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2023 IEEE International conference on Big Data, BigData 2023

作者： D'Onofrio, Anthony Hossain, Amir Santana, Lesther Machlovi, Naseem Stein, Samuel Liu, Jinwei Li, Ang Mao, Ying Fordham University Computer and Information Science Department United States United States Florida A&M University United States

ISBN: (纸本)9798350324457

The rapid advancement of quantum computing has pushed classical designs into the quantum domain, breaking physical boundaries for computing-intensive and data-hungry applications Given its immense potential, quantum-based computing systems have attracted increasing attention with the hope that some systems may provide a quantum speedup. For example, variational quantum algorithms have been proposed for quantum neural networks to train deep learning models on qubits, achieving promising results. Existing quantum learning architectures and systems rely on single, monolithic quantum machines with abundant and stable resources, such as qubits. However, fabricating a large, monolithic quantum device is considerably more challenging than producing an array of smaller devices. In this paper, we investigate a distributed quantum system that combines multiple quantum machines into a unified system. We propose DQuLearn, which divides a quantum learning task into multiple subtasks. Each subtask can be executed distributively on individual quantum machines, with the results looping back to classical machines for subsequent training iterations. Additionally, our system supports multiple concurrent clients and dynamically manages their circuits according to the runtime status of quantum workers. Through extensive experiments, we demonstrate that DQuLearn achieves similar accuracies with significant runtime reduction, by up to 68.7% and an increase per-second circuit processing speed, by up to 3.99 times, in a 4-worker multi-tenant setting. © 2023 IEEE.

关键词： Distributed Quantum Computing Multi-node Quantum Quantum Resource Management

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Garbage classification recognition based on improved YOLOv8 model

Garbage classification recognition based on improved YOLOv8 ...

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Signal processing and Intelligent Computing (SPIC), International conference on

作者： Lijuan Qin Xiaoyu Tang Feng Zhang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The use of intelligent garbage classification methods and devices for waste sorting is set to become the future trend in waste management. With the development of high-precision image recognition algorithms, research in intelligent garbage classification has gradually matured. The core lies in utilizing image recognition technology to analyze garbage image information, thereby surpassing the limitations of manual sorting and achieving higher standards of speed and accuracy. This paper proposes a target detection scheme based on the introduction of the C2f_RepGhost module into YOLOv8, aiming to construct a model system for efficient garbage identification and classification. Thanks to its streamlined design and real-time response characteristics, this model can operate efficiently on platforms such as smart mobile devices, enabling real-time intelligent automatic classification of various types of waste.

关键词： Waste management Accuracy Image recognition Computational modeling Streaming media Real-time systems Data models Usability Standards Sorting

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Enhancing Blurred Image processing with IoT Integration for Improved Clarity

Enhancing Blurred Image Processing with IoT Integration for ...

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Emerging Research in Computational Science (ICERCS), International conference on

作者： Geetamma Tummalapalli Summia Parveen H Ramesh S M Monica P Santhiya B Arun Sekar Rajasekaran Department of Electronics and Communication Engineering GMR Institute of Technology Rajam India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering GST GITAM Deemed to be University Visakhapatnam India Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering SR University Warangal India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains.

关键词： Image sensors Cloud computing Privacy Image processing Image edge detection Noise Visual effects Real-time systems Internet of Things Object recognition

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Distributed Deep Reinforcement Learning for Autonomous Iot Healthcare devices in the Cloud 1

Distributed Deep Reinforcement Learning for Autonomous Iot H...

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1st International conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Shukla, Aasheesh Pokhariya, Hemant Singh Michaelson, Jacob Laxminarayanamma, K. Kumar, Mukesh Krishna, Om Gla University Department of Electronics and Communication Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The ethical and philosophical problems concerning the cooperation of AI systems and human artists are also examined in this study. In addressing authorship, agency, and the very essence of creation, the changing position of artists as co-creators with intelligent algorithms is explored. It also looks at how AI can question and change conventional ideas of creative competence. Additionally, this study looks into how AI affects the promotion and distribution of art. AI-driven marketing tactics provide improved targeting of customers, personalized experiences, and optimal promotional efforts by utilizing insights based on data and predictive analytics. The study focuses on how these developments transform the relationships between galleries and artists and their patrons, ultimately fostering a more varied and inclusive art scene. The system's potential in many healthcare scenarios has been validated through simulations and practical experiments, which have received excellent feedback from healthcare providers. A critical review emphasizes the need to address practical deployment issues and data security concerns, while also highlighting the exciting convergence of IoT healthcare devices and DDRL. The paper ends with suggestions for more research, highlighting the significance of ethical issues, user interface improvements, and real-world validation. Through the smooth integration of DDRL-enhanced Internet of Things (IoT) medical equipment into clinical practice, our research eventually improves patient care and transforms the delivery of healthcare. © 2023 IEEE.

