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D2-MLP: Dynamic Decomposed MLP Mixer for Medical image Segmentation

D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segme...

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

作者： Yang, Jin Yang, Jing Yu, Xiaobing Qiu, Peijie Prajapat, Sunil Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Department of Computer System and Technology University of Malaya Kuala Lumpur Malaysia Central University of Himachal Pradesh Dharamshala India

ISBN: (纸本)9798350368741

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it uses novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods. © 2025 ieee.

关键词： Channel and Spatial Features Dynamic Networks Medical image Segmentation MLP Mixer

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Asteroid image Inpainting Method based on Accelerated Diffusion Model 9

Asteroid Image Inpainting Method Based on Accelerated Diffus...

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9th international conference on Intelligent Computing and signal Processing, ICSP 2024

作者： Zheng, Yunyun Huang, Xiangyu Beijing Institute of Control Engineering Science and Technology Space Intelligence Control Laboratory Beijing China

ISBN: (纸本)9798350376548

To mitigate the threat of asteroid impact on life on Earth, non-nuclear kinetic energy high-speed impact has been identified as the most viable strategy. To ensure accurate impact on the asteroid's center of mass with maximum kinetic energy, high-precision and high-speed autonomous optical navigation is necessary during the probe's approach to the asteroid. Autonomous optical navigation is crucial due to the difficulty of using historical tracking data such as astrometry on the ground, given the long distance between the asteroid and the probe. The navigation image is used to extract the centroid of the target asteroid, with the center of gravity of the asteroid profile serving as the impact point to provide a stable impact direction. It is important to note that asteroids have irregular shapes, fast spin, and rapidly changing bright regions. The shadow area of the asteroid is constantly changing due to illumination, which causes bias in the impact point due to the incomplete profile. To complete the contour of the target asteroid, a method based on the accelerated diffusion model (ADM) is proposed to inpainting the shadow area. ADM is fast enough for mission and has been found to have the highest accuracy index and the lowest centroid deviation after restoration when compared to several advanced image restoration methods. © 2024 ieee.

关键词： Restoration

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image classification method of bearing fault based on BOA optimization ResNet-18

Image classification method of bearing fault based on BOA op...

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2023 ieee international conference on Sensors, Electronics and Computer Engineering, ICSECE 2023

作者： Jiang, Shuai Hua, Chuanqi Yuan, Muye Hebei University of Technology College of Mechanical Engineering Tianjin China Shenyang Ligong University College of Mechanical Engineering Shenyang China Xi'An Aeronautical University College of Mechanical Engineering Xi'an China

ISBN: (纸本)9798350313734

Aiming at some inefficient and difficult classification problems caused by the difficulty of determining parameters in data-driven bearing fault image classification, a bearing fault image classification method based on BOA ResNet-18 is proposed. Firstly, the original vibration signal data of the bearing is added to the noise and converted into an image method by signal processing such as two-dimensional wavelet transform for preprocessing, and the corresponding target data set is collected. Then, the ResNet-18 network model that has optimized the corresponding parameters by the BOA group optimization algorithm is trained and fine-tuned on the generated target dataset. The final adjusted ResNet-18 model is applied to the fault image classification of bearings. The method was verified on the bearing dataset of Case Western Reserve University, and the result was that the surface method could obtain an average accuracy of 99.5% of the bearing fault image classification, which has certain engineering application prospects and is of great significance for solving the bearing fault image classification. © 2023 ieee.

关键词： image classification

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Comparative Study on image Classification Models of Ancient Ceramic Types 6

Comparative Study on Image Classification Models of Ancient ...

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2023 ieee 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Yue, Xiaorui Chen, Jihua Zhuang, Zhehao Hubei Urban Construction Vocational and Technological College Wuhan China South China University of Technology Guangzhou China

ISBN: (纸本)9798350313444

One of the main problems faced by image big data is how to efficiently automate image classification. The current mainstream approach is to use image recognition models for training. Although there are many different image recognition models applicable to the field of image classification at present, there is a lack of comparative research on multiple neural network models for specific usage scenarios. This article focuses on the recognition and classification of ancient pottery and porcelain types, and compares and studies three mainstream image recognition models in the industry. Multiple batches of dual parameter optimization training schemes are used to obtain multiple sets of comparable training results. The test data shows that the accuracy of the three image recognition models (Inception, ResNet, MobileNet) is up to 90%, and they can effectively recognize ceramic types. Under the same hardware conditions, the MobileNet model has the fastest recognition speed, with an average of 21.35ms per image, and its computational power requirement (FLOPS) is only 0.56G, far lower than the other two recognition models, making it suitable for mobile internet application scenarios such as mobile phone image recognition. © 2023 ieee.

