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Content Addressable Memories (CAMs) are high-speed hardware lookup tables that are crucial in routing, packet classification, search, and other fast look-up applications. Despite having the advantage of high speed, they have limitations due to their power hungriness. This paper addresses the limitations of existing CAMs with two novel modifications in the proposed Search Line Pre-Charge-Free Low-Power-Content Addressable Memory (SLPFCAM) structure. The first novelty is their gated evaluation matching circuit and the second is a match line pre-charge controller. The proposed design reduces the energy consumption and improves the speed of the CAM by eliminating the SLprecharge phase in the CAM operation. The pre-charge controller prevents the attempt of match line pre-charge when it is not required. The design has been implemented using Cadence Virtuoso in a 90-nm CMOS technology with 1-V supply, and the post-layout simulation results show that the SLPFCAM makes significant improvements with a minimum of 83% less power consumption, a 91% less search delay, and the average energy consumption being 0.765 fJ/bit/search. [ABSTRACT FROM AUTHOR]

This study presents a framework for gray texture classification based on the fusion of wavelet and curvelet features. The two main frequency domain transformations Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT) are analyzed. The features are extracted from the DWT and DCT decomposed image separately and their performance is evaluated independently. Then feature fusion technique is applied to increase the classification accuracy of the proposed approach. Brodatz texture images are used for this study. The results show that, only two texture images D105 and D106 are misclassified by the fusion approach and 99.74% classification accuracy is obtained. [ABSTRACT FROM AUTHOR]

In this paper, the task of texture image classification is analyzed by using Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT). The wavelet and curvelet coefficients are used to describe the textures in the given image. These coefficients are obtained by the decomposition process. First, the texture image is decomposed by using DWT and DCT at multiscale. As the sub-bands in the decomposed image carries diverse information about the texture, predefined number of coefficients is selected in each sub-band image. Before selecting the coefficients, sub-band coefficients are sorted in order to account high energy coefficients. The results show that the classification accuracy of DWT based features outperforms the DCT energies. The classification accuracy of DWT is 5% higher than DCT features at 2-level decomposition with 50% of coefficients used. [ABSTRACT FROM AUTHOR]