关键词： data processing healthcare Internet of Things learning process technology

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Algorithm for Changing the Size of a Multi-Element Aperture of a Two-dimensional Recursive-Separable Five-Stage Filter for Digital Image processing

Algorithm for Changing the Size of a Multi-Element Aperture ...

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Annual Siberian Russian Workshop on Electron devices and Materials (EDM)

作者： Andrey V. Kamenskiy Kristina S. Kupriyanova Andrey V. Podnebesnyy Mikhail I. Kuryachiy Natalya Borodina Akaeva M. Tatyana Dept. of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The paper presents a description of the developed algorithm for changing the size of a multi-element aperture of a recursive-separable five-stage filter for processing digital images generated by specialized optical systems. The developed algorithm has two features: it has the ability to change the size of a multi-element aperture during use; it makes it possible to use the recursive and separable properties. These capabilities make it possible to reduce the processing time of incoming data, which has a positive effect on the efficiency of the optical-electronic imaging system used. The paper presents a block diagram of a two-dimensional recursive-separable five-stage filter, with the help of which an algorithm was developed for changing the size of a multi-element aperture, and a study of the influence of the aperture size on the performance of the developed filter was carried out. As part of the performance evaluation, the developed algorithm was compared with the classical two-dimensional convolution algorithm. Developed algorithm was tested on three test images of different sizes and formats: $1300 \times 700$ Bitmap Picture, 1200x1200 Tagged Image File, 2900x1900 Joint Photographic Experts Group. During the processing of test images, the dimensions of the multi-element aperture of the developed filter changed. As the aperture size increases, the performance evaluation results also increase. This shows that speeding up the algorithm is an actual task. However, the developed algorithm takes less time than the classical two-dimensional convolution algorithm, from which it can be concluded that the developed solution is effective for both conventional images and images from optoelectronic systems.

关键词： optical filters Performance evaluation Convolution Digital images Image processing Apertures Filtering algorithms

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Adding ears to intelligent connected vehicles by combining microphone arrays and high definition map

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IET INTELLIGENT TRANSPORT systems 2021年第10期15卷 1228-1240页

作者： Jiang, Kun Yang, Diange Wijaya, Benny Zhang, Bowei Yang, Mengmeng Zhang, Kai Tang, Xuewei Tsinghua Univ Sch Vehicle & Mobil Ctr Intelligent Connected Vehicles & Transportat State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

Ears are responsible for the sense of hearing, which is indispensable for a human to understand the environment. It is similar to an intelligent vehicle where acoustical data contains useful and valuable information to comprehend the environment to enable autonomous driving. One important application of acoustical data processing is to localize sound sources. However, due to the complexity of microphone array setup and the unstable localization performance against noises, few research articles tried to localize surrounding vehicles based on on-board microphone arrays. The main contribution is the first implementation to combine roof-mounted microphones and high definition maps as vehicle' s ears for online 3D localization of surround vehicles. This article designed a six-microphones sensor array dedicated to the on-board application and improved sound source localization algorithms using the observer technique to adapt to dynamic driving scenarios. A map-based localization enhancement method is also proposed to make the system more robust to noises and model errors. Feasibility tests are conducted with real vehicles in the scenarios of pass-by and overtaking. Experimental results validate the feasibility of using microphones to localize surrounding vehicles with lane-level accuracy.

关键词： Speech and audio signal processing optical, image and video signal processing Signal processing and detection Sensing devices and transducers Digital signal processing Computer vision and image processing techniques

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A Comparative Assessment of Deep Learning algorithms in Network Intrusion Detection System

A Comparative Assessment of Deep Learning Algorithms in Netw...

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International conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： Varadhaganapathy S Shanthakumari R Vinothkumar Sengottuvelu Kavya S Kanishkar K M Barath S Department of Information Technology Kongu Engineering College Erode India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Deep learning procedures like ANN, RNN, LSTM, and CNN advanced threat detection in network systems. Thus, there must be advanced IDS with increased accuracy in identifying intrusions into its web connected devices. Leverage UNSW-NB15 benchmark dataset, which is strong bench marking in network security research. The models that we have thus far are accurate to the extent of 97%. The detection rate is also improved substantially with minimal false positives. Each deep learning approach was tailored to handle different issues inherent to intrusion detection - handling sequential data, capturing spatial patterns, and most importantly, privacy through distributed learning. This paper clearly details comparisons of these algorithms in depth. It will outline the merits and shortcomings of each method concerning IDS. The results will eventually herald the hope of advanced deep learning techniques using LSTM and CNN as promising high-performance intrusion detection solutions that could be adaptable to the dynamically changing cyberthreat landscape.

