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Vehicular Indoor Localization and Tracking System
In this chapter, we implement a system prototype for vehicle localization and tracking in an indoor environment. First, we leverage ubiquitous...
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Deep Q-Learning-Based Adaptive Multimedia Streaming in Vehicular Edge Intelligence
In this chapter, we present an architecture for Adaptive-BitRate (ABR)-based multimedia streaming in heterogeneous IoV, where each multimedia file is...
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Distributed Task Offloading and Workload Balancing in IoV
MEC is an emerging paradigm to offload computation from the cloud in vehicular networks, aiming at better supporting computation-intensive services...
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Temporal Data Uploading and Dissemination in Real-Time Vehicular Networks
Temporal information services are critical in implementing emerging ITSs. Nevertheless, it is challenging to realize timely temporal data update and...
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Non-line-of-sight Collision Warning System
VEC has been envisioned as a promising paradigm for enabling a variety of emerging ITSs. However, due to inevitable yet non-negligible issues in...
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See Through System
With the continuous advancements in sensing technologies and wireless communication, video applications have gained significant popularity in IoV....
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Future Directions
This chapter outlines future research directions from three aspects, including vehicle–road–cloud integration, cyber-physical fusion, and generative...
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Conclusion
In this monograph, we have presented the latest advancements of connected, cooperative, and intelligent IoV, and introduced five typical application...
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Collaborative Incentive Mechanism for Mobile Crowdsensing
In this chapter, we propose PTASIM, an incentive mechanism that explores cooperation with POI-tagging App for Mobile Edge Crowdsensing (MEC). PTASIM...
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A Brief Introduction
In this chapter, we first introduce the background regarding Mobile Crowdsensing (MCS) and present an overview of MCS. Then, we specifically state...
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Neural Networks to Infer Traditional Chinese Medicine Prescriptions from Indications
Ith increasing digitization of Chinese medicine-related books and extraction and analysis of the ingredients in herbs, it now becomes feasible to use...
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Integration of Convolutional Neural Networks and Autoencoding for Generating Reconfigurable Intelligent Surfaces
This paper presents a method utilizing convolutional neural networks (CNN) and autoencoding for generating a reconfigurable intelligent surface (RIS)...
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Lay Summarization of Biomedical Documents with Discourse Structure-Based Prompt Tuning
Transforming complex biomedical texts into accessible lay summaries is a critical endeavor in Natural Language Generation (NLG). This study addresses...
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Robust Influence-Based Training Methods for Noisy Brain MRI
Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on...
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SASBO: Sparse Attack via Stochastic Binary Optimization
Deep Neural Networks have shown vulnerability to sparse adversarial attack, which involves perturbing only a limited number of pixels. Identifying...
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Personalized EDM Subject Generation via Co-factored User-Subject Embedding
This paper introduces the Co-Factored User-Subject Embedding based Personalized EDM Subject Generation Framework (COUPES), a model for creating...
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On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises
Recommender Systems are a popular and common means to extract relevant information for users. Small and medium-sized enterprises make up a large...
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Probabilistic Guarantees of Stochastic Recursive Gradient in Non-convex Finite Sum Problems
This paper develops a new dimension-free Azuma-Hoeffding type bound on summation norm of a martingale difference sequence with random individual...
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Contrastive Learning for Unsupervised Sentence Embedding with False Negative Calibration
Contrastive Learning, a transformative approach to the embedding of unsupervised sentences, fundamentally works to amplify similarity within positive...
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Neural Additive and Basis Models with Feature Selection and Interactions
Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability. The neural additive model...