scholarly journals Sparse Matrix for ECG Identification with Two-Lead Features

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kuo-Kun Tseng ◽  
Jiao Luo ◽  
Robert Hegarty ◽  
Wenmin Wang ◽  
Dong Haiting
Keyword(s):  
Feature Extraction ◽  
Matrix Method ◽  
Sparse Matrix ◽  
Human Identification ◽  
Two Dimensional ◽  
Ecg Signals ◽  
Biometric Security ◽  
Dimensional Matrix ◽  
The Matrix ◽  
Sparse Matrix Techniques

Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

scholarly journals Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4138
Author(s):  
Kuo-Kun Tseng ◽  
Jiao Lo ◽  
Chih-Cheng Chen ◽  
Shu-Yi Tu ◽  
Cheng-Fu Yang
Keyword(s):  
Matrix Method ◽  
Sparse Matrix ◽  
Human Identification ◽  
Recognition Algorithm ◽  
Blood Oxygen ◽  
Dimensional Approach ◽  
Identification Rate ◽  
Ecg Signals ◽  
Biometric Security ◽  
Biometric Information

Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

Matrix Based Feature Measurement and Extraction for Face Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 643-647
Author(s):  
Qi Zhu ◽  
Jin Rong Cui ◽  
Zi Zhu Fan
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy ◽  
Face Image ◽  
Two Dimensional ◽  
The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

A THEORETICAL FRAMEWORK FOR MATRIX-BASED FEATURE EXTRACTION ALGORITHMS WITH ITS APPLICATION TO IMAGE RECOGNITION

2008 ◽  
Vol 08 (01) ◽  
pp. 1-23 ◽  
Author(s):  
GUIYU FENG ◽  
DAVID ZHANG ◽  
JIAN YANG ◽  
DEWEN HU
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Theoretical Framework ◽  
Small Sample ◽  
Point Of View ◽  
Two Dimensional ◽  
Linear Discriminant ◽  
The Matrix ◽  
Small Sample Sizes ◽  
Special Case

Recently proposed matrix-based methods, two-dimensional Principal Component Analysis (2DPCA), two-dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Locality Preserving Projections (2DLPP) have been shown to be effective ways to avoid the problems of high dimensionality and small sample sizes that are associated with vector-based methods. In this paper, we propose a general theoretical framework for matrix-based feature extraction algorithms from the point of view of graph embedding. Our framework can be applied to extend two recently proposed vector-based algorithms, i.e. Unsupervised Discriminant Projection (UDP) and Marginal Fisher Analysis (MFA) algorithms, to their matrix-based versions. Further, our framework can also be used as a platform to generate new matrix-based feature extraction algorithms by designing meaningful graphs, e.g. two-dimensional Discriminant Embedding Analysis (2DDEA) in this paper. It is shown that 2DLDA is actually a special case of the 2DDEA method. Experiments on three publicly available image databases demonstrate the effectiveness of the proposed algorithm. Our results fit into the scene for a better picture about the matrix-based feature extraction algorithms.

Teaching and Examining Sparse Matrix Methods

1995 ◽  
Vol 23 (4) ◽  
pp. 336-342
Author(s):  
F. W. Williams
Keyword(s):  
Matrix Method ◽  
Mental Arithmetic ◽  
Sparse Matrix ◽  
Simultaneous Equations ◽  
Matrix Methods ◽  
The Matrix ◽  
Gauss Elimination ◽  
The Way

A method is presented for concise teaching and examining of the principles and advantages of sparse matrix methods. The method uses only mental arithmetic and is illustrated using Gauss elimination for the solution of simultaneous equations. Indications are given of the ways in which the ideas can be extended to methods other than Gauss elimination and to types of sparse matrix method other than those considered in detail. Indications are also given of how the material can be taught so as to integrate with related matters, such as the evaluation of determinants and the way that the savings obtained by using the most sophisticated sparse matrix methods increase rapidly as the order of the matrix increases.

scholarly journals SYNTHESIS OF PIECEWISE-LINEAR CHAOTIC MAPS: INVARIANT DENSITIES, AUTOCORRELATIONS, AND SWITCHING

2008 ◽  
Vol 18 (08) ◽  
pp. 2169-2189 ◽  
Author(s):  
ALAN ROGERS ◽  
ROBERT SHORTEN ◽  
DANIEL M. HEFFERNAN ◽  
DAVID NAUGHTON
Keyword(s):  
Matrix Method ◽  
Chaotic Maps ◽  
Piecewise Linear ◽  
Image Generation ◽  
Two Dimensional ◽  
Invariant Density ◽  
Interesting Application ◽  
The Matrix ◽  
Simple Matrix ◽  
Invariant Densities

In this paper, we give a review of the Inverse Frobenius–Perron problem (IFPP): how to create chaotic maps with desired invariant densities. After describing some existing methods for solving the IFPP, we present a new and simple matrix method of doing this. We show how the invariant density and the autocorrelation properties of the maps can be controlled independently. We also give some fundamental results on switching between a number of different chaotic maps and the effect this has on the overall invariant density of the system. The invariant density of the switched system can be controlled by varying the probabilities of choosing each individual map. Finally, we present an interesting application of the matrix method to image generation, by synthesizing a two-dimensional map, which when iterated, generates a well-known image.

scholarly journals Approaches to Political Commentary in Scandinavia

2015 ◽  
Vol 36 (1) ◽  
pp. 5-18
Author(s):  
Mette Bengtsson
Keyword(s):  
Initial Phase ◽  
Public Debate ◽  
Future Research ◽  
Two Dimensional ◽  
Scholarly Literature ◽  
Dimensional Matrix ◽  
Political Commentary ◽  
The Matrix ◽  
Text Context

Abstract Political commentary is a contested genre that has attracted a great deal of attention in the Scandinavian public debate, whereas the scholarly literature on it is still in an initial phase. In order to strengthen future research, the present paper suggests a two-dimensional matrix indexing the research on Scandinavian political commentary along the dimensions text/context and descriptive/evaluative. The matrix enables us to see more clearly what we already know and where we lack knowledge. It enables us to see how each category can be developed, the interplay among them, and the obvious lack of textual, evaluative approaches. The author argues that a joint, cross-disciplinary engagement is necessary if we are to adequately understand the potentials and problems of political commentary.

scholarly journals Global Astrometric Solutions with Sparse Matrix Techniques

2000 ◽  
Vol 180 ◽  
pp. 127-131
Author(s):  
Richard L. Branham
Keyword(s):  
Linear Systems ◽  
Sparse Matrix ◽  
Nested Dissection ◽  
Numerical Experimentation ◽  
The Matrix ◽  
Hall Property ◽  
Sparse Matrix Techniques ◽  
Dynamic Data Structure ◽  
Large Linear Systems ◽  
Dynamic Hashing

AbstractModern astrometric techniques lead to large, linear systems solved by the precepts of least-squares. These systems are usually sparse, and one should take advantage of the sparsity to facilitate their solution. As long as the matrix A of the equations of condition possesses the weak Hall property, characteristic of linear systems derived from astrometric reductions, it is possible to find a sparse Cholesky factor. Before the equations of condition are accumulated, by use of the fast Givens transformation, a symbolic factorization of A using Tewarson’s length of intersection technique determines the ordering of the columns of A that result in low fill-in. The non-null elements are stored in a sparse, dynamic data structure by use of dynamic hashing. Numerical experimentation shows that this competes well with alternatives such as nested dissection, and large, but sparse, linear systems with several thousand unknowns can be solved in a reasonable amount of time, even on personal computers.

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