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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hanbin Lee Gihwan Kim Sangho Ha Hyungshin Kim Department of Computer Science and Engineering Embedded Systems Laboratory (ESLAB) Chungnam National University Daejeon Republic of Korea

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.

关键词： Accuracy Quantization (signal) Disasters Computational modeling Semantic segmentation Autonomous aerial vehicles Real-time systems

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How to Make Artificial Intelligence Self Manifest the Sense of Humour

How to Make Artificial Intelligence Self Manifest the Sense ...

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Future of Information and Communication Conference, FICC 2021

作者： Benazzouz, Yazid Boudour, Rachid Embedded Systems Laboratory Computer Science Department Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9783030731021

In everyday life, sense of humor usually comes through the production of jokes. The ability to be funny or to be amused by a joke is not an easy task for human because of cultural background and their capacity to perceive the ludicrous. This makes it a big challenge for artificial intelligence to imitate this behaviour. It is an advanced capability to create artificial life and interactive robots in particular. This technology could be interesting for many applications such as aerospace. Astronauts are called to stay in space for long periods of time and their emotional state must be monitored through interactions in order to keep stress and anxiety levels very low. Laughter can be part of the solution, especially since it comes from a machine. In this paper, we propose a machine learning based architecture to allow a machine not only understand jokes but also to generate them automatically according to discourse context. Moreover, multiple scenarios are evaluated to test the feasibility of our approach: emotional response to jokes during human machine interaction and autonomic robot reactions when reading a book or watching funny TV series. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Speech recognition

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CREAI 2022 - Preface to the First Workshop on Artificial Intelligence and Creativity 1

CREAI 2022 - Preface to the First Workshop on Artificial Int...

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1st Workshop on Artificial Intelligence and Creativity, CREAI 2022

作者： De Filippo, Allegra Milano, Michela Presutti, Valentina Saffiotti, Alessandro Department of Computer Science and Engineering University of Bologna Italy Department of Modern Languages Literature and Cultures University of Bologna Italy AASS Cognitive Robotic Systems Laboratory Örebro University Sweden

In recent years, Artificial Intelligence (AI) has gained increasing popularity in the area of art creation, by demonstrating its great potential. Research in this topic has developed AI systems able to generate creative outputs in fields such as music, painting, games, design and scientific discovery, either autonomously or in collaboration with humans. Therefore, AI also helped to analyze and study the mechanisms of creativity from a broader perspective: from the socio-anthropological to psychological, as well as cognitive impact of the autonomous creative processes of artificial intelligence. These advances are leading to new opportunities research perspectives, while also posing challenging questions related to authorship, integrity, bias and evaluation of AI artistic outputs. CREAI, the workshop on AI and creativity, tries to address these research lines and aims to provide a forum for the AI community to discuss problems, challenges and innovative approaches in the various sub-fields of AI and creativity. © 2021 Copyright for this paper by its authors.

关键词： Artificial intelligence

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Using Sequence to Sequence Model to Transform Recognized ASL Words to Understandable Sentences

Using Sequence to Sequence Model to Transform Recognized AS...

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Future Technologies Conference, FTC 2020

作者： Benazzouz, Yazid Boudour, Rachid Embedded Systems Laboratory Computer Science Department Badji Mokhtar University Annaba Algeria

ISBN: (纸本)9783030631277

There was widespread agreement that Communication can be a struggle for some deaf and hearing impaired people. The big breakthrough is the development of sign language (SL) which has enabled this category of people to communicate. SL has enabled them to integrate into modern society, and access to education and work. Although sign language is very well documented and taught it does not allow them to interact with all people. It still hard for them to conduct a real life as the rest of the society, for example, participating in work meetings, reading any books, taking classes at the university, nor to use the available means of communication, such as video chatting and conferencing. With the recent development in artificial intelligence solutions began to appear on the horizon to accentuate the integration of deaf and dumb people. This paper attempts to answer a small corner of these needs. It presents a technique for transforming sign language into understandable sentences that can in turn be synthesized into speech or sent as it is by email or SMS. The aim is to allow the society understand SL without making a learning effort. In order to show the feasibility of this approach, a real-time prototype is developed by integrating an existing sign recognition model. © 2021, Springer Nature Switzerland AG.

关键词： Audition

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TariqAmn's Innovative Road Safety Paradigm through Smart Technology

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Periodica Polytechnica Transportation Engineering 2024年第4期52卷 372-381页

作者： Malek, Nada Ahmed Boudour, Rachid Embedded Systems Laboratory Computer Science Department Faculty of Technology BADJI Mokhtar- Annaba University P.O.B. 12 Annaba23000 Algeria

