In many real engineering applications, the distribution of training scenarios (source domain) and the distribution of testing scenarios (target domain) is different, thus the classification performance decreases sharply when simply applying the classifier trained in source domain directly to the target domain. At present, most of the existing domain adaptation methods are based on the probability-inference. For the problem of domain adaptation image classification, a collaborative representation based unsupervised method was proposed from the view of image representation. Firstly, all of the source samples were taken as the dictionary. Secondly, the three target samples closest to the target sample in the target domain were exploited to robustly represent the local-neighbor geometrical information. Thirdly, the target sample was encoded by combining the dictionary and the local-neighbor information. Finally, the classification was completed by using the nearest classifier. Since the collaborative representations have stronger robustness and discriminative ability by absorbing the target local-neighbor information, the classification method based on the new representations has better classification performance. The experimental results on the domain adaptation dataset confirm the effectiveness of the proposed method.

Focused on the problem of low storage density in embedded systems, in this paper, a new NAND device management solution with high storage density was proposed. In the proposed solution, a generalized mode of information structure in NAND page was designed by researching a great number of NAND storage structures and BCH(Bose-Chaudhuri-Hocquenghem) parity coding programs. In the mode, data layout in Out of Band (OOB) could achieve Error Correcting Code (ECC) capability while accommodating device management information of partition, thus the main page could be completely used for data storage, which can be treated as a basis for development of device read-write solution and Wear Leveling mechanism. The experimental results show that the proposed solution improves storage density up to 98%, and it is superior to most current common file systems. Having an excellent data storage density, as well as relatively stable device read-write efficiency and Program/Erase (P/E) endurance, the solution has good application advantages in embedded systems.

Recently, collective chaos in coupled oscillators networks has become a new hot spot in the chaos study. On account of short growing history of collective chaos and lacking of mature theories and methods, the main means of research is still concentrated on the rough ones such as numerical computation, power spectrum and Lyapunov exponent, which are not strict judgment in mathematics and hard to describe the mechanism of chaos. By means of topological horseshoe theory, the authors studied deeply collective chaos of one four-dimensional continuous system consisting of a pair of coupled oscillators and found that topological horseshoe with expanding in one direction in the phase space of the corresponding Poincare map. It not only strictly demonstrates by numerical way that the coupled oscillator system is chaotic, but also reveals the dynamic mechanism of chaos.

Shortcomings of image encryption schemes based on chaos currently can be mainly classified into three types as follows: firstly, low dimensional chaotic sequences cause degradation of chaos; secondly, the structure of chaotic system adopted is too simple; thirdly, the algorithm is merely related to the structure of chaotic system and key. Concerning the shortcomings above, this paper presented a new image group encryption algorithm. In order to overcome the degradation of chaos, the algorithm generated pixel diffusion matrix by coupling chaotic sequences of high dimensional hyperchaos system with chaotic sequences of one dimensional chaotic mapping by means of matrix tensor product. In the process, the generation of pixel diffusion matrix was controlled by plaintext, thus the algorithm was related to plaintext, which enhanced the security of the algorithm.