Hiroshi Toda

Bio: Hiroshi Toda is an academic researcher from Toyohashi University of Technology. The author has contributed to research in topics: Wavelet & Wavelet transform. The author has an hindex of 10, co-authored 62 publications receiving 298 citations.

Perfect translation invariance with a wide range of shapes of hilbert transform pairs of wavelet bases

Abstract: It is well known that a Hilbert transform pair of wavelet bases improves the lack of translation invariance of the discrete wavelet transform. However, its shapes and improvement are limited by the difficulty in applying the Hilbert transform pair to a discrete signal. In this paper, novel Hilbert transform pairs of wavelet bases, which are based on a Meyer wavelet and have a wide range of shapes, are proposed to create perfect translation invariance, and their calculation method is designed to apply these wavelet bases to any discrete signal. Therefore, perfect translation invariance is achieved with a wide range of shapes of the Hilbert transform pairs of wavelet bases.

The design of complex wavelet packet transforms based on perfect translation invariance theorems

Abstract: The useful theorems for achieving perfect translation invariance have already been proved, and based on these theorems, dual-tree complex discrete wavelet transforms with perfect translation invariance have been proposed. However, due to the complication of frequency divisions with wavelet packets, it is difficult to design complex wavelet packet transforms with perfect translation invariance. In this paper, based on the aforementioned theorems, novel complex wavelet packet transforms are designed to achieve perfect translation invariance. These complex wavelet packet transforms are based on the Meyer wavelet, which has the important characteristic of possessing a wide range of shapes. In this paper, two types of complex wavelet packet transforms are designed with the optimized Meyer wavelet. One of them is based on a single Meyer wavelet and the other is based on a number of different shapes of the Meyer wavelets to create good localization of wavelet packets.

Nonstationary signal analysis using the RI-Spline wavelet

Abstract: In our first report, we have proposed a complex type wavelet, the Real-Imaginary Spline Wavelet (RI-Spline wavelet) for the continuous wavelet transform and demonstrated the advantages of our approach. In this study, we develop our RI-Spline wavelet for the Discrete Wavelet Transform (DWT) that uses a fast algorithm based on Multi-resolution analysis. The DWT has a translation variance problem, so it can not catch features of the signals exactly although it has been widely used in signal analysis. In order to overcome this translation variance problem, we first develop a Complex Discrete Wavelet Transform (CDWT) using the RI-Spline wavelet and propose the Coherent Dual-Tree algorithm for the RI-Spline wavelet without increasing the computational cost very much. Then we apply this translation invariant CDWT to translation invariant de-noising. Experimental results show that our method, when applied to ECG data and music data, can obtain better de-noising results than conventional Wavelet Shrinkage.

Directional selection of two-dimensional complex discrete wavelet transform and its application to image processing

Abstract: In this study, a novel direction selection method using the two-dimensional complex discrete wavelet transform (2D-CDWT) is proposed. In order to achieve arbitrary direction selection, the directional filters are first designed. Calculation procedure of directional selection can be shown as follows: (1) The 16 sub-images are generally generated from the original image by the 2D-CDWT without a down-sampling process and the 12 sub-images that correspond to the high-frequency components are selected. (2) The 12 sub-images are filtered by using the designed directional filter. (3) The down-sampling process is carried out and the resulting images are obtained. Furthermore, this method is applied to the surface analysis of a wafer, and it is confirmed that our method is effective in detecting irregular direction components.

Perfect translation invariance with a wide range of shapes of hilbert transform pairs of wavelet bases

Abstract: It is well known that a Hilbert transform pair of wavelet bases improves the lack of translation invariance of the discrete wavelet transform. However, its shapes and improvement are limited by the difficulty in applying the Hilbert transform pair to a discrete signal. In this paper, novel Hilbert transform pairs of wavelet bases, which are based on a Meyer wavelet and have a wide range of shapes, are proposed to create perfect translation invariance, and their calculation method is designed to apply these wavelet bases to any discrete signal. Therefore, perfect translation invariance is achieved with a wide range of shapes of the Hilbert transform pairs of wavelet bases.

High Temporal Resolution Path-Average Rain Gauge with 50-GHz Band Microwave

Abstract: Rain radar measures instantaneous spatial-average rainfall, while conventional rain gauges directly measure point rainfall with low temporal resolution. Thus differences in the resolution of the sensors create difficulties for rain radar validation, especially for spaceborne rain radar. Accordingly, rainfall measurement by microwave link has been proposed for several decades, as it estimates instantaneous path-average rainfall. Thus it is expected that the microwave link rain gauge will overcome, at least partly, the problems in the rain radar validation, toward which a 50-GHz band microwave link [the path-averaged rain gauge (PRG)] was developed that has been in operation since September 2000. In this paper, the authors show the potential of the PRG system by a simple model and rainfall comparison with a disdrometer and a tipping-bucket rain gauge. Differences observed by the instruments were within 15% (within 10% in half of the cases) during actual rain events in 2003. This confirmed that the ...

An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT

Abstract: Brain tumor is an uncontrolled mass of tissues in the brain which originate due to mutated growth of tissues. Brain tumor has become a leading cost of death in modern day environment and researchers are inclined to find ways to mitigate the proliferation of this disease. A lot of methods have been applied in brain tumor detection ranging from image processing to signal based analysis. In this study a robust image processing based method is applied using MRI images. MRI images are preferred due to their simplicity and low noise presence. In this study first a clustering based method is used to segment the image and then SVM is applied for tumor detection. A total seven features were considered and were analyzed by the classifiers. SVM with 94.6% accuracy gave a robust result.

The design of complex wavelet packet transforms based on perfect translation invariance theorems

Abstract: The useful theorems for achieving perfect translation invariance have already been proved, and based on these theorems, dual-tree complex discrete wavelet transforms with perfect translation invariance have been proposed. However, due to the complication of frequency divisions with wavelet packets, it is difficult to design complex wavelet packet transforms with perfect translation invariance. In this paper, based on the aforementioned theorems, novel complex wavelet packet transforms are designed to achieve perfect translation invariance. These complex wavelet packet transforms are based on the Meyer wavelet, which has the important characteristic of possessing a wide range of shapes. In this paper, two types of complex wavelet packet transforms are designed with the optimized Meyer wavelet. One of them is based on a single Meyer wavelet and the other is based on a number of different shapes of the Meyer wavelets to create good localization of wavelet packets.