Synthesis methods for linear-phase FIR filters with a piecewise-polynomial impulse response

his thesis concentrates on synthesis methods for linear-phase finite-impulse response filters with a piecewise-polynomial impulse response. One of the objectives has been to find integer-valued coefficients to efficiently implement filters of the piecewise-polynomial impulse response approach introduced by Saram¨aki and Mitra. In this method, the impulse response is divided into blocks of equal length and each block is created by a polynomial of a given degree. The arithmetic complexity of these filters depends on the polynomial degree and the number of blocks. By using integer-valued coefficients it is possible to make the implementation of the subfilters, which generates the polynomials, multiplication-free. The main focus has been on finding computationally-efficient synthesis methods by using a piecewise-polynomial and a piecewise-polynomial-sinusoidal impulse responses to make it possible to implement high-speed, low-power, highly integrated digital signal processing systems. The earlier method by Chu and Burrus has been studied. The overall impulse response of the approach proposed in this thesis consists of the sum of several polynomial-form responses. The arithmetic complexity depends on the polynomial degree and the number of polynomial-form responses. The piecewise-polynomial-sinusoidal approach is a modification of the piecewise-polynomial approach. The subresponses are multiplied by a sinusoidal function and an arbitrary number of separate center coefficients is added. Thereby, the arithmetic complexity depends also on the number of complex multipliers and separately generated center coefficients. The filters proposed in this thesis are optimized by using linear programming methods

Partial discharge denoising for power cables

Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising.Partial discharge (PD) diagnostics is considered a major and effective tool for the monitoring of insulating conditions of power cables. As such, a large amount of off-line or online PD measurements have been deployed in power cables during the past decades. However, challenges still exist in PD diagnostics for power cables. Noise is one of the challenges involved in PD measurement. This thesis develops new algorithms based on the characteristics of both PD signals and noise to improve the effectiveness of wavelet-based PD denoising. In the meantime, it presents new findings in the application of empirical mode decomposition (EMD) in PD denoising. Wavelet-based technique has received high attention in the area of PD denoising, it still faces challenges, however, in wavelet selection, decomposition scale determination, and noise estimation. It is therefore the first area of interest in this thesis to improve the effectiveness of existing wavelet-based technique in PD detection by incorporating proposed algorithms. These new algorithms were developed based on the difference of entropy between transformed PD signals and noise, and the sparsity of transformed PD signals corrupted by noise. One concern commonly expressed by critics of wavelet-based technique is a pre-defined wavelet is applied in wavelet-based technique. EMD is an algorithm that can decompose a signal based on the signal itself. Thus, the second area of interest in this thesis is to further investigate the application of EMD in PD denoising; a technique that does not require the selection of a pre-defined signal to represent the "unknown" signal of interest. A new method for relative mode selection (RMS) was proposed based on the entropy of each intrinsic mode function (IMF). Although this new method cannot outperform the existing ones, it reveals that RMS is not as important as claimed in the application of EMD in signal denoising. Also, PD signals, especially those with lower magnitudes, can receive serious distortion through EMD-based denoising. Finally, comparisons between wavelet-based and EMD-based denoising were implemented in the following aspects, i.e., executing time, distortion, effectiveness, adaptivity and robustness. Results unveil that improved wavelet-based technique is more preferable as it can present better performance in PD denoising

