Towards Real Time Implementation of Sparse Representation Classifier (SRC) Based Heartbeat Biometric System

2014 ◽  
pp. 189-202 ◽  
Author(s):  
W. C. Tan ◽  
H. M. Yeap ◽  
K. J. Chee ◽  
D. A. Ramli
Keyword(s):  
Sparse Representation ◽  
Real Time ◽  
Biometric System ◽  
Sparse Representation Classifier

scholarly journals A Sparse Representation Classification Approach for Near Real-Time, Physics-Based Functional Monitoring of Aerosol Jet-Fabricated Electronics

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Roozbeh (Ross) Salary ◽  
Jack P. Lombardi ◽  
Darshana L. Weerawarne ◽  
M. Samie Tootooni ◽  
Prahalada K. Rao ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Real Time ◽  
Functional Properties ◽  
Printed Electronics ◽  
Imaging System ◽  
Monitoring And Control ◽  
Wide Range ◽  
Sparse Representation Classification ◽  
Aerosol Jet Printing

Abstract Aerosol jet printing (AJP) is a direct-write additive manufacturing (AM) method, emerging as the process of choice for the fabrication of a broad spectrum of electronics, such as sensors, transistors, and optoelectronic devices. However, AJP is a highly complex process, prone to intrinsic gradual drifts. Consequently, real-time process monitoring and control in AJP is a bourgeoning need. The goal of this work is to establish an integrated, smart platform for in situ and real-time monitoring of the functional properties of AJ-printed electronics. In pursuit of this goal, the objective is to forward a multiple-input, single-output (MISO) intelligent learning model—based on sparse representation classification (SRC)—to estimate the functional properties (e.g., resistance) in situ as well as in real-time. The aim is to classify the resistance of printed electronic traces (lines) as a function of AJP process parameters and the trace morphology characteristics (e.g., line width, thickness, and cross-sectional area (CSA)). To realize this objective, line morphology is captured using a series of images, acquired: (i) in situ via an integrated high-resolution imaging system and (ii) in real-time via the AJP standard process monitor camera. Utilizing image processing algorithms developed in-house, a wide range of 2D and 3D morphology features are extracted, constituting the primary source of data for the training, validation, and testing of the SRC model. The four-point probe method (also known as Kelvin sensing) is used to measure the resistance of the deposited traces and as a result, to define a priori class labels. The results of this study exhibited that using the presented approach, the resistance (and potentially, other functional properties) of printed electronics can be estimated both in situ and in real-time with an accuracy of ≥ 90%.

Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation

2016 ◽  
Vol 76 (20) ◽  
pp. 20457-20482 ◽  
Author(s):  
Nesrine Charfi ◽  
Hanene Trichili ◽  
Adel M. Alimi ◽  
Basel Solaiman
Keyword(s):  
Sparse Representation ◽  
Palmprint Recognition ◽  
Biometric System ◽  
Hand Shape

Real-Time Grayscale-Thermal Tracking via Laplacian Sparse Representation

2016 ◽  
pp. 54-65 ◽  
Author(s):  
Chenglong Li ◽  
Shiyi Hu ◽  
Sihan Gao ◽  
Jin Tang
Keyword(s):  
Sparse Representation ◽  
Real Time
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