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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Analyzing α-divergence in Gaussian Rate-Distortion-Perception Theory

Analyzing α-divergence in Gaussian Rate-Distortion-Percepti...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Martha V. Sourla Giuseppe Serra Photios A. Stavrou Marios Kountouris School of Electrical & Computer Engineering Technical University of Crete Chania Greece Communication Systems Department EURECOM France Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

The problem of the estimation of the information rate-distortion-perception function (RDPF), which is a relevant information-theoretic quantity in goal-oriented lossy compression and semantic information reconstruction, is investigated here. Specifically, we study the RDPF tradeoff for Gaussian sources subject to a mean-squared error (MSE) distortion and a perception measure that belongs to the family of α-divergences. Assuming a jointly Gaussian RDPF, which forms a convex optimization problem, we characterize an upper bound for which we find a parametric solution. We show that evaluating the optimal parameters of this parametric solution is equivalent to finding the roots of a reduced exponential polynomial of degree α. Additionally, we determine which disjoint sets contain each root, which enables us to evaluate them numerically using the well-known bisection method. Finally, we validate our analytical findings with numerical results and establish connections with existing results.

关键词： Wireless communication Upper bound Semantics Measurement uncertainty Estimation Distortion Polynomials Convex functions Distortion measurement Information theory

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Crop Yield Prediction Using Deep Reinforcement Learning 2

Crop Yield Prediction Using Deep Reinforcement Learning

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2nd International Conference on Trends in Electrical, Electronics and computer Engineering, TEECCON 2023

作者： Deivasigamani, Sudhakar Jaya Mabel Rani, A. Natchadalingam, R. Vijayakarthik, P. Bala Sendhil Kumar, G. Reddy, Pundru Chandra Shaker Digipulse Technologies Inc Scrum Master NJ United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India R.L.Jalappa Institute of Technology Computer Science and Engineering Karnataka Bengaluru India Sri Manakula Vinayagar Engineering College Master of Business Administration Puducherry India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350339949

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models. © 2023 IEEE.

关键词： Crops

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Autoencoder based Temporal Convolutional Network for Anomaly Detection in data Streams 1

Autoencoder based Temporal Convolutional Network for Anomaly...

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1st International Conference on Intelligent Systems and Computational networks, ICISCN 2025

作者： Tamilselvan, K. Riadhwsein, Ramee Sundaram, S. Meenakshi Vigneshwaran, K.S. Abhinaya, S. Avs Engineering College Department of Information Technology Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India

ISBN: (纸本)9798331529246

Nowadays, the rapid growth of data stream from different sources has needed the development of effective Anomaly Detection (AD) methods. The existing unsupervised Deep Learning (DL) approaches learnt various patterns and relationships in data streams without labeled data. However, detecting anomalies in data streams led to significant challenges such as handling high-dimensional data and capturing temporal dependencies. Hence, this research proposes an Autoencoder based Temporal Convolutional Network (AE-TCN) for AD in data streams. Initially, the input data is collected from Numenta Anomaly Benchmark (NAB) repository and further preprocessed with Regression Imputation (RI) to handle missing values and logarithmic scaling to normalize the data respectively. After that, Robustly Optimized Bidirectional Encoder Representation from Transformer approach (RoBERTa) is employed to extract the semantic features. Finally, AE-TCN is introduced to identify the anomalies in data streams by extracting temporal features from reconstructed nominal patterns. From the results, the proposed AE-TCN established high results in terms of Accuracy (Acc), F1-score, and Area Under Curve (AUC) of 99.89%, 94.06%, and 99.85% respectively when compared with existing Deep Autoencoder Neural Network with Adamax Optimization (DANN-AO). © 2025 IEEE.

关键词： Spatio-temporal data

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Research on The Method of Constructing Knowledge Graph of Affective Disorder Based on Multi-Source data Fusion 22

Research on The Method of Constructing Knowledge Graph of Af...

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Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering

作者： Yang Wang Weihong Yang School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781450396806

Affective disorder (Depression) has the characteristics of high incidence rate, low recognition rate, high disability and death rate, and has become a serious public health problem that needs to be solved urgently. It is particularly important to use machine learning and big data mining technology to build a complete knowledge graph of affective disorders. In this paper, multi-source data fusion is used to construct the original corpus of the knowledge graph of affective disorders. In the information extraction stage, we designed a machine learning algorithm based on Hidden Markov Model (HMM). The algorithm can extract entities and entity mapping relationships from corpus to form entity database and triplet relationship database. In the knowledge fusion stage, multi-source heterogeneous knowledge fusion algorithm is used to complete entity alignment and relationship deduction. Finally, the open-source graphic database Neo4j is used as the underlying storage structure, so as to realize the visualization of the knowledge graph of affective disorders.

