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2023 IEEE International Workshop on Mechatronics Systems Supervision, IW_MSS 2023

作者： Akermi, Wafa Alyaoui, Nouha Benfradj Guiloufi, Awatef Chabir, Karim Gafsa University Higher Institute of Applied Sciences and Technology Automatic Industrial Systems Department Gafsa Tunisia Gabes Tunisia Gafsa University Higher Institute of Applied Sciences and Technology Computer Science and Telecommunications Department Gafsa Tunisia Gabes Tunisia

ISBN: (纸本)9798350327564

In the present day, the Internet of Things is becoming more and more popular. Enabling continuous connectivity and packet routing in this kind of network is difficult because of the resource limitations that are a feature of IoT. Reduce end-to-end latency, conserve energy, and balance network load are the three basic objectives of lightweight routing protocol. In this paper, we give a quick review of the IPv6 Routing Protocol for Low-Power and Lossy Networks-based routing protocols of VANET already in use and discuss their advantages and disadvantages. More specifically, we examine a few chosen VANET routing protocols and contrast their performance based on metrics like packet delivery ratio, Throughput, End to end delay, Routing- Cost, etc. © 2023 IEEE.

关键词： Routing protocols

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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HyenaPixel: Global Image Context with Convolutions 27

HyenaPixel: Global Image Context with Convolutions

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Spravil, Julian Houben, Sebastian Behnke, Sven Fraunhofer IAIS Germany University of Applied Sciences Bonn-Rhein-Sieg Germany University of Bonn Computer Science Institute VI Center for Robotics Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

ISBN: (纸本)9781643685489

In computer vision, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, its quadratic complexity limits its applicability to tasks that benefit from high-resolution input. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to bidirectional data and two-dimensional image space. We scale Hyena's convolution kernels beyond the feature map size, up to 191×191, to maximize ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 84.9% and 85.2%, respectively, with no additional training data, while outperforming other convolutional and large-kernel networks. Combining HyenaPixel with attention further improves accuracy. We attribute the success of bidirectional Hyena to learning the data-dependent geometric arrangement of pixels without a fixed neighborhood definition. Experimental results on downstream tasks suggest that HyenaPixel with large filters and a fixed neighborhood leads to better localization performance. © 2024 The Authors.

关键词： Complex networks

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Merging Realities: Exploring Mixed Reality as a Research Tool for Human-Robot Interaction in Real-World Settings

Merging Realities: Exploring Mixed Reality as a Research Too...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Jan Schulten André Helgert Alexander Arntz Carolin Straßmann Sabrina C. Eimler Institute of Computer Science Ruhr West University of Applied Sciences Bottrop Germany

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

The application of Virtual Reality (VR) in Human-Robot Interaction (HRI) research is growing due to its capacity to create adaptable yet controlled study environments. While VR often achieves data validity comparable to real-world studies, it faces limitations when physical interaction with robots is required. Mixed Reality (MR) offers a potential solution by enabling interactions with virtual robots in real-world settings, allowing participants to engage with realistic, interactive simulations that adapt to physical environments. Given the limited use of MR in HRI studies and the need for further understanding of its effectiveness in generating transferable results, this study examines MR's potential as a tool for simulating social-robot interaction. We conducted a study involving 21 participants interacting with a virtual robot performing navigation tasks in a real library and assessed self-efficacy, presence, technology acceptance, perceived realism, and social characteristics of the robot. The results show that most participants perceived the interaction with the robot as successful, the engagement was high and the robot interaction with MR provided excellent usability. These results suggest that MR has potential as a research and training tool for simulating human-robot interaction in navigation tasks within public spaces.

关键词： Training Solid modeling Navigation Merging Human-robot interaction Mixed reality Virtual reality Libraries Usability Robots

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Measuring Decubitus Wounds Using Augmented Reality Glasses - A User Interface Study 24th

Measuring Decubitus Wounds Using Augmented Reality Glasses -...

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24th International Conference on Human-computer Interaction, HCII 2022

作者： Bauer, Mathias Leidner, Nico Willhauk, Wladislaw Gansohr, Carina Geisler, Stefan Institute of Computer Science and Institute of Positive Computing Hochschule Ruhr West University of Applied Sciences Bottrop Germany

ISBN: (纸本)9783031196782

We present a study on the usability of three different user interfaces for an augmented reality glasses system. The application is intended for use in hospitals by nursing professionals. During wound care, images can be taken with the Microsoft HoloLens 2 and measured directly. The photos must always be taken at the same distance from the wound to follow the healing process. We present and juxtapose three aim assistants to take a picture of the wound and acquire wound data. To gain a first impression about the goodness of usability and the preferred targeting assistant, we conducted a small usability study. Participants were asked to perform wound measuring tasks using our application’s varying user interfaces. In this study, we identified strengths and weaknesses for each user interface based on the conducted research and identified approaches for further improvements. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Supporting Hospital Nurses During Medication Dispensation with Augmented Reality – A Participatory Approach 24th

Supporting Hospital Nurses During Medication Dispensation wi...

