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7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Das, Mousumi Choudhary, Tilendra Bhuyan, M.K. Sharma, L.N. Pallab Jyoti Dutta, H. Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Department of Electronics and Electrical Engineering Assam Guwahati India

ISBN: (纸本)9781665496483

In recent years, camera-based non-contact heart rate (HR) measurement technology has grown immensely. The system captures the reflection of light from the facial tissues and lead to the formation of a remote photoplethysmogram (rPPG) signal that can be used to measure physiological parameters for cardiac health assessment. Due to environmental interferences, extraction of a reliable rPPG signal is a challenging task and thus, requires a robust denoising algorithm. In this paper, a discrete wavelet transform (DWT)-based multiresolution method is used to remove the noises from the video frames caused due to illumination variation and motion artifacts. Subsequently, rPPG signal is extracted and HR is measured from two region of interests (ROIs), facial and forehead regions. The study evaluates the performance of the proposed method on each of the RGB color channels from both the ROIs. The performance results for the COHFACE dataset show that the proposed method works well for the estimation of HR values. Furthermore, they reveal that the forehead region on the green channel is more suitable for HR measurement. © 2022 IEEE.

关键词： Discrete wavelet transforms

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The Smart Coverage Path Planner for Autonomous Drones Using TSP and Tree Selection 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Sundarrajan, M. Jothi, Akshya Prabakar, D. Kadry, Seifedine SRM Institute of Science and Technology Chennai India Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The problem of efficiently covering an area with a limited number of sensors or cameras is an important research topic in various fields such as surveillance, monitoring, and inspection. This problem can be posed as a Traveling Salesman Problem (TSP), where the optimal path needs to be found to visit all the coverage points and return to the starting point. However, the computational complexity of solving TSP increases exponentially with the number of coverage points, making it impractical for large-scale applications. This paper proposes a novel approach to solving the coverage problem as a TSP by incorporating tree selection algorithms to improve efficiency and accuracy. The proposed approach represents the study area as a graph, with each node representing a coverage point and the edges representing the distance between them. To reduce computational complexity, we use various tree selection algorithms to select the best possible starting point for the TSP algorithm. To evaluate the effectiveness of the proposed approach, we conduct simulations using different scenarios with varying numbers of coverage points. The simulation results demonstrate that the proposed approach significantly improves the quality of coverage while reducing computational complexity compared to traditional TSP algorithms. Moreover, the proposed approach can be applied in a wide range of real-world applications, such as surveillance, monitoring, or inspection, where efficient and accurate coverage of an area is essential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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MedSay-Tamil: A Pharmacological-Translator Mobile Application for the aid of Native Tamil Speakers

MedSay-Tamil: A Pharmacological-Translator Mobile Applicatio...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Joshua Alfred, J. Sai Srivathsan, V. Sasithradevi, A. Mohamed Mansoor Roomi, S. Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798350312126

Medication error is one of the major causes of death, as victims intake the wrong medicine or dosage which can cause chronic effects. One such cause of medication error comes from the misinterpretation of information due to language barrier. There is a need for translation of information in medicinal strips to create awareness to the user. For this purpose, we propose an Innovative Mobile Application Using Flutter and Dart, empowering all native-Tamil speakers who find difficulty in acquiring information from tablet strips. The app is designed to revolutionize the way people obtain information from tablet strips such as general information, dosage, side effects, etc. with the help of web scraping. With its advanced Optical Character Recognition (OCR) engine, the app can accurately recognize text from images and convert it into editable text. The app's user-friendly interface with its state-of-art innovation mainly focusing native-Tamil speakers, built using the Flutter framework, allows users to quickly and easily scan and process strings. The application then translates and displays the information present in the Tamil language along with a text-to-speech feature. Upon testing, the application displayed a 90% accuracy in retrieving relevant information about the tablet present in images. © 2023 IEEE.

关键词： Optical character recognition

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Structural damage detection via hierarchical damage information with volumetric assessment

arXiv

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arXiv 2024年

作者： Agyemang, Isaac Osei Adjei-Mensah, Isaac Acheampong, Daniel Boateng, Gordon Owusu Baffour, Adu Asare Data Science and Big Data Technology Stirling College Chengdu University Chengdu610054 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu610054 China Lutgert College Florida Gulf Coast University United States School of Science and Engineering University of Missouri-Kansas City United States

Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure, but complex image environments, noisy labels, and reliance on manual damage assessments often hinder its effectiveness. This study introduces the Guided Detection Network (Guided-DetNet), a framework designed to address these challenges. Guided-DetNet is characterized by a Generative Attention Module (GAM), Hierarchical Elimination Algorithm (HEA), and Volumetric Contour Visual Assessment (VCVA). GAM leverages cross-horizontal and cross-vertical patch merging and cross-foreground-background feature fusion to generate varied features to mitigate complex image environments. HEA addresses noisy labeling using hierarchical relationships among classes to refine instances given an image by eliminating unlikely class instances. VCVA assesses the severity of detected damages via volumetric representation and quantification leveraging the Dirac delta distribution. A comprehensive quantitative study and two robustness tests were conducted using the Pacific Earthquake Engineering Research (PEER) Hub Image-Network dataset, and a drone-based application, which involved a field experiment, was conducted to substantiate Guided-DetNet's promising performances. In triple classification tasks, the framework achieved 96% accuracy, surpassing state-of-the-art classifiers by up to 3%. In dual detection tasks, it outperformed competitive detectors with a precision of 94% and a mean average precision (mAP) of 79% while maintaining a frame rate of 57.04 frames-per-second, suitable for real-time applications. Additionally, robustness tests demonstrated resilience under adverse conditions, with precision scores ranging from 79% to 91%. Guided-DetNet is established as a robust and efficient framework for SHM, offering advancements in automation and precision, with the potential for widespread application in drone-based infrastructure inspections. © 2024, CC BY.

关键词： Damage detection

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WPMamba: Enhanced Wind Power Forecasting Model Based on Mamba with Weather Forecast data

WPMamba: Enhanced Wind Power Forecasting Model Based on Mamb...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Tengyuan Liu Lei Liu Xue Dong Qiuju Chen Bin Li University of Science and Technology of China Hefei China School of Information Science and Technology University of Science and Technology of China Hefei China Power China Huadong Engineering Corporation Limited Hangzhou China Laboratory for Big Data and Decision Changsha China

ISBN: (数字)9798350377460

ISBN: (纸本)9798350377477

As global warming intensifies, the development of wind energy becomes increasingly important. Given the inherent unpredictability of wind, there is a growing demand for sophisticated forecasting techniques to better incorporate wind energy into the existing power grid. Previous studies mostly relied on historical data to predict power generation without considering future climate conditions. These methods were only effective for short-term predictions, not for long-term predictions. To address the aforementioned issues, future weather forecast information is incorporated into the traditional framework within the paper. According to the new framework, a novel network model named WPMamba is proposed for predicting the output power of wind farm based on the Mamba model. Concurrently, a data preprocessing method, specifically tailored for wind power forecasting (WPF), is also introduced in this paper. Experiments show that the proposed WPMamba model demonstrates convincing performance in short-term, medium-term and long-term WPF tasks, achieving significant performance advantages over existing models. The ablation experiments also demonstrate that the introduction of future weather forecast information and our proposed data processing method can both enhance the model's forecasting accuracy.

关键词： Renewable energy sources Wind energy Time series analysis Predictive models Wind power generation Wind farms data models Power grids Wind forecasting Forecasting Climate change Global warming

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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Mouse Cusor Control Using Facial Movements - An HCI Application

Mouse Cusor Control Using Facial Movements - An HCI Applicat...

