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13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023

作者： Kastner, Steffen Ebner, Markus Bullmann, Markus Fetzer, Toni Deinzer, Frank Grzegorzek, Marcin Technical University of Applied Sciences Würzburg-Schweinfurt Center for Artificial Intelligence and Robotics Würzburg Germany Technical University of Applied Sciences Würzburg-Schweinfurt Faculty of Computer Science Würzburg Germany University of Lübeck Institute of Medical Informatics Lübeck Germany

ISBN: (纸本)9798350320114

This work presents SIMUL, a new dataset consisting of 550 minutes of annotated motion data from six synchronized IMUs placed consistently at the same strategically chosen positions on the bodies of 32 participants. With a focus on indoor localization, the selection fell on hand, feet, and trouser pockets. Due to the sensor's synchronization, this selection allows, for example, to label the data recorded in the hand based on the data captured at the feet. For a better generalizability, the dataset was recorded freestyle under many different environmental conditions and walking speeds. Thus, indoors, numerous floor coverings such as stone, wood, carpet and PVC, as well as stairs are included in the data. The outdoor recordings additionally contain uneven surfaces such as paving stones and slopes. The annotation of the participant's currently performed activity additionally allows this dataset to be used for activity recognition. © 2023 IEEE.

关键词： Activity Recognition Dataset IMU PDR Stairs Step Counting Step Identification Walking

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Empowering Industry 4.0 with Generative and Model-Driven SDK Development

Empowering Industry 4.0 with Generative and Model-Driven SDK...

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Annual Conference of Industrial Electronics Society

作者： Nico Braunisch Marko Ristin-Kaufmann Robert Lehmann Martin Wollschlaeger Hans Wernher van de Venn TU Dresden Institute of Applied Computer Science Zurich University of Applied Sciences (ZHAW) Institute of Mechatronic Systems (IMS)

Industry 4.0 developers need high-quality libraries in their preferred programming languages to efficiently and ergonomically create, edit and exchange digital twins as Asset Administration Shell (AAS). Existing specifications of the AAS can not be directly translated into programming code. This leads to many, often manual, re-implementations of the AAS in different projects and programming languages. We present an approach for automatic generation of software libraries based on the formalized AAS meta-model. This shows that the development with our approach is 5× faster for a new language and 10× faster for a new version of the AAS meta-model, respectively.

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The Semiconfined Harmonic Oscillator with a Position-Dependent Effective Mass: Exact Solution, Dynamical Symmetry Algebra and Quasiprobability Distribution Functions

The Semiconfined Harmonic Oscillator with a Position-Depen...

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International Workshop on Lie Theory and Its Applications in Physics, LT 2023

作者： Jafarov, E.I. Nagiyev, S.M. Van der Jeugt, J. Institute of Physics Ministry of Science and Education Javid av. 131 BakuAZ1143 Azerbaijan Department of Applied Mathematics Computer Science and Statistics Faculty of Sciences Ghent University Krijgslaan 281-S9 Gent9000 Belgium

ISBN: (纸本)9789819764525

We present some details of the new model of the quantum harmonic oscillator that is semiconfined due to a specific change of its mass by position. We succeeded in solving exactly the Schrödinger equation corresponding to this model. Moreover, we found its dynamical symmetry algebra and could compute analytically its Kirkwood, Husimi and Wigner joint quasiprobability functions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Schrodinger equation

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Machine Learning Techniques for Pose Based Human Activity Recognition 3

Machine Learning Techniques for Pose Based Human Activity Re...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Koranga, Ravindra Singh Rai, Arun Kumar Mohan, Lalit Singh, Shubham Priya, Pritika School of Computer Science and Engineering Galgotias University Greater Noida India Birla Institute of Applied Sciences Bhimtal India

ISBN: (纸本)9798331519582

Activity recognition is the task of identifying human actions and physical activities. It can have applications in broad areas including medical, sports and fitness, healthcare, behaviour analysis and security. Activity recognition is a widely sought after area of research in both Machine Learning (ML) and smart devices. The activity recognition plays an important role in the field of machine learning. There are several machine learning techniques which can be applied for pose based activity recognition. These techniques involve training machine learning models for the dataset. The dataset is obtained by using OpenPose framework. This framework provides keypoints from an input image. These keypoints represent the raw data determining the pose of a human. The machine learning model can predict the pose after training on the dataset. models © 2025 IEEE.

关键词： Active learning

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Matrix Structure of Classical Z2×Z2 Graded Lie Algebras

Matrix Structure of Classical Z2×Z2 Graded Lie Algebras

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International Workshop on Lie Theory and Its Applications in Physics, LT 2023

作者： Stoilova, N.I. Van der Jeugt, J. Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Boul. Tsarigradsko Chaussee 72 Sofia1784 Bulgaria Department of Applied Mathematics Computer Science and Statistics Ghent University Krijgslaan 281-S9 Gent9000 Belgium

ISBN: (纸本)9789819764525

A Z2×Z2-graded Lie algebra g is a Z2×Z2-graded algebra g with a bracket [[·,·]] that satisfies certain graded versions of the symmetry and Jacobi identity. In particular, despite the common terminology, g is not a Lie algebra. We construct classes of Z2×Z2-graded Lie algebras corresponding to the classical Lie algebras, in terms of their defining matrices. For the Z2×Z2-graded Lie algebra of type A, the construction coincides with the previously known class. For the Z2×Z2-graded Lie algebra of type B, C and D our construction is new and gives rise to interesting defining matrices closely related to the classical ones but undoubtedly different. We also give some examples and possible applications to parastatistics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Matrix algebra

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Contextual Decision-Making with Knapsacks Beyond the Worst Case 38

Contextual Decision-Making with Knapsacks Beyond the Worst C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhaohua Ai, Rui Yang, Mingwei Pan, Yuqi Wang, Chang Deng, Xiaotie School of Computer Science Peking University Haidian Beijing China IDSS LIDS Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Management Science and Engineering Stanford University StanfordCA94305 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States School of Computer Science Institute for Artificial Intelligence Peking University Haidian Beijing China

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an Oe(√T) worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an Ω(√T) distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an Oe(1) regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal Oe(√T) regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed. © 2024 Neural information processing systems foundation. All rights reserved.

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Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

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computers, Materials & Continua 2022年第12期73卷 5543-5557页

作者： Thavavel Vaiyapuri S.Srinivasan Mohamed Yacin Sikkandar T.S.Balaji Seifedine Kadry Maytham N.Meqdad Yunyoung Nam College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering College of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India Department of Applied Data Science Noroff University CollegeKristiansand4608Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillahBabil51001Iraq Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

In past decades,retinal diseases have become more common and affect people of all age grounds over the *** examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated *** analyze the retinal malady,the system proposes a multiclass and multi-label arrangement ***,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive ***,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)*** this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus *** proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological ***,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly *** addition,SqueezeNet based feature extractor is employed to generate a collection of feature ***,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal *** efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different *** experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.

关键词： Multi-retinal disease computer aided diagnosis fundus images deep learning segmentation intelligent models

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Comparative Study of Image to Image Translation Models for Underwater Image Enhancement 4

Comparative Study of Image to Image Translation Models for U...

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4th International Conference on Robotics and Automation in Industry, ICRAI 2021

作者： Siddiqua, Maria Akhter, Naeem Khurshid, Javaid Pakistan Institute of Engineering And Applied Sciences Department of Computer And Information Sciences Islamabad Pakistan

ISBN: (纸本)9781665423434

Understanding the underwater environment is important for navigation, surveillance, and exploration. The aim is to restore underwater images degraded by light absorption and scattering in water medium resulting in poor visibility, low brightness, and low contrast of the scene. In this paper, we introduce a network based on the image to image translation that is Contextual Conditional Generative Adversarial Network (CCGAN) and compare its performance with the available image to image translation models namely, Unsupervised Image Translation (UNIT), cycleGAN, Contrastive Unpaired Translation (CUT) and fastCUT qualitatively and quantitatively. Both supervised and unsupervised models are analyzed and we show that the proposed model outrun the alternatives. © 2021 IEEE.

关键词： Generative adversarial networks

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Cross-Correlation-Based Time-Aligned Interpolation of Head-Related Impulse Responses

Cross-Correlation-Based Time-Aligned Interpolation of Head-R...

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International Conference on Spatial and Immersive Audio 2023

作者： Pörschmann, Christoph Lübeck, Tim Arend, Johannes M. Institute of Computer and Communication Technology TH Köln - University of Applied Sciences CologneD-50679 Germany Audio Communication Group Technische Universität Berlin BerlinD-10587 Germany

ISBN: (纸本)9781713884620

In a previous publication [1] we introduced a method for Spherical Array Interpolation by Time Alignment (SARITA). The basic idea of this method is to spatially upsample the input signals of spherical microphone arrays by adding virtual microphone signals, which are interpolated by an appropriate weighting of the time and amplitude differences of the signals of the neighboring microphones. In this study, we apply the method to head-related impulse responses (HRIRs) and combine it with other state-of-the-art pre- and postprocessing methods. For technical evaluation, we compare the performance of the SARITA method with other HRIR interpolation methods. The results show that the SARITA method performs similar to current state-of-the-art methods. Further advantages, such as higher robustness of SARITA to noise and measurement artifacts compared to the other methods, are briefly discussed. © 2023 International Conference on Spatial and Immersive Audio. All Rights Reserved.

关键词： Interpolation

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