Bücher von Tareek Pattewar

Patient monitoring is to have a quantitative assessment of the important physiological variables of patients during critical periods of biological functions. For diagnostic and research purposes, it is necessary to know their actual value or trend of change. Telemonitoring systems are used for measuring continuously or at regular intervals, automatically, the values of the patient¿s important physiological parameters. Telemedicine system based on embedded computer deals with real time continuous monitoring and recording of parameters like body temperature, electrocardiogram, blood pressure and respiration as well as their analysis. With the advent of computerization in biomedical field, this dissertation has very wide scope, due to the computerized data acquisition, monitoring and control incorporated in it. It reduces the workload of doctors and also gives more accurate results in patients survival.

As a part of an early stage project, it has been the goal of this project to serve as a prototype for such venture having set the paths to a wide range of new opportunities in the field of remote controlled robots interaction. The project is designed to develop a robotic vehicle that is controlled by a Bluetooth device. Bluetooth operation is achieved by any smart phone/Tablet etc., with Android OS, upon a GUI (Graphical User Interface) based touch screen operation. It consists of a HC-06 Bluetooth module circuit and ATMEGA2560 micro-controller is used for the desired operation. At the transmitting end user send the command using android application, commands are sent to the receiver to control the movement of the robot either to move forward, backward and left or right etc. At the receiving end two motors are interfaced to the micro-controller where they are used for the movement of the vehicle. The android application device transmitter acts as a remote control. While the receiver end Bluetooth device is fed to the micro-controller to drive servo motors via motor driver IC for necessary work.

Online Social Networks (OSNs) have become fundamental parts of our online lives, and their popularity is increasing at a surprising rate every day. Growing popularity of Twitter has attracted the attention of attackers who attempt to manipulate the features provided by Twitter to gain some advantage, such as driving Twitter users to other websites that they post as short URLs (Uniform Resource Locators) in their tweets.Even short URLs are also used to avoid sharing overly long URLs and save limited text space in tweets. Significant numbers of URLs shared in the OSNs are shortened URLs. Despite of its potential benefits from genuine usage, attackers use shortened URLs to hide the malicious URLs, which direct users to malicious pages. Although, OSN service providers and URL shortening services utilize certain detection mechanisms to prevent malicious URLs from being shortened, research has found that they fail to do so effectively.In this project, we developed mechanism to develop a machine learning classifier which detect malicious short URLs. And also shows comparative analysis of detection with various methods.