A novel image encryption scheme based on compressed sensing and blind source separation is proposed in this work, where there is no statistical requirement to plaintexts. In the proposed method, for encryption, the plaintexts and keys are mixed with each other using a underdetermined matrix first, and then compressed under a project matrix. As a result, it forms a difficult underdetermined blind source separation (UBSS) problem without statistical features of sources. Regarding the decryption, given the keys, a new model will be constructed, which is solvable under compressed sensing (CS) frame. Due to the usage of CS technology, the plaintexts are compressed into the data with smaller size when they are encrypted. Meanwhile, they can be decrypted from parts of the received data packets and thus allows to lose some packets. This is beneficial for the proposed encryption method to suit practical communication systems. Simulations are given to illustrate the availability and the superiority of our method.

MOTIVATION: Distribution analysis is one of the most basic forms of statistical analysis. Thanks to improved analytical methods, accurate and extensive quantitative measurements can now be made of the mRNA, protein and metabolite from biological systems. Here, we report a large-scale analysis of the population abundance distributions of the transcriptomes, proteomes and metabolomes from varied biological systems. RESULTS: We compared the observed empirical distributions with a number of distributions: power law, lognormal, loglogistic, loggamma, right Pareto-lognormal (PLN) and double PLN (dPLN). The best-fit for mRNA, protein and metabolite population abundance distributions was found to be the dPLN. This distribution behaves like a lognormal distribution around the centre, and like a power law distribution in the tails. To better understand the cause of this observed distribution, we explored a simple stochastic model based on geometric Brownian motion. The distribution indicates that multiplicative effects are causally dominant in biological systems. We speculate that these effects arise from chemical reactions: the central-limit theorem then explains the central lognormal, and a number of possible mechanisms could explain the long tails: positive feedback, network topology, etc. Many of the components in the central lognormal parts of the empirical distributions are unidentified and/or have unknown function. This indicates that much more biology awaits discovery.

This paper investigates variable selection (VS) and classification for biomedical datasets with a small sample size and a very high input dimension. The sequential sparse Bayesian learning methods with linear bases are used as the basic VS algorithm. Selected variables are fed to the kernel-based probabilistic classifiers: Bayesian least squares support vector machines (BayLS-SVMs) and relevance vector machines (RVMs). We employ the bagging techniques for both VS and model building in order to improve the reliability of the selected variables and the predictive performance. This modeling strategy is applied to real-life medical classification problems, including two binary cancer diagnosis problems based on microarray data and a brain tumor multiclass classification problem using spectra acquired via magnetic resonance spectroscopy. The work is experimentally compared to other VS methods. It is shown that the use of bagging can improve the reliability and stability of both VS and model prediction.

This paper deals with the application of the General Mapping Regressor (GMR) neural network to the direct torque control DTC of an induction motor. In particular it shows that the GMR neural network is able to correctly learn the classical DTC, as well as any other more involved control strategy. A suitable test bench has been set up in order to verify the performance of the neural controller.

The Generalized Mapping Regressor (GMR) neural network is able to solve for inverse problems even when multiple solutions are given. In this case, it does not only identify these solutions (even if infinite, e.g. contours), but also specifies to which branch of the underlying mapping it belongs. It is also able to model mapping with discontinuities. The basic idea is the transformation of the mapping problem in a pattern recognition problem in a higher dimensional space (where the function branches are represented by clusters). Training is given by a multiresolution quantization represented by a pool of neurons whose number is determined by the training set. Then, neurons are linked each other by using some kind of local principal component analysis (LPCA). This phase is the most important and original. Other techniques (e.g. SVM's, mixture-of-experts) could work a priori on the same problems, but are not able to understand automatically when to stop the data quantization. This linking phase can be viewed as a reconstruction phase in which the correct clusters are recovered. The production phase uses a Gaussian kernel interpolation technique. Some examples conclude the paper.