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In a system that performs both encryption and lossy compression, the conventional way is to compress first and then encrypt the compressed data. This separation approach has been proved to be optimal. In certain applications where sensitive information should be protected as early as possible, it is preferable to perform the encryption first and then compress the encrypted data, which leads to the concept of the reversed system. Johnson et al. proposed an achievable scheme for the reversed system, where a modulo-sum encryption is followed by a compression using the Wyner–Ziv distributed source coding with side information. However, in general, this reversed system performs worse than the conventional system in the sense that it requires more compression rate and secrecy key rate. In this paper, we propose a new achievable scheme for the reversed system, where the encryption is conducted by a permutation cipher, and then, the encrypted data is compressed using the optimal rate-distortion code. The proposed scheme can achieve the optimal compression rate and secret key rate. As a result, we show that reversing the order of the encryption and compression does not necessarily compromise the performance of an encryption-compression system. We show that the proposed system attains strong secrecy, and the information leakage vanishes exponentially. [ABSTRACT FROM PUBLISHER]

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super- resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel Mamdani-type fuzzy model, referred to as the all-permutations fuzzy rule base (APFRB), and show that it is mathematically equivalent to a standard feedforward neural network. We describe several applications of this equivalence between a neural network and our fuzzy rule base (FRB), including knowledge extraction from and knowledge insertion into neural networks. [ABSTRACT FROM AUTHOR]