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30th Telecommunications Forum, TELFOR 2022

作者： Malesevic, Branko Banjac, Bojan Sesum-Cavic, Vesna Korolija, Nenad University of Belgrade School of Electrical Engineering Department of Applied mathematics Belgrade Serbia University of Novi Sad Computer graphics chair Faculty of Technical sciences Novi Sad Serbia Digital innovation team Intens d.o.o. Novi Sad Serbia University of Belgrade Faculty of Civil Engineering Department for Geoinformatics Belgrade Serbia University of Belgrade School of Electrical Engineering Department for Computer science and information technology Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

This paper considers one problem of automated proving of MTP inequalities f(x) > 0 over [0, π/2] on boundary points. It suggests one algorithm for symbolic checking for f(0) = 0 or f(π/2) = 0 without use of numerical estimations. With stated approach it gives one contribution to theory and practice in this part of the field of automated proving. © 2022 IEEE.

关键词： Rational functions

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360ORB-SLAM: A Visual SLAM System for Panoramic Images with Depth Completion Network

arXiv

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arXiv 2024年

作者： Chen, Yichen Pan, Yuqi Liu, Ruyu Zhang, Haoyu Zhang, Guodao Sun, Bo Zhang, Jianhua School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Haixi Institutes Chinese Academy of Sciences Quanzhou Institute of Equipment Manufacturing Quanzhou362000 China Department of Digital Media Technology Hangzhou Dianzi University Hangzhou310018 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

To enhance the performance and effect of AR/VR applications and visual assistance and inspection systems, visual simultaneous localization and mapping (vSLAM) is a fundamental task in computer vision and robotics. However, traditional vSLAM systems are limited by the camera’s narrow field-of-view, resulting in challenges such as sparse feature distribution and lack of dense depth information. To overcome these limitations, this paper proposes a 360ORB-SLAM system for panoramic images that combines with a depth completion network. The system extracts feature points from the panoramic image, utilizes a panoramic triangulation module to generate sparse depth information, and employs a depth completion network to obtain a dense panoramic depth map. Experimental results on our novel panoramic dataset constructed based on Carla demonstrate that the proposed method achieves superior scale accuracy compared to existing monocular SLAM methods and effectively addresses the challenges of feature association and scale ambiguity. The integration of the depth completion network enhances system stability and mitigates the impact of dynamic elements on SLAM performance. Copyright © 2024, The Authors. All rights reserved.

关键词： System stability

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Reducing the Complexity of Operational Domain Computation in Silicon Dangling Bond Logic 23

Reducing the Complexity of Operational Domain Computation in...

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Proceedings of the 18th ACM International Symposium on Nanoscale Architectures

作者： Marcel Walter Jan Drewniok Samuel Sze Hang Ng Konrad Walus Robert Wille Chair for Design Automation Technical University of Munich Germany TUM School of Computation Information and Technology Technical University of Munich Germany Department of Electrical and Computer Engineering University of British Columbia Canada Technical University of Munich & SCCH GmbH Germany

ISBN: (纸本)9798400703256

Silicon Dangling Bonds (SiDBs) constitute a beyond-CMOS computational nanotechnology platform that enables higher integration density and lower power consumption than contemporary CMOS nodes. Recent manufacturing breakthroughs in the domain sparked the interest of academia and industry alike in the race for a green computation future at the nanoscale. However, as the fabrication of SiDBs requires atomic precision, SiDB logic systems are inherently susceptible to environmental defects and material variations, which inevitably occur. The Operational Domain is a methodology to evaluate the resilience of SiDB logic against physical parameter variations. However, state-of-the-art implementations require a quadratic number of exponentially complex physical simulator calls to assess the operational domain. This paper presents two novel algorithms to obtain operational domains in an efficient fashion: one based on flood fill, and one based on contour tracing. Experimental evaluations confirm that they reduce the number of required simulator calls by 70.87% and 95.29 %, respectively. Particularly contour tracing achieves the shift from a quadratic to a linear relation, thereby reducing the complexity and paving the way for realizing reliable SiDB-based computing systems.

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Intelligent Strategic Decision-Making for Optimized Project Management

Intelligent Strategic Decision-Making for Optimized Project ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Tariq Samarah Muder Almiani Ala Mughaid Shadi AlZu'bi Amer Al-Rahayfeh Electronic Marketing and Social Media Zarqa University Zarqa Jordan Department of MIS GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University of Science & Technology Kuwait Department of Computer Science Alzaytoonah University of Jordan Amman Jordan Faculty of information technology The World Islamic Sciences and Education University Amman Jordan

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This research explores the application of AI-driven strategies in engineering project management, focusing on optimizing decision-making in areas such as risk assessment, resource distribution, and performance tracking. The study reveals that AI-based predictive analytics enhance risk identification accuracy, allowing for proactive measures that reduce project delays. Resource optimization models effectively streamline allocations, minimizing waste and ensuring consistent resource availability. Furthermore, real-time performance monitoring provides project managers with actionable insights, improving responsiveness to emerging challenges and contributing to reliable projects. While these methods demonstrate significant advantages, the study identifies challenges related to technical expertise and data requirements, which may limit AI implementation. The findings underscore AI's role in supporting complex project decisions, suggesting that further research could expand its applicability and establish ethical guidelines for its use in decision-making frameworks.

