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Application of domain-specific modeling in kinetography and bipedal humanoid robot control

PeerJ computer Science 2025年 11卷

作者： Djukić, Verislav Oros, Dragana Penčić, Marko Lu, Zhenli Faculty of Computer Engineering Union University Serbia Belgrade Serbia Djukic Software GmbH Nürnberg Germany Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia School of Electrical Engineering and Automation Suzhou University of Technology Changshu China

The article presents a new approach in the development of software for bipedal humanoid robot controllers, based on the construction and application of graphic domain-specific languages (DSLs). The notations used to describe dance movements and gestures are typical examples of DSLs. With certain extensions, related to the description of foot topology, sensors and actuators, such DSLs are applicable for modeling dance movements that would be performed by a robot. The existing software development methodologies in robotics have a purely mechanistic approach to understanding and implementing robotic tasks. Such an approach in humanoid robotics complicates the understanding of the problem, as well as the specification and implementation of solutions. Our approach, which uses DSLs, adopts complex movements and gestures performed by the feet of dancers using professional dancers, people with above-average motor skills, as reference. We believe that the developed software can also be successfully applied to assistive robots that would help people with special needs whose mobility is significantly lower than average. © 2025 Djukić et al.

关键词： Anthropomorphic robots

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Collaboration Energy Efficiency with Mobile Edge Computing for Target Tracking in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Yu, Gang Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining and Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786205

In this paper, the target tracking problem is investigated with mobile edge computing (MEC) mechanism in internet of things (IoT), where the challenge of energy efficiency is a significant issue when the target tracking event is driven. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing target tracking effects. We deign the sensor selection scheme for carrying out the tracking tasks according to energy distribution of the sensor node. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency without reducing the accuracy of target tracking. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the target state. Simulations demonstrate the efficiency for tracking performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Target tracking

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Progressive LiDAR Adaptation for Road Detection

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 693-702页

作者： Zhe Chen Jing Zhang Dacheng Tao IEEE the UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science Faculty of Engineering and Information Technologies University of Sydney the School of Automation Hangzhou Dianzi University the University of Technology Sydney

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection,because LiDAR data is less susceptible to visual noises. However,the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection(PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-theart road detection models and is currently top of the publicly accessible benchmark leader-board.

关键词： Autonomous driving computer vision deep learning LiDAR processing road detection

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Collaboration Energy Efficiency with Mobile Edge Computing for Data Collection in IoT 7th

Collaboration Energy Efficiency with Mobile Edge Computing f...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Zou, Guobin Zhang, Jian Tang, Jian Zhou, Junwu College of Information Science and Engineering Northeastern University Shenyang110819 China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing102600 China BGRIMM Technology Group Co. Ltd. Beijing102600 China

ISBN: (纸本)9783030786144

In this paper, we investigated collaboration energy efficiency with mobile edge computing (MEC) mechanism in internet of things (IoT), which is a challenge issue. In order to prolong the lifetime of IoT, we adopt dynamic clustering methods to improve energy efficiency while guaranteeing energy balance. We deign the sensor selection scheme for collecting data according to Pareto optimality. Concretely, by considering the reality of random deployment and introducing the definition of node density for IoT, we develop a Pareto optimality for sensor nodes selection in terms of energy efficiency. Furthermore, we recruit voluntary mobile devices as mobile edge computing servers to offload the data from selected sensor nodes in the cluster and process them to the estimate the data state. Simulations demonstrate the efficiency for the performance on energy balance in terms of efficiently prolonging the IoT lifetime. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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An experiential learning approach to learn AI in an online workshop

An experiential learning approach to learn AI in an online w...

