Jiao Lo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jiao Lo has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Cheng-Fu Yang
Department of Chemical and Materials Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan
1 shared publication - Chih-Cheng Chen
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
1 shared publication - Kuo-Kun Tseng
Harbin Institute of Technology
1 shared publication - Shu-Yi Tu
Mathematics Department, University of Michigan, Flint, Ann Arbor, MI 48502, USA
1 shared publication
Latest Publications
- Open Access
Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches
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.
Open AccessElectrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches
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.