A novel ROI-based reversible data hiding scheme is proposed for medical images, which is able to hide electronic patient record (EPR) and protect the region of interest (ROI) with tamper localization and recovery. The proposed scheme combines prediction error expansion with the sorting technique for embedding EPR into ROI, and the recovery information is embedded into the region of non-interest (RONI) using histogram shifting (HS) method which hardly leads to the overflow and underflow problems. The experimental results show that the proposed scheme not only can embed a large amount of information with low distortion, but also can localize and recover the tampered area inside ROI.

A reversible watermark algorithm with large capacity has been developed by applying the difference expansion of a generalized integer transform. In this algorithm, a watermark signal is inserted in the LSB of the difference values among pixels. In this paper, we apply the prediction errors calculated by a predictor in JPEG-LS for embedding watermark, which contributes to increase the amount of embedded information with less degradation. As one of the drawbacks discovered in the above conventional method is the large size of the embedded location map introduced to make it reversible, we decrease the large size of the location map by vectorization, and then modify the composition of the map using the local characteristics. We also exclude the positions such that the modification in the embedding operation cannot increase the capacity but merely degrade the image quality, which can be applicable to the conventional methods.

In this article, we considered the asymptotic expectations of the prediction error and the fitting error of a regression model, in which the component functions are chosen from a finite set of orthogonal functions. Under the least squares estimation, we showed that the asymptotic bias in estimating the prediction error based on the fitting error includes the true number of components, which is essentially unknown in practical applications. On the other hand, under a suitable shrinkage method, we showed that an asymptotically unbiased estimate of the prediction error is given by the fitting error plus a known term except the noise variance.

The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.

This paper proposes a practical training algorithm for artificial neural networks, by which both the optimally pruned model and the optimally trained parameter for the minimum prediction error can be found simultaneously. In the proposed algorithm, the conventional information criterion is modified into a differentiable function of weight parameters, and then it is minimized while being controlled back to the conventional form. Since this method has several theoretical problems, its effectiveness is examined by computer simulations and by an application to practical ultrasonic image reconstruction.

A narrowband interference in direct sequence spread spectrum communication systems also affects the characteristics of a delay lock loop. In this paper, the delay errors of a baseband delay lock loop (DLL) in the presence of the interference which consists of a narrowband Gaussian noise and several tones are examined, and when a filter is used to reject the interference, the characteristics of the DLL are analyzed using the Fourier method. Furthermore, from the calculation results of the delay error in case where a prediction error filter with two-sided taps is used as the rejection filter, it is shown that the filter is necessary to keep the DLL in the lock-on state.