This document provides a summary of Madhavi Tippani's resume. She has a Master's degree in Biomedical Engineering and is currently working as a graduate research assistant at UT Southwestern Medical Center. Her skills include using MATLAB for image processing, data analysis, and GUI creation. She has experience installing medical devices, troubleshooting equipment, and performing statistical data analysis. Past projects involve segmenting medical images, developing analytical tools for corneal diagnosis, and using frequency domain techniques to measure tissue properties.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
Determination with Deep Learning and One Layer Neural Network for Image Proce...IJERA Editor
Today’s world Coronary artery disease is the most common cause of death worldwide and thus early diagnosis. Well-timed opportune of this disease can lead to significant reduction in its morbidityand mortality in both younger and older for angiogram test. In this research multi slice CT scanner is used for heart angiogram test. With the help of this multi slice CT angiogram image we detect the hart diseased or not. For this disease identification and classification of angiogram images many machine learning algorithms are previously proposed those are SVM RBF and RBF neural network. Problem with SVM isnon-liner method when use any type of application will miss most liner ways of blood vessels and lack of speed in process. For non linear classification we are using RBF SVM. Problem with RBF neural network is not solve the hierarchal and component based problems, so resolve the problem using deep learning. This issue drastically improves the estimation efficiency for real time application. This methodology consumes less time for both learning as well as testing comparatively than any other methods. This issue highly improves the estimation efficiency and accuracy for real time 256, 512 slices CT scan angiogram image.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
Professor Harrison Bai, Artificial Intelligence Applications in Radiology_mHe...Levi Shapiro
Artificial Intelligence Applications in Radiology, presentation by Dr Harrison Bai, Assistant Professor of Diagnostic Imaging, Warren Alpert Medical School, Brown University. His research interests focus on AI, machine learning, and computer vision as applied to medical image analysis. Dr Bai is an associate editor for the journal Radiology: Artificial Intelligence and is currently a principal investigator for an RSNA Research Scholar grant and an NIH grant. The AI Radiology Lab has various areas of work including COVID-19; Treatment response assessment on imaging (brain, TACE, lung, colorectal); Rapid diagnosis of large-vessel ischemic stroke, patient selection and outcome prediction; Tumor characterization on imaging; Infrastructure development; Federated learning; Image registration (CT-guided tumor ablation); Radiology reports natural language processing. The AI pipeline includes DIANA system, Diagnosis model, severity model and progression model across various automated features and the value proposition. One Technique for dealing with missing sequence and imaging artifact- Sequence dropout. Human-in-the-loop AI. In the short- to mid-term, the utilization of AI needs to be combined with human intervention and supervision. Active learning strategy – annotation. Treatment response evaluation on imaging. Automatic quality estimation to flag the failed cases for humans to review and/or edit. Human in the loop annotation. Automatic quality estimation. Federated learning. Semi-supervised and unsupervised learning. AWS NVIDIA Clara Train SDK using TensorFlow 1.14. Annotations vary across imaging sites. Share weights without sharing data. Domain shift – distribution difference between source data and target data leading to performance degradation.
Development of Computational Tool for Lung Cancer Prediction Using Data MiningEditor IJCATR
The requirement for computerization of detection of lung cancer disease arises ever since recent-techniques which involve
manual-examination of the blood smear as the first step toward diagnosis. This is quite time-consuming, and their accurateness depends
upon the ability of operator's. So, prevention of lung cancer is very essential. This paper has surveyed various techniques used by previous
authors like ANN (Artificial Neural Network), image processing, LDA (Linear Dependent Analysis), SOM (Self Organizing Map) etc.