In response to escalating road safety challenges within Algeria, marked by rising traffic volumes and urban development, the "TariqAmn Algeria" initiative emerges as a groundbreaking approach in traffic management. This study delineates the development and deployment of this innovative system, which leverages intelligent traffic signs, LiDAR (Light Detection and Ranging, using laser light to measure distances) radar, and proactive brake control technologies, embedded within a robust traffic management framework. The system's dynamic adaptation to real-time traffic conditions and its enforcement of speed compliance are central to its design. Rigorous evaluations through SUMO (Simulation of Urban Mobility, a traffic simulation software) simulations and field tests in high-risk urban settings affirm its efficacy, demonstrating a 60% reduction in traffic incidents and a 50% reduction in speeding violations. By marrying local cultural insights with cutting-edge technology, "TariqAmn Algeria" not only enhances immediate road safety but also establishes a new paradigm in traffic management systems. The findings illuminate the significant scalability potential of the system, promising broader applications in enhancing road safety and adherence to traffic regulations across various regions, such as densely populated urban areas, high-traffic corridors, and accident-prone black spots. This initiative underscores the critical role of Intelligent Transportation systems (ITS) in modern road safety strategies and sets a precedent for future technological advancements in the field. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Traffic signs

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Traffic Sign Detection With Event Cameras and DCNN 25

Traffic Sign Detection With Event Cameras and DCNN

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25th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2022

作者： Wzorek, Piotr Kryjak, Tomasz Embedded Vision Systems Group Agh University of Science and Technology Computer Vision Laboratory Department of Automatic Control and Robotics Kraków Poland Silesian University of Technology Department of Digital Systems Gliwice Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used in vision systems as an alternative or supplement to traditional cameras. They are characterised by high dynamic range, high temporal resolution, low latency, and reliable performance in limited lighting conditions - parameters that are particularly important in the context of advanced driver assistance systems (ADAS) and self-driving cars. In this work, we test whether these rather novel sensors can be applied to the popular task of traffic sign detection. To this end, we analyse different representations of the event data: event frame, event frequency, and the exponentially decaying time surface, and apply video frame reconstruction using a deep neural network called FireNet. We use the deep convolutional neural network YOLOv4 as a detector. For particular representations, we obtain a detection accuracy in the range of 86.9-88.9% mAP@0.5. The use of a fusion of the considered representations allows us to obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the detector for the frames reconstructed with FireNet is characterised by an accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of event cameras in automotive applications, either as standalone sensors or in close cooperation with typical frame-based cameras. © 2022 Division of Signal Processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Cameras

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Real-time FPGA implementation of the Semi-Global Matching stereo vision algorithm for a 4K/UHD video stream

arXiv

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arXiv 2023年

作者： Grabowski, Mariusz Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics Agh University of Science and Technology Krakow Poland

In this paper, we propose a real-time FPGA implementation of the Semi-Global Matching (SGM) stereo vision algorithm. The designed module supports a 4K/Ultra HD (3840 ×2160 pixels @ 30 frames per second) video stream in a 4 pixel per clock (ppc) format and a 64-pixel disparity range. The baseline SGM implementation had to be modified to process pixels in the 4ppc format and meet the timing constrains, however, our version provides results comparable to the original design. The solution has been positively evaluated on the Xilinx VC707 development board with a Virtex-7 FPGA device. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Real-time FPGA implementation of the Semi-Global Matching stereo vision algorithm for a 4K/UHD video stream

TechRxiv

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TechRxiv 2023年

作者： Grabowski, Mariusz Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

In this paper, we propose a real-time FPGA implementation of the Semi-Global Matching (SGM) stereo vision algorithm. The designed module supports a 4K/Ultra HD (3840×2160 pixels @ 30 frames per second) video stream in a 4 pixel per clock (ppc) format and a 64-pixel disparity range. The baseline SGM implementation had to be modified to process pixels in the 4ppc format and meet the timing constrains, however, our version provides results comparable to the original design. The solution has been positively evaluated on the Xilinx VC707 development board with a Virtex-7 FPGA device. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Hierarchical Knowledge Representation of Complex Tasks Based on Dynamic Motion Primitives 7th

Hierarchical Knowledge Representation of Complex Tasks Based...

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7th International Conference on Cognitive systems and Information Processing, ICCSIP 2022

作者： Miao, Shengyi Zhong, Daming Miao, Runqing Sun, Fuchun Wen, Zhenkun Huang, Haiming Zhang, Xiaodong Wang, Na College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China College of Modern Post Beijing University of Posts and Telecommunications Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Shenzhen University Shenzhen518060 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Application Beijing100083 China

ISBN: (纸本)9789819906161

Current service robots without learning ability are not qualified for many complex tasks. Therefore, it is very significant to decompose the complex task into repeatable execution unit. In this paper, we propose a complex task representation method based on dynamic motion primitives, and use hierarchical knowledge graph to represent the analytic results of complex tasks. To realize the execution of complex robot manipulation tasks, we decompose the semantic tasks into the minimum motion units that can be executed by the robot and combine the multi-modal information: posture, force and robot joint parameters, obtained by the sensors. We use the knowledge graph to record the end-effector required by the robot to perform different tasks and make appropriate selection of end-effector according to different needs. Finally, Taking the long sequence complex task of service scene as an example, we use UR5 robot to verify the effectiveness and feasibility of this design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： End effectors

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