Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Double image encryption by using iterative random binary encoding in gyrator domains

Abstract: We propose a double image encryption by using random binary encoding and gyrator transform. Two secret images are first regarded as the real part and imaginary part of complex function. Chaotic map is used for obtaining random binary matrix. The real part and imaginary part of complex function are exchanged under the control of random binary data. An iterative structure composed of the random binary encoding method is designed and employed for enhancing the security of encryption algorithm. The parameters in chaotic map and gyrator transform serve as the keys of this encryption scheme. Some numerical simulations have been made, to demonstrate the performance this algorithm.

A face and palmprint recognition approach based on discriminant DCT feature extraction

Abstract: In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

Filtering for Discrete-Time Networked Nonlinear Systems With Mixed Random Delays and Packet Dropouts

Abstract: In this technical note, a new class of discrete-time networked nonlinear systems with mixed random delays and packet dropouts is introduced, and the H∞ filtering problem for such systems is investigated The mixed stochasitc time-delays consist of both discrete and infinite distributed delays and the packet dropout phenomenon occurs in a random way Furthermore, new techniques are presented to deal with the infinite distributed delay in the discrete-time domain Sufficient conditions for the existence of an admissible filter are established, which ensure the asymptotical stability as well as a prescribed H∞ performance Finally, examples are given to demonstrate the effectiveness of the proposed filter design scheme in this technical note