In this paper, the repeatable hybrid parallel implementation of inverse matrix computation using SMW formula is proposed. The authors' had previously proposed a hybrid parallel algorithm for inverse matrix computation. It is reasonably fast for a one time computation of an inverse matrix, but it is hard to apply this algorithm repeatedly for consecutive computations since the relocation of the large matrix is required at the beginning of each iterations. In order to eliminate the relocation of the large input matrix which is the output of the inverse matrix computation from the previous time step, the computation algorithm has been redesigned so that the required portion of the input matrix becomes the same as the output portion of the previously computed matrix in each node. This makes it possible to repeatedly and efficiently apply the SMW formula to compute inverse matrix in a time-series simulation.

A simple but efficient method to improve readability of discrete pseudo time-frequency representations (TFRs) of nonstationary signals by the reassignment of the representations in discrete frequency dimension is presented. The method does not rely on the nonzero time derivative of the window function employed in the estimation of pseudo TFR. This property of the reassignment method is advantageous because the method can provide an improved readability in the situation when a known reassignment method is unefficient. The reassignment of the TFRs of corrupted signals is discussed. Numerical examples are included to illustrate the performance of the proposed method.

We propose a fine structure imaging for the surface and its inside of solid material such as coated drill bits with TiN (Titanium Nitride). We call this method i-MSE (innovative MSE) since the fine structure is visualized with a local mechanical strength (the local erosion rate) which is obtained from a set of erosion depth profiles measured with Micro Slurry-jet Erosion test (MSE). The local erosion rate at any sampling point is estimated from the depth profile using a sliding window regression and for the rest of the 2-dimensional points it is interpolated with the mean value coordinate technique. The interpolated rate is converted to a 2D image (i-MSE image) with a color map. The i-MSE image can distinguish layers if the testing material surface is composed of coats which have different resistance to erosion (erosive wear), while microscopic image such as SEM (Scanning Electron Microscope) and a calotest just provides appearance information, not physical characteristics. Experiments for some layered specimens show that i-MSE can be an effective tool to visualize the structure and to evaluate the mechanical characteristics for the surface and the inside of solid material.

In this paper, a hybrid parallel implementation of inverse matrix computation using SMW formula is proposed. By aggregating the memory bandwidth in the hybrid parallel implementation, the bottleneck due to the memory bandwidth limitation in the authors previous multicore implementation has been dissolved. More than 8 times of speed up is also achieved with dual-core 8-nodes implementation which leads more than 20 simulation steps per second, or near real-time performance.

In region-of-interest (ROI) image coding based on wavelet transforms, the tap length of the wavelet filter as well as energy compaction characteristics affect the quality of the restored image. This paper presents a wavelet transform comprised of two wavelet filter sets with different tap lengths. The wavelet filter is switched to the shorter-length set to code a ROI of an image and to the longer-length one for the remaining region, the region of non-interest (RONI). ROI coding examples demonstrate that this switching wavelet transform provides better quality levels than fixed transforms under the same total bits; the quality of the recovered ROI is improved in the lossy coding of both regions while that of the full image is improved in the lossless coding of the ROI.

The paper shows some of benefits of multi-unitary decomposition in signal analysis applications. It is emphasized that decompositions of complex discrete-time signals onto a single basis provide an incomplete and in such way potentially misleading image of the signals in signal analysis applications. It is shown that the multi-unitary decimated filter banks which decompose the analyzed signal onto several bases of the given vector space can serve as a tool which provides a more complete information about the signal and at the same time the filter banks can enjoy efficient polyphase component implementation of maximally decimated, i. e. nonredundant, filter banks. An insight into the multi-unitary signal decomposition is provided. It is shown that the multiple-bases representation leads to an efficient computation of frequency domain representations of signals on a dense not necessarily uniform frequency grid. It is also shown that the multiple-bases representation can be useful in the detection of tones in digital implementations of multifrequency signaling, and in receivers of chirp systems. A proof is provided that there are possible benefits of the multiple-bases representations in de-noising applications.