Flexible hardware architectures for robust Cyberphysical systems

2020 ◽  
Author(s):  
Michael Hubner
Keyword(s):  
Cyberphysical Systems ◽  
Flexible Hardware ◽  
Hardware Architectures

Flexible Architectures for Cryptographic Algorithms — A Systematic Literature Review

2019 ◽  
Vol 28 (03) ◽  
pp. 1930003 ◽  
Author(s):  
Muhammad Rashid ◽  
Malik Imran ◽  
Atif Raza Jafri ◽  
Turki F. Al-Somani
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Hardware Implementation ◽  
Comprehensive Investigation ◽  
Flexible Hardware ◽  
Cryptographic Algorithms ◽  
Hardware Architectures ◽  
Flexible Architectures ◽  
Secure Transmission

Symmetric and asymmetric cryptographic algorithms are used for a secure transmission of data over an unsecured public channel. In order to use these algorithms in real-time applications, many flexible hardware architectures have been proposed and implemented with multiple design constraints. Therefore, a systematic study is required to analyze various implementation approaches. This paper has focused on the identification and classification of recent research practices pertaining to the flexible hardware implementation of cryptographic algorithms. We have used Systematic Literature Review (SLR) process to identify 51 research articles, published during 2008–2017. The identified researches have been classified according to three design approaches: (1) crypto processor, (2) crypto coprocessor and (3) multicore crypto processor. Consequently, a comparative analysis of various cryptographic algorithms in terms of flexibility, throughput, area, power and implementation technology has been presented. A comprehensive investigation of flexible architectures for implementing cryptographic algorithms facilitates researchers and designers of the domain to select an appropriate design approach for a particular algorithm and/or application according to their needs.

Evaluation of Hardware Architectures for HF Antenna Arrays

1988 ◽  
Author(s):  
R. K. Balasubramaniam ◽  
Victor Frost ◽  
Roger Spohn ◽  
Ryan Moates ◽  
Stephen Fechtel
Keyword(s):  
Antenna Arrays ◽  
Hardware Architectures

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

scholarly journals Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
Kshirasagar Naik ◽  
Mahesh D. Pandey ◽  
Anannya Panda ◽  
Abdurhman Albasir ◽  
Kunal Taneja
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Finite State Machine ◽  
State Machine ◽  
Plant Performance ◽  
Series Data ◽  
Cyberphysical Systems ◽  
Finite State ◽  
Machine Representation

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

scholarly journals Formal Verification of Cyberphysical Systems

Computer ◽  
2021 ◽  
Vol 54 (9) ◽  
pp. 15-24
Author(s):  
James Bret Michael ◽  
Doron Drusinsky ◽  
Duminda Wijesekera
Keyword(s):  
Formal Verification ◽  
Cyberphysical Systems

scholarly journals Embedded Deep Learning Prototyping Approach for Cyber-Physical Systems: Smart LIDAR Case Study

2021 ◽  
Vol 10 (1) ◽  
pp. 18
Author(s):  
Quentin Cabanes ◽  
Benaoumeur Senouci ◽  
Amar Ramdane-Cherif
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Computation Time ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Research Technology ◽  
Standard Design ◽  
Hardware Architectures ◽  
Voxel Grid

Cyber-Physical Systems (CPSs) are a mature research technology topic that deals with Artificial Intelligence (AI) and Embedded Systems (ES). They interact with the physical world via sensors/actuators to solve problems in several applications (robotics, transportation, health, etc.). These CPSs deal with data analysis, which need powerful algorithms combined with robust hardware architectures. On one hand, Deep Learning (DL) is proposed as the main solution algorithm. On the other hand, the standard design and prototyping methodologies for ES are not adapted to modern DL-based CPS. In this paper, we investigate AI design for CPS around embedded DL. The main contribution of this work is threefold: (1) We define an embedded DL methodology based on a Multi-CPU/FPGA platform. (2) We propose a new hardware design architecture of a Neural Network Processor (NNP) for DL algorithms. The computation time of a feed forward sequence is estimated to 23 ns for each parameter. (3) We validate the proposed methodology and the DL-based NNP using a smart LIDAR application use-case. The input of our NNP is a voxel grid hardware computed from 3D point cloud. Finally, the results show that our NNP is able to process Dense Neural Network (DNN) architecture without bias.

scholarly journals Formal Methods in Cyberphysical Systems

Computer ◽  
2021 ◽  
Vol 54 (9) ◽  
pp. 25-29
Author(s):  
James Bret Michael ◽  
Doron Drusinsky ◽  
Duminda Wijesekera
Keyword(s):  
Formal Methods ◽  
Cyberphysical Systems
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