关键词： ceramic shape image classification model optimization

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Improving image Fidelity using Skip Connections Autoencoder 16

Improving Image Fidelity using Skip Connections Autoencoder

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16th international conference on Information technology and Electrical Engineering, ICITEE 2024

作者： Rumaksari, Atyanta Nika Hidayat, Risanuri Hartanto, Rudy Universitas Gadjah Mada Dept. of Electrical Engineering and Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350375817

This study presents an advanced autoencoder model with skip connections designed to enhance the image fidelity quality. Traditional methods often face challenges in preserving high-resolution details and textures, particularly in tasks involving noise reduction, super-resolution, and low-light enhancement. Our model leverages skip connections to maintain essential spatial information across different layers, effectively addressing the vanishing gradient problem and improving gradient propagation during training. We compare our model against several state-of-the-art methods, including the Overall Improved Autoencoder (OIAE), Cascade Decoders-based Autoencoders, and Edge-Aware Autoencoder Design, evaluating performance based on Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Our autoencoder consistently achieves superior results, with MSE of 5.889e-05, PSNR consistently above 40 dB, and SSIM close to 1. These findings demonstrate the significant advantages of incorporating skip connections, which allow for better retention of high-resolution features and more accurate image reconstructions. © 2024 ieee.

关键词： image reconstruction

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Simulation of Stator Current signal Fault Characteristics of Induction Motor based on ANSYS 25

Simulation of Stator Current Signal Fault Characteristics of...

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25th international conference on Advanced Communication technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Rahman, Atiqur Badhon, Azruph Islam USTB Sch Mech Engn Beijing Peoples R China Chittagong Dry Dock Ltd Chittagong Bangladesh

ISBN: (纸本)9791188428106

The diagnosis of induction motor problems is a crucial operation in industrial settings. Among the different problems that can occur in induction motors (IM), gear system faults account for almost 60 percent of all faults. At this paper, the detection of stator and rotor failures utilizing electrical signals from IM under various structural factors. The evaluation results indicate that both stator and rotor faults enhance the amplitude of the sidebands of the motor current signature in a Maxwell finite element 2D design. The Finite Element Method is used to examine the electromagnetic field of an induction motor with a faulty gear system that is fed by both the mains and a three-phase voltage source (FEM). Analyses are conducted on electromagnetic field parameters such as voltage distribution, load torque distribution, and coil pitch.

关键词： Finite Element Induction Motor Electromagnetic Field ANSYS Load Torque Coil Pitch Gear System signal Frequency Fault Diagnosis

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A PROBABILITY-based ALL-ZERO BLOCK EARLY TERMINATION ALGORITHM FOR QSHVC 30

A PROBABILITY-BASED ALL-ZERO BLOCK EARLY TERMINATION ALGORIT...

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30th ieee international conference on image Processing (ICIP)

作者： Wang, Dayong Lu, Xin Dufaux, Frederic Wang, Qianmin Li, Weisheng Hang, Bo Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Paris France Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Hubei Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

ISBN: (纸本)9781728198354

To seamlessly adapt to time-varying network bandwidths, Quality Scalable High-Efficiency Video Coding (QSHVC) is developed. However, its coding process is overwhelmingly complex, and this seriously limits its wide applications in realtime environments. Therefore, it is of great significance to study fast coding algorithms for QSHVC. In this paper, we propose a novel probability-based All-Zero Block (AZB) early termination algorithm for QSHVC. We observe that the generated residual coefficients follow the Laplace distribution if a CU is accurately predicted. based on this observation, we derive the sum of squared differences-based AZB decision condition. Second, the probability of each coding mode and coding depth being chosen as the best ones are combined with AZBs to derive the probability-based early termination condition. The experimental results show that the proposed algorithm can improve the average coding speed by 74.95% with a 0.26% increase in BDBR.

关键词： SHVC probability AII-Zero Block

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Fluorescence Feature image: Fluorescence Enhancement Method based on Illumination Adaptation

Fluorescence Feature Image: Fluorescence Enhancement Method ...

