Global ETD Search

1	Fingerprints recognition Dimitrov, Emanuil January 2009 (has links) Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501. fingerprints fingerprints recognition biometrics verification identification authentication image enhancement fingerprints matching biometric sensors
2	Fingerprints recognition Dimitrov, Emanuil January 2009 (has links) <p>Nowadays biometric identification is used in a variety of applications-administration, business and even home. Although there are a lot of biometric identifiers, fingerprints are the most widely spread due to their acceptance from the people and the cheap price of the hardware equipment. Fingerprint recognition is a complex image recognition problem and includes algorithms and procedures for image enhancement and binarization, extracting and matching features and sometimes classification. In this work the main approaches in the research area are discussed, demonstrated and tested in a sample application. The demonstration software application is developed by using Verifinger SDK and Microsoft Visual Studio platform. The fingerprint sensor for testing the application is AuthenTec AES2501.</p> fingerprints fingerprints recognition biometrics verification identification authentication image enhancement fingerprints matching biometric sensors
3	An Enhanced Body Area Network to Wirelessly Monitor Biometric Information Moore, Levi M. January 2017 (has links) No description available. Electrical Engineering Computer Engineering Body Area Network Remote Healthcare Monitoring Biometric Sensors Bluetooth Low Energy ARM-based Microcontroller
4	Adaptive Learning Grundtman, Per January 2017 (has links) The purpose of this project is to develop a novel proof-of-concept system in attempt to measure affective states during learning-tasks and investigate whether machine learning models trained with this data has the potential to enhance the learning experience for an individual. By considering biometric signals from a user during a learning session, the affective states anxiety, engagement and boredom will be classified using different signal transformation methods and finally using machine-learning models from the Weka Java API. Data is collected using an Empatica E4 Wristband which gathers skin- and heart related biometric data which is streamed to an Android application via Bluetooth for processing. Several machine-learning algorithms and features were evaluated for best performance. adaptive learning machine learning e-learning biosyncing biometric sensors Empatica E4 Intelligent Tutoring Systems WEKA Computer and Information Sciences Data- och informationsvetenskap Engineering and Technology Teknik och teknologier

1

Page generated in 0.0787 seconds