E-BALL TECHNOLOGY A report submitted as a part of General Proficiency requirement for the degree of B-Tech in Computer Science and Engineering GREATER NOIDA INSTITUTE OF TECHNOLOGY SUPERVISED BY: Mr. Important.!About computer science seminar reports pdf is Not Asked Yet? Please ASK FOR computer science seminar reports pdf BY CLICK HERE.Our Team/forum members are ready to help you in free of cost. Below is stripped version of available. Seminar Report and PPT for CSE Students. Published on Nov 1. Abstract. As smartphones evolve researchers are studying new techniques to ease the human- mobile interaction. We propose Eye. Phone, a novel “hand- free” interfacing system capable of driving mobile applications/functions using only the user’s eyes movement and actions (e. Eye. Phone tracks the user’s eye movement across the phone’s display using the camera mounted on the front of the phone; more specifically, machine learning algorithms are used to: i) track the eye and infer its position on the mobile phone display as a user views a particular application; and ii) detect eye blinks that emulate mouse clicks to activate the target application under view. TechnologyEPCglobal2UHFRFIDProtocolV109122005.pdf.RFID Technology Tracking Techniques seminar Report pdf, ppt. Talekar K.T.Patil College of Computer Science B C S III rd.ABSTRACT : Radio Frequency Identification RFID is a new. Huge Collection of Computer Science Seminar Topics with Abstract and 2013 MCA Research Topics or Ideas in Reports,PDF,PPT,Powerpoint Presentation can be found by visiting this link. Semiar Reports Read More 51. Electronic Waste Seminar report. Quantum computing seminar pdf Computer proofread pdf science has a classical soul many definitions implicitly contain ideas from the time.Seminar Report Quantum Computing. Introduction: Civilization has advanced as people discovered new ways of exploiting. Important.!About computer science industrial training report pdf is Not Asked Yet? Please ASK FOR computer science industrial training report pdf BY CLICK HERE.Our Team/forum members are ready to help you in free of cost. Below is stripped version of. Sample seminar report 71,841 views Share Like Download Farman Khan, Student at Works at no where Follow. A SEMINAR REPORT Submitted by Mr. XYZ in partial fulfillment for the award of the degree of MASTER OF TECHNOLOGY IN COMPUTER 6.
We present a prototype implementation of Eye. Phone on a Nokia N8. At no time does the user have to physically touch the phone display. Human- Computer Interaction (HCI) researchers and phone vendors are continuously searching for new approaches to reduce the effort users exert when accessing applications on limited form factor devices such as mobile phones. The most significant innovation of the past few years is the adoption of touchscreen technology introduced with the Apple i. Phone . The touchscreen has changed the way people interact with their mobile phones because it provides an intuitive way to perform actions using the movement of one or more fingers on the display (e. More specifically, the distinguishing factors of the mobile phone environment are mobility and the lack of sophisticated hardware support, i. HCI applications. In what follows, we discuss these issues. One of the immediate products of mobility is that a mobile phone is moved around through unpredicted context, i. HPI application. A mobile phone is subject to uncontrolled movement, i. It is almost impossible to predict how and where people are going to use their mobile phones. A HPI application should be able to operate reliably in any encountered condition. Consider the following examples: two HPI applications, one using the accelerometer, the other relying on the phone’s camera. Imagine exploiting the accelerometer to infer some simple gestures a person can perform with the phone in their hands, e. What is challenging is being able to distinguish between the gesture itself and any other action the person might be performing. For example, if a person is running or if a user tosses their phone down on a sofa, a sudden shake of the phone could produce signatures that could be easily confused with a gesture. There are many examples where a classifier could be easily confused. In response, erroneous actions could be triggered on the phone. Similarly, if the phone’s camera is used to infer a user action . In addition, video frames blur due to the phone movement. Because HPI application developers cannot assume any optimal operating conditions (i. HPI application to be reliable and scalable. As opposed to HCI applications, any HPI implementation should not rely on any external hardware. Asking people to carry or wear additional hardware in order to use their phone might reduce the penetration of the technology. Moreover, state- of- the art HCI hardware, such as glass mounted cameras, or dedicated helmets are not yet small enough to be conformably worn for long periods of time by people. Any HPI application should rely as much as possible on just the phone’s on- board sensors. Although modern smartphones are becoming more computationally capable , they are still limited when running complex machine learning algorithms . HPI solutions should adopt lightweight machine learning techniques to run properly and energy efficiently on mobile phones. Eyephone Design. One question we address in this paper is how useful is a cheap, ubiquitous sensor, such as the camera, in building HPI applications. We develop eye tracking and blink detection mechanisms based algorithms . We show the limitations of an off- the- shelf HCI technique . The Eye. Phone algorithmic design breaks down into the following pipeline phases: 1) an eye detection phase; 2) an open eye template creation phase; 3) an eye tracking phase; 4) a blink detection phase. In what follows, we discuss each of the phases in turn. By applying a motion analysis technique which operates on consecutive frames, this phase consists on finding the contour of the eyes. The eye pair is identified by the left and right eye contours. While the original algorithm identifies the eye pair with almost no error when running on a desktop computer with a fixed camera (see the left image in Figure 1), we obtain errors when the algorithm is implemented on the phone due to the quality of the N8. Figure 1: Left figure: example of eye contour pair returned by the original algorithm running on a desktop with a USB camera. The two white clusters identify the eye pair. Right figure: example of number of contours returned by Eye. Phone on the Nokia N8. The smaller dots are erroneously interpreted as eye contours. Based on these experimental observations, we modify the original algorithm by: i) reducing the image resolution, which according to the authors in reduces the eye detection error rate, and ii) adding two more criteria to the original heuristics that filter out the false eye contours. In particular, we filter out all the contours for which their width and height in pixels are such that widthmin . The widthmin, widthmax, heightmin, and heightmax thresholds, which identify the possible sizes for a true eye contour, are determined under various experimental conditions (e. This design approach boosts the eye tracking accuracy considerably. Applications Eye. Menu. An example of an Eye. Phone application is Eye. Menu as shown in Figure 4. Eye. Menu is a way to shortcut the access to some of the phone’s functions. The set of applications in the menu can be customized by the user. The idea is the following: the position of a person’s eye is mapped to one of the nine buttons. A button is highlighted when Eye. Phone detects the eye in the position mapped to the button. If a user blinks their eye, the application associated with the button is lunched. Driving the mobile phone user interface with the eyes can be used as a way to facilitate the interaction with mobile phones or in support of people with disabilities. Car Driver Safety. Eye. Phone could also be used to detect drivers drowsiness and distraction in cars. While car manufactures are developing technology to improve drivers safety by detecting drowsiness and distraction using dedicated sensors and cameras . Campbell, Computer Science Department, Dartmouth College,Hanover, NH, USAMobi. Held 2. 01. 0, August 3. New Delhi, India.
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