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Studies in Big Data 2021年 73卷 v-vii页

作者： Chakraborty, Chinmay Banerjee, Amit Kolekar, Maheshkumar H. Garg, Lalit Chakraborty, Basabi Department of Electronics and Communication Engineering Birla Institute of Technology Ranchi Mesra India Department of Electrical Engineering Indian Institute of Technology Patna Patna India Faculty of Software and Information Science Iwate Prefectural University Takizawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta Msida Malta

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A voxel-based filtering algorithm for mobile LiDAR data

A voxel-based filtering algorithm for mobile LiDAR data

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2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing

作者： Qin, Hejuan Guan, Haiyan Yu, Yongtao Zhong, Liang College of Remote Sensing and Geometics Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian223003 China Changjiang Spatial Information Technology Engineering Co. Ltd. Wuhan410010 China

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds. © Authors 2018. CC BY 4.0 License.

关键词： Computational efficiency

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A Study of WIFI Control Wheeled Robot System with Ultrasonic Obstacle Avoidance

A Study of WIFI Control Wheeled Robot System with Ultrasonic...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hai-Wu Lee Zi-Chen Fang Jin-Yu Chen Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian City Jiang Su China

With the progress of science and technology, wheeled robots have been widely used in various fields to help people complete tasks. However, when performing a task, the robot and the object could be damaged if the robot collides with the object due to negligence of the operator. Therefore, the safety issue is the most important consideration. This paper presents a multifunction system, including Arduino controller, rotary ultrasonic obstacle avoidance and WIFI control system. Red warning led and buzzers on the fuselage will alert operators is time of danger and feedback warning information to the phone. The system is proved to be feasible by experiment.

关键词： Mobile robots Robot sensing systems Acoustics Wireless fidelity Collision avoidance Mobile handsets

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Explaining predictions of deep neural classifier via activation analysis

arXiv

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arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Integration of smart grid with Industry 5.0: Applications, challenges and solutions

Measurement: Energy

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Measurement: Energy 2025年 5卷

作者： Sunawar khan Tehseen Mazhar Tariq Shahzad Muhammad Amir khan Ateeq Ur Rehman Habib Hamam Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar 62300 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah 31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore 54000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal 57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia School of Computing Gachon University Seongnam 13120 Republic of Korea Faculty of Engineering Uni de Moncton Moncton NB E1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

Introduction In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two. Objectives In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage. Method ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0. Findings and discussion In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures. Conclusion Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.

关键词： Smart grid Energy efficiency Industry 5.0 Haman-machine collaboration Advanced metering infrastructure (AMI) Real-time data analytics

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Traffic Signs Detection and Augmented Reality Based on Multithreading

Traffic Signs Detection and Augmented Reality Based on Multi...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Wenting Li Qian Li Shangbing Gao Chuangxin Cai Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian P.R. China

ISBN: (纸本)9781538684986;9781538684979

The detection algorithm of existing traffic signs was improved, and the implementation of traffic sign detection and recognition was described in detail. This article mainly contains the following: Through targeted feature extraction, a trained cascaded classifier is used to obtain the location of traffic signs. Combined with the previous detection results, feature extraction is performed on the specified part of the target. The results are analyzed according to the location of detection or the results of the trained support vector machine are used to classify, and the classification results are obtained to realize the recognition of traffic signs. The total number of samples used for goal training in this article has reached thousands. The test results show that the detection rate of the traffic sign under the trained scene has been more than 90%. The algorithm has been optimized by the multi-thread method and combined with augmented reality to achieve real-time feedback.

