Kalman filtering theory
A Linear Algebraic Model of Algorithm-Based Fault Tolerance
IEEE Transactions on Computers
Design & analysis of fault tolerant digital systems
Design & analysis of fault tolerant digital systems
Discrete-time signal processing
Discrete-time signal processing
Real-Number Codes for Fault-Tolerant Matrix Operations on Processor Arrays
IEEE Transactions on Computers
Kalman filtering: with real-time applications (2nd ed.)
Kalman filtering: with real-time applications (2nd ed.)
Error-Correction Coding for Digital Communications
Error-Correction Coding for Digital Communications
Principles of Digital Communication and Coding
Principles of Digital Communication and Coding
Error Control Coding, Second Edition
Error Control Coding, Second Edition
An Efficient Algorithm-Based Fault Tolerance Design Using the Weighted Data-Check Relationship
IEEE Transactions on Computers
Concurrent Error Detection in Fast Unitary Transform Algorithms
DSN '01 Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS)
Low-power MIMO signal processing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on the 2001 international conference on computer design (ICCD)
Failure-Detecting Arithmetic Convolutional Codes and an Iterative Correcting Strategy
IEEE Transactions on Computers
Concurrent Error Detection in Wavelet Lifting Transforms
IEEE Transactions on Computers
Fault Tolerance Design in JPEG 2000 Image Compression System
IEEE Transactions on Dependable and Secure Computing
Optimal real number codes for fault tolerant matrix operations
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Periodic and non-concurrent error detection and identification in one-hot encoded FSMs
Automatica (Journal of IFAC)
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An extension to Algorithm-Based Fault Tolerance (ABFT) methodologies shows how parity values dictated by a real convolutional code can be employed by Kalman estimation techniques to perform real number correction for protecting linear processing systems. Intermittent failures appearing in the output samples are detected and corrected using only the syndromes normally generated in ABFT schemes. The algebraic structure of a real convolutional code provides separation needed by recursive Kalman state estimators to affect mean-square error correction. State and parity measurement equations model faults and computational noise in both the linear processing and parity generation subassemblies, and, in a departure from previous models, the noise sources are considered time-varying. The Kalman one-step estimator which makes decisions on all parity values up to the present point is determined, and it separates naturally into detection and correction operations permitting corrective action only when the detection levels exceed thresholds based on roundoff noise energy. The detector/corrector uses efficient multirate block processing techniques as determined by the real convolutional code.A smoothed fixed-lag Kalman estimator which uses parity values for a fixed amount beyond the point of interest is needed to complete the correction. It employs one-step estimator quantities and implementation simplifications are possible. Examples showing the correction behavior and mean-square error performance are presented, and the size of overhead calculations for detection and correction is estimated. A protected processing system is constructed by introducing additional subassemblies, mostly comparators, with the detection and correction parts already described. Under the usual assumptions of at most a single subassembly failure, no improperly detected or corrected data leave the overall protected configuration.