scholarly journals A Sparse Representation Classification for Noise Robust Wrist-based Fall Detection

2021 ◽  
Author(s):  
Farah Othmen ◽  
André Lazzaretti ◽  
Mouna Baklouti ◽  
Marwa Jmal ◽  
Mohamed Abid
Keyword(s):  
Sparse Representation ◽  
Fall Detection ◽  
Sparse Representation Classification ◽  
Noise Robust

scholarly journals Gesture recognition based on an improved local sparse representation classification algorithm

2017 ◽  
Vol 22 (S5) ◽  
pp. 10935-10946 ◽  
Author(s):  
Yang He ◽  
Gongfa Li ◽  
Yajie Liao ◽  
Ying Sun ◽  
Jianyi Kong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Gesture Recognition ◽  
Classification Algorithm ◽  
Sparse Representation Classification ◽  
Local Sparse Representation

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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%.

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