关键词： Optimization models Decision making Project management Real-time systems Resource management Risk management Reliability Predictive analytics Monitoring Guidelines

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A method for extracting corneal reflection images from multiple eye images

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computers in Biology and Medicine 2024年 177卷 108631-108631页

作者： Du, Mengqi Zhang, Jiayu Zhi, Yuyi Zhang, Jianhua Liu, Ruyu Zhang, Guodao Wang, Jing College of Computer Science and Technology Zhejiang University of Technology Liuhe 288 Hangzhou310023 China Department of Ophthalmology The Third Affiliated Hospital of Wenzhou Medical University Ruifeng 168 Ruian325200 China Jianxing Honors College Zhejiang University of Technology Liuhe 288 Hangzhou310023 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Institute of Intelligent Media Computing Hangzhou Dianzi University Hangzhou310018 China Department of Ophthalmology Zhongshan Hospital Fudan University Shanghai200032 China

The incident light reflected from the cornea is rich in information about the human surroundings, and these reflected rays are imaged by the camera, which can be used for research on human consciousness and gaze analysis, and produce certain help in the fields of psychology, human computer interaction and disease diagnosis. However, limited by the low corneal reflection ability, when a high-definition camera captures corneal reflecting rays, a large amount of color and texture interference from the iris can seriously contaminate the corneal reflection images, resulting in low usability and ubiquity of corneal reflection images. In this paper, we propose a corneal reflection image extraction method with multiple eye images as input. We align the iris regions of multiple eye images with the help of iris localization method, and by comparing multiple iris regions, we obtain the complementary iris regions, so that the iris interference in the corneal reflection region can be stripped completely. A large number of experiments have demonstrated that our work can effectively mitigate iris interference and effectively improve the quality of corneal reflection images. © 2024 Elsevier Ltd

关键词： Extraction

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Data Science collaboration in Human AI: Decision Optimization using Human-centered Automation

Data Science collaboration in Human AI: Decision Optimizatio...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Sudheer Choudari Ritika Sanwal Neetu Sharma Suryakant Shastri D. Anto Pravin Singh G. Deepa Department of Civil Engineering Centurion University of Technology and Management Viziangaram AP India Department of Media & Mass Communication Graphic Era Hill University Haldwani India Department of Chemistry Graphic Era Deemed to be University Dehradun India Computer Science and Engineering IEC College of Engineering Greater Noida U.P. India Department of MBA SRM Institute of Science and Technology Chennai India Department of Information Technology Dr. M.G.R. Educational and Research Institute Chennai India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Through human-centered automation, the fusion of artificial intelligence (AI) and human expertise has enabled notable progress in decision optimization. This work examines the relationship between data science and human-AI cooperation, with a focus on improving decision-making across a range of fields. AI systems are capable of processing and interpreting enormous volumes of data, finding patterns and insights that may escape human analysts, by utilizing sophisticated machine learning algorithms and data analytics. But to refine these automated procedures and guarantee morally sound, transparent, and contextually appropriate results, human judgment must be incorporated. This paper explores methods for maximizing decision-making through the integration of human-centered and AI-driven automation. It presents case studies of how cooperation between humans and AI has enhanced productivity, accuracy, and adaptability in challenging decision-making situations. It also tackles issues like user trust, bias prevention, and the dynamic interaction between machine precision and human intuition. The outcomes address how critical it is to promote a mutually beneficial partnership between humans and AI, supporting systems that enhance human skills while upholding diversity and responsibility. This study adds to the continuing conversation about the nature of employment in the future by putting forth a well-balanced paradigm in which AI efficiencies and human insights work together to produce the best possible decisions.

关键词： Training Productivity Automation Prevention and mitigation Decision making Buildings Companies Data science Artificial intelligence Optimization

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Efficient Contextformer: Spatio-Channel Window Attention for Fast Context Modeling in Learned Image Compression

arXiv

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arXiv 2023年

作者： Burakhan Koyuncu, A. Jia, Panqi Boev, Atanas Alshina, Elena Steinbach, Eckehard The Technical University of Munich School of Computation Information and Technology Department of Computer Engineering Media Technology Munich80333 Germany The Friedrich-Alexander University Department of Electrical-Electronic-Communication Engineering Multimedia Communications and Signal Processing Erlangen91058 Germany The Huawei Munich Research Center Munich80992 Germany