Project report 3D visualization of medical imaging dataShashank
Report of my engineering research on 3D visualisation of medical images obtained from slices of human male and female cadevars. Courtesy NIH (USA), IIIT (Allahabad)
Determination with Deep Learning and One Layer Neural Network for Image Proce...IJERA Editor
Today’s world Coronary artery disease is the most common cause of death worldwide and thus early diagnosis. Well-timed opportune of this disease can lead to significant reduction in its morbidityand mortality in both younger and older for angiogram test. In this research multi slice CT scanner is used for heart angiogram test. With the help of this multi slice CT angiogram image we detect the hart diseased or not. For this disease identification and classification of angiogram images many machine learning algorithms are previously proposed those are SVM RBF and RBF neural network. Problem with SVM isnon-liner method when use any type of application will miss most liner ways of blood vessels and lack of speed in process. For non linear classification we are using RBF SVM. Problem with RBF neural network is not solve the hierarchal and component based problems, so resolve the problem using deep learning. This issue drastically improves the estimation efficiency for real time application. This methodology consumes less time for both learning as well as testing comparatively than any other methods. This issue highly improves the estimation efficiency and accuracy for real time 256, 512 slices CT scan angiogram image.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
1. MADHAVI TIPPANI
400 Kerby street, #308, Arlington, Texas - 76013, USA Email: madhavi.tippani@mavs.uta.edu Mobile: +1(469) 525 7794
BIO MEDICAL ENGINEERING PROFESSIONAL
Research and laboratory assistant, proficient in imaging and data processing using MATLAB. Experienced in installation of medical devices,
trouble shooting and calibration. Skilled at statistical data analysis using MS Excel, R programming and MATLAB and their documentation.
Detail oriented, team leading ability and passionate to learn about new technologies.
FUNCTIONAL EXPERTISE and SKILLS
Application packages: MATLAB (image reconstruction, image processing and analysis, GUI creation), Statistical data analysis (using ‘R’,
MATLAB and MS Excel), Python, LabVIEW, Solidworks, C, MS Office Suite, Image J, QlikView.
Others: Optical fiber polishing, Knowledge about GMP, GLP, GCP and FDA Regulations.
EDUCATION
The University of Texas at Arlington and the University of Texas South Western Medical Centre at Dallas CGPA-4.0/4.0
Master of Science in Bio-Medical Engineering. May 2016
Jawaharlal Technological University, India CGPA-3.3/4.0
Bachelor of Technology in Bio-Medical Engineering May 2013
COURSEWORK
Human Anatomy and Physiology, Bio-Optics Laboratory, Laboratory Principles, Basic Clinical Sciences, Bio-Medical Equipment and Signal
Processing, Statistical Methods, Image Processing and Pattern Recognition, Basic Simulation-MATLAB, Medical Informatics, Python.
PROFESSIONAL EXPERIENCE
Graduate Research Assistant - Ophthalmology department, UT South Western Medical Center, Dallas, Texas. Sep’15 - Present
Installed system software following manufacturer's instruction manual
Data processing, Data modelling and Data visualization of medical images in MATLAB
Generating text, Excel, image output files through MATLAB, as diagnostic results
Observed in vivo experiments, learnt measurement procedures and system protocols and documented the same
Programmed User defined MATLAB functions for decoding header files to load different medical data like DICOM, Volume files
GUI creation using MATLAB for quantitative data analysis of 3-Dimensional confocal imaging in vivo
Interactive 2D and 3D image reconstruction and signal processing using MATLAB
Graduate Research Assistant - Bioengineering department, UT Arlington, Texas. Oct’14 – May’15
Performed timely administration, calibration and troubleshooting of lab equipment
Performed Data collection, Data scrubbing, Data analysis, Data modelling and Data visualization using MS-Excel and MATLAB
Performed repeated Frequency Domain measurements and interpreted the results for real time diagnosis of prostate cancer
Prepared different concentrations of intra lipid and stocked it for experiments, and maintained lab notes for concentration calculations
Conducted experiments using 32 laser diode, 4 PMT detector Imagent system to calculate scattering and absorption values of different
phantoms using slope algorithms and documented the results for conclusions
Designed different probe geometries following predefined principles, for effective signal detection
Observed and recorded signal output from all probe geometries and drew conclusions for suitable probe designs
Collaborated with technicians: provided them the timely equipment performance and documented the machine technical errors
Supported laboratory quality and safety initiatives
Sales Executive -Aspirejobz.com Services Pvt. Ltd., Hyderabad, India. Dec’13 – Jun’14
Achieved increased productivity as a team lead (team of 4 members)
Marketed company products and services, thus increasing company sales by 8%
Implemented improved marketing strategies
Intern - Care Hospitals, Hyderabad, India. May’12 – June’12
Gained knowledge about the operation of life saving equipment in Bio-Medical department
Trained on hospital regulations, patient care protocols, hazardous chemicals and medical lab record policies
Performed installation, service and maintenance of medical equipment and documented their specifications and working
Monitored equipment in ICU, ICCU and operation theaters in the hospital and observed biological parameters
Prompt reporting of abnormal working conditions of equipment in all wards to the Bio-Medical department in the hospital
2. PROJECTS
Image processing and 3D reconstruction of Brain images
Developed MATLAB code to project the montage view of series of brain images and to display the contour map of tumor and brain tissue
for cancer diagnosis.
