Semi-tensor product-based one-bit compressed sensing [PDF]
EURASIP Journal on Advances in Signal Processing, 2023 The area of one-bit compressed sensing (1-bit CS) focuses on the recovery of sparse signals from binary measurements. Over the past decade, this field has witnessed the emergence of well-developed theories.
Jingyao Hou, Xinling Liu
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A novel associative memory model based on semi-tensor product (STP) [PDF]
Frontiers in Computational NeuroscienceA good intelligent learning model is the key to complete recognition of scene information and accurate recognition of specific targets in intelligent unmanned system.
Yanfang Hou, Hui Tian, Chengmao Wang
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Construction of Structured Random Measurement Matrices in Semi-Tensor Product Compressed Sensing Based on Combinatorial Designs [PDF]
Sensors, 2022 A random matrix needs large storage space and is difficult to be implemented in hardware, and a deterministic matrix has large reconstruction error. Aiming at these shortcomings, the objective of this paper is to find an effective method to balance these
Junying Liang +3 more
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Diagnosability of composite automata based on semi-tensor product
Systems Science & Control Engineering, 2021 Fault diagnosis is an important issue of partially observed discrete event systems (DESs). In this problem, fault detection and isolation are two associated tasks, where a fault is diagnosable if it can be detected certainly with a finite delay ...
Zengqiang Chen +3 more
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Low storage space for compressive sensing: semi-tensor product approach [PDF]
EURASIP Journal on Image and Video Processing, 2017 Random measurement matrices play a critical role in successful recovery with the compressive sensing (CS) framework. However, due to its randomly generated elements, these matrices require massive amounts of storage space to implement a random matrix in ...
Jinming Wang +3 more
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Generalised semi‐tensor product of matrices [PDF]
IET Control Theory & Applications, 2019 By introducing matrix multiplier and vector multiplier two kinds of semi‐tensor products (STPs), called matrix–matrix (MM) STPs and matrix–vector (MV) STPs, are introduced. They are generalisations of conventional matrix product, and contain standard STP as a particular case. Certain properties are revealed.
Daizhan Cheng +3 more
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A survey on applications of the semi‐tensor product method in Boolean control networks with time delays [PDF]
Engineering Reports, 2023 Summary As regulatory networks that simulate and establish gene interactions, Boolean networks (BNs) exhibit significant delays in state updates or trajectory tracking due to environmental factors in practical applications.
Tiantian Mu, Jun‐e Feng
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Semi-Tensor Product of Hypermatrices with Application to Compound Hypermatrices [PDF]
2023 42nd Chinese Control Conference (CCC), 2023 The semi-tensor product (STP) of matrices is extended to the STP of hypermatrices. Some basic properties of the STP of matrices are extended to the STP of hypermatrices. The hyperdeterminant of hypersquares is introduced. Some algebraic and geometric structures of matrices are extended to hypermatrices. Then the compound hypermatrix is proposed.
Daizhan Cheng, Xiao Zhang, Zhengping Ji
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Research Status of Nonlinear Feedback Shift Register Based on Semi-Tensor Product [PDF]
Mathematics, 2022 Nonlinear feedback shift registers (NFSRs) are the main components of stream ciphers and convolutional decoders. Recent years have seen an increase in the requirement for information security, which has sparked NFSR research.
Zhe Gao, Jun-e Feng
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Exponentiation Representation of Boolean Matrices in the Framework of Semi-Tensor Product of Matrices [PDF]
IEEE Access, 2019 Semi-tensor product of matrices (STP of matrices) is a new matrix product and has been successfully applied to many fields, especially to logical dynamic systems.
Jumei Yue, Yongyi Yan
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