Hasil untuk "Transportation and communications"

Sharadhi Gunathilake, Ampalavanapillai Nirmalathas, Kosala Herath et al.

The growing passion for indoor optical wireless networks reflects their immense capability to deliver consistent high-quality data connectivity across diverse indoor environments. This study examines how polarization can be engineered to enhance near-field beam focusing in optical wireless indoor networks using a modular clustered optical phased array aperture. The aperture follows a ceiling-mounted phased array embedded within a phased array layout, associating planar clusters of dipole nano-emitters, supported by a dual-carrier architecture for grating lobe mitigation. We introduce a polarization-adaptive synthesis strategy that determines the cluster-level emitter polarization to replicate any desired polarization at the focal spot. The optimization model accommodates both unconstrained and constrained modes, enabling electric (E)-field matching from linear to general elliptical receiver states. Moreover, we analyze how quantization can be applied to these optimized orientations and how it affects the final performance. To learn the benefits of polarization orthogonality in a multi-receiver environment, we extend the aperture to simultaneously manage multiple focused beams via sub-cluster segmentation. At this level, we apply two polarization control strategies: continuous-domain polarization optimization and binary polarization assignment to match user-specific polarization states and suppress inter-user interference. Numerical estimates of per-receiver signal-to-interference-plus-noise ratio (SINR), E-field patterns, beam characteristics, and mean SINR trends with increasing user availability confirm the superior performance of the proposed techniques over systems that do not consider polarization. Under realistic system- and hardware-level constraints, our results deepen understanding of polarization-engineered modular optical phased arrays and demonstrate their potential for efficient and secure next-generation indoor networks.

Yang Liu, Ruo Jia, Jieping Ye et al.

Fangjie Liu, Muhammad Shafique, Xiaowei Luo

Vijay M. Galshetwar, Praful Hambarde, Prashant W. Patil et al.

Adverse weather conditions such as haze, rain, and snow significantly degrade the quality of images and videos, posing serious challenges to intelligent transportation systems (ITS) that rely on visual input. These degradations affect critical applications including autonomous driving, traffic monitoring, and surveillance. This survey presents a comprehensive review of image and video restoration techniques developed to mitigate weather-induced visual impairments. We categorize existing approaches into traditional prior-based methods and modern data-driven models, including CNNs, transformers, diffusion models, and emerging vision-language models (VLMs). Restoration strategies are further classified based on their scope: single-task models, multi-task/multi-weather systems, and all-in-one frameworks capable of handling diverse degradations. In addition, we discuss day and night time restoration challenges, benchmark datasets, and evaluation protocols. The survey concludes with an in-depth discussion on limitations in current research and outlines future directions such as mixed/compound-degradation restoration, real-time deployment, and agentic AI frameworks. This work aims to serve as a valuable reference for advancing weather-resilient vision systems in smart transportation environments. Lastly, to stay current with rapid advancements in this field, we will maintain regular updates of the latest relevant papers and their open-source implementations at https://github.com/ChaudharyUPES/A-comprehensive-review-on-Multi-weather-restoration

Vitou That, Kimchheang Chhea, Jung-Ryun Lee

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the Whale Optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian Optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

Léah Camarcat, Yuxiang Feng, Nicolette Formosa et al.

Wei-Ting Chen, Chung-Yu Chen

Prasetyaning Diah Rizky Lestari, Ronghui Liu, Richard Batley

Nathan Huynh, Majbah Uddin, Chu Cong Minh

With the growth of intermodal freight transportation, it is important that transportation planners and decision makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities to intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolution, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation system. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques through relatively simple examples, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.

Yan Cheng, Thomas Hatzichristos, Anastasia Kostellou et al.

The needs for transit station classification are ever-growing as the planning process, be it at a strategic or operational level, becomes increasingly automated, data-oriented, and short-cycled. Whilst most existing models have used binary methods, this study applied a fuzzy clustering approach and examined cluster memberships (i.e., to what degree a station belongs to each cluster) of London rail transit stations by using entry and exit data with intra-day and intra-week variations. A method of hyperparameter selection in fuzzy clustering considering the context of transportation and a framework of ridership variation analysis was proposed. The results suggest that fuzzy clustering can maximise the information from high-resolution temporal passenger flow data of urban rail transit. The membership breakdowns allow users to have a better understanding of station characteristics and help to avoid inadequate plans by treating the stations belonging to multiple clusters in a different manner from the binary clustering, where each station only belongs to one cluster. Furthermore, fuzzy clustering can capture the ridership variation patterns and reveal special clusters. The results can be potentially applied in operation planning, such as service timetabling, station staff working-hour designs and fare strategy designs, etc.

Qing Xue, Chengwang Ji, Shaodan Ma et al.

