Andjela Draganic

Andjela Draganic

The characterization of multicomponent signals with a particular emphasis on musical and communication signals is one of the problems studied in the dissertation. In order to provide an efficient analysis of the multicomponent signals, the possibility to separate signal components is observed. The procedure for decomposition and classification of the signal components whose energy and physical characteristics differ in the time-frequency domain is proposed in this work. A special focus in the dissertation is on the application of the compressive sensing approach in multicomponent signals. The compressive sensing method becomes popular in the field of signal processing until recently, and its application in various fields can increase the acquisition and transmission speed, reduce the complexity of devices, and reduce energy consumption. The procedure that applies the compressive sensing in the classification of the wireless communication signals is proposed. The algorithms for reconstruction of the compressive sensed signals are intensively developing, and therefore special emphasis in the dissertation is devoted to the hardware implementation of one of the algorithms for sparse signal reconstruction.

Zoja Vulaj, Milos Brajovic, Andjela Draganic et al.

Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. SVM represent a special type of classification techniques commonly used in medical applications. Automatic classification of ECG could make the work of cardiologic departments faster and more efficient. It would also reduce the number of false diagnosis and, as a result, save lives. The working principle of the SVM is based on translating the data into a high dimensional feature space and separating it using a linear classificator. In order to provide an optimal representation for SVM application, the Hermite transform domain is used. This domain is proved to be suitable because of the similarity of the QRS complex with Hermite basis functions. The maximal signal information is obtained using a small set of features that are used for detection of irregular QRS complexes. The aim of the paper is to show that these features can be employed for automatic ECG signal analysis.

Andjela Draganic, Milan Maric, Irena Orovic et al.

Although the protection of ownership and the prevention of unauthorized manipulation of digital images becomes an important concern, there is also a big issue of image source origin authentication. This paper proposes a procedure for the identification of the image source and content by using the Public Key Cryptography Signature (PKCS). The procedure is based on the PKCS watermarking of the images captured with numerous automatic observing cameras in the Trap View cloud system. Watermark is created based on 32-bit PKCS serial number and embedded into the captured image. Watermark detection on the receiver side extracts the serial number and indicates the camera which captured the image by comparing the original and the extracted serial numbers. The watermarking procedure is designed to provide robustness to image optimization based on the Compressive Sensing approach. Also, the procedure is tested under various attacks and shows successful identification of ownership.