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2022 International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Shashidhar, R. Snehith, K. Abhishek, P.K. Vishwagna, Abhishek B Pavitha, N. Sri Jayachamarajendra College of Engineering Jss Science Technology University Department of Electronics and Communication Engineering Mysuru India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Maharashtra Pune India

ISBN: (纸本)9781665478847

Because of various limitations, some people are unable to operate computers. Cursor controls based on facial expression are extremely useful not just for the future of natural input, but also for the handicapped and disadvantaged. Furthermore, creating a control system allows them to operate computers without the assistance of a third party. While this is not an issue for a healthy person, it may be an insurmountable barrier for persons who have a limited range of motion in their limbs. In these instances, input techniques based on the region's greater skills, such as eye movements and facial expressions, would be preferable. A system was created that uses a low-cost way to permit alternative input methods like controlling a mouse cursor on a computer screen. Using a camera we can capture the image of the eye and head movement. The mouse cursor is controlled by head movement as moving face up, down, sidewise movements and mouse events such as right and left click are controlled through eye blinks and squinting. This system is mainly aimed at disabled people to have effective communication with the computer. It is quite beneficial to implement a controlling system that allows them to move without the assistance of another person. © 2022 IEEE.

关键词： Computer vision

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Consensus Graph Filter Learning for Multiple Graph Clustering

Consensus Graph Filter Learning for Multiple Graph Clusterin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiale Zou Yan Chen Peng Zhou Chao Wen Liang Du Yuhua Qian School of Computer and Information Technology Shanxi University Taiyuan China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China School of Computer Science and Technology Anhui University Hefei China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-view Clustering (MVC) has gained significant attention for its ability to utilize consistent and complementary information from multiple views. Graph filter-based MVC methods have recently demonstrated promising performance, attracting growing interest. However, existing graph filter-based methods typically rely on a single filter for each view. These filters are usually derived from either a specific view or a consensus graph across all views, which limits their effectiveness in fully integrating multi-view information. To address this limitation, we propose a novel method, Consensus Graph Filter Learning for Multiple Graph Clustering (CGFMVC). Unlike existing methods that rely on a single filter per view, we construct multiple graph filters by integrating information from all views. For each view, we generate intra-view graph filters to intermediate high-order information and bridge local-global structures. This approach facilitates neighborhood smoothing and preserves local consistency within each view. By learning a consensus graph filter from these multiple filters, we effectively capture global complementary information across views while preserving local consistency. CGFMVC demonstrates excellent efficiency and effectiveness on various benchmark datasets, outperforming state-of-the-art methods. The code is publicly available at https://***/Sean-zjl/CGFMVC.

关键词： Bridges Smoothing methods Codes Benchmark testing Signal processing Information filters Acoustics Speech processing

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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How to apply deep reinforcement learning to multi-dimensional intelligent reasoning

How to apply deep reinforcement learning to multi-dimensiona...

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2022 International Symposium on Artificial Intelligence Control and Application technology, AICAT 2022

作者： Wu, Guobin Liang, Jian Jin, Xi Wang, Xiao Zhang, Zhewen DiDi Technology Ecosystem and Development Didi Chuxing Beijing China Data Science and Artificial Intelligence Department Draweast Tech Beijing China

ISBN: (纸本)9781510656710

With the rapid development of artificial intelligence, the research on perceptual intelligence is becoming more and more mature. For the next stage of cognitive intelligence research, knowledge graph (KG) is one of the key directions. Knowledge reasoning is an important part of knowledge graph and has a wide range of application requirements. To solve the problems of current large-scale KG, including poor interpretability, low accuracy and efficiency of knowledge reasoning, this paper proposes a method RLPTransE which combines knowledge representation, relational path with deep reinforcement learning. This method generates a path vector by semantically combining the vectors of all relations on the path. Then, a reinforcement learning environment is established in the vector space, and through training a multi-step inference policy network based on diverse reward functions, the reinforcement learning agent can efficiently complete inference in the process of interacting with the environment. This paper conducted link prediction and fact prediction experiments with the RLPTransE method on NELL-995 and FB15K-237 datasets, and compared with knowledge representation-based, relational path-based and fusion-based methods, the experimental results show that RLPTransE method achieves better performance on large-scale dataset inference tasks. © 2022 SPIE.

关键词： Knowledge representation

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