Efficient implementation of empirical mode decomposition in FPGA Using Xilinx System Generator

Prince, A. Amalin; Sriram, Ganesh; Verma, Prakhar Kumar; George, Philip; Raju, D.

doi:10.1109/iecon.2016.7793218

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…In Table 4, we give a detailed comparison between the proposed architecture and existing state-of-the-art designs [16]- [19], [21]- [23] in terms of maximum operating frequency, throughput, hardware platform, envelope generation technique, architecture design and used data format. Since existing FPGA-based architectures for EMD have been implemented on Xilinx Spartan 6 (XC6SLX150), Altera Stratix III (EP3SL150F1152C2) equivalent to Xilinx Virtex-5 (ML506) and Kintex 7 platforms, we also implemented our proposed design on all the above platforms to give a fair comparison with state-of-the-art designs.…”

Section: Comparison With the State-of-the-art Designsmentioning

confidence: 99%

“…Kintex XC7K480T/XC7K325T). The XSG [23] uses Xilinx system generation(XSG) tools for implementation of EMD. The design uses linear interpolation scheme and achieves the maximum frequency of 33.3MHz.…”

Section: Comparison With the State-of-the-art Designsmentioning

confidence: 99%

“…All designs were implemented on FPGA with maximum operating frequency of 50MHz; the execution time to perform the EMD operation on 1024 samples with CSI was 560µs. Another architecture based on Xilinx System Generator (XSG) has been presented in [23] for implementation of EMD on FPGA using linear interpolation scheme. The design was able to achieve maximum frequency up to 33MHz and the input signal was sampled at 50 kHz.…”

Section: Introductionmentioning

confidence: 99%

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FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture

2019

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Empirical mode decomposition (EMD) is a data driven method for nonstationary data analysis which has proven to be extremely useful in diverse applications in biomedical engineering. In its original formulation, the EMD method works collectively on a batch of data and hence is not suitable for online applications that demand continuous data processing capability in real-time. To cater for such applications, few complete field-programmable gate array (FPGA) based designs for EMD computations have emerged in recent years, yet they have found limited practical applications. The main reason could be that existing complete FPGA based architectures invariably work on integer data sets thus ignoring the sensitivity EMD to truncation errors. Moreover, these architectures mostly implement simplistic linear interpolation technique for sifting process within EMD, thereby compromising its accuracy. To that end, we develop a complete FPGA-based architecture for EMD which works on fixed point data formats thereby alleviating the main source of error in existing EMD based architectures. We also implement well established and accurate cubic spline interpolation technique in addition to giving provision to linear interpolation within the sifting process in EMD, which further improves its accuracy. Our proposed pipeline design ensures that despite implementing computationally expensive EMD related tasks, the proposed architecture exhibits lowest execution time among all existing EMD architectures. We provide examples of computation of EMD decomposition through proposed architecture for both synthetic and real world biomedical signals to demonstrate the prowess of our work.INDEX TERMS Empirical mode decomposition, FPGA, time frequency analysis.

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Section: Comparison With the State-of-the-art Designsmentioning

confidence: 99%

Section: Comparison With the State-of-the-art Designsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture

2019

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“…In the time-frequency domain, the MeanEnvelopeEnergy feature was generated to analyze tool wear in milling processes. This variable captures the mean energy of the upper and lower envelopes of each intrinsic mode function (IMF), providing insight into the tool's condition during operation [41][42][43][44].…”

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confidence: 99%

Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

Kurek,

Świderska,

Szymanowski

2024

Applied Sciences

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The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges.

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Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic Systems

Mittal

Prince

2022

Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders

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Efficient implementation of empirical mode decomposition in FPGA Using Xilinx System Generator

Cited by 11 publications

References 23 publications

FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture

FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture

Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

Catalogic Systematic Literature Review of Hardware-Accelerated Neurodiagnostic Systems

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