Browsing by Author "Can, Yekta Said."

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    A biometric authentication technique using spread spectrum audio watermarking
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2014., 2014.) Can, Yekta Said.; Alagöz, Fatih.
    Watermarking has become important in the last decade because of the copyright protection applications. Embedding information into an audio le is more di cult as compared to images, because human auditory system is more sensitive than human visual system. Therefore, the proposed watermarking algorithms for digital audio have been less than those for digital image and video. This thesis presents a biometric authentication scheme based on spread spectrum watermarking technique. We add a biometric authentication system to the Sipdroid open source VoIP program. Firstly, senders must register to the system with their unique biometric features. T.C Identity number, keystroke dynamics and voice are used as biometric features. After registration, these biometric features are used as watermarked material. Before embedding, the watermark is spread with the Direct Sequence Spread Spectrum (DSSS) technique. While talking, this watermark material is embedded to speech and sent to receiver using Frequency Hopping Spread Spectrum(FHSS) technique. The watermarked biometric data is constructed in the receiver's phone after conversation is nished. This method does not need the original audio carrier signal when extracting watermark because it is using the blind extraction. The experimental results demonstrate that the embedding technique is not only less audible but also more robust against the common signal processing attacks like low-pass lter, adding white Gaussian noise, shearing, and compression. In order for receiver to be able to login to the system, biometric features of the user should match with the watermarked biometric data.
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    Stress detection and management in daily life using wearable sensors
    (Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020.) Can, Yekta Said.; Ersoy, Cem.
    Stress has become an integral part of our modern society. Researchers investi gated ways to cope with it to alleviate its negative effects on human health, society and economy. At this point, widespread usage of smartphones, smartwatches and smart wrist-bands raised the question of whether we can detect and alleviate stress with them. Although research has traditionally been conducted in laboratory settings, a set of new studies have recently begun to be conducted in ecological environments with unobtrusive wearable devices. In this thesis, we developed a stress detection system for daily life. Unobtrusive wearable devices were used for physiological data collection. For that purpose, we used heart rate variability (HRV) and electrodermal activity (EDA) signals. Modality specific artifact detection and removal algorithms, feature extraction and advanced machine learning methods were proposed. We tested our system in a lab oratory environment, restricted, semi-restricted and unrestricted real-life environments by collecting data in each environment. We proposed different techniques to improve the state of the art in real life environments. We worked on prominent environment specific research questions. We further examined different stress alleviation methods including those which can be applied indoors. We also discussed promising techniques, alleviation methods and research challenges for daily life stress management.
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    Unsupervised
    (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2022., 2022) Başaran, Osman Tugay.; Ersoy, Cem.; Can, Yekta Said.
    Stress is one of the most important problems of today. Although it seems to be a part of modern human life, it is known to cause serious health problems. Many researchers from different disciplines have been working on this subject, which has personal and social effects, for many years. Psychologists, behavioral scientists, and psychiatrists continue their research in the clinical setting. However, when the stress factor is considered as a part of daily life, clinical environments or controlled experimental areas may be insufficient in terms of stress classification. Thanks to developing sensor technologies, wearable devices, and machine learning methods, stress classification has become an area of interest for computer scientists. Although developments in wearable sensors, ubiquitous computing, and machine learning continue, they bring new challenges to this field. The data labeling burden is one of these challenges. It requires significant effort and resources to have the subjects who have stress problems fill out questionnaires periodically in their daily life and to synchronize the physiological data with the questionnaire results. Being aware of this labeling burden, we aimed to find a new solution by using a less amount of labeled data from the multi-sensor physiological dataset that we collect in daily life. For this reason, this thesis focuses on what will be the performance of a system using a less amount of labeled data and semi-supervised learning techniques.