Reconfigurable intelligent surface (RIS)-based localization has received significant attention in wireless communication systems due to its potential for enabling non-line-of-sight (NLoS) wireless sensing. However, hardware impairments (HWIs) in non-ideal hardware adversely affect the localization performance. To address the issue, this work studies a RIS-aided passive sensing system with the presence of HWIs, where communications signals are used to sense targets. We propose a method to address a DOA estimation problem in three-dimensional (3D) space. Specifically, a low-rank matrix is first recovered by solving an atomic norm minimization (ANM) problem, which is not affected by interference signal and additive noise. Subsequently, a block Hankel matrix is constructed using the correlation information of the low-rank matrix. The elevation and azimuth angles between targets and RIS are then estimated by applying the modified matrix pencil (MMP) method on the block Hankel matrix. We also derive the Cramér-Rao Bound (CRB) with closed-form expressions for DOA estimation based on the proposed system model. The proposed algorithm can sense multiple targets and achieve parameter automatic pairing. Numerical results verify the superiority of the proposed algorithm over the traditional schemes.

Featured by low power losses and strong heat dissipation capability, hairpin winding has been widely accepted as a key enabler of boosting performance for traction/propulsion electrical machines in automotive and aerospace sectors. Continuous hairpin winding (CHW), distinguished by its further improvement brought to machines' key performance indicators, being power density, efficiency, and reliability, is emerging as a more promising winding solution. This paper will provide a critical technology review on CHW, with focus on highlighting its different features compared with those of other typical winding types, manufacturing/assembly process, layout design rules, as well as current and future development trends. Based on illustration of the unique winding process, the so-called “radial shift' feature that inherently exists in CHW will be introduced. The key elements in winding layout development, including transposition, terminal and jumper connections, parallel branch number, will be summarised for CHW. Moreover, new technology bricks and research ideas that strive to tackle manufacturing challenges, enhance design flexibility, as well as improve overall performance have been highlighted. Finally, the paper concludes by proposing future research directions, with the vision of increasing its Technology Readiness Level (TRL) for future net-zero transportation.

As automated vehicles progress toward increasing levels of autonomy, the need for thorough testing of such systems in all relevant environments increases. These safety-critical systems often rely on visible-spectrum cameras to perceive the environment. Therefore, these systems must perform reliably under a range of adverse weather conditions. This study investigates the impact of rain on the quality of images taken in an experimental setting designed to vary rain intensity in a controlled manner. This study analyzes the impact of rain using low-level metrics such as contrast and spatial frequency response. In addition, overall image quality was evaluated using a range of full-reference image quality metrics. The results show a 45% reduction in SNR at 40 m and 38 mm/h, a 70% maximum decrease in Weber contrast at 30 m and 38 mm/h, and a 42% increase in color error as a result of rain in the environment. Consequently, degradation in image quality is likely to affect subsequent downstream computer vision performance. The results of this study highlight the need for robust testing and optimization of camera systems.

Integrating visible light communication (VLC) with the reconfigurable intelligent surface (RIS) significantly enhances physical layer security by enabling precise directional signal control and dynamic adaptation to the communication environment. These capabilities strengthen the confidentiality and security of VLC systems. This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. Specifically, two frameworks are proposed to maximize both the minimum secrecy rate (SR) and the minimum secrecy energy efficiency (SEE) by jointly optimizing power allocation, RIS association, and RIS elements orientation angles for both power-domain NOMA and RSMA-based VLC systems. The proposed frameworks consider random device orientation and guarantee the minimum user-rate requirement. The proposed optimization frameworks belong to the class of mixed integer nonlinear programming, which has no known feasible solution methodology to guarantee the optimal solution. Moreover, the increased degree of freedom and flexibility from the joint consideration of power control, RIS association and element orientation results in a large set of decision variables and constraints, which further complicates the optimization problem. To that end, we utilize a genetic algorithm-based solution method, which through its exploration and exploitation capabilities can obtain a good quality solution. Additionally, comprehensive simulations show that the RSMA scheme outperforms the power-domain NOMA scheme across both the SR and SEE metrics over various network parameters. Furthermore, useful insights on the impact of minimum user rate requirement, number of RIS elements, and maximum VLC AP transmit power on the minimum SR and SEE performances are provided.

The progression of low-carbon aviation policies and the maturation of electric vertical take-off and landing (eVTOL) technology have engendered considerable prospects for the advancement of short-haul intercity and intra-city transportation systems. To harness the potential of eVTOL travel in ameliorating transportation carbon emissions and alleviating ground transportation congestion, the judicious selection of optimal eVTOL stop sites emerges as a pivotal consideration. This study delineates a framework for the delineation of intra-city and short-distance inter-city eVTOL site selection predicated on comprehensive analysis of ground transportation system interconnections. The initial phase of the framework entails the identification of potential optimal take-off and landing sites through a multi-faceted assessment of factors encompassing vehicular and passenger traffic flows, regional economic dynamics, travel behavioral patterns, and prevailing eVTOL flight regulations across heterogeneous ground transportation networks. Employing an enhanced iteration of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means algorithm, this phase undertakes the clustering of optimal takeoff and landing locations, thereby discerning their spatial distribution to effectively alleviate ground traffic congestion while aligning with eVTOL vertical port requirements and airspace regulatory mandates. The second phase involves the establishment of a demand gravity model to validate the optimal take-off and landing coordinate sites of eVTOL and further assess a service index indicative of traffic flow optimization. The case shows that six optimal eVTOL take-off and landing locations have been discerned by our model within the Beijing-Tianjin-Xiong'an (Hebei) region. These locations are anticipated to yield a cumulative service index of 75,465 instances, thereby efficaciously mitigating travel pressure on ground transportation infrastructure.

