and other aspects to improve overall image lossless compression capability. By applying Huffman encoding and LZW compression algorithms to images separately, the results show that Huffman encoding can dynamically construct the optimal encoding based on the frequency of characters appearing in image data, while LZW technology is good at utilizing repeated patterns and contextual information in image data to further improve compression efficiency by establishing dictionaries. The complementary advantages of the two can further improve the lossless compression ability of images. ©2024 IEEE.

关键词： image compression

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Audio Feature Extraction algorithms and Implementation Technologies Analysis 5

Audio Feature Extraction Algorithms and Implementation Techn...

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5th International conference on Information Science, Parallel and Distributed systems, ISPDS 2024

作者： Li, Shaoxuan Xi'an Shiyou University Computer College Xian China

ISBN: (纸本)9798350352719

LIBROSA is a powerful Python audio data processing library introduced in recent years. Based on LIBROSA provided source codes, two types of feature data extraction algorithms are analyzed in detail, including MEL power spectrum and CHROMA syllable spectrums. By each algorithm, 128, 12 floating point numbers are extracted as the feature data of their corresponding spectrums. In addition, from multi perspectives of basic music theory, mathematical analysis, signal processing, computer programming and so on, deeply discusses LIBROSA library functions involved Discrete Fourier Transform (DFT), Triangular filter, Gaussian filter, and other programming technologies. The DFT algorithm, time domain window function, syllable feature data extraction, band-pass filter code analysis and other contents discussed have important reference value in data processing, deep learning and other fields of underlying model creation, source codes development and so on © 2024 IEEE.

关键词： Discrete Fourier transforms

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Leveraging NVIDIA Clara for Real-Time Cardiac image Segmentation and Diagnosis

Leveraging NVIDIA Clara for Real-Time Cardiac Image Segmenta...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Navaneethan, S. Hemanth, Goalla Nikilkumar, R. Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Medical imaging is crucial for heart diagnosis, but outdated algorithms and hardware result in delayed processing and low accuracy. Using NVIDIA Clara, a GPU-accelerated platform, the study proposes real-time cardiac picture segmentation and diagnosis. The proposed system uses advanced DL algorithms built for cardiac imaging to enhance processing speed and accuracy. Employing rigorous data preprocessing and deep learning model able to perform precise partitioning of cardiac structures and positioning defects that lead to quick heart arrhythmia diagnose in a timely manner. It also substantially reduces processing times compared to older systems while improving the contour quality and diagnosability. The system achieves the following DSC values: 0.93 for the left ventricle;0.92 for right;0.90 for left atrium;0.89 for right ventricle and 0.91 for the major blood vessels. With diagnostic accuracy scores higher than the current system Sensitivity 94.2%, Specificity 92.8%, and Accuracy 93.5%. The method has potential to enhance cardiac care by facilitating earlier interventions leading better patient outcomes. © 2024 IEEE.

关键词： image segmentation

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Quantitative Measurement of Hydrodynamics in a Binary Fluidized Bed by Capacitance Probes

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2024年第14期63卷 6297-6312页

作者： Wan, Zhen Wang, Hao Lu, Youjun Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xian 710049 Shaanxi Peoples R China

The hydrodynamics of binary particle mixing in a gas-solid fluidized bed were measured by using the capacitance probe method. A novel output voltage signal processing method based on a double threshold was proposed. Originally employed for voidage measurement in monodisperse systems, the capacitance probe method has been successfully extended to assess bubble and mixing characteristics in binary systems by establishing bubble and emulsion phase search algorithms. Through direct comparison with digital image analysis (DIA), quantiles of 0.15 and 0.45 were determined as thresholds for identifying emulsion and bubble phase, respectively. Experimental results demonstrated that under steady-state conditions, the volume ratio of jetsam decreased with increasing bed height, while higher gas velocities enhanced longitudinal and transverse mixing. Average bubble size and rising velocity increased with both the probe height and gas velocity. An improved empirical correlation for bubble diameter was proposed based on variations in local bed mixing degree, yielding a relative error below 20%. The average bubble rising velocity exhibited a direct relationship with the bubble diameter. Relative errors of measured velocities obtained through the capacitance probe method, DIA method, and empirical relation all remained below 15% across each operating condition.

关键词： Bubbles Electrical properties Emulsions Mathematical methods Probes

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Adapting Spatial Transformer Networks Across Diverse Hardware Platforms: A Comprehensive Implementation Study 6

Adapting Spatial Transformer Networks Across Diverse Hardwar...