Communication in millimeter wave (mmWave) and even terahertz (THz) frequency bands is ushering in a new era of wireless communications. Beam management, namely initial access and beam tracking, has been recognized as an essential technique to ensure robust mmWave/THz communications, especially for mobile scenarios. However, narrow beams at higher carrier frequency lead to huge beam measurement overhead, which has a negative impact on beam acquisition and tracking. In addition, the beam management process is further complicated by the fluctuation of mmWave/THz channels, the random movement patterns of users, and the dynamic changes in the environment. For mmWave and THz communications toward 6G, we have witnessed a substantial increase in research and industrial attention on artificial intelligence (AI), reconfigurable intelligent surface (RIS), and integrated sensing and communications (ISAC). The introduction of these enabling technologies presents both open opportunities and unique challenges for beam management. In this paper, we present a comprehensive survey on mmWave and THz beam management. Further, we give some insights on technical challenges and future research directions in this promising area.

Mohamed Sana, Emilio Calvanese Strinati

We consider a multi-user semantic communications system in which agents (transmitters and receivers) interact through the exchange of semantic messages to convey meanings. In this context, languages are instrumental in structuring the construction and consolidation of knowledge, influencing conceptual representation and semantic extraction and interpretation. Yet, the crucial role of languages in semantic communications is often overlooked. When this is not the case, agent languages are assumed compatible and unambiguously interoperable, ignoring practical limitations that may arise due to language mismatching. This is the focus of this work. When agents use distinct languages, message interpretation is prone to semantic noise resulting from critical distortion introduced by semantic channels. To address this problem, this paper proposes a new semantic channel equalizer to counteract and limit the critical ambiguity in message interpretation. Our proposed solution models the mismatch of languages with measurable transformations over semantic representation spaces. We achieve this using optimal transport theory, where we model such transformations as transportation maps. Then, to recover at the receiver the meaning intended by the teacher we operate semantic equalization to compensate for the transformation introduced by the semantic channel, either before transmission and/or after the reception of semantic messages. We implement the proposed approach as an operation over a codebook of transformations specifically designed for successful communication. Numerical results show that the proposed semantic channel equalizer outperforms traditional approaches in terms of operational complexity and transmission accuracy.

Adam Millard-Ball, Natasha Timmons

We explore whether policies to promote electric vehicles (EVs) impede efforts to reduce vehicle travel. We hypothesize that the presence of EV chargers reduces respondents' willingness to support (i) the removal of on-street parking to make space for bicycle lanes, and (ii) infill development on surface parking lots. We also hypothesize that the availability of EVs reduces public support for broader vehicle travel reduction policies. Using a randomized survey-based experiment, we find no evidence to support any of these hypotheses.

Durga Prasad Bavirisetti, Herman Ryen Martinsen, Gabriel Hanssen Kiss et al.

In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input. We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model. Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks. Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy. Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy of the model when real-world data is limited.

Jacob Larsson, Musa Furkan Keskin, Bile Peng et al.

Anton Smirnov, Evgeniy Smolokurov, Yuriy Fir

Zhaoyang Su, Liu Liu, Jiachi Zhang et al.

Vehicles have been an important role in people's life. However, traffic accidents caused by vehicles are increasing exponentially with the popularization of vehicles. As an important part of the intelligent transportation system (ITS), vehicle-to-everything (V2X) can improve the traffic efficiency and safety through intercommunication in the transportation system. Urban is a typical scenario in V2X communication with numerous vehicles and infrastructures. The urban channels are non-stationary due to the high speed of vehicles and scatterers. Therefore, we carried out measurement campaigns and analyzed the non-stationarity of vehicle-to-vehicle (V2V) as well as vehicle-to-infrastructure (V2I) channels. In this paper, measurement campaigns for the urban channels at 5.9 GHz were performed including 3 scenarios. The local region of stationarity (LRS) method is used to analyze the non-stationary channel based on actual measured channel data. We focus on the power delay profile (PDP), stationarity interval, and temporal PDP correlation coefficient (TPCC) of the channels. In addition, the statistics analysis of stationarity distance is also provided.

Sandeep Badrinath, Hamsa Balakrishnan

A significant fraction of communications between air traffic controllers and pilots is through speech, via radio channels. Automatic transcription of air traffic control (ATC) communications has the potential to improve system safety, operational performance, and conformance monitoring, and to enhance air traffic controller training. We present an automatic speech recognition model tailored to the ATC domain that can transcribe ATC voice to text. The transcribed text is used to extract operational information such as call-sign and runway number. The models are based on recent improvements in machine learning techniques for speech recognition and natural language processing. We evaluate the performance of the model on diverse datasets.

Bart van Arem, Shadi Sharif Azedeh, Maaike Snelder et al.

Ivan Doroshin, Ruben Kazaryan