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Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application 27

Research on the behaviour of Bluetooth Low Energy protocol i...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Balas, Zuzanna Tokarz, Krzysztof Zielinski, Bartlomiej Guzniczak, Tomasz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bluetooth Low Energy Health monitoring Internet of Things Smart wristband Wireless data transmission

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance 28th

Domain-specific data characteristics: A study on meaning of ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Stanczyk, Urszula Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies. © 2024 The Authors.

关键词： Data assimilation

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Observations of data characteristics and irregularities through domain-oriented transformations of attributes 28th

Observations of data characteristics and irregularities thro...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics. © 2024 The Authors.

关键词： Data assimilation

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SLAM Methods for Augmented Reality systems for Flight Simulators 23rd

SLAM Methods for Augmented Reality Systems for Flight Simu...

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23rd International Conference on Computational Science, ICCS 2023

作者： Nwobodo, Onyeka J. Wereszczyński, Kamil Cyran, Krzysztof Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

ISBN: (纸本)9783031359941

In this paper, we present the review and practical evaluation of the flight simulators of Simultaneous Localization and Mapping methods. We present a review of recent research and development in the SLAM application in a wide range of domains, like autonomous driving, robotics and augmented reality (AR). Then we focus on the methods selected from the perspective of their usefulness in the AR systems for training and servicing the flight simulators. The localization and mapping in such an environment are much more complex than in others since the flight simulator is relatively small and close area. Our previous experiments showed that the built-in SLAM system in HoloLens is insufficient for such areas and has to be enhanced with additional elements, like QR codes. Therefore, the presented study on other methods can improve the localization and mapping of AR systems in flight simulators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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A review on tracking head movement in augmented reality systems 27

A review on tracking head movement in augmented reality syst...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Nwobodo, Onyeka J. Wereszczynski, Kamil Cyran, Krzysztof Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

This paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. The main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. This paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. The review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： augmented reality deep learning DOF Head tracking immersive experience pose estimation

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Data loss in quantum image representation methods 27

Data loss in quantum image representation methods

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Werner, Krzysztof Kordasz, Michal Michalczuk, Agnieszka Cyran, Krzysztof Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice44-100 Poland

Quantum Image Representation is a well-studied area in quantum informatics. The first proposed algorithm was the qubit lattice method introduced in 2003. This method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. The most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： FRQI LPIQE NEQR PDU quantum computing Quantum image encoding quantum information theory quantum sampling

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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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Should supervised discretisation always be trusted unreservedly? On combining characteristics of supervised and unsupervised discretisation algorithms in two-step processing 27

Should supervised discretisation always be trusted unreserve...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents a description of the research methodology dedicated to a two-step discretisation process applied to the input numeric data, with combining the characteristics of selected supervised and unsupervised algorithms, which leads to extended processing of some attributes in train and test sets. The methodology was illustrated with the investigations carried out in the domain of stylometric analysis of texts, for two datasets prepared for the task of binary authorship attribution. The several variants of transformed input data obtained were subjected to exploration using two selected machine learning methods capable of inducing knowledge from both continuous and categorical forms, namely the PART and J48 classifiers. The results from the experiments indicate that, as can be expected, supervised transformations of data work well enough, however, they do not always return the best outcome. The two-step processing of some attributes shows sufficient promise to warrant a closer study, as opposed to always unconditionally relying only on supervised algorithms as outperforming all other approaches. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Authorship attribution Data representation Pattern recognition Stylometry Supervised discretisation Unsupervised discretisation

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How transformations of representation for input data can affect the properties of induced decision reducts and rules 27

How transformations of representation for input data can aff...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Stanczyk, Urszula Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

Decision reducts and rules belong to forms used for the representation of knowledge learnt from input data while using a rough set approach in the exploration stage. As with any patterns that capture properties of data, the size is considered as the most important indicator of their quality, as a more general and concise description allows for improved understanding and simplified interpretation. The size of a reduct is given by its cardinality, and for a rule it is a number of included conditions, both referred to as lengths. These lengths undergo transformations along with data transformations, so such processing as discretisation leads to changed characteristics of existing patterns and through them also reducts and rules. The paper presents research with contributions in the observations focused on the influence of supervised and unsupervised discretisation on properties of attributes, induced decision reducts, and rules, offering a deeper insight into relations among them. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Data exploration decision reduct decision rule discretisation knowledge representation rough set theory transformation

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Optimizing Artificial Neural Networks Trough Weight Adjustments 28th

Optimizing Artificial Neural Networks Trough Weight Adjustme...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Akber, Syed Muhammad Abrar Szczesna, Agnieszka Kazmi, Sadia Nishat Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Longer training times pose a significant challenge in artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network. © 2024 The Authors.

关键词： Deep neural networks

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