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6th International conference on AI Circuits and systems (AICAS)

作者： Bettayeb, Meriem Hassan, Eman Khan, Muhammad Umair Halawani, Yasmin Saleh, Hani Mohammad, Baker Khalifa Univ Syst On Chip SoC Lab Comp & Commun Engn Dept Abu Dhabi U Arab Emirates Univ Dubai Coll Engn & IT Dubai U Arab Emirates

ISBN: (纸本)9798350383638;9798350383645

The field of artificial intelligence (AI) holds a variety of algorithms designed with the goal of achieving high accuracy at low computational cost and latency. One popular algorithm is the vision transformer (ViT), which excels at various computer vision tasks for its ability to capture long-range dependencies effectively. This paper analyzes a computing paradigm, namely, spatial transformer networks (STN), in terms of accuracy and hardware complexity for image classification tasks. The paper reveals that for 2D applications, such as image recognition and classification, STN is a great backbone for AI algorithms for its efficiency and fast inference time. This framework offers a promising solution for efficient and accurate AI for resource-constrained Internet of Things (IoT) and edge devices. The comparative analysis of STN implementations on the central processing unit (CPU), Raspberry Pi (RPi), and Resistive Random Access Memory (RRAM) architectures reveals nuanced performance variations, providing valuable insights into their respective computational efficiency and energy utilization.

关键词： Spatial Transformer Network image Classification vision transformer raspberry Pi hardware platforms artificial intelligence

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A Dual Branch Segmentation Network for Forward-looking Sonar image

A Dual Branch Segmentation Network for Forward-looking Sonar...

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2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： He, Ju Chen, Jianfeng Xu, Hu School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350316728

Accurate segmentation of marine objects holds crucial significance and value in underwater resource exploration and marine rescue. However, in complex and noisy underwater environments, existing CNN-based algorithms face limitations such as insufficient global feature extraction capabilities and incomplete segmentation of certain targets in the sonar image. To overcome these inevitable problems, this article innovatively introduces a segmentation network based on transformer blocks. The algorithm encompasses a dual-branch structure that involves local and global feature extraction. The main components of the algorithm include four parts: image preprocessing, Local feature extraction branch, Global feature extraction branch and Segmentation head. Finally, we conducted experiments on the public dataset, and the mIoU value of the proposed method surpasses other CNN-based algorithms. The results illustrate the effectiveness and feasibility of the segmentation network. © 2023 IEEE.

关键词： Feature extraction

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A Hardware Architecture for Frequency-Domain image processing Based on Split-Radix 2-4 FFT 39

A Hardware Architecture for Frequency-Domain Image Processin...

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39th conference on Design of Circuits and Integrated systems, DCIS 2024

作者： Tian, Dian Yu, Ningmei Tang, Jiahao Fu, Tao Hernandez, Alvaro Urena, Jesus Xi'An University of Technology School of Automation and Information Engineering Xi'an China Electronics Department Spain

ISBN: (纸本)9798350364392

Fast Fourier Transform (FFT) is a necessary and widely used algorithm in frequency-domain image processing, and the iterative calculation of 2D FFTs and iFFTs (inverse Fast Fourier Transform) is commonly applied. The general sequence of the global calculations often implies reiterative processes of data loading and storing. On the other hand, the detection-in-frequency split-radix 2-4 FFT has advantages in terms of resources and latency, compared to the radix-2 or the radix-4 schemes. In this way, a hardware architecture for frequency-domain image processing is proposed here, where a split-radix 2-4 multi-path delay commutation architecture (MDC) FFT structure with four parallel inputs and outputs is designed to achieve 100 \% resource utilization. In addition, a dynamic ping-pong switching between the FFT and the iFFT is designed to implement a non-stalled stream on calculation by means of a feedback path without reloading data. Furthermore, as for the conjugate symmetry of the transfer function, the processing order of the image is designed to reduce the number of calculations or data loading. Comparing with other architectures, the proposed one requires less buffers, complex multiplications and complex additions, whereas it also provides a low latency and a wide range of sequence lengths. In this way, in a frequency-Time domain processing for an image, only 2 loads and 2 stores are required per iteration because of the dynamic ping-pong switching. © 2024 IEEE.

关键词： Inverse problems

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Comparing the Effectiveness and Performance of image processing algorithms in Face Recognition 25

Comparing the Effectiveness and Performance of Image Process...

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25th International Carpathian Control conference (ICCC)

作者： Rostum, Hussam M. Vasarhelyi, Jozsef Univ Miskolc Inst Automat & Infocommunicat Miskolc Hungary

ISBN: (纸本)9798350350708;9798350350715

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.

关键词： Deep Learning Facial Recognition Convolutional Neural Networks VggFace ArcFace FaceNet Cosine Similarity Euclidean Distance

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