Vehicular Ad Hoc Networks (VANETs) represent promising technologies for comfort driving and entertainment applications which rely heavily on the data downloading. Due to the rapid change of network topology and interm...
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Vehicular Ad Hoc Networks (VANETs) represent promising technologies for comfort driving and entertainment applications which rely heavily on the data downloading. Due to the rapid change of network topology and intermittent connection, it is a big challenge to satisfy the download requirements from multi-vehicles at the same time. This paper proposes a Bus-Based Content Downloading (BBCD) which aims to maximize the volume of downloaded data from bus to vehicles while the download opportunity fairness of each vehicle is taken into consideration. By predicting the number of buses which the vehicle would encounter in its future path and estimating the connection duration that the vehicle stays in the coverage of bus, the proposed BBCD schedules the download service for vehicles slot by slot such that the volume of downloaded data can be guaranteed while achieving the download opportunity fairness. The effectiveness of the proposed algorithm is evaluated by extensive simulations.
Several concepts for the construction of large antennas have been proposed and discussed. This paper presents the first results obtained during a sounding rocket experiment of the Furoshiki-type concept: a large net d...
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ISBN:
(纸本)9781605600390
Several concepts for the construction of large antennas have been proposed and discussed. This paper presents the first results obtained during a sounding rocket experiment of the Furoshiki-type concept: a large net deployed by spacecraft forms the antenna surface, on which small robots either deploy to perform simple tasks on antenna elements or constitute individual elements of a larger retro-directive phase array antenna. The present paper discusses the trade-off of the robotic options and presents results obtained from robots on the flight experiment.
This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority...
This paper delves into the challenges of binary classification using imbalanced datasets, particularly when instances of interest are infrequent. It explores a comprehensive approach that integrates Synthetic Minority Over-sampling Technique (SMOTE), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) to enhance classification outcomes. Traditional classification models tend to favor the majority class, while the impact of imbalanced misclassification costs is often overlooked. The integration of SMOTE, GANs, and VAEs in binary classification, or SMOTE-GAN-VAE, addresses these challenges by generating synthetic instances, refining data representations, and capturing latent features. To evaluate the effectiveness of various data generation methods, a credit card fraud dataset is used. The performance metrics considered include F0.5-score, F1-score, and F2-score, which account for both precision and recall. The results indicate that SMOTE-GAN-VAE outperforms individual methods, such as SMOTE, GANs, and VAEs, demonstrating its potential to enhance data representation and classification accuracy, and outperformed the β- VAE filtered approach employed in previous literature.
In this paper, an experimental evaluation is presented for a Model Predictive control (MPC) algorithm controlling powertrains dynamics in vehicles with Automated Manual Transmissions (AMTs). This model based control e...
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In this paper, an experimental evaluation is presented for a Model Predictive control (MPC) algorithm controlling powertrains dynamics in vehicles with Automated Manual Transmissions (AMTs). This model based control enables online optimization by using sub-optimal solutions directly linked to the accelerator pedal position. Transmission stability constraints are explicitly handled as well as saturations on the control inputs. This MPC control is tested on line during vehicle start-up in a mild hybrid city car demonstrator equipped with a natural gas engine. A comparison with a PI-based control is made to show the convenience of the proposed MPC control.
The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily ...
The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.
With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has rise...
With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).
Leakage-resilient public key encryption (PKE) schemes are designed to resist "memory attacks", i.e., the adversary recovers the cryptographic key in the memory adaptively, but subject to constraint that the ...
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In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for...
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ISBN:
(数字)9798331518158
ISBN:
(纸本)9798331518165
In dynamic and unpredictable work environments such as manufacturing, logistics, and automated warehouses, achieving high-precision self-localization estimation for efficient object picking are critical challenges for mobile robots. Ensuring accuracy and adaptability is essential for their successful operation. This paper presents a new method that integrates RGB cameras with Contrastive Learning (CL) for self-localization estimation and advanced object picking for mobile manipulators. The effectiveness of the proposed method for location estimation in object picking is verified in indoor environments.
Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captu...
Self-localization is a crucial task for robots, demanding high accuracy. In this work, we propose a new robot localization method based on the Variational Autoencoder (VAE). In our method, the robot utilizes the captured image to generate robot localization in indoor environments. The utilization of VAE makes the system adaptive to varying environmental conditions. Our findings demonstrate that utilizing both the robot's coordinates and images as training data significantly enhances the accuracy of robot self-localization estimation and improves the robustness of the system due to sensor data noise.
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