The proportion of persons over 65 years old is projected to increase worldwide between 2022 and 2050. The increasing burden on medical staff and the shortage of human resources are growing problems. Bedsores are injuries caused by prolonged pressure on the skin and stagnation of blood flow. The more the damage caused by bedsores progresses, the longer the treatment period becomes. Moreover, patients require surgery in some serious cases. Therefore, early detection is essential. In our research, we are developing a non-contact bedsore detection system using electromagnetic waves at 10.5GHz. In this paper, we extracted appropriate information from a scalogram and utilized it to detect the sizes of bedsores. In addition, experiments using a phantom were conducted to confirm the basic operation of the bedsore detection system. As a result, using the approximate curves and lines obtained from prior analysis data, it was possible to estimate the volume of each defected area, as well as combinations of the depth of the defected area and the length of the defected area. Moreover, the experiments showed that it was possible to detect bedsore presence and estimate their sizes, although the detection results had slight variations.

In JPEG2000, the Cohen-Daubechies-Feauveau (CDF) 9/7-tap wavelet filter was implemented by using the conventional lifting scheme. However, the filter coefficients remain complex, and the conventional lifting scheme disregards image edges in the coding process. In order to solve these issues, we propose a lifting scheme in two steps. In the first step, we select the appropriate filter coefficients; in the second step, we employ a median operator to regard image edges. Experimental results show that the peak signal-to-noise ratio (PSNR) value of the proposed lifting scheme is significantly improved, by up to 0.75 dB on average, compared to that of the conventional lifting scheme in the CDF 9/7-tap wavelet filter of JPEG2000.

An analytic approach for the generation of non-periodic and periodic complementary sequences is advanced for lengths that are powers of two. The periodic complementary sequences can be obtained using symmetric or anti-symmetric extensions. The properties of their autocorrelation functions are studied. The non-periodic complementary sequences are the intersection between anti-symmetric and symmetric periodic sequences. These non-periodic and periodic complementary sequences are identified to be special cases of non-periodic and periodic (or cyclic) orthogonal wavelet transforms. This relationship leads to the novel approach.

Several lifting implementation techniques for invertible deniterlacing are proposed in this paper. Firstly, the invertible deinterlacing is reviewed, and an efficient implementation is presented. Next, two deinterlacer-embedded lifting architectures of discrete wavelet transforms (DWT) is proposed. Performances are compared among several architectures of deinterlacing with DWT. The performance evaluation includes dual-multiplier and single-multiplier architectures. The number of equivalent gates shows that the deinterlacing-embedded architectures require less resources than the separate implementaion. Our experimental evaluation of the dual-multiplier architecture results in 0.8% increase in the gate count, whereas the separate implementation of deinterlacing and DWT requires 6.1% increase from the normal DWT architecture. For the proposed single-multiplier architecture, the gate count is shown to result in 4.5% increase, while the separate counterpart yields 10.7% increase.

In this paper, we have proposed a new method for diffuse liver disease classification with sonogram, including the normal liver, hepatitis and cirrhosis, from a new point of view "scale. " The new system utilizes a multiscale analysis tool, called wavelet transforms, to analyze the ultrasonic liver images. A new set of features consisting of second order statistics derived from the wavelet transformed images is employed. From these features, we have found that the third scale is the representative scale for the classification of the considered liver diseases, and the horizontal wavelet transform can improve the representation of the corresponding features. Experimental results show that our method can achieve about 88% correct classification rate which is superior to other measures such as the co-occurrence matrices, the Fourier power spectrum, and the texture spectrum. This implies that our feature set can access the granularity from sonogram more effectively. It should be pointed out that our features are powerful for discriminating the normal livers from the cirrhosis because there is no misclassification samples between the normal liver and the cirrhosis sets. In addition, the experimental results also verify the usefulness of "scale" because our multiscale feature set can gain eighteen percent advantage over the direct use of the statistical features. This means that the wavelet transform at proper scales can effectively increase the distances among the statistical feature clusters of different liver diseases.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

Two drawbacks of pyramidal wavelet transforms for finite-length sequences are the lack of conservation of the support and the boundary effect. In this letter, the structure of cyclic wavelet transforms (CWT) is used to permute the input and output data to map them into a linear array. Systolic realization of cyclic wavelet packet transforms (CWPT) is also presented to adequately deal with finite-length sequences which have dominant information on high or median frequency channels. The VLSI architectures designed in this letter are very attractive because adaptive processing can be achieved by just programming the filter coefficients.