Efficient Spatial Variation Modeling of Nanoscale Integrated Circuits Via Hidden Markov Tree

Liao, Changhai; Tao, Jun; Zeng, Xuan; Su, Yangfeng; Zhou, Dian; Li, Xin

doi:10.1109/tcad.2015.2481868

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Knowing that many signals satisfy the sparsity property, the applications of CS can be listed as follows: speech and audio signal processing [95,96], underwater signal processing, power system monitoring [97,98], acoustic and linear frequency modulated signal processing [99], image reconstruction [100][101][102][103], radar and communications [104][105][106], pattern recognition [107][108][109][110][111], video processing [112,113], micro and nano electronics and VLSI [114][115][116] and biomedical applications [117][118][119][120][121][122].…”

Section: Compressive Sensing For Ecg Signalmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

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Section: Compressive Sensing For Ecg Signalmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

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“…This review is also significant, since CS has been implemented in various research areas and applications, such as compressive imaging [24]- [26], biomedical applications [27], [28], pattern recognition [29], [30], sound processing [31]- [34], video processing [35], [36], and microelectronics applications [37], [38], and its most well-known implementation is in the field of communication systems, such as wireless networks and antenna [39]- [47]. It has been shown that this field is growing and has a wide opportunity for exploration using this technique of compression.…”

Section: Algorithm 1 Omp Algorithmmentioning

confidence: 99%

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

Nadzri

Ahmad

2022

IJEECS

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Orthogonal matching pursuit (OMP) is the most efficient algorithm used for the reconstruction of compressively sampled data signals in the implementation of compressive sensing. OMP operates in an iteration-based nature, which involves optimisation and least-square problem (LSP) as the main processes. However, optimisation and LSP processes comprise complex mathematical operations that are computationally demanding, and software-based implementations are slow, power-consuming, and unfit for real-time applications. To fill the research gap, we reviewed the optimisation and LSP techniques implemented on the FPGA platform as the hardware accelerator. Aspects that contributed to the performance, algorithm, and methods involved in the implemented works were discussed and compared. The methods were found to be improved when modified or combined. However, the best approach still depends on the requirement of the system to be developed, and this review is significant as a reference.

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“…At the same time, because compressed sensing deals with the fundamental problem of sampling and reconstruction, it has found numerous applications in substantially different fields of science and engineering. While this thesis focuses on the applications of compressed sensing in computer graphics and image processing, the following subjects exemplify the diversity of research fields that use compressed sensing: wireless sensor networks [321,322], face/gesture/speech recognition [63,323,324,325], biomedical applications [326,327], radar imaging [328,329], integrated circuits [330,331,332], audio source separation [333,334], and many more.…”

Section: H a P T E R 6 Compressed Sensing In Graphics And Imagingmentioning

confidence: 99%

Sparse representation of visual data for compression and compressed sensing

Miandji

2018

Linköping Studies in Science and Technology. Dissertations

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The cover of this thesis depicts the sparse representation of a light field. From the back side to front, a light field is divided into small elements, then these elements (signals) are projected onto an ensemble of two 5D dictionaries to produce a sparse coefficient vector. I hope that the reader can deduce where the dimensionality of those Rubik's Cube looking matrices come from after reading Chapter 3 of this thesis. The light field used on the cover is a part of the Stanford Lego Gantry database (http://lightfield.stanford.edu/)

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Efficient Spatial Variation Modeling of Nanoscale Integrated Circuits Via Hidden Markov Tree

Cited by 6 publications

References 38 publications

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

A review of optimisation and least-square problem methods on field programmable gate array-based orthogonal matching pursuit implementations

Sparse representation of visual data for compression and compressed sensing

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