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

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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AI-based Adaptive Programming Education for Socially Disadvantaged Students: Bridging the Digital Divide

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TechTrends 2025年 1-18页

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

In the context of the digital economy, programming proficiency is an essential competency that promotes upward socio-economic mobility and expands career opportunities. However, students from socially disadvantaged backgrounds often face significant barriers to acquiring these skills, such as limited access to technology and educational resources. This study explores the impact of AI-based adaptive programming education on socially disadvantaged students' learning outcomes and engagement levels. The 122 participants in the research were divided into an experimental group (EG) that received AI-driven adaptive instruction, and a control group (CG) taught through the traditional curriculum during the 13-week experimental period. In the research, the combined pre-test/post-test assessments and self-report engagement questionnaires were used to focus on behavioural, emotional, and cognitive engagement. The findings showed that the EG demonstrated higher levels in programming knowledge and full-scale engagement across behavioural, emotional, and cognitive dimensions compared to the CG. This difference was confirmed through ANOVA, while ANCOVA, which controlled for students' socio-economic factors, further confirmed that the AI system had a positive impact on students’ results regardless of their socio-economic background. Indeed, current research findings suggest that AI-based adaptive learning environments can hold a promising opportunity to narrow educational gaps, boost engagement, and contribute toward better learning outcomes for socially disadvantaged students. © The Author(s) 2025.

关键词： Adaptive learning AI-based education Disadvantaged students Programming education Student engagement

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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Computers and Education: Artificial Intelligence 2025年 8卷

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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EquiNO: A physics-informed neural operator for multiscale simulations

arXiv

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arXiv 2025年

作者： Eivazi, Hamidreza Tröger, Jendrik-Alexander Wittek, Stefan Hartmann, Stefan Rausch, Andreas Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld38678 Germany Institute of Applied Mechanics Clausthal University of Technology Clausthal-Zellerfeld38678 Germany

Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too expensive for many-query scenarios, e.g., uncertainty quantification, remeshing applications, topology optimization, and so forth. This limitation has motivated the application of data-driven surrogate models, where the microscale computations are substituted with a surrogate, usually acting as a black-box mapping between macroscale quantities. These models offer significant speedups but struggle with incorporating microscale physical constraints, such as the balance of linear momentum and constitutive models. In this contribution, we propose Equilibrium Neural Operator (EquiNO) as a complementary physics-informed PDE surrogate for predicting microscale physics and compare it with variational physics-informed neural and operator networks. Our framework, applicable to the so-called multiscale FE 2 computations, introduces the FE-OL approach by integrating the finite element (FE) method with operator learning (OL). We apply the proposed FE-OL approach to quasi-static problems of solid mechanics. The results demonstrate that FE-OL can yield accurate solutions even when confronted with a restricted dataset during model development. Our results show that EquiNO achieves speedup factors exceeding 8000-fold compared to traditional methods and offers an optimal balance between data-driven and physics-based strategies. © 2025, CC BY.

关键词： Uncertainty analysis

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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From simulation to operation: Using design time artifacts to ensure the safety of advanced driving assistance systems at runtime

From simulation to operation: Using design time artifacts to...

引用

International Workshop on Modelling in Automotive software engineering, MASE 2015 - co-located with ACM/IEEE 18th International Conference on Model Driven engineering Languages and systems, MODELS 2015

作者： Mauritz, Malte Howar, Falk Rausch, Andreas Institute for Applied Software Systems Engineering Technical University Clausthal. Clausthal-ZellerfeldD-38678 Germany

Advanced driver assistance systems and (semi-)autonomous mobility systems will arguably be the biggest disruption of our every- day life in the next couple of years. The development of such systems comes with legal and technical challenges: Product liability regulations impose high standards on manufacturers regarding the safe operation of advanced driver assistance systems (ADAS). In the Automotive domain, sufficient safety has yet to be proven through extensive and expensive testing. As a consequence, car manufacturers try to move testing effort from the road to simulation. It is not clear, however, how results ob- tained from simulations transfer to the road. In this paper, we present an approach for leveraging simulation results during road tests. Our approach utilizes runtime monitors that are generated from specifications, test scenarios, and simulated components. These monitors can be used during road tests and during operation for identifying untested situations and for checking functional correctness of an ADAS.

关键词： Advanced driver assistance systems

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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Slovakia Information Systems and Software Engineering Slovak University of Technology Institute of Informatics Slovakia

ISBN: (纸本)9798350318845

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead. © 2024 IEEE.

关键词： Error correction

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Control of Composite Manufacturing Processes Through Deep Reinforcement Learning 22

Control of Composite Manufacturing Processes Through Deep Re...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Stieber, Simon Heber, Leonard Obertscheider, Christof Reif, Wolfgang Institute for Software & Systems Engineering University of Augsburg Augsburg Germany University of Applied Sciences Wiener Neustadt Wiener Neustadt Austria

ISBN: (纸本)9798350345346

Resin transfer molding (RTM) is a composite manufacturing process that uses a liquid polymer matrix to create complex-shaped parts. There are several challenges associated with RTM. One of the main challenges is ensuring that the liquid polymer matrix is properly distributed throughout the composite material during the molding process. If the matrix is not evenly distributed, the resulting part may have weak or inconsistent properties. This is the challenge we tackle with the approach presented in this work. We implement an online control using deep reinforcement learning (RL) to ensure a complete impregnation of the reinforcing fibers during the injection phase, by controlling the input pressure on different inlets. This work uses this self-learning paradigm to actively control the injection of an RTM process, which has the advantage of depending on a reward function instead of a mathematical model, which would be the case for model predictive control. A reward function is more straightforward to model and can be applied and adapted to more complex problems. RL algorithms have to be trained through many iterations, for which we developed a simulation environment with a distributed and parallel architecture. We show that the presented approach decreases the failure rate from 54 % to 27 %, by 50 % compared to the same setup with steady parameters. © 2023 IEEE.

关键词： Control Reinforcement Learning Resin Transfer Molding

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1st workshop on innovative software engineering education

1st workshop on innovative software engineering education

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2018 Combined Workshops of the German software engineering Conference, SE-WS 2018

作者： Krusche, Stephan Kuhrmann, Marco Schneider, Kurt Department of Informatics Technical University of Munich Germany Institute for Applied Software Systems Engineering Clausthal University of Technology Germany Software Engineering Group Leibniz Universität Hannover Germany

Due to the growing numbers of students, courses can no longer be offered in high quality without systematic approaches. Hence, this workshop aims at presenting and discussing innovative teaching approaches in software engineering education, which are highly relevant for teaching at universities, colleges, and in online courses. © 2018 CEUR-WS. All rights reserved.

关键词： Teaching

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