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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pan, Zexu Wang, Wupeng Borsdorf, Marvin Li, Haizhou Institute of Data Science Singapore Singapore Nus Department of Electrical and Computer Engineering Singapore Singapore University of Bremen Machine Listening Lab Germany The Chinese University of HongKong Shenzhen Research Institute of Big Data School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

The speaker extraction technique seeks to single out the voice of a target speaker from the interfering voices in a speech mixture. Typically an auxiliary reference of the target speaker is used to form voluntary attention. Either a pre-recorded utterance or a synchronized lip movement in a video clip can serve as the auxiliary reference. The use of visual cue is not only feasible, but also effective due to its noise robustness, and becoming popular. However, it is difficult to guarantee that such parallel visual cue is always available in real-world applications where visual occlusion or intermittent communication can occur. In this paper, we study the audio-visual speaker extraction algorithms with intermittent visual cue. We propose a joint speaker extraction and visual embedding inpainting framework to explore the mutual benefits. To encourage the interaction between the two tasks, they are performed alternately with an interlacing structure and optimized jointly. We also propose two types of visual inpainting losses and study our proposed method with two types of popularly used visual embeddings. The experimental results show that we outperform the baseline in terms of signal quality, perceptual quality, and intelligibility. © 2023 IEEE.

关键词： Extraction

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Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home data

Privacy-Enhanced Living: A Local Differential Privacy Approa...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Waheed, Nazar Khan, Fazlullah Mastorakis, Spyridon Jan, Mian Ahmad Alalmaie, Abeer Z. Nanda, Priyadarsi School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Abdul Wali Khan University Department of Computer Engineering Mardan Pakistan University of Notre Dame Computer Science and Engineering Department United States

ISBN: (纸本)9798350346473

The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities. © 2023 IEEE.

关键词： Internet of things

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System 3

Decision Making and Recognition of String Characters using N...

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3rd International Conference on Machine Learning and data engineering, ICMLDE 2024

作者： Henge, Santosh Kumar Bhagat, Avinash Mandal, Sanjeev Kumar Viraati, Nikhil Reddy Singh, Ravleen Kedhar, K. Rushi Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal506371 India School of Computer Applications Lovely Professional University Punjab Phagwara India Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddewaram India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach;it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate. © 2025 The Author(s).

关键词： Optical character recognition

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Research on Revit-Based Turning Algorithm of Climbing Frame Type Mold for High-Rise Building Construction

Research on Revit-Based Turning Algorithm of Climbing Frame ...

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2023 China Automation Congress, CAC 2023

作者： Zhong, Hui Huang, JingKai School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysisline Laboratory of Urban Construction Shenyang China

ISBN: (纸本)9798350303759

To assist the Construction of a climbing frame for high-rise building construction and enrich the family of climbing frame parts, this paper proposes an algorithm for the molding of climbing frame parts, which automatically molds the Revit 3D model and forms the family file according to the CAD 2D drawings of climbing frame parts. The original information, such as text and geometric elements in the drawing of the part, is extracted in Revit. The algorithm is designed to calculate the data such as bottom profile, height, and hole position, and the 3D model of the part is built, and the family of the part is formed by stretching the bottom profile and cutting the hole. For the same type of parts, the differences between the parts are parameterized, and the structure of the parts is determined according to the parameters. The model is built, and the family of parts is formed. It improves the design efficiency of the climbing frame for high-rise building construction, lays the foundation for the subsequent overall design of the climbing frame, and provides specific value in promoting the application of BIM technology in the architectural design industry. © 2023 IEEE.

关键词： Architectural design

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Outage Prediction and Improvement in 6G for UAV Swarm Relays Using Machine Learning

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Progress In Electromagnetics Research B 2024年 107卷 33-45页

作者： Khalil, Hisham Ali, Gauhar Rahman, Saeed Ur Asim, Muhammad Affendi, Mohammed El Department of Intelligent Systems Faculty of Information Technology University of Lahore Pakistan EIAS Data Science and Block chain Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia School of Electronic Engineering Xidian University Xi’an China

The issue of signal outages in sub-THz frequency communication for future 6G networks is addressed by this research. A machine learning method is proposed, employing Random Forest and K-Means algorithms to predict the optimal frequency band and outage probabilities for UAV relays. Both space and frequency diversity are explored to enhance signal strength, and metasurface-carrying UAVs are introduced with a 16 × 16 mm2 design. This design significantly reduces the predicted outage probability from 0.1% to 0.0178%. Finally, triangular and hexagonal UAV swarm formations with metasurfaces are investigated, demonstrating improved performance through heatmap results. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第11期9卷 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3D CNN on Nodule data 2

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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IoT Smart City Air Quality Monitoring System Using Arduino

IoT Smart City Air Quality Monitoring System Using Arduino

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Gopinath, N. Chinnasamy, A. Sachin, K. Sakthiraj, S. Ravindharan, S. Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srmist Kattankulathur Campus Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350394580

Many factors, including population growth, increased vehicle use, industrialization, and urbanisation, have contributed to an increase in pollution levels throughout time, which has a negative impact on human wellbeing by adversely affecting the health of those exposed to it. To keep an eye on In this project, we'll build an IOT-based air pollution monitoring system that will track the air quality via an internet-connected web server and sound an alarm whenit drops below a certain threshold, which occurs when harmful gases like CO2, smoke, alcohol, benzene, and NH3 are present in sufficient quantities. On the LCD and on the website, the air quality will be displayed in PPM for easy monitoring. You can use your mobiledevice to check the pollution level as part of this IOT project from anywhere. © 2023 IEEE.

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Design and Implementation of a Power Grid Fault State Analysis System Based on Deep Learning 4

Design and Implementation of a Power Grid Fault State Analys...

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4th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2023

作者： Wen, Xuan Sun, Haimeng Xiamen Institute of Technology School of Data and Computer Science Xiamen China Chengyi College Jimei University Electronic Information Engineering Xiamen China

ISBN: (纸本)9798350308082

The aim of this paper is to propose an intelligent solution to power equipment fault. The emphasis is to improve the feature extraction for different types of power equipment fault Based on the deep learning technology, a method of acquiring feature map and constructing knowledge map for Power Equipment Fault Operation Detection is proposed. This method can analyze and diagnose the running state of power equipment, quickly identify and locate equipment faults, and provide corresponding maintenance schemes, so as to improve the ability of fault treatment and upgrade the level of equipment operation and maintenance services. In addition, this research also designed a kind of immersive full-color hybrid AR virtual environment electrical equipment fault teaching drill and Training Mode method, it enables operators to master the operation principle of power equipment and the skills of handling power equipment faults, so as to improve their practical operation ability and the accuracy of fault handling. © 2023 IEEE.

关键词： Neural network models

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Research on Algorithm of Building Overall Contour for Planar CAD Drawing

Research on Algorithm of Building Overall Contour for Planar...

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2023 China Automation Congress, CAC 2023

作者： Zhong, Hui Fan, Lei School of Computer Science and Engineering Liaoning Province Big Data Management and Analysisline Laboratory of Urban Construction Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350303759

In the study of automatic modeling of building climbing frame based on building CAD plans, recognizing the building's contour is a very critical step. In order to realize the automatic recognition of building contour and realize from 2D building drawings to 3D building entities, this paper generates 3D Autodesk Revit model from building plan AutoCAD 2D graphic recognition of building contour to realize BIM reconstruction. On the basis of adaptive chunking algorithm, combined with convex packet algorithm and depth search algorithm, an algorithm based on the recognition and processing of straight line Line and multi-segmented line PolyLine in architectural CAD drawings is proposed, and the building contour is automatically acquired and generated into a 3D entity. The algorithm links the CAD drawings in Revit, reads the text information by connecting to the. dwg database with the help of Teigha library, filters the straight lines and polylines of the target layer, processes them, and obtains and stores the endpoint information. The points on the building contour are obtained by scanning and comparing the endpoint coordinates. Finally, all the points on the contour are connected in an orderly manner to get the 2D contour of the building. Finally, the contour is solidly stretched in Revit to get the 3D building solid, which lays a certain foundation for the subsequent research on the automatic generation of the 3D model of the climbing frame. The algorithm is of great significance for realizing the automatic modeling from AutoCAD 2D graphics to Revit 3D model and the automatic modeling of building climbing frame, which has strong application value in the field of computer-aided building construction design. © 2023 IEEE.

关键词： Architectural design

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