Irena Orovic

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.