arXiv subjects

Andjela Draganic

Publications and source records attributed to Andjela Draganic.

8 recordsLinked to original sources

arXiv2019

Analysis of non-stationary multicomponent signals with a focus on the Compressive Sensing approach

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.

eess.SP↗

arXiv2017

Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine

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.

cs.CV↗

arXiv2017

A tool for ECG signal analysis using standard and optimized Hermite transform

The development of a system that would ease the diagnosis of heart diseases would also fasten the work of the cardiologic department in hospitals and facilitate the monitoring of patients with portable devices. This paper presents a tool for ECG signal analysis which is designed in Matlab. The Hermite transform domain is exploited for the analysis. The proposed transform domain is very convenient for ECG signal analysis and classification. Parts of the ECG signals, i.e. QRS complexes, show shape similarity with the Hermite basis functions, which is one of the reasons for choosing this domain. Also, the information about the signal can be represented using a small set of coefficients in this domain, which makes data transmission and analysis faster. The signal concentration in the Hermite domain and consequently, the number of samples required for signal representation, can additionally be reduced by performing the parametization of the Hermite transform. For the comparison purpose, the Fourier transform domain is also implemented within the software, in order to compare the signal concentration in two transform domains.

cs.CE↗

arXiv2017

On some common compressive sensing recovery algorithms and applications - Review paper

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.

cs.IT↗

arXiv2017

Identification of image source using serialnumber-based watermarking under Compressive Sensing conditions

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.

cs.MM↗

arXiv2016

A System for Compressive Sensing Signal Reconstruction

An architecture for hardware realization of a system for sparse signal reconstruction is presented. The threshold based reconstruction method is considered, which is further modified in this paper to reduce the system complexity in order to provide easier hardware realization. Instead of using the partial random Fourier transform matrix, the minimization problem is reformulated using only the triangular R matrix from the QR decomposition. The triangular R matrix can be efficiently implemented in hardware without calculating the orthogonal Q matrix. A flexible and scalable realization of matrix R is proposed, such that the size of R changes with the number of available samples and sparsity level.

cs.IT↗

arXiv2015

Sparse Time-Frequency Representation for Signals with Fast Varying Instantaneous Frequency

Time-frequency distributions have been used to provide high resolution representation in a large number of signal processing applications. However, high resolution and accurate instantaneous frequency (IF) estimation usually depend on the employed distribution and complexity of signal phase function. To ensure an efficient IF tracking for various types of signals, the class of complex time distributions has been developed. These distributions facilitate analysis in the cases when standard distributions cannot provide satisfactory results (e.g., for highly nonstationary signal phase). In that sense, an ambiguity based form of the forth order complex-time distribution is considered, in a new compressive sensing (CS) context. CS is an intensively growing approach in signal processing that allows efficient analysis and reconstruction of randomly undersampled signals. In this paper, the randomly chosen ambiguity domain coefficients serve as CS measurements. By exploiting sparsity in the time-frequency plane, it is possible to obtain highly concentrated IF using just small number of random coefficients from ambiguity domain. Moreover, in noisy signal case, this CS approach can be efficiently combined with the L-statistics producing robust time-frequency representations. Noisy coefficients are firstly removed using the L-statistics and then reconstructed by using CS algorithm. The theoretical considerations are illustrated using experimental results.

cs.IT↗

arXiv2015

Hardware Architecture for Single Iteration Reconstruction Algorithm

A hardware architecture for the single iteration algorithm is proposed in this paper. Single iteration algorithm enables reconstruction of the full signal when small number of signal samples is available. The algorithm is based on the threshold calculation, and allows distinguishing between signal components and noise that appears as a consequence of missing samples. The proposed system for hardware realization is divided into three parts, each part with different functionality. The system is suitable for the FPGA realization. Realization of the blocks for which there are no standard components in FPGA, is discussed as well.

cs.IT↗