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2024 ieee international conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Shi, Lemin Feng, Xin Song, Dianxin Gong, Ping Yue, Ming Si, Yuan Changchun University of Science and Technology School of Computer Science and Technology Changchun China Changchun University of Science and Technology School of Life Science and Technology Changchun China Changchun University of Science and Technology College of Physics Changchun China

ISBN: (纸本)9798350362107

In recent years, with the continuous development of computer technology in the field of molecular diagnosis, deep learning has shown great potential in enhancing fluorescence features in fluorescence in situ hybridization (FISH) images, allowing researchers to observe fluorescence distribution and abnormalities more clearly. Condition. of genetic material. Health status. Improves observation clarity and overall image quality of genetic material. However, due to problems such as uneven dyeing and lighting differences, the model training and generalization capabilities of deep learning are limited, thus affecting the feature recognition and accuracy of the algorithm. This method is approached from the perspective of image enhancement and feature recognition. By dynamically adjusting the lighting factors of the image to match the actual lighting distribution, this adjustment helps reduce the impact of lighting changes and makes the brightness of the image more uniform. At the same time, this method uses heat maps to quickly locate high-intensity signal areas in the image, that is, the parts with the most obvious fluorescence signals. By calculating 16 environments around these areas. Combined with the linear segmentation strategy and dynamically adjusting processing parameters, it not only highlights and enhances the clarity of image details, but also obtains accurate enhanced images. To avoid excessive enhancement of fluorescence characteristics, the fluorescence characteristics are compensated through the Cycle-Gan network to obtain the final compensation result. Finally, the effect of this method in image enhancement was verified through experimental analysis. © 2024 ieee.

关键词： III-V semiconductors

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Deployment of Proposed EfficientNeXt on NXP *** 8M Mini 6

Deployment of Proposed EfficientNeXt on NXP *** 8M Mini

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6th international conference on Information and Computer Technologies, ICICT 2023

作者： Deokar, Abhishek El-Sharkawy, Mohamed Purdue School of Engineering and Technology Indianapolis Department of Electrical and Computer Engineering Indianapolis United States

ISBN: (纸本)9798350300956

Convolutional Neural Networks make tasks of computer vision like image classification and object tracking possible. The advances in accelerator hardware have made the progress in neural networks possible. Accelerator hardware is prevalent on desktops and high-end computing systems and may not always be available on low compute devices deployed on the Edge of internet of Things. The capabilities of neural network need to be ported to hardware that can run without accelerators. Benchmark setting neural networks like EfficientNet are too heavy for deployment on systems with low compute capabilities and can benefit from reduction in their memory footprint and optimized to improve their inference times. To this end we propose the design of EfficientNeXt and demonstrate its inference capabilities with reduced memory footprint (by 56%), increased accuracy and reduced inference time (by 30%) on an ARM based device. © 2023 ieee.

关键词： internet of things

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CabiNet: A Deep Learning Framework for Multiclass Medical image Segmentation from Multiple Single Class Datasets

CabiNet: A Deep Learning Framework for Multiclass Medical Im...

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

作者： Soni, Aman Maiti, Ishita Ghosh, Nirmalya Electrical Engineering IIT Kharagpur Kharagpur India School of Medical Science and Technology IIT Kharagpur Kharagpur India

ISBN: (纸本)9798350368741

The development of deep learning based image segmentation algorithms is often hindered by the lack of adequately annotated datasets, and this issue becomes a severe bottleneck in multi-class segmentation. The models can learn binary image segmentation since the datasets are often labeled for a single class. However, the task becomes more challenging for multi-class annotations and segmentation of regions of interest, e.g., multi-organ segmentation that plays a vital role in computer-assisted diagnosis, surgery planning, and other related applications. This study proposes a framework, CabiNet, that attempts to solve data bottleneck by learning multi-class healthy organ segmentation from partially annotated abdominal MRI data, i.e., multiple single-class datasets. Multiple organ specific expert networks and a Jack of All (JoA) network are trained to generate posterior probability maps of individual organs simultaneously which are accumulated to get the final posterior probability distributions over different organs for each pixel. On benchmark Combined Healthy Abdominal Organ Segmentation (CHAOS) MRI dataset CabiNet yielded very promising dice scores: liver (90.39%), right kidney (87.41%), left kidney (81.09%), spleen (90.78%) and overall average dice score as 87.4%. © 2025 ieee.

关键词： Computer Assisted Diagnosis Distributed learning Multi-Organ Segmentation Sparse annotation

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