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Guojun Qin
Hunan International Economics University

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An Embedded Iris Image Acquisition Research Dangui Chen; Guojun Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 5, No 1: January 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v5.i1.pp90-98

Abstract

In view of the limitation of traditional identification, it is easy to lose and copy keys, cards or ID cards, and it is easy to forget the password. Here, an embedded application system was designed based on the iris identification technology, the functions of gathering, inputing, and registering the iris information and identification can be realized. The system architecture was designed by using the embedded microprocessor of advanced RISC machines (ARM), which is used as the core. The iris sensor was used to gather the iris information, and the development of software was accomplished with the embedded OS Windows CE. The system can be used on the company entrance guard system, customs security of airport and criminal identification.
Random Sampling and Signal Bregman Reconstruction Based on Compressed Sensing Guojun Qin; Jingfang Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 2: November 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i2.pp365-372

Abstract

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programing random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal descriptions and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate,but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free sampling frequency need not obey the Nyquist limit and higher frequency resolution.
A Compressed Sensing Signal Processing Research Guojun Qin; Jingfang Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 1: July 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i1.pp119-125

Abstract

The classical Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. Nowadays in many applications, because of the restriction of the Nyquist rate, we end up with too many samples and it becomes a great challenge for further transmission and storage. In recent years, an emerging theory of signal acquirement, compressed sensing(CS), is a ground-breaking idea compared with the conventional framework of Nyquist sampling theorem. It considers the sampling in an novel way, and open up a brand new field for signal sampling process. It also reveals a promising future of application. In this paper, we review the background of compressed sensing development. We introduce the framework of CS and the key technique and illustrate some naïve application on image process.
Co-Authors Dangui Chen Jingfang Wang