关键词： Multi-source data Knowledge Graph Affective Disorder Knowledge Fusion Information Extraction

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What should a neuron aim for? Designing local objective functions based on information theory

arXiv

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arXiv 2024年

作者： Schneider, Andreas C. Neuhaus, Valentin Ehrlich, David A. Makkeh, Abdullah Ecker, Alexander S. Priesemann, Viola Wibral, Michael Faculty of Physics Institute for the Dynamics of Complex Systems University of Göttingen Germany Max Planck Institute for Dynamics and Self-Organization Germany Campus Institute for Dynamics of Biological Networks University of Göttingen Germany Institute of Computer Science Campus Institute Data Science University of Göttingen Germany

In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. We here show how self-organization between individual artificial neurons can be achieved by designing abstract bio-inspired local learning goals. These goals are parameterized using a recent extension of information theory, Partial Information Decomposition (PID), which decomposes the information that a set of information sources holds about an outcome into unique, redundant and synergistic contributions. Our framework enables neurons to locally shape the integration of information from various input classes, i.e. feedforward, feedback, and lateral, by selecting which of the three inputs should contribute uniquely, redundantly or synergistically to the output. This selection is expressed as a weighted sum of PID terms, which, for a given problem, can be directly derived from intuitive reasoning or via numerical optimization, offering a window into understanding task-relevant local information processing. Achieving neuron-level interpretability while enabling strong performance using local learning, our work advances a principled information-theoretic foundation for local learning strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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Topological Dependencies in Deep Learning for Mobile Edge: Distributed and Collaborative High-Speed Inference 2

Topological Dependencies in Deep Learning for Mobile Edge: D...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Abd Algani, Yousef Methkal Kumar, A Suresh Ala Walid, Md. Abul Balu, S. Velayutham, Priya Sasi Kumar, A. The Arab Academic College for Education in Israel-Haifa Department of Mathematics Israel Rathinam Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore India Khulna9203 Bangladesh Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamil Nadu Namakkal India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Inurture Education Solutions Pvt Ltd Bangalore India Srinivas University Institute of Engineering & Technology Department of Cloud Technology & Data Science Karnataka State India

ISBN: (纸本)9798350346640

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. Distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Edge computing

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time 26

Exploring Patent Transformation Event: Forecasting Patent Tr...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Huo, Jinchen Liu, Weidong Li, Yang Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events. © 2023 IEEE.

关键词： Forecasting

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Research on patent recommendation method based on graph neural network 26

Research on patent recommendation method based on graph neur...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Enchao Liu, Weidong Liu, Shi Cao, Yan Institute of Scientific and Technical Information of China China College of Computer Science Inner Mongolia University China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350331684

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation. © 2023 IEEE.

关键词： Patents and inventions

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Detecting the Human Activities of Aging People using Restricted Boltzmann Machines with Deep Learning Technique in IoT 3

Detecting the Human Activities of Aging People using Restric...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Manimaran, M. Sasi Kumar, A. Natteshan, N.V.S. Baranitharan, K. Mahaveerakannan, R. Sudhakar, K. Sri Krishna Adithya College of Arts and Science Department of Computer Science Coimbatore India Srinivas University Institute of Engineering and Technology Department of Cloud Technology and Data Science Surathkal Dakshina Kannada District Mukka India S.A. Engineering College Department of Cse Chennai India Vsb Engineering College Department of Cse India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Ece Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Approximately 962,000,000 persons worldwide are 60 or older. Despite the widespread adoption of techniques for human activity detection, there has been a dearth of study into the specific challenge of identifying the tasks performed by the elderly. Given the proliferation of wearable and mobile devices, the Internet of Healthcare Things is assuming a larger role in Human Activity Recognition (HAR). This article focuses on assisting the elderly by tracking their movements in both indoor and outdoor settings. The dataset includes human actions like sitting, walking, ascending and descending stairs, standing, and lying down. Here, how deep learning might improve HAR in Internet of Home Things settings has been examined. For better HAR results, a semi-supervised deep learning framework has been developed that makes effective use of the imperfectly labelled sensor data in order to fine-tune the classifier learning model. An intelligent auto-labelling system is built on top of Deep Q-Network to enhance learning performance in Internet of Things (IoT) environment and address the issue of insufficient labelled samples. Finally, real-world data is used in trial and evaluation to prove the method's utility and efficacy. © 2023 IEEE.

关键词： Internet of things

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