Future-facing cities increasingly integrate smart and autonomous objects for their smooth functioning and operations, which ultimately benefit city dwellers and the ecosystem at large. In such highly complex and digital environments, the increased situational awareness is very important for the safety of road participants. In this paper, we propose a new bandwidth-aware framework that maximizes the situational awareness of a given region, using mobile digital boxes and 360° cameras, mounted on connected vehicles, taking into account the constrained uplink capacity. The proposed framework leverages the multi-view spectral clustering approach and the K-Means++ algorithms to ensure efficient clustering of vehicles based on their GPS coordinates. The clustering step is crucial for larger spatial coverage and, thus, higher situational awareness. Vehicle selection and video quality attribution, under limited uplink constraints, are then performed per cluster to fairly cover the region. Extensive simulations and comparisons against state-of-the-art solutions have been conducted to evaluate the performance of the proposed framework, in terms of region coverage rate and normalized mutual information score, at both small- and large-scale deployments. The results obtained demonstrate the superiority of the proposed approach.

Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in real-life field tests. Results have indicated that the E2E network latency of the perception information sent is around 30ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries during trips. Simultaneously, personal data use for analytics is growing at an unprecedented rate, raising concerns for privacy. One standard for formalising location privacy is geo-indistinguishability as a generalisation of local differential privacy. However, the noise must be tuned properly, considering the implications of potential utility losses. In this paper, we introduce the notion of approximate geo-indistinguishability (AGeoI), which allows EVs to obfuscate their query locations while remaining within their area of interest. It is vital because journeys are often sensitive to a sharp drop in quality of service (QoS). Our method applies AGeoI with dummy data generation to provide two-fold privacy protection for EVs while preserving a high QoS. Analytical insights and experiments demonstrate that the majority of EVs get “privacy-for-free” and that the utility loss caused by the gain in privacy guarantees is minuscule. In addition to providing high QoS, the iterative Bayesian update allows for a private and precise CS occupancy forecast, which is crucial for unforeseen traffic congestion and efficient route planning.

Unprecedented growth in wireless data traffic, and ever-increasing demand for highly secured, and low-latency wireless communication has motivated the research community to move towards sixth-generation (6G) technology, where networks can cater to the rising need for ubiquitous secure wireless connectivity. One of the promising technologies for 6G wireless communication is the reconfigurable intelligent surface (RIS) concept that is proposed to successfully deal with increasing security threats by smartly controlling the wireless channel conditions. This survey paper presents a detailed literature review on RIS-assisted physical layer security (PLS) for next-generation wireless communications. Firstly, we briefly discuss the PLS concept, its importance, the PLS performance metrics, and its applicability in different wireless networks. Next, we discuss the applications of RIS in the 6G scenario. Then, a detailed and systematic classification of the various RIS-assisted wireless system topologies exhibiting multiple scenarios, system models, channel fading models, performance metrics and objectives is done. The existing state-of-art approaches for PLS such as secret key generation (SKG), optimization algorithms, namely semidefinite relaxation-successive convex approximation (SDR-SCA) to optimize RIS coefficients, and optimal placement of RIS units are discussed for single-input single-output (SISO) case. For multiple-input single-output (MISO) case, PLS strategies such as inducing artificial noise (AN), optimization algorithms, alternating optimization (AO), machine learning (ML) and deep learning (DL), and reflect matrices are discussed. Similarly, for multiple-input multiple-output (MIMO) setup, block coordinate descent (BCD) and AN induction are some of the PLS methods used to analyze the secrecy. Lastly, we present some of the technical challenges and future directions based on the survey.

Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonetheless, PSO has its limitations, notably its propensity for slow convergence. Traditionally, PSO operates by guiding swarms through positions determined by their initial velocities and acceleration components, encompassing cognitive and social information. In pursuit of expedited convergence, we introduce a novel approach: the Cognitive and Social Information-Based Hyperbolic Tangent Particle Swarm Optimization (HT-PSO) algorithm. This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. To combat the issue of slow convergence, we reengineer the cognitive and social acceleration coefficients of the PSO algorithm, leveraging the power of the hyperbolic tangent function. This strategic adjustment fosters a dynamic balance between exploration and exploitation, unleashing PSO's full potential. Our experimental trials encompass thirteen benchmark functions spanning unimodal and multimodal landscapes. Besides that, the proposed algorithm is also applied to different UAV path planning scenarios, underscoring its real-world relevance. The outcomes underscore the prowess of HT-PSO, showcasing significantly better convergence rates compared to the state-of-the-art.