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Medical imaging is that the technique and method of making visual representations of the inside of a body for clinical analysis and medical intervention, furthermore as visual illustration of the performance of some organs or tissues (physiology). In computer vision, image segmentation is the method of partitioning a digital image into its constituent parts with identical pixel values. The goal of segmentation is to modify the illustration of a picture into one thing that is a lot of purposeful and easier to investigate. A foot ulceration is associate degree open sore on the foot. Foot ulceration may be a shallow red crater that involves solely the surface skin. Foot ulceration can also be terribly deep. There are many issues with current practices for treating diabetic foot ulcers. First, patients should move to their wound clinic on a daily basis to own their wounds checked by their clinicians. Second, a clinician’s wound assessment method is predicated on visual examination. Technology using image analysis techniques may be a potential resolution to each these issues. The wound image is captured by the camera on the smart phone with the help of a picture capture box. In the Image pre-processing step, the electronic image is first down-sampled into high-resolution image. Second, the pictures are sleeked to get rid of noise (which is assumed primarily to be Gaussian noise made by image acquisition process) by mistreatment the Gaussian blur methodology whose variance with 0.5 was through empirical observation judged to be optimum supported multiple experiments. The foot outline is decided by using Sobel edge detection methodology. Then, Region of interest is developed by keeping the foot as foreground and rest as background. Color segmentation is performed by partitioning pixels into totally different clusters supported color similarity and abstraction relation. The RYB image is transformed to HSV and color thresholding is performed to find the outer area.

In this current scenario, covid pandemic breaks analysis is becoming popular among the researchers. The various data sources from the different countries analyzed to predict the possibility of coronavirus transition from one person to another person. The datasets are not providing more information about the causes of the corona. Many authors provided the solution by using chest X-ray and CT images to predict the corona. In this paper, the covid pandemic transition process from one person to another person was classified using traditional machine learning algorithms. The input labels are encoded and transformed, utilizing the label encoder technique. The XG boost algorithm was outperformed all the other algorithms with overall accuracy and F1-measure of 99%. The Naive Bayes algorithm provides 100% accuracy, precision, recall, and F1-Score due to its improved ability to handle lower datasets.

Semiconductor industry is still looking for the enhancement of breakdown voltage in Silicon on Insulator (SOI) Metal Oxide Semiconductor Field Effect Transistor (MOSFET). Thus, in this paper, heavy n-type doping below the channel is proposed for SOI MOSFET. Simulation of SOI MOSFET is carried out using 2D TCAD physical simulator. In the conventional device, with no p-type doping is used at the bottom silicon layer. While, in proposed device, p-type doping of 1×1018 cm-3 is used. Physical models are used in the simulation to achieve realistic performance. The models are mobility model, impact ionization model and ohmic contact model. Using TCAD simulation, electron/hole current density, impact generation, recombination and breakdown phenomena are analyzed. It is found that the proposed with p-type doping of 1×1018 cm-3 for SOI MOSFET yields high breakdown voltage. In contrast to conventional device, 20% improvement in breakdown voltage is achieved for proposed device.

The full adder is an important component for controller or processor design like microprocessors, digital signal processors etc. It is also used to do arithmetic and logical operations. The objective of this project is to reduce power, delay and increase the stability factor of a full adder by using various 1bit full adder designs and techniques. Here 10T full adder circuits using CMOS technology plots the minimum power consumption rather than others. Because CMOS technology dissipates low power. A comparative data analysis is shown for power, delay and stability using SERF (Static Energy Recovery Full Adder), GDI(Gate Diffused Input) method with different number of transistors which is used to extend the battery life. The adders are designed and implemented in the virtuoso platform using Cadence 45nm tool.

The architecture of a MIPS (Microprocessor without Interlocked Pipeline Stages) based RISC or Reduced Instruction Set of Computers is a type of microprocessor which was designed by Harvard type data path structure to execute high speed using a small set of Instructions. This project explains the design and implementation of a 4-stage pipelining based low power processor. This feature leads to increase the reliability and speed of the system. The pipelining includes fetch, decode, execute and memory read/write operations. Low power was obtained by using clock gating technique. Clock gating is used to eliminate the unwanted clock usage when the module is not used. The main aim of the project is to design a 4-stage pipelined RISC processor starting from RTL to GDSII (Physical Design). The processor was coded by Verilog HDL language and implemented in Cadence Encounter Compiler tool. Calculated area, power, delay and clock gating using Cadence RTL compiler using slow and fast libraries of 45nm technology.

Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. In this paper explaining current segmentation approaches in medical image segmentation and then reviewed with an emphasis on the advantages and disadvantages of these methods and showing the implemented outcomes of the thresholding, clustering