Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronge...
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ISBN:
(数字)9798350364941
ISBN:
(纸本)9798350364958
Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. this work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. this approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. this cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.
Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check th...
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ISBN:
(数字)9781665422543
ISBN:
(纸本)9781665446617
Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. there is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. the most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants withthe property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. the private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database.
the development of a parallel adaptive mesh refinement (AMR) scheme is described for solving the governing equations for multi-phase (gas-particle) core flows in solid propellant rocket motors (SRMs). An Eulerian form...
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the development of a parallel adaptive mesh refinement (AMR) scheme is described for solving the governing equations for multi-phase (gas-particle) core flows in solid propellant rocket motors (SRMs). An Eulerian formulation is used to describe the coupled motion between the gas and particle phases. A cell-centred upwind finite-volume discretization and the use of limited linear reconstruction, Riemann solver based flux functions for the gas and particle phases, and explicit multi-stage time-stepping allows for high solution accuracy and computational robustness. A Riemann problem is formulated for prescribing boundary data at the burning surface and a mesh adjustment algorithm has been implemented to adjust the multi-block quadrilateral mesh to the combustion interface. A flexible block-based hierarchical data structure is used to facilitate automatic solution-directed mesh adaptation according to physics-based refinement criteria. Efficient and scalable parallel implementations are achieved with domain decomposition on distributed memory multi-processor architectures. Numerical results are described to demonstrate the capabilities of the approach for predicting SRM core flows.
As the technology node shrinks down to 90 nm and below, high power becomes one of the major critical issues for CMOS high-speed computing circuits (e.g. logic and cache memory) due to the increasing leakage currents a...
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ISBN:
(纸本)9781479906185
As the technology node shrinks down to 90 nm and below, high power becomes one of the major critical issues for CMOS high-speed computing circuits (e.g. logic and cache memory) due to the increasing leakage currents and data traffic. Emerging non-volatile memories are under intense investigation to bring the non-volatility into the logic circuits and then eliminate completely the standby power issue. thanks to its quasi-infinite endurance, high speed and easy 3D integration at the back-end process of CMOS IC fabrication, Magnetic RAM (MRAM) is considered as one of the most promising candidates. A number of hybrid MRAM/CMOS logic circuits have been proposed and prototyped successfully in the last years. In this introduction paper for the invited special session at NEWCAS 2013, we present an overview and current status of these logic circuits and discuss their potential applications in the future.
Background: State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. the high-throughput sequencers ...
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Background: State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. the high-throughput sequencers generate lengthier reads with greater sequencing depththan those generated by previous technologies. Two major challenges exist in using the high-throughput technology for de novo assembly of genomes. First, the amount of physical memory may be insufficient to store the data structure of the assembly algorithm, even for high-end multicore processors. Moreover, the graph-theoretical model used to capture intersection relationships of the reads may contain structural defects that are not well managed by existing assembly algorithms. Results: We developed a distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush. the assembler includes a novel edge-adjustment algorithm to detect structural defects by examining the neighboring reads of a specific read for sequencing errors and adjusting the edges of the string graph, if necessary. CloudBrush is evaluated against GAGE benchmarks to compare its assembly quality withthe other assemblers. the results show that our assemblies have a moderate N50, a low misassembly rate of misjoins, and indels of > 5 bp. In addition, we have introduced two measures, known as precision and recall, to address the issues of faithfully aligned contigs to target genomes. Compared withthe assembly tools used in the GAGE benchmarks, CloudBrush is shown to produce contigs with high precision and recall. We also verified the effectiveness of the edge-adjustment algorithm using simulated datasets and ran CloudBrush on a nematode dataset using a commercial cloud. CloudBrush assembler is available at https://***/ice91/CloudBrush.
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