Finansijski sistem i ekonomski rast

Finansijski sistem, kao integralni deo privrednog sistema, ima ključnu ulogu u povezivanju štednje i investicija. Stoga je nesporno važna uloga finansijskog sistema u funkcionisanju svake tržišne privrede. Osim mobilizacije i trnasfera štednje od finansijski suficitarnih ka finansijski deficitarnim subjektima, finansijski sistem uključuje funkcionisanje odgovarajućeg mehanizma kontrole korporativnog upravljanja, omogućava upravljanje rizicima imanentnim finansijskom poslovanju i olakšava razmenu dobara i usluga. Svaka od navedenih funkcija ima uticaj na akumulaciju kapitala i razvoj tehnoloških inovacija kao primarnih determinanti ekonomskog rasta. S druge strane, ekspanzija realnog sektora podrazumeva rast obima finansijskih transakcija. To dalje predstavlja osnovu za uvećanje obima i kvaliteta finansijskih proizvoda i usluga. Polazeći od različitih teorijskih modela i empirijskih rezultata, predmet istraživanja u doktorskoj disertaciji je međuzavisnost razvoja finansijskog sistema i ekonomskog rasta. S obzirom da su u tranzicionim privredama mehanizmi transfera finansijskih sredstava nedovoljno razvijeni, a tempo ekonomskog rasta spor, definisano područije istraživanja ima poseban teorijski i praktičan značaj. Shodno tome, osnovni cilj doktorske disertacije je da teorijsko-metodološki i empirijski sagleda vezu između razvoja finansijskog sistema i ekonomskog rasta. U doktorskoj disertaciji su najpre analizirani osnovni elementi finansijskog sistema. Predmet detaljne analize bile su funkcije finansijskog sistema koje objašnjavaju zbog čega se u svakoj privredi finansijskom sistemu pridaje izuzetna važnost. Analizirani su modeli finansijskog sistema, sa posebnim osvrtom na njihove prednosti i nedostake prilkom obavljanja osnovnih funkcija. Kao jedno od istraživačkih područja izdvajilo se pitanje determinanti i pokazatelja razvoja finasijskog sistema i ekonomskog rasta. Posebna pažnja posvećena je modelima koji dovode u vezu razvoj finansijskog sistema i ekonomski rast. Empirijskim istraživanjem na primeru Republike Srbije utvrđena je pozitivna korelacija između razvoja finansijskog posredovanja banaka i stope ekonomskog rasta. Takođe, otkrivena je jednosmerna kauzalna veza koja ide iz prvaca razvoja finansijskog posredovanja banaka ka stopi ekonomskog rasta. Otuda, smatra se opravdanim koncipiranje politika i strategija koje uključuju stimulisanje daljeg razvoja bankarskog sektora i podsticanje razvoja tržišta kapitala i nebankarskih finansijkih posrednika.The financial system, as an integral part of the economic system, plays a key role in linking savings and investment. Therefore, the role of the financial system in the functioning of every market economy is undoubtedly important. In addition to the mobilization and transfer of savings from entities with financial surplus to those with financial deficit, the financial system involves the functioning of appropriate corporate governance control mechanisms, enables risk management, imminent in financial operations, and facilitates the exchange of goods and services. Each of these functions has an impact on capital accumulation and development of technological innovation, as the primary determinants of economic growth. At the same time, the expansion of the real sector means increase in the volume of financial transactions. It stands for the basis for the increase in volume and quality of financial products and services. Starting from different theoretical models and empirical results, the research subject in the doctoral thesis is the correlation between financial system development and economic growth. Given that, in transition economies, mechanisms of funds transfer are insufficiently developed, and the pace of economic growth slow, the defined research area has special theoretical and practical significance. Consequently, the basic objective of the doctoral thesis is to, from the theoretical, methodological, and empirical aspects, examine the correlation between financial system development and economic growth. The doctoral thesis first analyzed the basic elements of the financial system. The subject of a detailed analysis referred to the functions of the financial system, which explain why each economy gives particular importance to the financial system. The analysis focused on the models of the financial system, with particular emphasis on their advantages and disadvantages in performing basic functions. The issue of determinants and indicators of financial system development and economic growth stood out as one of the research areas. Special attention was paid to models that connect financial system development and economic growth. Empirical research into the example of the Republic of Serbia established positive correlation between the development of financial intermediation by banks and economic growth rates. What is more, one-way causality from the development of financial intermediation by banks to the economic growth rate was revealed. Therefore, it is considered justifiable to design policies and strategies that include stimulating further development of the banking sector and encourage the development of capital markets and non-bank financial intermediaries

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports