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Showing 3 results for Encoder
High-Speed and Low-Power Flash ADCs Encoder
M. Soleimani, S. Toofan,
Volume 14, Issue 3 (9-2018)
Abstract
This paper presents a high-speed, low-power and low area encoder for implementation of flash ADCs. Key technique for design of this encoder is performed by convert the conventional 1-of-N thermometer code to 2-of-M codes (M = ¾ N). The proposed encoder is composed from two-stage; in the first stage, thermometer code are converted to 2-of-M codes by used 2-input AND and 4-input compound AND-OR gates. In the second stage by two ROM encoders, 2-of-M codes determine n-1 MSB bits and one LSB bit. The advantages of the proposed encoder rather than other similar works are high speed, low power consumption, low active area, and low latency with same bubble error removing capability. To demonstrate the mention specifications, 5-bit flash ADCs with conventional and proposed encoders in their encoder blocks, are simulated at 2-GS/s and 3.5-GS/s sampling rates in 0.18-μm CMOS process. Simulation results show that the ENOB of flash ADCs with conventional and proposed encoders are equal. In this case, the proposed encoder outputs are determined almost 30-ps faster rather than the conventional encoder at 2-GS/s. Also, the power consumptions of the conventional and proposed encoders were 17.94-mW and 11.74-mW at 3.5-GS/s sampling rate from a 1.8-V supply, respectively. Corresponding, latencies of the conventional and proposed encoders were 3 and 2 clock cycles. In this case, number of TSPC D-FFs and logic gates of the proposed encoder is decreased almost 39% compared to the conventional encoder.

A Novel Multiply-Accumulator Unit Bus Encoding Architecture for Image Processing Applications
R. Samanth, S. G. Nayak, P. B. Nempu,
Volume 19, Issue 1 (3-2023)
Abstract
In the CMOS circuit power dissipation is a major concern for VLSI functional units. With shrinking feature size, increased frequency and power dissipation on the data bus have become the most important factor compared to other parts of the functional units. One of the most important functional units in any processor is the Multiply-Accumulator unit (MAC). The current work focuses on the development of MAC unit bus encoders as well as the identification of an improved architecture for image processing applications. To reduce the power consumption in these functional units, two bus encoding architectures were developed by encoding data before it was sent on the data buses. One is MSB reference encoding, and another is Fourth and Fifth bit ANDing (FFA) without the need for an extra bus line with fewer transitions by using gray codes. The comparison of the proposed encoding architectures with the existing encoding architectures from the literature revealed an 8% to 36% significant improvement in power dissipation. The simulation was done with Xilinx ISE, and the Cadence RTL Compiler tool was utilized for the synthesis, which was done with the 180nm technology library. And also, the image filtering is analyzed using MATLAB.

Deep Learning-Based Ultrasound Image Despeckling by Noise Model Estimation
O. Mahmoudi Mehr, M. R. Mohammadi, M. Soryani,
Volume 19, Issue 3 (9-2023)
Abstract
Speckle noise is an inherent artifact appearing in medical images that significantly lowers the quality and accuracy of diagnosis and treatment. Therefore, speckle reduction is considered as an essential step before processing and analyzing the ultrasound images. In this paper, we propose an ultrasound speckle reduction method based on speckle noise model estimation using a deep learning architecture called “speckle noise-based inception convolutional denoising neural network" (SNICDNN). Regarding the complicated nature of speckle noise, an inception module is added to the first layer to boost the power of feature extraction. Reconstruction of the despeckled image is performed by introducing a mathematical method based on solving a quadratic equation and applying an image-based inception convolutional denoising autoencoder (IICDAE). The results of various quantitative and qualitative evaluations on real ultrasound images demonstrate that SNICDNN outperforms the state-of-the-art methods for ultrasound despeckling. SNICDNN achieves 0.4579 dB and 0.0100 additional gains on average for PSNR and SSIM, respectively, compared to other methods. Denoising ultrasound based on its noise model estimation is not only a novel approach in comparison to traditional denoising autoencoder models but also due to the fact that it uses mathematical solutions to recover denoised images, SNICDNN shows a greater power in ultrasound despeckling.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.