Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly a...
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ISBN:
(纸本)9798400700361
Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack [9]. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. this three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.
In recent years, industries have automated processes which mean the amount of human participation has decreased, resulting in the Fourth Industrial Revolution. A highly distributed self-organizing system known as a Wi...
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One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. the global incidence of terrorist activities has been considerably reduced by the development of...
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the advent of connected autonomous vehicles (CAVs) is bringing forth a revolutionary new era of technology transforming transportation. For traffic to be optimized and safe, efficient vehicle-to-everything collaborati...
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ISBN:
(纸本)9798350361261;9798350361278
the advent of connected autonomous vehicles (CAVs) is bringing forth a revolutionary new era of technology transforming transportation. For traffic to be optimized and safe, efficient vehicle-to-everything collaboration and improved autonomous vehicles (AV) decision-making are crucial. It becomes essential to make decisions in real time using information from vehicle sensors, software, and traffic data. As a part of such an In-Vehicle Network (IVN), over-the-air (OTA) software update service in CAVs needs to be facilitated rapidly, reliably, and securely. However, by taking advantage of vulnerabilities, attackers may quickly target the OTA software update as part of botnets to execute distributed denial-of-service (DDoS) attacks. the enormous volume and widespread nature of these DDoS cyber-attacks make it vital for the CAV industry to work quickly on identifying and preventing these threats. this paper proposes proof-of-concept experiments withthe Hyperledger Fabric (HLF) Blockchain model to detect and prevent DDoS attacks in CAVs' OTA update systems. the proposed method implements Practical Byzantine Fault Tolerance (PBFT) as the consensus mechanism and a distributed firewall to ensure the ledger is secure and tamper-proof. the system is tested on the Amazon Elastic Compute Cloud (EC2) Blockchain (BC) platform. the results show that the proposed approach effectively prevents DDoS attacks while ensuring fast transaction execution time.
the integration of distributed Generation (DG) systems, particularly wind turbine generators (WTGs), into power grids presents significant challenges in protection and coordination due to their unique characteristics....
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Withthe continuous expansion of photovoltaic grid-connected capacity, precise prediction of photovoltaic power has become essential for ensuring the secure and stable operation of new energy power systems. this paper...
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the emergence of smart storage systems for onions in modern agriculture presents farmers with both challenges and opportunities. these systems, equipped withsensors and control mechanisms, monitor temperature, humidi...
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In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. this technique aims to safegu...
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ISBN:
(纸本)9798350363883;9798350363876
In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their local data. this technique aims to safeguard privacy, countering the vulnerabilities of conventional centralized learning methods. Traditional federated learning approaches often rely on a central server to coordinate model training across clients, aiming to replicate the same model uniformly across all nodes. However, these methods overlook the significance of geographical and local data variances in vast networks, potentially affecting model effectiveness and applicability. Moreover, relying on a central server might become a bottleneck in large networks, such as the ones promoted by edge computing. Our paper introduces a novel, fully-distributed federated learning strategy called proximity-based self-federated learning that enables the self-organised creation of multiple federations of clients based on their geographic proximity and data distribution without exchanging raw data. Indeed, unlike traditional algorithms, our approach encourages clients to share and adjust their models with neighbouring nodes based on geographic proximity and model accuracy. this method not only addresses the limitations posed by diverse data distributions but also enhances the model's adaptability to different regional characteristics creating specialized models for each federation.
A Short Time Fourier Transform (STFT)-based approach is introduced to enhance power quality in distributed energy integration with low-voltage substations. this method utilizes STFT to segment signals with a time-freq...
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'Wireless sensor Networks (WSNs) have gained considerable attention for their versatility in diverse applications, from environmental monitoring to healthcare. Nonetheless, effective energy management poses a sign...
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