scholarly journals A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1617 ◽  
Author(s):  
Hui Huang ◽  
Shiyan Hu ◽  
Ye Sun
Keyword(s):  
Consumer Electronics ◽  
High Signal ◽  
Wearable Electronics ◽  
Signal Distortion ◽  
Data Smoothing ◽  
Discrete Curvature ◽  
Ecg Denoising ◽  
Curvature Estimation ◽  
Low Distortion ◽  
Distortion Reduction

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.

CIRCULAR ARC SEGMENTATION BY CURVATURE ESTIMATION AND GEOMETRIC VALIDATION

2012 ◽  
Vol 12 (04) ◽  
pp. 1250024 ◽  
Author(s):  
SHYAMOSREE PAL ◽  
RAHUL DUTTA ◽  
PARTHA BHOWMICK
Keyword(s):  
Geometric Analysis ◽  
Circular Arc ◽  
Discrete Curvature ◽  
Circular Arcs ◽  
Initial Stage ◽  
Benchmark Datasets ◽  
Curvature Estimation ◽  
Digital Curve ◽  
Curvature Information ◽  
Novel Algorithm

A novel algorithm to detect circular arcs from a digital image is proposed. The algorithm is based on discrete curvature estimated for the constituent points of digital curve segments, followed by a fast geometric analysis. The curvature information is used in the initial stage to find the potentially circular segments. In the final stage, the circular arcs are merged and maximized in length using the radius and center information of the potentially circular segments. Triplets of longer segments are given higher priorities; doublets and singleton arcs are processed at the end. Detailed experimental results on benchmark datasets demonstrate its efficiency and robustness.

scholarly journals Visible to long-wave infrared chip-scale spectrometers based on photodetectors with tailored responsivities and multispectral filters

Nanophotonics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 3197-3208 ◽  
Author(s):  
Jasper J. Cadusch ◽  
Jiajun Meng ◽  
Benjamin J. Craig ◽  
Vivek Raj Shrestha ◽  
Kenneth B. Crozier
Keyword(s):  
Consumer Electronics ◽  
Wearable Electronics ◽  
Ir Detector ◽  
Detector Arrays ◽  
Long Wave ◽  
Low Weight ◽  
Recent Developments ◽  
Infrared Spectral ◽  
Small Device ◽  
Long Wave Infrared

AbstractChip-scale microspectrometers, operational across the visible to long-wave infrared spectral region will enable many remote sensing spectroscopy applications in a variety of fields including consumer electronics, process control in manufacturing, as well as environmental and agricultural monitoring. The low weight and small device footprint of such spectrometers could allow for integration into handheld, unattended vehicles or wearable-electronics based systems. This review will focus on recent developments in nanophotonic microspectrometer designs, which fall into two design categories: (i) planar filter-arrays used in conjunction with visible or IR detector arrays and (ii) microspectrometers using filter-free detector designs with tailored responsivities, where spectral filtering and photocurrent generation occur within the same nanostructure.

Research of Discrete Curvature Estimation Method Based on Local Space Parabola

2014 ◽  
Vol 577 ◽  
pp. 802-805 ◽  
Author(s):  
Jian Wei Ma ◽  
Zhen Yuan Jia ◽  
Fu Ji Wang
Keyword(s):  
Point Cloud ◽  
Simulation Experiment ◽  
Estimation Method ◽  
Circular Arc ◽  
Discrete Curvature ◽  
Fitting Method ◽  
Local Space ◽  
Scan Line ◽  
Curvature Estimation ◽  
Line Point

Curvature estimation of 3-dimension discrete points performs an important role in dealing with scan line point cloud and is difficult to calculate. A discrete curvature estimation method based on local space parabola is proposed. Method in this paper is contrasted with circular arc fitting method and simulation experiment shows that the proposed method is feasible and effective with high precision.

scholarly journals Wearable and Flexible Textile Electrodes for Biopotential Signal Monitoring: A review

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 479 ◽  
Author(s):  
Gizem Acar ◽  
Ozberk Ozturk ◽  
Ata Jedari Golparvar ◽  
Tamador Alkhidir Elboshra ◽  
Karl Böhringer ◽  
...  
Keyword(s):  
Point Of Care ◽  
Consumer Electronics ◽  
Wearable Electronics ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Textile Electrodes ◽  
Wearable Systems ◽  
Routine Monitoring ◽  
Care Health ◽  
Signal Monitoring

Wearable electronics is a rapidly growing field that recently started to introduce successful commercial products into the consumer electronics market. Employment of biopotential signals in wearable systems as either biofeedbacks or control commands are expected to revolutionize many technologies including point of care health monitoring systems, rehabilitation devices, human–computer/machine interfaces (HCI/HMIs), and brain–computer interfaces (BCIs). Since electrodes are regarded as a decisive part of such products, they have been studied for almost a decade now, resulting in the emergence of textile electrodes. This study presents a systematic review of wearable textile electrodes in physiological signal monitoring, with discussions on the manufacturing of conductive textiles, metrics to assess their performance as electrodes, and an investigation of their application in the acquisition of critical biopotential signals for routine monitoring, assessment, and exploitation of cardiac (electrocardiography, ECG), neural (electroencephalography, EEG), muscular (electromyography, EMG), and ocular (electrooculography, EOG) functions.

