An Improved Power Efficient Clock Pulsed D Flip-flop Using Transmission Gate

Recent digital applications will require highly efficient and high-speed gadgets and it is related to the minimum delay and power consumption.The proposed work deals with a low-power clock pulsed data flip-flop(D flip-flop)using a transmission gate.To accomplish a power-efficient pulsed D flip-flop,clock gating is proposed.The gated clock reduces the unnecessary switching of the transistors in the circuit and thus reduces the dynamic power consumption.The clock gating approach is employed by using an AND gate to disrupt the clock input to the circuit as per the control signal called Enable.Due to this process,the clock gets turned off to reduce power consumption when there is no change in the output.The proposed transmission gate-based pulsed D flip-flop’s performance with clock gating and without clock gating circuit is analyzed.The proposed pulsed D flip-flop power consumption is 1.586μw less than the without clock gated flip-flop.Also,the authors have designed a 3-bit serial-in and parallel-out shift register using the proposed D flip-flop and analyzed the performance.Tanner Electronic Design Automation tool is used to simulate all the circuits with 45 nm technology.

Underwater Image Enhancement Using MIRNet

In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.

Stressed Coral Reef Identification Using Deep Learning CNN Techniques

Deep learning is a machine learning technique that allows the computer to process things that occur naturally to humans.Today,deep learning techniques are commonly used in computer vision to classify images and videos.As a result,for challenging computer vision problems,deep learning provides state of the art solutions to it.Coral reefs are an essential resource of the earth.A new study finds the planet has lost half of its coral reefs since 1950.It is necessary to restore and prevent damage to coral reefs as they play an important role in maintaining a balance in the marine ecosystem.This proposed work helps to prevent the corals from bleaching and restore them to a healthy condition by identifying the root cause of the threats.In the proposed work,using deep learning CNN techniques,the images are classified into Healthy and Stressed coral reefs.Stressed coral reefs are an intermediate state of coral reef between healthy and bleached coral reefs.The pre-trained models Resnet50 and Inception V3 are used in this study to classify the images.Also,a proposed CNN model is built and tested for the same.The results of Inception V3 and Resnet50 are improved to 70%and 55%by tuning the hypermeters such as dropouts and batch normalisation.Similarly,the proposed model is tuned as required and obtains a maximum of up to 90%accuracy.With large datasets,the optimum amount of neural networks and tuning it as required brings higher accuracy than other methods.