- Used Image processing techniques like Erosion, Linear transformation, Region growing, Segmentation, and Contour mapping
- The code loads 53 MRI DICOM images of brain and executes User defined functions to display the contrast map of brain and tumor.
- Segmentation of brain tissue from skull and scalp was achieved by selecting three seeds (pixels) from brain region in the image and
overlapping the three images grown from the seeds for an accurate image.
- Also generated 3D view of brain while differentiating brain and tumor with different colors to visualize the approximate position and size
of tumor in the brain. Documented the project results with comments on the code.
Corneal Image Processing Analytical tool
Developed an analytical tool for corneal diagnosis that generates a text file as an analysis report. This tool, built in MATLAB with a Graphical
User Interface (GUI) is used to study the cellular events of wound healing after a corneal surgery or an infection. Corneal data of mice and
rabbits was collected through in vivo confocal microscope (HRT-RCM).
The tool has features (built with User defined functions) allowing a user to:
- Input Volume files from a confocal microscope of different sizes.
- Projects side, front, top and 3D views of the 2D corneal image stack and pick a desired Region of Interest (ROI) from the stack.
- Perform analysis such as Smoothening with different masks
- Calculate the area of the selected portion of the intensity curve and calculate the epithelial, stromal and endothelial thickness of the cornea.
Currently working on documenting the entire progress of the project, results and writing comments on the code.
• Segmentation of Heart structure from surroundings
Different Segmentation techniques like Thresholding, Region Growing, Erosion, Dilation, Opening and Closing etc. were performed on a
CT slice of a human heart and all the results were stored and compared. Segmentation was performed to examine the contrast enhanced
heart by eliminating the additional structures like bones and parts of large blood vessels of lung surrounding the heart and parts of patient
table.
Study on effect of cancer severity on lifespan of patients
Formulated a Hypotheses and performed statistical data analysis on the data set containing an assay on cutaneous melanoma (malignant
cancer), to determine if the average lifespan of patients decreased with increase in cancer severity. As a part of Data Analysis Hypotheses
testing was performed in ‘R’. Loaded data into R and segregated it into nodule categories based on disease severity, F-tests and T-tests were
performed on all combinations of categories and thus it was concluded that average lifetime decreased with increased severity. Documented
the results of five number summary, boxplot, normality test and hypotheses tests.
Study on dependency of patient disease history on improvement of his exercise duration
Performed statistical data analysis on the data set and determined that the percentage improvement of exercise duration is in negative linear
relation with the patient disease history. Hypothesis testing for the linearity is performed in R assuming a 5% level of significance and the
result of negative linearity was rechecked with the plot between the two data variables. The assumptions used to formulate the linear model
where checked and the results were documented.
Frequency Domain Photon migration in tissue
Absorption coefficient and reduced scattering coefficient of intralipid (which mimics tissue scattering characteristics) are measured using
Frequency Domain optical migration technique. Experiment was setup with 1% concentration intralipid as a medium and a source light of
633nm laser with a frequency of 90MHz was used. Output signal is taken at different source detector separations and amplitude and phase
differences are calculated with the reference signal. Absorption, scattering coefficients are then calculated using slope algorithms and
documented the results and conclusions. Laboratory safety rules for using laser were followed and learned side effects of laser.
Wireless Data Communication and Energy Transfer for Implantable Devices
Developed a magnetic coil to achieve non-invasive energy transfer into the implantable devices, to avoid multiple surgeries for recharging
the implantable. ‘Witricity’, a phenomenon of magnetic resonance is proposed to exchange energy efficiently between strongly coupled
resonant objects in a certain range. Additionally, RF communication was established with the implantable device using a microcontroller
AT89C52 (programmed using Keil microvision 3) to modify the operational parameters as per the requirements
