Fast approximate truncated SVD

Shishkin, Serge L.; Shalaginov, Arkadi; Bopardikar, Shaunak D.

doi:10.1002/nla.2246

Cited by 15 publications

(4 citation statements)

References 15 publications

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“…The analytical BER computed from the derived PDSNR/ PDSINR is represented as "ana" and simulated BER is denoted as "sim." As the analytical BER given in (37) is inversely proportional to the PDSNR/PDSINR, therefore, the BER performance of linear detection schemes shown in Figure 6 follows an inverse trend with reference to PDSNR/PDSINR. A substantial improvement in the BER is witnessed for TSVD-based schemes when compared with the MRC, direct pseudo-inverse, and ZF schemes.…”

Section: Linear Detection Scheme Complexitymentioning

confidence: 90%

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Developing analytical models for the post‐detection SINR of TSVD‐based techniques with imperfect CSI

Eduru,

Rangaswamy

2024

Int J Communication

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SummaryIn massive multiple input multiple output (m‐MIMO) uplink, the performance gap between zero forcing (ZF) or direct pseudo‐inverse and minimum mean square error (MMSE) detection schemes increases with an increase in the load factor. To report this issue, truncated singular value decomposition (TSVD) is used in both ZF and direct pseudo‐inverse detection techniques using dynamic threshold. Moreover, the analytical models for the post‐detection signal‐to‐interference‐plus‐noise ratio (PDSINR) of TSVD‐based detection schemes are derived with imperfect channel state information (CSI), and subsequently, bit error rate (BER) is evaluated. Later, an optimum hard threshold for the TSVD‐based detection schemes is deduced from the empirical analysis. Further, the tightness of the analysis is verified through Monte Carlo trails. The simulation result shows that at lower values of average signal‐to‐noise ratio (SNR), TSVD‐based detection schemes with imperfect CSI offer comparable performance and low complexity when compared with the MMSE technique. However, the BER of TSVD‐based detection schemes decreases and gets saturated with an increase in the average SNR, whereas the BER of the MMSE detection scheme increases and approaches the BER of ZF or direct pseudo‐inverse detection schemes.

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Section: Linear Detection Scheme Complexitymentioning

confidence: 90%

“…a TSVD-based ZF using randomized schemes [36][37][38] In this section, Monte Carlo trails are used to demonstrate the effect of PDSINR on the BER of TSVD-based detection schemes. The simulation parameters used in this paper are given in Table 2.…”

Section: Linear Detection Scheme Complexitymentioning

confidence: 99%

Developing analytical models for the post‐detection SINR of TSVD‐based techniques with imperfect CSI

Eduru,

Rangaswamy

2024

Int J Communication

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“…Besides sparsity, a more reasonable property to leverage is fast eigenvalue decay, which opens onto a variety of methods for truncated approximate SVD. Deterministic methods allow to compute an r -rank approximation in O(r N 2 ) [52], whereas randomized methods can further reduce the complexity to O(log r N 2 + r 2 N ) [32,40]. We also remark that the actual Monte Carlo approximation of a given signal is in principle a different problem than the computation of the frame itself, and as such may in some cases be more tractable.…”

Section: Computational Considerationsmentioning

confidence: 96%

Construction and Monte Carlo Estimation of Wavelet Frames Generated by a Reproducing Kernel

Vito

Kereta

Naumova

et al. 2021

J Fourier Anal Appl

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We introduce a construction of multiscale tight frames on general domains. The frame elements are obtained by spectral filtering of the integral operator associated with a reproducing kernel. Our construction extends classical wavelets as well as generalized wavelets on both continuous and discrete non-Euclidean structures such as Riemannian manifolds and weighted graphs. Moreover, it allows to study the relation between continuous and discrete frames in a random sampling regime, where discrete frames can be seen as Monte Carlo estimates of the continuous ones. Pairing spectral regularization with learning theory, we show that a sample frame tends to its population counterpart, and derive explicit finite-sample rates on spaces of Sobolev and Besov regularity. Our results prove the stability of frames constructed on empirical data, in the sense that all stochastic discretizations have the same underlying limit regardless of the set of initial training samples.

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“…Next, the inverse document frequency (IDF) value of each word was calculated to build a custom IDF dictionary, enhancing the accuracy of keyword extraction. The TF-IDF algorithm was then applied to extract keywords from all the tower crane safety texts collected [55]. Using the constructed tower crane safety domain feature dictionary, feature matching was performed on the extracted keywords, obtaining the feature attributes of each tower crane safety accident text.…”

Section: Text Mining For Tower Crane Accidentsmentioning

confidence: 99%

Enhancing Human Reliability Prediction in Smart Tower Crane Interfaces: A Refined Approach Using Simplified Plant Analysis Risk–Human Reliability Assessment and the Decision Making Trial and Evaluation Laboratory–Analytic Network Process

Si,

Niu

2024

Buildings

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With the advent of Industry 4.0, the prevalence of tower cranes equipped with hook visualization is increasing. However, the introduction of new interface management tasks has led to novel patterns of human errors for operators. The Simplified Plant Analysis Risk–Human Reliability Assessment (SPAR-H) method has emerged as a relevant approach for the prediction of human reliability in smart construction tower crane operations. However, the current SPAR-H method is only partially applicable and does not fully meet the requirements of this study. Initially, a text mining approach (TF-IDF-TruncatedSVD-ComplementNB) was employed to identify operator error-specific terms in tower crane operations. These terms were then correlated with the eight Performance Shaping Factors (PSFs) of the SPAR-H method, and corresponding failure modes and potential causes were determined from the literature. This ensured a more objective selection of influencing factors and PSFs during the stratification process, which was validated through questionnaire surveys. Furthermore, standards for SPAR-H PSF levels were established based on the characteristics of tower crane operators. Given the inherent complexity of relationships among SPAR-H PSFs, the DEMATEL-ANP method was applied. This involved analyzing logical interactions and causal relationships between first-level and second-level indicators of PSFs, obtaining weights, and integrating these with the SPAR-H method to determine human reliability. Finally, an analysis and validation were conducted using a case study of an accident involving a smart construction tower crane, confirming the subsequent reliability of operator actions. The result of the accident case study yielded a reliability measure of 4.2 × 10−5. These findings indicate that the evaluation process of this method aligns with scenarios encountered in smart construction tower crane operations.

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Fast approximate truncated SVD

Cited by 15 publications

References 15 publications

Developing analytical models for the post‐detection SINR of TSVD‐based techniques with imperfect CSI

Developing analytical models for the post‐detection SINR of TSVD‐based techniques with imperfect CSI

Construction and Monte Carlo Estimation of Wavelet Frames Generated by a Reproducing Kernel

Enhancing Human Reliability Prediction in Smart Tower Crane Interfaces: A Refined Approach Using Simplified Plant Analysis Risk–Human Reliability Assessment and the Decision Making Trial and Evaluation Laboratory–Analytic Network Process

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