Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to add...
Teleoperation, or remote driving, constitutes a crucial transitional phase toward the widespread adoption of fully autonomous vehicles. Nevertheless, to enable seamless real-time teleoperation, it is imperative to address the time delay between the driver and the vehicle. Collision-free path generation has emerged as a vital technique facilitating both teleoperation and autonomous driving, particularly in high-level path planning for vehicles. In the context of real-time teleoperation, a generated collision-free path serves as a valuable guide for the teleoperator, effectively mitigating the impact of time delay. In this research, we present a framework dubbed dual transformer network (DTNet), designed to cater to the needs of teleoperation by addressing road scene understanding. The proposed DTNet employs two transformer-based networks to effectively segment the road free space and detect road objects. Additionally, we introduce an innovative fusion mechanism that leverages the combined information from both networks to predict a collision-free path. The efficacy of the DTNet is extensively evaluated using a large-scale BDD100k dataset, substantiating its superior performance in road free space segmentation and road object detection tasks. Remarkably, DTNet achieves a mean intersection over union score of 83.89% for road free space segmentation and an impressive mean average precision score of 34.20% for road object detection. The experimental findings affirm the effectiveness of the DT-Net framework in addressing the challenges of road scene understanding, making it a promising solution to provide a robust and efficient approach for collision-free path generation, with broader implications for the advancement of autonomous driving technologies.
Existing hardware platforms are typically optimized for either realtime or high-performance applications, which poses challenges when running a mix of both on the same platform. This work aims to address this issue by...
Existing hardware platforms are typically optimized for either realtime or high-performance applications, which poses challenges when running a mix of both on the same platform. This work aims to address this issue by proposing a hybrid platform that can effectively execute both types of applications without compromising timing predictability or performance optimization. The proposed solution presents a hybrid HW/SW architecture template capable of dynamically switching between realtime and high-performance execution modes at runtime. The integration and implementation of this architecture template are described on an FPGA, utilizing an open-source RISC-V processor system and FreeRTOS as the software management layer. We have successfully applied the TACLe benchmark suite for the evaluation of our proposed approach. Through an integrated measurement infrastructure, the software functionality, execution timing, and switching times are analyzed on a single-core implementation of the proposed architecture template.
The effects of multipath on the statistical cell-edge user service quality is for the first time investigated for mm-wave multi-user communication systems. The focus is given on setting the user spacing constraints an...
The effects of multipath on the statistical cell-edge user service quality is for the first time investigated for mm-wave multi-user communication systems. The focus is given on setting the user spacing constraints and the transmit array topology via thinning, which can be used to enhance wireless security or decrease analog/digital complexity. A hybrid line-of-sight/non-line-of-sight channel is created by using a statistical model following the communication standards. The multipath signal components are included in the model by using non-coherent or coherent modes of operation. It is shown in simulation that selection, by the medium access control layer, of large angular spacings between the simultaneously served users and application of antenna array thinning at the array edges improves the system performance.
Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based metho...
Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated.
In today's digital era, credit card fraud is a preva-lent issue that costs financial institutions and individuals billions of dollars. To prevent such fraud, fraud detection systems are implemented that use machin...
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ISBN:
(数字)9798350370997
ISBN:
(纸本)9798350371000
In today's digital era, credit card fraud is a preva-lent issue that costs financial institutions and individuals billions of dollars. To prevent such fraud, fraud detection systems are implemented that use machine learning algorithms to analyze patterns and detect transaction anomalies. However, these systems heavily rely on historical data, and if the data is limited or biased, the system's accuracy decreases significantly. This study addresses this issue by investigating a real credit card transaction dataset and determining different consumer behaviors. Synthetic datasets are generated based on consumer behaviors to enhance the accuracy of detecting fraudulent activities. According to the findings, the Logistic Regression (LR) model exhibited superior performance in both experiments. It achieved an impressive accuracy of 96.4% with remarkable time efficiency in the first experiment, and 94.5% accuracy in the second experiment, while still maintaining excellent time efficiency. This research goes through the procedures involved in analyzing a real dataset, understanding consumer behaviors, and generating synthetic datasets.
This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in...
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One major advantage of microservice cloud architectures is the agility with which microservices can be replicated to help improve the overall quality of service and meet service-level contracts. Their challenge is to ...
One major advantage of microservice cloud architectures is the agility with which microservices can be replicated to help improve the overall quality of service and meet service-level contracts. Their challenge is to carefully balance the horizontal microservice replicas with the vertical resources of CPU, memory, and IO that are allocated to each microservice. The objective of such balancing act is, of course, to avoid both service bottlenecks and resource wastage. In this paper, we present OSμS, a new open-source microservice prototyping platform that has been developed and instrumented from the ground up with the objective of collecting fine-grained, non-proprietary metrology on microservice mesh performance. We will illustrate the use of OSμS for developing and evaluating machine-learning algorithms for the horizontal and vertical autoscaling of microservice architectures. A hybrid algorithm based on decision-tree learning will be implemented on OSμS and compared with the academic state of the art and existing cloud-provider solutions. The advantages of such algorithm in improving horizontal and vertical resource utilization will be highlighted.
Heterogeneity in federated learning (FL) is a critical and challenging aspect that significantly impacts model performance and convergence. In this paper, we propose a novel framework by formulating heterogeneous FL a...
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This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhance...
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Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authenticati...
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