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Yuan Xu Ching Sing Chai Helen Meng Savio Wai Ho Wong King Woon Yau Thomas K. F. Chiu Irwin King Yeung Yam Faculty of Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Curriculum and Instruction The Chinese University of Hong Kong Hong Kong SAR China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong SAR China Department of Educational Psychology The Chinese University of Hong Kong Hong Kong SAR China Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR China

As educating K-12 students about artificial intelligence (AI) gains prominence, the question of which pedagogical approach could promote the learning of AI needs exploration. This study aims to investigate the feasibility and effectiveness of learning fundamental AI concepts through online experiential learning. A total of 673 secondary school students participated in a two-day online workshop. A set of pre-and-post questionnaires was applied to assess students' understanding of fundamental AI concepts and their perceptions to learn AI. The results showed the online workshop was effective in improving students’ knowledge of AI and their motivation to learn about it. These findings suggested that the use of online experiential learning approach to learn AI is not only feasible but also appropriate for secondary school students. Furthermore, this study discussed the practical implications of incorporating experiential learning into online learning experience for K-12 students, providing insights into the potential of online workshops as a means of learning AI.

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Ivomfuse: An Image Fusion Method Based on Infrared-to-Visible Object Mapping

SSRN

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SSRN 2022年

作者： Zhang, Xiangbo Huang, Lei Liu, Gang Ren, Qin Bavirisetti, Durga Prasad School of Automation Engineering Shanghai University of Electric Power Shanghai201306 China Library Shanghai University of Electric Power Shanghai201306 China School of Software and Microelectronics Peking University Beijing102600 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Due to the significant difference in the structure of infrared and visible images, most of the existing algorithms use globally consistent strategy, which may weaken the contrast of the target region. To solve this problem, we propose an infrared and visible image fusion method based on infrared-to-visible object mapping (IVOMFuse) First of all, we use the probability induced intuitionistic fuzzy C-means algorithm (PIIFCM) to process the infrared image, and extract the target region of the infrared image according to the image structure features. However, because of the poor contrast of the target of the visible image, it can not be segmented directly. For this reason, we map the infrared target region to the visible image, and the target region with the same coordinates of the two images can be obtained very quickly. Secondly, due to the significant differences in image structure, we set a hybrid fusion strategies for different regions. We use the fusion strategy based on the Expectation-Maximization (EM) algorithm to iterate and optimize the probability model for the target region. The fourth-order partial differential equation (FPDE) is used for the background region to decompose the source image into high-frequency texture and low-frequency approximation images respectively. Then, the principal component analysis (PCA) and the average fusion strategy are used to obtain the fused image. Finally, we evaluated our algorithm with thirteen other state-of-the-art image fusion methods on three commonly used datasets, namely TNO, CVC14, and RoadScene. Among the ten evaluation indexes, our algorithm achieved the best six times. © 2022, The Authors. All rights reserved.

关键词： Mapping

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Ultracolor Multiplexing in QR Codes for Data Encryption and Transmission

Ultracolor Multiplexing in QR Codes for Data Encryption and ...

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2024 Frontiers in Optics, FiO 2024

作者： Ignacio-Cerrato, Sara Pacios, David Rodríguez, José Miguel Ezquerro Vázquez-Poletti, José Luis Mariño, María Estefanía Avilés Stravakakis, Konstantinos Di Iorio, Alessio Cesarano, Clemente Schetakis, Nikolaos Optics Department Faculty of Optics and Optometry Universidad Complutense de Madrid Calle Arcos de Jalón 118 Madrid28037 Spain Department of Computer Architecture and Automation Universidad Complutense de Madrid Madrid Spain Alma Sistemi Srl Guidonia Rome00012 Italy Section of Mathematics International Telematic University Uninettuno Corso Vittorio Emanuele II 39 Rome Italy Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología Universidad Politécnica de Madrid Calle de José Gutiérrez Abascal 2 Madrid28006 Spain Computational Mechanics and Optimization Laboratory School of Production Engineering and Management Technical University of Crete Chania73100 Greece Quantum Innovation Pc Chania73100 Greece

This paper introduces an innovative encryption method using ultracolor codification via the Prism Algorithm, which effectively encodes large data volumes into images with base64 encoding, demonstrating practical reliability and potential for secure data transmission. © Optica Publishing Group 2024, © 2024 The Author(s)

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An invisible DWT watermarking algorithm using noise removal with application to dermoscopic images 9

An invisible DWT watermarking algorithm using noise removal ...

