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arXiv 2020年

作者： Wahab, Abdul Khan, Shujaat Khan, Farrukh Zeeshan Department of Mathematics School of Sciences and Humanities Nazarbayev University 53 Kabanbay Batyr Avenue Nur-Sultan010000 Kazakhstan Bio-Imaging Signal Processing and Learning Lab. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Computer Science University of Engineering and Technology Taxila Taxila47080 Pakistan

The purpose of this article is to discuss some aspects of the convergence analysis performed in the paper [Design of momentum fractional LMS for Hammerstein nonlinear system identification with application to electrically stimulated muscle model, Eur. Phys. J. Plus (2019) 134: 407]. It is highlighted that the way the authors prove convergence suffers a lack of correct and valid mathematical justifications. Copyright © 2020, The Authors. All rights reserved.

关键词： Momentum

Feedback alfa-rooting algorithm for medical image enhancement 17

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Feedback alfa-rooting algorithm for medical image enhancemen...

17th Image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods. © 2019, Society for Imaging Science and technology.

关键词： Clinical research

Multiple reconstruction compression framework based on PNG image

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arXiv 2019年

作者： Lu, Zhiqing Yin, Zhaoxia Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

It is shown that neural networks (NNs) achieve excellent performances in image compression and reconstruction. However, there are still many shortcomings in the practical application, which eventually lead to the loss of neural network image processing ability. Based on this, this paper proposes a joint framework based on neural network and zoom compression. The framework first encodes the incoming PNG or JPEG image information, and then the image is converted into binary input decoder to reconstruct the intermediate state image, next we import the intermediate state image into the zooming compressor and re-pressurize it, and reconstruct the final image. From the experimental results, this method can better process the digital image and suppress the reverse expansion problem, and the compression effect can be improved by 4 to 10 times as much as that of using RNN alone, showing better ability in application. In this paper, the method is transmitted over a digital image, the effect is far better than the existing compression method alone, the Human visual system can not feel the change of the effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Image compression

The quaternion-based anisotropic gradient for the color images 17

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The quaternion-based anisotropic gradient for the color imag...

17th Image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

Image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and technology.

关键词： Polynomial approximation

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

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arXiv 2020年

作者： Oliari, Vinícius Goossens, Sebastiaan Häger, Christian Liga, Gabriele Bütler, Rick M. van den Hout, Menno van der Heide, Sjoerd Pfister, Henry D. Okonkwo, Chigo Alvarado, Alex Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering Duke University Durham United States High Capacity Optical Transmission Lab Electro-Optical Communication Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch". One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance–complexity tradeoffs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work. Copyright © 2020, The Authors. All rights reserved.

关键词： Light transmission

Focal-SAM: Focal Sharpness-Aware Minimization for Long-Tailed Classification

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arXiv 2025年

作者： Li, Sicong Xu, Qianqian Yang, Zhiyong Wang, Zitai Zhang, Linchao Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China China Electronics Technology Group Corporation China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China BDKM University of Chinese Academy of Sciences China

Real-world datasets often follow a long-tailed distribution, making generalization to tail classes difficult. Recent methods resorted to long-tail variants of Sharpness-Aware Minimization (SAM), such as ImbSAM and CC-SAM, to improve generalization by flattening the loss landscape. However, these attempts face a trade-off between computational efficiency and control over the loss landscape. On the one hand, ImbSAM is efficient but offers only coarse control as it excludes head classes from the SAM process. On the other hand, CC-SAM provides fine-grained control through class-dependent perturbations but at the cost of efficiency due to multiple backpropagations. Seeing this dilemma, we introduce Focal-SAM, which assigns different penalties to class-wise sharpness, achieving fine-grained control without extra backpropagations, thus maintaining efficiency. Furthermore, we theoretically analyze Focal-SAM’s generalization ability and derive a sharper generalization bound. Extensive experiments on both traditional and foundation models validate the effectiveness of Focal-SAM. © 2025, CC BY.

