Biblio

Distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

Living in the age of digital transformation, companies and individuals are moving to public and private clouds to store and retrieve information, hence the need to store and retrieve data is exponentially increasing. Existing storage technologies such as DAS are facing a big challenge to deal with these huge amount of data. Hence, newer technologies should be adopted. Storage Area Network (SAN) is a distributed storage technology that aggregates data from several private nodes into a centralized secure place. Looking at SAN from a security perspective, clearly physical security over multiple geographical remote locations is not adequate to ensure a full security solution. A SAN security framework needs to be developed and designed. This work investigates how SAN protocols work (FC, ISCSI, FCOE). It also investigates about other storages technologies such as Network Attached Storage (NAS) and Direct Attached Storage (DAS) including different metrics such as: IOPS (input output per second), Throughput, Bandwidths, latency, cashing technologies. This research work is focusing on the security vulnerabilities in SAN listing different attacks in SAN protocols and compare it to other such as NAS and DAS. Another aspect of this work is to highlight performance factors in SAN in order to find a way to improve the performance focusing security solutions aimed to enhance the security level in SAN.

Mixed-Criticality Systems (MCS) are real-time systems characterized by two or more distinct levels of criticality. In MCS, it is imperative that high-critical flows meet their deadlines while low critical flows can tolerate some delays. Sharing resources between flows in Network-On-Chip (NoC) can lead to different unpredictable latencies and subsequently complicate the implementation of MCS in many-core architectures. This paper proposes a new virtual channel router designed for MCS deployed over NoCs. The first objective of this router is to reduce the worst-case communication latency of high-critical flows. The second aim is to improve the network use rate and reduce the communication latency for low-critical flows. The proposed router, called DAS (Double Arbiter and Switching router), jointly uses Wormhole and Store And Forward techniques for low and high-critical flows respectively. Simulations with a cycle-accurate SystemC NoC simulator show that, with a 15% network use rate, the communication delay of high-critical flows is reduced by 80% while communication delay of low-critical flow is increased by 18% compared to usual solutions based on routers with multiple virtual channels.