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17th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2024

作者： Sahare, Natasha Fuentealba, Patricio Salvi, Rutuja Burmann, Anja Henze, Jasmin Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany Technical University of Dortmund Dortmund Germany Instituto de Electricidad y Electrónica Facultad de Ciencias de la Ingeniería Universidad Austral de Chile Valdivia Chile IDTM GmbH Recklinghausen Germany

Biometric identification allows to secure sensitive information. Since existing biometric traits, such as finger prings, voice, etc. are associated with different limitations, we exemplified the potential of blood flow sounds for biometric authentication in previous work. Therefore, we used measurements from seven different users acquired with a custom-built auscultation device to calculate the spectrograms of these signals for each cardiac cycle using continuous wavelet transform (CWT). The resulting spectral images were then used for training of a convolutional neural network (CNN). In this work, we repeated the same experiment with data from twelve users by adding more data from the original seven users and data from five more users. This lead to an imbalanced dataset, where the amount of available data for the new users was much smaller, e.g., U1 had more than 900 samples per side whereas the new user U9 had less than 100 samples per side. We experienced a lower performance for the new users, i.e. their sensitivity was 18-21% lower than the overall accuracy. Thus, we examined whether the augmentation of data leads to better results. This analysis was performed using generative adversarial networks (GANs). The newly generated data was then used for training of a CNN with several different settings, revealing the potential of GAN-based data augmentation for increasing the accuracy of biometric authentication using blood flow sounds. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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Concept About Shared Digital Twin of EV Batteries to Improve the Data Exchange in the Context of Battery Transport

Concept About Shared Digital Twin of EV Batteries to Improve...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Max Plotnikov Arkadius Schier Software & Information Engineering Fraunhofer Institute for Material Flow & Logistics Dortmund Germany

The present work is based on a concept to improve the information exchange during off-site transport of batteries in the context of electromobility with different actors of the value chain using descriptive research design. The challenges regarding the exchange of information with corresponding actors such as the responsible authorities or safety institutions form the main research subject of this thesis. A measure of the European Commission for the electronic provision of transport information combined with the Shared Digital Twin of a battery by means of Battery Passport makes it possible to improve the error-prone current process, especially in the B2A sector. Hereby, synergy effects arise for different actors in terms of data availability, - integrity and - sovereignty through cloud-based provision of the transport information of batteries.

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Deep neural network applications on pose estimation and action recognition for precision dairy farming 10

Deep neural network applications on pose estimation and acti...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Rejas-Haddioui, D. Purcell, W. Neubauer, T. Information and Software Engineering Group Institute of Information Systems Engineering Faculty of Informatics TU Wien Favoritenstrasse 9-11/194 Vienna1040 Austria

ISBN: (纸本)9788396536006

Recent advances in Computer Vision (CV) have yielded great improvements in tasks such as Pose Estimation (PE) and Activity Recognition (AR) and their application to the field of Precision Livestock Farming (PLF) have the potential to enable truly non-intrusive animal monitoring. These systems can help detect health issues by providing detailed behaviour analysis and automatically detect problematic conditions like lameness. Past solutions rely on RFID chips and inertial measurement unit (IMU) to identify and classify cattle behaviour, while existing research of CV solutions for animal PE or AR often focus on scenes with single animals and in very clear conditions. Our work aims to study the capabilities and limitations of Computer Vision systems applied under industrial conditions. We train a Deep Neural Network (DNN) system that can predict the pose of each animal in the image, and to also predict the activity they are performing. Furthermore, we explore the deployment capabilities of these systems in industrial settings by studying the effect of neural network pruning in the inference accuracy and cost of the system in an effort to help future solutions be light-weight and with affordable hardware requirements. © ECPLF 2022. All rights reserved.

关键词： Computer vision

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security 5

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Dilshan, K.N. Rathnayake, H.A.C.K. Pathirana, L.P.T.L. Dhanapala, D.H.S.V. Fernando, Harinda Abeygunawardhana, Pradeep K.W Sri Lanka Institute of Information Technology Faculty of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

关键词： Blockchain

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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Establishing Pattern Sequences Using Artificial Neural Networks with an Application to Organizational Patterns 10

Establishing Pattern Sequences Using Artificial Neural Netwo...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Matovič, Viktor Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava84216 4 Slovakia

Explicit relationships included in textual description of patterns are insufficient to establish pattern sequences. This article brings a new method of establishing pattern sequences using artificial neural networks. The method is based on extracting implicit relationships between organizational patterns using the softmax regression model by Zhang et al., which are learned by the artificial neural network and encoded in its weights. The implementation of the method is based on the mini-batch stochastic gradient descent method by Zhang et al. and the Keras application programming interface of the TensorFlow framework. To evaluate the method, we conducted four experiments on selected organizational patterns documented by Coplien and Harrison. Two pattern sequences were established in each experiment. For each pattern sequence, a pattern story was created in order to check its meaningfulness. © 2023 Copyright for this paper by its authors.

关键词： artificial neural networks organizational pattern pattern relationships pattern sequence softmax regression

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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