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9th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2020

作者： Moldovanu, S. Michis, F.A. Moraru, L. Dunarea de Jos University of Galati Department of Computer Science and Information Technology Faculty of Automation Computers Sciences Electronics and Electrical Engineering 2 Stiintei Street GalatiRO-800210 Romania Department of Chemistry Physics and Environment Faculty of Sciences and Environment Dunarea de Jos University of Galati 47 Domneasca Street Galati800008 Romania Dunarea de Jos University of Galati Modelling and Simulation Laboratory Galati Romania

A new approach for the digital watermarking process is proposed to be part of the pre-processing stage of a computer-aided diagnosis system. We propose to embed a denoised image acting as the watermark image in the original host image with the final goal of improving the quality of demoscopic images for further image processing operation related to CAD. The proposed algorithm uses Discrete Wavelet Transform (DWT) corroborated with some basic properties of Human Visual System such as Contrast Sensitive Function (CSF) and Noise Visibility Function (NVF) with the goal of correlating the texture properties and noise. This approach hides the watermark (i.e. denoised version of the host image) in high-pass subbands that are focused on image features. The main concern is to evaluate the distortion produced to the host image by watermarking and an objective quality measure function, i.e. Weighted Peak Signal-to-Noise Ratio (WPSNR), is used to evaluate the existing differences between the original and watermarked images. The proposed approach is tested using the available skin lesion images from the digital image archive of the Department of Dermatology of the University Medical Center Groningen. The experiment results show the improved performance of the proposed scheme against a 3 × 3 median filtering attack in comparison with the a 5 × 5 median filtering attack. © Published under licence by IOP Publishing Ltd.

关键词： Discrete wavelet transforms

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Image encryption application and security analysis based on current modulated edge-emitting semiconductor lasers with pulse packages embedded in the microcontroller

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Optik 2023年第1期287卷

作者： Subramani, Rajeshkanna Emin, Berkay Kingni, Sifeu Takougang Akgül, Akif Center for Artificial Intelligence Chennai Institute of Technology Tamilnadu Chennai600069 India Department of Electronics and Automation Osmancık Omer Derindere Vocational School Hitit University Corum19500 Turkey Department of Mechanical Petroleum and Gas Engineering National Advanced School of Mines and Petroleum Industries University of Maroua P.O. Box 46 Maroua Cameroon Department of Computer Engineering Faculty of Engineering Hitit University Corum19030 Turkey Laboratory of Products Development and Entrepreneurship Institute of Innovation and Technology P.O. Box 8210 Yaounde Cameroon

This paper investigates the dynamical characteristics of current-modulated edge-emitting semiconductor lasers (CMEESLs) and their image encryption application. The associated dynamical characteristic of the CMEESL is divulged in the two-dimensional coordinate space of the system parameters by altering the modulation's frequency and amplitude. The CMEESL divulges cyclical pulse packages preceded by cyclic-doubling cascade follow by complex pulse packages when the modulation current density frequency is limited below 1 GHz. The microcontroller implementation of the CMEESL is realized and its results find agreement compared with the numerical dynamics of the CMEESL. The chaotic equations describing CMEESL are first modeled in the LabVIEW program and random number generation (RNG) is performed. The obtained random numbers are subjected to the NIST 800–22 statistical standard package, proving its worth to be employed for encryption applications. Employing the random numbers gotten, the LabVIEW environment is used in encrypting the image. The encrypted image is subjected to histogram, correlation, number of pixel change rates (NPCR) - unified average changing intensity (UACI), and entropy analyses. The reliability of the method is proven by the success achieved in the encryption process. © 2023 Elsevier GmbH

关键词： Microcontrollers

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Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning

arXiv

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arXiv 2022年

作者： Wei, Shoulin Li, Yadi Lu, Wei Li, Nan Liang, Bo Dai, Wei Zhang, Zhijian Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming650500 China National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China Faculty of Science Kunming University of Science and Technology Kunming650500 China

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveysa) © 2022, CC BY.

关键词： Surveys

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