关键词： Training Visualization Rendering (computer graphics) Feature extraction Streaming media Eigenvalues and eigenfunctions Augmented reality

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Research on Face Detection under Different Lighting

Research on Face Detection under Different Lighting

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International Conference on Applied System Innovation

作者： Hai-Wu Lee Fan-Fan Peng Xiu-Yun Lee Hong-Nian Dai Ying Zhu Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian City Jiang su China

Face detection is a biometric identification technology based on human facial feature information. This study uses the logitech C310 camera to collect images with faces and automatically detect faces in the images, and then carry out a series of technical processing on the detected faces. The traditional face detection technology is mainly based on visible images, which is also familiar with the detection method. But this kind of method has insurmountable defect, especially when the light environment changes, the detection effect will fall sharply, cannot meet the need of the actual system. The purpose of this study is to solve the classic problem of face detection under different lights, and to develop an intelligent and efficient human face detection method on Visual Studio 2015 platform software and OpenCV technology.

关键词： Face detection Webcam Visual studio 2015 and Opencv technology

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer Graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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Deep learning driven interpretable and informed decision making model for brain tumour prediction using explainable AI

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Scientific Reports 2025年第1期15卷 1-23页

作者： Adnan, Khan Muhammad Ghazal, Taher M. Saleem, Muhammad Farooq, Muhammad Sajid Yeun, Chan Yeob Ahmad, Munir Lee, Sang-Woong Pattern Recognition and Machine Learning Lab Faculty of Artificial Intelligence and Software Gachon University Seongnam-si 13557 South Korea Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19111 Jordan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Cyber Security NASTP Institute of Information Technology Lahore 58810 Pakistan Center for Secure Cyber-Physical Systems (C2PS) Computer Science Department Khalifa University Abu Dhabi United Arab Emirates University College Korea University Seoul 02841 South Korea

Brain Tumours are highly complex, particularly when it comes to their initial and accurate diagnosis, as this determines patient prognosis. Conventional methods rely on MRI and CT scans and employ generic machine learning techniques, which are heavily dependent on feature extraction and require human intervention. These methods may fail in complex cases and do not produce human-interpretable results, making it difficult for clinicians to trust the model’s predictions. Such limitations prolong the diagnostic process and can negatively impact the quality of treatment. The advent of deep learning has made it a powerful tool for complex image analysis tasks, such as detecting brain Tumours, by learning advanced patterns from images. However, deep learning models are often considered “black box” systems, where the reasoning behind predictions remains unclear. To address this issue, the present study applies Explainable AI (XAI) alongside deep learning for accurate and interpretable brain Tumour prediction. XAI enhances model interpretability by identifying key features such as Tumour size, location, and texture, which are crucial for clinicians. This helps build their confidence in the model and enables them to make better-informed decisions. In this research, a deep learning model integrated with XAI is proposed to develop an interpretable framework for brain Tumour prediction. The model is trained on an extensive dataset comprising imaging and clinical data and demonstrates high AUC while leveraging XAI for model explainability and feature selection. The study findings indicate that this approach improves predictive performance, achieving an accuracy of 92.98% and a miss rate of 7.02%. Additionally, interpretability tools such as LIME and Grad-CAM provide clinicians with a clearer understanding of the decision-making process, supporting diagnosis and treatment. This model represents a significant advancement in brain Tumour prediction, with the potential to enhance pat

关键词： Brain tumour CT scans MRI XAI

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The Study of Using Arduino to Control Aircraft Combined with Obstacle Avoidance Warning Function

The Study of Using Arduino to Control Aircraft Combined with...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Hai-Wu Lee Xian-Zhi Chen Xiao-Hao Tan Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian City Jiang Su China Faculty of Transportation Engineering Huaiyin Institute of Technology Huaian City Jiang Su China

With the rapid development of UAV technology, more and more sophisticated and simple unmanned aerial vehicles have been brought to market. For the general public however the issue of greatest concern has always been security. At present, it is a key issue for UAV development to realize air safety flight, and the obstacle avoidance function plays a key role in this issue. This thesis adopted the Arduino UNO, rotary ultrasonic, three color light and buzzer as performance of obstacle avoidance warning system, and send warning signals to the mobile phone APP through the WIFI to remind staff operating in advance to avoid an obstruction on the flight path, greatly reduce various kind of harm for error, etc.

关键词： Unmanned aerial vehicles Wireless fidelity Acoustics Alarm systems Mobile handsets Collision avoidance Sensors

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