Important extrema of time series

Fink, Eugene; Gandhi, H.S.

doi:10.1109/icsmc.2007.4414161

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…The maxima and minima points can present at unequal portions ( Figure 3A) of a signal as well as at the flat portions ( Figure 3B) of a signal. 26 In Figure 3, we have shown two signals having extrema points present at unequal and flat portions. The method proposed in Hong et al 18 can't detect extrema points at the flat portions, which means the method can't properly decompose a signal that has flat portions.…”

Section: Identification Of Extremamentioning

confidence: 94%

An efficient hardware realization of EMD for real‐time signal processing applications

Das

Pradhan

2020

Circuit Theory & Apps

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This paper presents a field-programmable gate array (FPGA) and applicationspecific integrated circuit (ASIC) based design for the real-time implementation of empirical mode decomposition (EMD) algorithm. Here, at the beginning, register-transfer-level (RTL) design of EMD algorithm is developed in the form of verilog-HDL code. Then, simulation-based testing of the RTL design is done. In this paper, two envelope computation methods are proposed: one using linear Bezier curve (LBC) and the other using cubic spline interpolation (CSI). For ASIC, the verilog-HDL code of EMD is synthesized using Genus tool of Cadence using SCL 180-nm technology library and Innovus tool of Cadence is used for the layout design. The core area of the proposed EMD ASIC is 1.16 mm 2 and can be operated at 62.5 MHz clock rate. The developed FPGA-based EMD architecture can be operated at 50 MHz clock rate and up to 50 MHz sampling rate. Here, an effort is also made to classify the normal and seizure/ictal electroencephalogram (EEG) signals, which are used as an input to EMD, with the help of a support vector machine (SVM). The classification accuracy obtained is above 99%, and here, MATLAB is used for feature calculation and classification purposes. K E Y W O R D S application specific integrated circuit (ASIC), empirical mode decomposition (EMD), field programmable gate array (FPGA), support vector machine (SVM)

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Section: Identification Of Extremamentioning

confidence: 94%

An efficient hardware realization of EMD for real‐time signal processing applications

Das

Pradhan

2020

Circuit Theory & Apps

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“…The algorithm for extracting important extrema of all types from a time series of length n is given by Fink and Gandhi [6]. The algorithm takes linear time and constant memory.…”

Section: T Ir and Dist(t I T Il ) R And Dist(t I T Ir ) Rmentioning

confidence: 99%

“…Fink and Gandhi [6] proposed the improved variant of the algorithm for finding important extreme points. Important extreme points are divided into two kinds: important minima and important [4] maxima.…”

Section: Extracting Significant Pointsmentioning

confidence: 99%

“…Fink and Gandhi [6] proposed some functions to compute the distance between two data values a, b, in a time series. In this work we use the distance dist(a, b) = |a − b|.…”

Section: Extracting Significant Pointsmentioning

confidence: 99%

“…Here, in time series segmentation we want that the start point and the end point of a segment should be change points. We extract the change points based on the combination of important extreme points [6] and turning points [4].…”

Section: Extracting Significant Pointsmentioning

confidence: 99%

See 2 more Smart Citations

A novel clustering-based method for time series motif discovery under time warping measure

Truong

Anh

2017

Int J Data Sci Anal

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The problem of time series motif discovery has attracted a lot of attention and is useful in many real-world applications. However, most of the proposed methods so far use Euclidean distance to deal with this problem. There has been one proposed method, called MDTW_WedgeTree, for time series motif discovery under DTW distance. But this method aims to deal with the case in which motif is the time series in a time series database which has the highest count of its similar time series within a range r. To adapt the above-mentioned method to the case in which motifs are frequently occurring subsequences of a longer time series, we modify MDTW_WedgeTree to a new algorithm for discovering "subsequence" motifs in time series under DTW. The proposed method consists of a segmentation method to divide the time series into motif candidates and a BIRCHbased clustering which can efficiently cluster motif candidate subsequences under DTW distance. Experimental results showed that our proposed method for discovering "subsequence" motifs performs very efficiently on large time series datasets while brings out high accuracy.

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Segmentation-Based Methods for Top-k Discords Detection in Static and Streaming Time Series Under Euclidean Distance

Thuy

Anh

Chau

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Important extrema of time series

Abstract: -We describe a technique for fast lossy compression of a time series based on the assignment of importance levels to its minima and maxima.

Cited by 19 publications

References 4 publications

An efficient hardware realization of EMD for real‐time signal processing applications

An efficient hardware realization of EMD for real‐time signal processing applications

A novel clustering-based method for time series motif discovery under time warping measure

Segmentation-Based Methods for Top-k Discords Detection in Static and Streaming Time Series Under Euclidean Distance

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