On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices

Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi

doi:10.1109/tbcas.2017.2779503

Cited by 45 publications

(29 citation statements)

References 49 publications

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“…The matrix Q represents an indicator matrix where q ij ∈ Q and q ij = 1, if no data for user i at time j 0, otherwise In order to improve anonymous success ratio, the first task of constructing anonymous candidate set is to complete the missing location in the anonymous server. As the correlation and similarity among users movement trajectories, the matrix X is a low-rank matrix [15]. Then,we can obtainX including the complete location of all users by the compression sensing technology [16].…”

Section: B Construction Of Anonymous Candidate Setmentioning

confidence: 99%

“…2 u (18) As the value range of the equation (15) and the equation (16) is [0,1], so u = 1. Based on the results of the descending formula, we obtain the sequence of location and selected the first k location to construct the anonymous set (AD) as shown in algorithm 2.…”

Section: Volume 8 2020mentioning

confidence: 99%

“…5: The Anonymous set, AD; 6: C = ∅; 7: S = ∅; 8: ε 1 = 0.5 × ε; 9: ε 2 = 0.5 × ε; 10: caculate the δ 2 according to the formate (15)…”

Section: Algorithm 2 Anonymous Set Construction Inputmentioning

confidence: 99%

“…The key idea of these methods is to avoid direct requests to the server of LBS from real request locations. Broadly speaking, existing works of location privacy-preserving can be classified into three categories: offset method [5]- [7], dummy method [2], [3], [8]- [11] and spatial cloaking method [4], [12]- [15]. The offset method is easy to implement but has poor availability.…”

Section: Introductionmentioning

confidence: 99%

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Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

et al. 2020

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The Location-Based Service has been widely used for mobile communication networks and location systems. However, privacy disclosure for incomplete collection location data in LBS was ignored in most of the existing works. To solve the problem of privacy disclosure, we propose a location privacy method based k-anonymity to prevent privacy disclosure in LBS constrained in incomplete data collection. The proposed scheme can provide effectively location privacy-preserving in the process of constructing the anonymous set, and against background attacks. In this method, we first designed a construct method for anonymous candidate set(ACS) with compressing sensing technology, to solve the problem of incomplete data of collection location. To prevent the privacy disclosure in the process of construct anonymous, we then adapt the differential privacy mechanism to construct the anonymous set(AS) with the ACS. we finally used the optimization method based on the Stackelberg game model to improve the privacy level of AS to against probabilistic attack. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in terms of privacy degree and applicability.

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Section: B Construction Of Anonymous Candidate Setmentioning

confidence: 99%

Section: Volume 8 2020mentioning

confidence: 99%

“…5: The Anonymous set, AD; 6: C = ∅; 7: S = ∅; 8: ε 1 = 0.5 × ε; 9: ε 2 = 0.5 × ε; 10: caculate the δ 2 according to the formate (15)…”

Section: Algorithm 2 Anonymous Set Construction Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

et al. 2020

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“…The deterministic sensing matrix has been exploited for BCI application. For example, W. Zhao et al [75] used deterministic quasi-cyclic array code (QCAC) matrix-based compressed sensing encoder architecture for wireless neural recording applications. Further, W. Zhao et al [76] also proposed the construction of the QCAC matrix and sparse random binary matrix (SRBM) and performed simulation experiments to reconstruct EEG signal from compressed measurement.…”

Section: Cs Sensing Matricesmentioning

confidence: 99%

Trends in Compressive Sensing for EEG Signal Processing Applications

Gurve

Delisle-Rodríguez

Bastos

et al. 2020

Sensors

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The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.

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On Reducing Test Data Volume for Circular Scan Architecture Using Modified Shuffled Shepherd Optimization

et al. 2021

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In this manuscript, a novel test data compression (TDC) system is proposes for reducing a test data volume (TDV) for circular scan (CS) architecture. A modified version of meta-heuristic population based optimization approach, hence it is called shuffled shepherd optimization (SSO) and it is used to optimize the conflicting bit minimization (CBM) problem. In CS framework, TDC is reached by upgrading only the conflicting bits among template pattern, real test pattern. Reduced test data volume and test application time are achieved by diminishing the Hamming distance between the currently captured response and the subsequent test vector. The CBM issue is formulated by a traveling salesman problem (TSP). Here, the test vectors are assumed by cities, the HD among test vectors a pair is assumed by distance of interurban. A modified version of the SSO algorithm called as MSSO is applied in combination using mutation operator for solving the issue of CBM. Experimental outcomes display the proposed TDC method using MSSO achieves an average development of 8.36% in compression ratio (CR) as well as 6.77% in TAT is attained by reducing a TDV. This proposed approach outperforms other state-of-art test data compression schemes by providing improved CR, TAT reduction and TDV reduction.

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On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices

Cited by 45 publications

References 49 publications

Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

Location Privacy Preservation Mechanism for Location-Based Service With Incomplete Location Data

Trends in Compressive Sensing for EEG Signal Processing Applications

On Reducing Test Data Volume for Circular Scan Architecture Using Modified Shuffled Shepherd Optimization

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