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24th International Conference on Human-computer Interaction, HCI International, HCII 2022

作者： Gansohr, Carina Thelen, Miriam B. Geisler, Stefan Eimler, Sabrina C. Institute of Computer Science and Institute of Positive Computing Hochschule Ruhr West University of Applied Sciences Bottrop Germany

ISBN: (纸本)9783031063930

The nursing profession is becoming increasingly complex: administrative and nursing tasks have to be performed in parallel, under time pressure and in shifts. These stressful working conditions also affect safety-critical processes such as the correct dispensing of medication. As digitization continues, new technologies such as augmented reality (AR) glasses are emerging that offer potential solutions. However, a practical implementation requires a deep understanding of the underlying problem and user needs. The aim of this paper is to first, discover pains and gains in the process of medication dispensation and second, develop a conceptual prototype for AR glasses as a basis for discussion on the applicability in practice. A participatory design process with nursing professionals and experts from technical and organizational fields was established. As result, we present a conceptual prototype, that (A) respects the context of use, (B) guides users while dispensing medication according to prescription, (C) displays useful information about medical preparations, procurement of alternate medicine and dosage variations and (D) uses checklists and error recognition to increase safety. Multidisciplinary feedback workshops indicate an overall positive resonance. We advise paying attention to the spatial and economic situation while using AR as a support tool providing flexibility for users. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

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computers, Materials & Continua 2022年第4期71卷 651-666页

作者： A.Devipriya P.Prabu K.Venkatachalam Ahmed Zohair Ibrahim Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhKSA.P.o.Box:84428Postal Code:11671

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

关键词： Deep residual learning convolutional neural network COVID-19 X-ray principal component analysis granulo metrics texture analysis

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Comparative Study of Optical Character Recognition Using Different Techniques on Scanned Handwritten Images 6th

Comparative Study of Optical Character Recognition Using Dif...

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6th International Conference on Microelectronics and Telecommunication Engineering, ICMETE 2022

作者： Singh, Anshika Garg, Sharvan Kumar Department of Computer Science Shaheed Rajguru College of Applied Sciences for Women University of Delhi Delhi India Department of Computer Science and Engineering Subharti Institute of Technology and Engineering Swami Vivekan and Subharti University Meerut India

ISBN: (纸本)9789811995118

Optical Character Recognition (OCR) is always a prominent area of research and has gained attention in academia and industries, and more advancements are being performed to improve accuracy and quality of text and images. OCR is a technique that converts the digital or optical image (which can be image of typed or printed document or scanned handwritten document or scene photo) into machine-encoded format. OCR takes an image containing text as input, scans the text character by character, analyzes it, and generates a text corresponding to the text in the image, thus reducing the storage size and making accessibility of data easy and faster. In this paper, we will be studying OCR using different techniques like Convolutional Neural Network, Recurrent Neural Network, pytesseract, Keras-OCR, EasyOCR and comparing their results on handwritten scanned images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 502-526页

作者： Xin ZENG Xiaoyu WANG Tengxiang ZHANG Yukang YAN Yiqiang CHEN Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China Goertek Inc. Bejing 261031China The Hong Kong University of Science and Technology Hong Kong 999077China Department of Computer Science Hajim School of Engineering and Applied SciencesUniversity of RochesterRochesterNY 14627US

The proliferation of computing devices requires seamless cross-device *** reality(AR)headsets can facilitate interactions with existing computers owing to their user-centered views and natural *** this study,we propose InputJump,a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical *** input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR *** also extracts semantic descriptions of inputs and elements using large language models(LLMs).Two types of information from different inputs(e.g.,gaze,gesture,mouse,and keyboard)were fused to map onto an interactive *** proposed method is explained in detail and implemented on both an AR headset and a desktop *** then conducted a user study and extensive simulations to validate our proposed *** results showed that InputJump can accurately associate a fused input with the target interactive element,enabling a more natural and flexible interaction experience.

关键词： Mixed reality Spatial Semantic Large language models

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