scholarly journals Machine Learning Based DWDM Design Using Regression Modelling

2020 ◽  
Author(s):  
Venkatesan K ◽  
Chandrasekar A ◽  
Ramesh P.G.V
Keyword(s):  
Design System ◽  
High Signal ◽  
Optical Parameters ◽  
Noise Power ◽  
Signal Power ◽  
Q Factor ◽  
Signal Distortion ◽  
Power Noise ◽  
Correlated Parameters ◽  
Dwdm System

Abstract In this paper, we discuss the non-linearity problems such as Four Wave Mixing (FWM) and high signal distortion with low Output Signal to Noise Ratio (OSNR) in the design of a 64-channel DWDM system using Regression learning technique. The occurrence of FWM in a DWDM system with high number of channels reduces the performance of an optical fiber system in terms of bandwidth and increases computational complexity. High signal distortion with low OSNR reduces network throughput, energy efficiency and thus forces re-transmission. To overcome the above problems, DWDM system with higher number of channel necessitates an optimized design based on correlation factors of optical dependent and independent variable factors such as BER, Q-factor, signal power, noise power and OSNR. Proposed here is a regression optimized DWDM system design. Regression is used here for correlating and optimizing the optical parameters. In this paper, the problem of non-linearity is solved through optimized DWDM design based on correlated parameters in 16, 32 and 64- Channeled DWDM system. The regression based correlated DWDM design (R-DWDM) is improvised mechanism over the independent parameter based simulations and thus improves accuracy. The R-DWDM design system shows higher accuracy through the derived R-value for optical parameters such as input power, channel spacing, optical gain and data rate. The enhancement achieved through the regression based optimized DWDM design is evaluated in terms of optical measurements such as signal power, noise power, Q-factor and BER.

scholarly journals Mid-gap Trap State Mediated Dark Current in Organic Photodiodes

2021 ◽  
Author(s):  
Christina Kaiser ◽  
Oskar Sandberg ◽  
Stefan Zeiske ◽  
Sam Gielen ◽  
Wouter Maes ◽  
...  
Keyword(s):  
Dark Current ◽  
Near Infrared ◽  
Low Cost ◽  
Open Circuit ◽  
Optoelectronic Properties ◽  
Consumer Electronics ◽  
Embodied Energy ◽  
Wearable Electronics ◽  
Trap States ◽  
New Applications

Abstract Photodiodes are ubiquitous in industry and consumer electronics. New applications for photodiodes are constantly emerging, such as the internet of things and wearable electronics that demand different mechanical and optoelectronic properties from those provided by conventional inorganic devices. This has stimulated considerable interest in the use of next generation semiconductors, particularly the organics, which provide a vast palette of available optoelectronic properties, can be incorporated into flexible form factor geometries, and promise extremely low cost, low embodied energy manufacturing from earth abundant materials. The sensitivity of a photodiode to low light intensities (typically important in these new applications) depends critically on the dark current. Organic photodiodes, however, are characterized by a much higher dark current than expected for thermally excited band-to-band transitions. Here, we show that the lower limit of the dark current is given by recombination via mid-gap trap states. This new insight is generated from temperature dependent dark current measurements of narrow-gap photodiodes for the near-infrared. Based on Shockley-Read-Hall statistics, a diode equation is derived which can be used to determine an upper limit for the specific detectivity and to explain the general trend observed for the light to dark current ratio as a function of the experimental open-circuit voltage for a series of organic photodiodes. A detailed understanding of the origins of noise in any detector is fundamental to defining performance limitations and thus is critical to materials and device selection, design and optimisation for all applications. Our work establishes these important principles for organic semiconductor photodiodes for the near-infrared.

Verification of Scanned Engineering Parts with CAD Models Based on Discrete Curvature Estimation

2005 ◽  
Vol 5 (2) ◽  
pp. 116-117 ◽  
Author(s):  
B. Lipshitz ◽  
A. Fischer
Keyword(s):  
Manufacturing Industry ◽  
Real Data ◽  
Current Work ◽  
Cad Model ◽  
Sampled Data ◽  
Discrete Curvature ◽  
Cad Models ◽  
Curvature Estimation ◽  
Prototype Design

The manufacturing industry constantly needs to verify machined objects against their original CAD models. Given a prototype design, an engineer should be able to determine whether the part was manufactured well; that is, whether it fits the CAD model exactly. However, derivative computations are unstable for real data, and the estimated curvature is thus very sensitive to noise. Moreover, in many cases, spatial fitting of corresponding points is not sufficient. The current work utilizes the curvature properties to inspect manufactured parts that have been reconstructed from noisy and densely sampled data.

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