Entropy estimation is essential for the performance of learned image compression. It has been demonstrated that a transformer-based entropy model is of critical importance for achieving a high compression ratio, however, at the expense of a significant computational effort. In this work, we introduce the Efficient Contextformer (eContextformer) - a computationally efficient transformer-based autoregressive context model for learned image compression. The eContextformer efficiently fuses the patch-wise, checkered, and channel-wise grouping techniques for parallel context modeling, and introduces a shifted window spatio-channel attention mechanism. We explore better training strategies and architectural designs and introduce additional complexity optimizations. During decoding, the proposed optimization techniques dynamically scale the attention span and cache the previous attention computations, drastically reducing the model and runtime complexity. Compared to the non-parallel approach, our proposal has ∼145x lower model complexity and ∼210x faster decoding speed, and achieves higher average bit savings on Kodak, CLIC2020, and Tecnick datasets. Additionally, the low complexity of our context model enables online rate-distortion algorithms, which further improve the compression performance. We achieve up to 17% bitrate savings over the intra coding of Versatile Video Coding (VVC) Test Model (VTM) 16.2 and surpass various learning-based compression models. © 2023, CC BY-NC-ND.

关键词： Image compression

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Diagnosis of COVID-19 from X-rays using combined CNN-RNN architecture with transfer learning

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第4期2卷 100088-100088页

作者： Islam, Md. Milon Islam, Md. Zabirul Asraf, Amanullah Al-Rakhami, Mabrook S. Ding, Weiping Sodhro, Ali Hassan Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Research Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Information Science and Technology Nantong University Nantong226019 China Department of Computer Science Kristianstad University KristianstadSE-29188 Sweden Shenzhen Institutes of Advanced technology Chinese Academy of Sciences Shenzhen China

Combating the COVID-19 pandemic has emerged as one of the most promising issues in global healthcare. Accurate and fast diagnosis of COVID-19 cases is required for the right medical treatment to control this pandemic. Chest radiography imaging techniques are more effective than the reverse-transcription polymerase chain reaction (RT-PCR) method in detecting coronavirus. Due to the limited availability of medical images, transfer learning is better suited to classify patterns in medical images. This paper presents a combined architecture of convolutional neural network (CNN) and recurrent neural network (RNN) to diagnose COVID-19 patients from chest X-rays. The deep transfer techniques used in this experiment are VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2, where CNN is used to extract complex features from samples and classify them using RNN. In our experiments, the VGG19-RNN architecture outperformed all other networks in terms of accuracy. Finally, decision-making regions of images were visualized using gradient-weighted class activation mapping (Grad-CAM). The system achieved promising results compared to other existing systems and might be validated in the future when more samples would be available. The experiment demonstrated a good alternative method to diagnose COVID-19 for medical staff. All the data used during the study are openly available from the Mendeley data repository at https://***/datasets/mxc6vb7svm. For further research, we have made the source code publicly available at https://***/Asraf047/COVID19-CNN-RNN. © 2023 The Authors

关键词： COVID-19

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine

Development of a Model for Discriminating Electroencephalogr...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Akifumi Tanabe Kyosuke Nakatani Kento Harachi Yusuke Yamamoto Ayumi Muramatsu Hajime Nagahara Noriko Takemura Shinji Shimojo Daisuke Furihata Yuko Mizuno-Matsumoto Kazue Kamae-Shigenobu Kazuhiro Shinosaki Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Department of Medical Engineering Aino University Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Software and Information Technology Aomori University Aomori Japan Cyber media center Osaka University Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients.

关键词： Support vector machines Electrodes Analytical models Accuracy Machine learning Brain modeling Electroencephalography Vectors Spectral analysis

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Lab Color Model and Contrast Modification as a Tool for Improving the Quality of Medical Images

Lab Color Model and Contrast Modification as a Tool for Impr...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Amer Abu-Jassar Mohammad Hamdan Kawther Hamash Sardor Boboyorov Vyacheslav Lyashenko Department of Computer Science College of Information Technology Amman Arab University Amman Jordan College of software engineering Amman Arab University Amman Jordan WellStar school of nursing Kennesaw state university Georgia USA Tashkent Medical Academy Termiz branch Termiz Uzbekistan Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper addresses the challenge of enhancing the quality of medical data interpretation, specifically using color microscopic medical images for effective visualization and further analysis. The method employed involves increasing contrast by adjusting the brightness intensity values within the Lab color model, which also considers the reference white point for horizontal light. To determine the parameters for the contrasting procedure, two strategies for selecting gamma correction of the input image are explored, based on the dynamics of the resulting image quality for various no-reference quality metrics. By analyzing these dynamics, the choice of correction conditions for the contrasting procedure can be justified, rather than relying on static metrics. The paper investigates the application of these proposed strategies on different types of images, presents a formal generalization of the strategies for selecting correction parameters, and offers practical recommendations for their use.

关键词： Measurement Image quality Visualization Image color analysis Microscopy Computational modeling Data visualization Quality assessment Computational intelligence Biomedical imaging

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