A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems

Montagna, Fabio; Buiatti, M.; Benatti, Simone; Rossi, Davide; Farella, Elisabetta; Benini, Luca

doi:10.1016/j.ymeth.2017.06.019

Cited by 11 publications

(8 citation statements)

References 47 publications

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“…The results presented in this paper were evaluated with an Intel Core I5-7400 and 20 GB of random-access memory, with the MATLAB software. The application of the proposed method in an embedded system could be a solution for the electrical inspections, improving the reliability of the electrical power network [75][76][77]. In this paper, the accuracy was used given by the coefficient of determination (R 2 ), calculated as follows:…”

Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

et al. 2021

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Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance is a difficult task as there are several levels of contamination that are hard to notice during inspections. To improve the quality of inspections, this paper proposes using k-nearest neighbors (k-NN) to classify the levels of insulator contamination based on images of insulators at various levels of contamination simulated in the laboratory. Computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, a statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed up to 85.17% accuracy using the k-fold cross-validation method, with an average accuracy higher than 82% for the multi-classification of contamination of insulators, being superior to the compared models.

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Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

et al. 2021

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“…The results presented in this paper were evaluated from Intel Core I5-7400, 20 GB of random-access memory, with the MATLAB software. The application of the proposed method in an embedded system could be a solution for the electrical inspections, improving the reliability of the electrical power network [54][55][56][57].…”

Section: Benchmarkingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Corso¹,

Perez²,

Stefenon³

et al. 2021

Preprint

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The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

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“…There are several variants of this algorithm; in this paper, we consider the decimation-in-frequency radix-2 variant. We consider a state-of-the-art supervised classifier, the Support Vector Machine (SVM), widely used in near-sensor applications [44]. We also include another classifier, named K-Means, which is an unsupervised ML algorithm able to inference an unknown outcome starting from input vectors.…”

Section: Benchmarksmentioning

confidence: 99%

A transprecision floating-point cluster for efficient near-sensor data analytics

Montagna¹,

Mach²,

Benatti³

et al. 2020

Preprint

Self Cite

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Recent applications in the domain of near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this paper, we propose a multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our design -based on the open-source RISC-V architecture -combines parallelization and sub-word vectorization with near-threshold operation, leading to a highly scalable and versatile system. We perform an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, with the aim to identify the most efficient configurations in terms of performance, energy efficiency, and area efficiency. We also provide a full-fledged software stack support, including a parallel runtime and a compilation toolchain, to enable the development of end-to-end applications. We perform an experimental assessment of our design on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. Finally, a comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors.

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A machine learning approach for automated wide-range frequency tagging analysis in embedded neuromonitoring systems

Cited by 11 publications

References 47 publications

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

A transprecision floating-point cluster for efficient near-sensor data analytics

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