This paper presents a sample rate conversion filter for decimation with flat passband. The proposed linear programming optimization (LPO) technique improves the magnitude response of filter with least computational complexity. Computational complexity has been a major factor in selection of decimation filter. Simulation results indicate that the proposed filter shows passband droop less than 0.007 dB with 50.5% decrease in computational complexity. The proposed filter eliminates the need of compensator.
In medical image diagnosis, performance is affected because of degradation in image resolution, imaging equipment and imaging parameters. Currently, deep learning has gained much attention from researchers due to its capability to maintain perceptual quality after reconstruction. Therefore, this letter is motivated by the advantages of deep learning and proposes a novel model termed as the correlation filter interleaved progressive convolution network (CFIPC). In this letter, dilated convolution interleaved with a correlation filter expands the receptive field without any pixel information losses. The result analysis was performed on DRIVE, CHASEB1, MRI, ultrasound and histopathological dataset medical datasets at 2x/4x/8x/16x upscaling factors and achieved the highest PSNR/SSIM value of 50.54/0.9986 at a 2x upscaling factor on the histopathological dataset.
In this paper a low power single ended 13T SRAM cell has been proposed for bit inter-leaving application. A column aware scheme is used in the cell to achieve stable SRAM cell with better performance than the existing designs. The proposed SRAM cell exhibit robust read operation and better read performance with lower power consumption. This proposed 13T SRAM has been compared with standard 6T SRAM and existing 9T SRAM (with bit-interleaving capability) in term of Power consumption, Delay and Power Delay Product (PDP) at various supply voltages as 1.8V, 1.6V and 1.4V. The simulations are carried out on Cadence Virtuoso at 180nm CMOS technology and the simulation results are analyzed to verify the superiority of the proposed design over the existing designs. The proposed 13T SRAM proves to be better in terms of power and PDP at all the supply voltages. At 1.8V power saving by the proposed circuit is 72.46% compared to standard 6T SRAM cell and significant improvement is observed at other supply voltages also.
Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.
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