Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based on machine learning

Cai, Mingxin; Wang, Shanshan; Wu, Chao

doi:10.1007/s11042-020-09847-w

Cited by 4 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the x-axis, y-axis, and F (amplitude) are all finite and discrete, then the image is a digital image. Thus, a digital image is an array of real values or complex values represented in a specific array of bits and a two-dimensional function [16,17,18].…”

Section: Methodsmentioning

confidence: 99%

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Rochendi,

Silalahi,

Simanjuntak

2024

Sinergi

View full text Add to dashboard Cite

This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

show abstract

Section: Methodsmentioning

confidence: 99%

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Rochendi,

Silalahi,

Simanjuntak

2024

Sinergi

View full text Add to dashboard Cite

show abstract

“…where the row vector represents a single parameter to sample the dataset over the time series. The m n  matrix represents the current latest sample data, n represents that the node has n monitoring parameters, and m represents the number of sampling periods of the node (assuming that each parameter has the same sampling period): For strongly correlated parameters (such as air temperature and soil temperature), a standard linear regression estimation model is used to establish a mathematical relationship equation between the parameters, as shown in (2), and the least squares method is used to determine the fitting equation when the fitting error RMSE is the smallest. Regression coefficients (a,b).…”

Section: Dynamicmentioning

confidence: 99%

“…Cai M et al regard wireless sensor networks as a new technology for information understanding, detection and processing. If the Internet recognizes that the world communicates for digital purposes and changes the way people communicate; then, WSN recognizes the connection between the digital world and the physical goals of the world, changes the interaction of people and events, and marks the coming of counting time [2]. Zhang J and others found that WSN originated from the combat needs of the U.S. military.…”

Section: Introductionmentioning

confidence: 99%

Label big data compression in Internet of things based on piecewise linear regression

Zhang

Zhao

et al. 2022

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In order to solve the key problem that most of the energy of wireless sensor network nodes is consumed in wireless data modulation, which is an extremely important and limited resource. The energy efficiency evaluation scheme of data compression algorithm based on the separation of hardware factor and algorithm factor is proposed; In order to improve the running efficiency of the compression algorithm and reduce the energy consumption of the algorithm itself, a program level energy-saving optimization method for the data compression algorithm is proposed; In order to keep the energy-saving benefits of the data compression algorithm when the wireless transmission power is adjusted, an adjustment mechanism of the compression algorithm which can adapt to the change of transmission power is proposed. The experiment shows that when the wireless transmission power is - 7dBm and below (k < 178.4), the data should be compressed by S-LZW algorithm, and when the wireless transmission power is - 5dBm and above (k > 178.4), the b ~ RLE algorithm should be used for compression. The validity of the method is verified.

show abstract

An ensemble mosaicing and ridgelet based fusion technique for underwater panoramic image reconstruction and its refinement

Jindal

Bharti

Kasana

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Research on real-time data transmission and multi-scale video image decomposition of embedded optical sensor array based on machine learning

Cited by 4 publications

References 26 publications

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Touchless palm print recognition system design using Gray Level Co-occurrence Matrix feature with K-Nearest Neighbor classification in MATLAB

Label big data compression in Internet of things based on piecewise linear regression

An ensemble mosaicing and ridgelet based fusion technique for underwater panoramic image reconstruction and its refinement

Contact Info

Product

Resources

About