关键词： Economic and social effects

Difficulty adjustable and scalab.e constrained multiobjective test problem toolkit

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Evolutionary Computation 2019年第3期28卷 339-378页

作者： Fan, Zhun Li, Wenji Cai, Xinye Li, Hui Wei, Caimin Zhang, Qingfu Deb, Kalyanmoy Goodman, Erik Department of Electronic Engineering Shantou University Guangdong515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China School of Mathematics and Statistics Xi’an Jiaotong University Shan’xi710049 China Department of Computer Science City University of Hong Kong Hong Kong BEACON Center for the Study of Evolution in Action Michigan State University East LansingMI United States

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, butmost of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalab.e CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalab.e CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrainedmany-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving conv

关键词： Hardness

Using Artificial Intelligence to Predict the Next Deceptive Movement Based on Video Sequence Analysis: A Case Study on a Professional Cricket Player's Movements

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Journal of Engineering Research

Journal of Engineering Research 2025年

作者： A.M. Mutawa Korupalli V Rajesh Kumar K Hemachandran M. Murugappan Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE- Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Chennai Tamilnadu India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis 02600 Perlis Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70% accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports.

关键词： Artificial Intelligence Deceptive Movements Machine Learning Cricket Performance Optimization

Automatic optic disc segmentation using a 3D Morphologic Fuzzy Histon Roughness Index

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Research on Biomedical Engineering 2019年第2期35卷 89-102页

作者： Cardoso, Cristiane de Fátima dos Santos Carrijo, Gilberto Arantes Ferreira, Júlio César dos Santos, Jucelino Cardoso Marciano Signal Processing Lab Faculty of Electrical Engineering Federal University of Uberlândia Campus Sta Mônica Av. João Naves de Ávila 2121 Bloco 1N UberlândiaMG38400-000 Brazil Computer Vision Lab Informatics Nucleo Goiano Federal Institute of Education Science and Technology Campus Urutaí Rodovia Geraldo Silva Nascimento km 2.5 Zona Rural Bloco de Informática UrutaíGO75790-000 Brazil

Purpose: Fundus images have become increasingly important due to the growing number of diabetics and their risk of developing diabetic retinopathy. Automated diagnosis of retinal images requires the separation and analysis of retinal elements. Among these elements, the optic nerve head is the most significant since it is used as a reference for detecting other anatomical structures such as the fovea and blood vessels. For this reason, optic disc segmentation is essential. Methods: This paper presents a method for locating and segmenting the optic disc using the circular Hough transform and the roughness index, respectively. We used a location algorithm based on the multiscale energy filter for fast location and the medial Hough transform as the decision criterion. Segmentation was performed with Atanassov’s Intuitionistic Fuzzy Set representation and an algorithm using the 3D roughness index and mathematical morphology to smoothly segmenting. Results: This approach to automatic segmentation was tested on the MESSIDOR database, achieving 99.59% accuracy and 91.56% sensitivity. Other types of roughness indices were also evaluated;however, color correlation within the color space of the object itself produced better results for the proposed algorithm. Conclusion: We hope these findings contribute to the development of reliable automatic diagnostic tools for retinal images. © 2019, Sociedade Brasileira de Engenharia Biomedica.

关键词： Mathematical morphology

A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps 2

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A novel segmentation framework for uveal melanoma in magneti...

2nd International Conference on Medical Imaging with Deep Learning, MIDL 2019

作者： Nguyen, Huu-Giao Pica, Alessia Hrbacek, Jan Weber, Damien C. La Rosa, Francesco Schalenbourg, Ann Sznitman, Raphael Cuadra, Meritxell Bach Proton Therapy Center Paul Scherrer Institut ETH Domain Villigen Switzerland Ophthalmic Technology Lab. ARTORG Center University of Bern Switzerland Switzerland Radiation Oncology Department Inselspital University of Bern Switzerland Signal Processing Lab. Ecole Polytechnique Fédérale de Lausanne Switzerland Adult Ocular Oncology Unit Jules-Gonin Eye Hospital Lausanne Switzerland Medical Image Analysis Laboratory CIBM University of Lausanne Switzerland

An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocula cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual lab.ls are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning. © 2019 H.-G. Nguyen, A. Pica, J. Hrbacek, D. Weber, F.L. Rosa, A. Schalenbourg, R. Sznitman & M. Bach Cuadra.

关键词： Magnetic resonance imaging