关键词： Deep learning Accuracy Heuristic algorithms Signal processing algorithms Network intrusion detection Telecommunication traffic Predictive models Feature extraction Data models Long short term memory

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Research of Gesture Recognition System Based on OpenCV

Research of Gesture Recognition System Based on OpenCV

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IEEE information Technology, Networking, Electronic and Automation Control conference

作者： Xiangfang Mao Yanhua Qiao Jianna Li Huijuan Ma School of information science and engineering Tianjin TianShi College Tianjin China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Currently, the primary modes of human-computer interaction still revolve around traditional devices like mice and keyboards. Technologies such as face recognition, emotion recognition, voice recognition, and gesture recognition have gained increasing attention, making the ways of human-computer interaction more flexible and vivid. This paper mainly focuses on gesture recognition systems in natural scenarios. OpenCV provides various image processing algorithms that can capture image and video information from digital cameras and convert them into binary image information recognized by computers through grayscale image processing. Then, the binary images are filtered and segmented. After extracting the shape of the hand, the contour features of the hand are analyzed. In a normal natural environment captured by a camera, corresponding commands are extracted based on the position and shape of the gestures, enabling simple human-computer interaction.

关键词： Human computer interaction Image segmentation Shape Keyboards Gesture recognition Speech recognition Feature extraction information filters Mice information technology

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Enhancing Video Stability with Object-Centric Stabilization

Enhancing Video Stability with Object-Centric Stabilization

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IEEE conference on Multimedia information processing and Retrieval (MIPR)

作者： Aparna Tiwari Hitika Tiwari K.S. Venkatesh Anuj Kumar Sharma Department of Mechatronics Engineering Center for Advance Studies A.K.T.U. Lucknow India Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

Video stabilization is essential for producing high-quality videos by eliminating unwanted camera motion, resulting in smooth, steady footage that enhances visual coherence. However, existing methods often pose several challenges such as the difficulties in accurately tracking specific objects, and high computational demands. These limitations hinder the ability to achieve optimal stabilization results and limit the practical applicability of video stabilization techniques in real-world scenarios. To address these challenges, we introduce a novel end-to-end method for object-centric video stabilization in the presence of multiple independent objects. Our approach introduces two algorithms: 1) Object-Centric Video Stabilization (OCViS), which facilitates effective detection and tracking of specific objects within each frame, followed by stabilization with respect to the object of interest, and 2) optical Flow-based Transformation Accumulation and Smoothing (OFTrAnS) to remove inherent shakiness and maintain consistent stabilization, even with multiple independent objects. Most importantly, our algorithms achieve these results without relying on deep neural networks or ground-truth data, providing a more efficient and accessible solution for enhancing video quality on mobile devices and handheld cameras. Our experiments demonstrate that the proposed approach significantly outperforms a strong baseline by a large margin emphasizing the efficacy of our method.

关键词： Visualization Smoothing methods Tracking optical computing Artificial neural networks Streaming media Cameras Quality assessment Reliability Video recording

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Mode Division Multiplexing Bidirectional Transmission System using Double Clad Fiber for Mobile In-Building Solutions

Mode Division Multiplexing Bidirectional Transmission System...

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International conference on Telecommunications and Signal processing (TSP)

作者： Ahmed S. Mohamed Eszter Udvary Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary ELKH-BME Information Systems Research Group Budapest Hungary

This paper shows the conceptual model of the possibility of simultaneously transmitting data and power on the same optical fiber by adopting the Power Over Fiber PWoF concept and Mode Division Multiplexing MDM. MDM is the data transmission multiplexing scheme of choice where two modes of light are considered; one mode carries the downlink, while the other mode carries the uplink information. Adopting the PWoF, remote devices can be powered by the optical energy transferred on the same optical fiber. Double Clad Fibers DCF are introduced as the transmission medium. DCF consists of the core, which is a few-mode fiber core capable of handling the two needed modes, the inner cladding which has a relatively large diameter and is capable of handling the optical power, and the outer cladding. The proposed system can reduce the complexity and the operations cost, suitable to be used in In-Building Solutions IBS.

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