The 8-bit design is able to process 256 times input combination in compare to 4-bit vedic multiplier, using approximates 6 times basic elements, 2 times IO buffers, approximate 1.5 times total power dissipation. HSTL_I_12, SSTL18_I and LVCMOS12 are the most energy efficient IO standards in HSTL, SSTL and LVCMOS family respectively. Device static power and design static power are two types of static power dissipation. Device static power is also known as Leakage power when the device is on but not configured. Design static power is power dissipation when bit file of design is downloaded on FPGA but there is no switching activity. Design static power dissipation of 8-bit Vedic multiplier is almost double of design static power dissipation of 4-bit Vedic multiplier. Device static (leakage) power dissipation of 8-bit Vedic multiplier is almost equal to device static power dissipation of 4-bit Vedic multiplier on 40nm FPGA.

The low power design of Very Large Scale Integration (VLSI) system is one of the hot topic in research. In this chapter, the low power design for VLSI based high-speed communication is realized over 28 nm VLSI chip packed in UltraScale Field Programming Gate Array (FPGA) using proposed technique. The high-speed communication system is taken as case study for the low power design of VLSI system. Similarly, various VLSI design system can be realized to achieve the low power VLSI system design goal. High-speed communication systems provide the smooth operation for global internet traffic and requires high power devices and components.IO standard is powerful interface tool that provides low power consumption using the fast signal termination by mean of electrical characteristics. In result for this work, more than 96% power reduction is achieved for VLSI based high-speed communication system, when operated at 500 GHZ, 900 GHz, 10 THz and 17 THz carrier frequencies using the High-Speed Unterminated Logic IO Standard. The power analysis is performed using XPA analyzer in Xilinx suite.

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR) . However, the generated image still suffers from undesirable artifacts such as the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN). We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using the WT, whereas the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merced Landsat remote sensing dataset and Set14 with each image size of 256 × 256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers.

Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super-resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low-resolution images. For remote sensing image super-resolution fast wavelet-based super-resolution (FWSR), we propose a novel, fast wavelet-based plexus framework that performs super-resolution convolutional neural network (SRCNN)-like extraction of features based on three hidden layers. First, wavelet sub-band images are combined into a pre-defined full-scale data training factor, including approximation and interchangeable stand-alone units (frequency subbands). Second, to speed up image recovery, mapping the sub-band image of the wavelet is then measured using its approximate image. Third, the added sub-pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub-band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their highfrequency data and preserved characteristics. Five current super-resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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