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The paper describes a method of segmented wavelet transform (SegWT) that makes it possible to compute the discrete-time wavelet transform of a signal segment-by-segment, with exactly the same result as if the whole signal were transformed at once. Due to its generality, the method can be utilized in many situations: for wavelet-type processing of a signal in real time or in case we want to process the signal in parallel or In case we need to process a long signal, but the available memory capacity is insufficient (e.g. in the DSPs). In the paper, the background theory and the emerging principles of both the forward and the inverse SegWT are explained. [ABSTRACT FROM AUTHOR]

In this paper, we present the application of adaptive wavelet in lossy image compression. The construction of this adaptive wavelet is realised by the use of lifting scheme. In our application the lifting scheme is composed by an adaptive update lifting step and a fixed prediction lifting step. Finally, experiments with The EBCOT (Embedded Block Coding with Optimized Truncations) algorithm applied on synthetic and real images are reported. [ABSTRACT FROM AUTHOR]

The essential problem in signal analysis is to find a numerically stable algorithm for reconstruction of a signal from its atomic decomposition [2]. This leads to the notion of frames [3, 7] which is a main ingredient in the analysis and synthesis of signals. In this paper, we have obtained the frame bounds for wavelet packet frames which are more general than that of wavelet frames. [ABSTRACT FROM AUTHOR]

In this paper, we consider random generalization of completely generalized nonlinear variational-like inclusions and propose an iterative algorithm for computing their approximate solutions. We prove that the approximate solutions obtained by proposed algorithm converge to the exact solution of our problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider random generalization of completely generalized nonlinear variational-like inclusions and propose an iterative algorithm for computing their approximate solutions. We prove that the approximate solutions obtained by proposed algorithm converge to the exact solution of our problem. [ABSTRACT FROM AUTHOR]