New Digital Audio Watermarking Algorithms for Copyright Protection

This thesis investigates the development of digital audio watermarking in addressing issues such as copyright protection. Over the past two decades, many digital watermarking algorithms have been developed, each with its own advantages and disadvantages. The main aim of this thesis was to develop a new watermarking algorithm within an existing Fast Fourier Transform framework. This resulted in the development of a Complex Spectrum Phase Evolution based watermarking algorithm. In this new implementation, the embedding positions were generated dynamically thereby rendering it more difficult for an attacker to remove, and watermark information was embedded by manipulation of the spectral components in the time domain thereby reducing any audible distortion. Further improvements were attained when the embedding criteria was based on bin location comparison instead of magnitude, thereby rendering it more robust against those attacks that interfere with the spectral magnitudes. However, it was discovered that this new audio watermarking algorithm has some disadvantages such as a relatively low capacity and a non-consistent robustness for different audio files. Therefore, a further aim of this thesis was to improve the algorithm from a different perspective. Improvements were investigated using an Singular Value Decomposition framework wherein a novel observation was discovered. Furthermore, a psychoacoustic model was incorporated to suppress any audible distortion. This resulted in a watermarking algorithm which achieved a higher capacity and a more consistent robustness. The overall result was that two new digital audio watermarking algorithms were developed which were complementary in their performance thereby opening more opportunities for further research

Diffusion and perfusion MRI and applications in cerebral ischaemia

Two MRI techniques, namely diffusion and perfusion imaging, are becoming increasingly used for evaluation of the pathophysiology of stroke. This work describes the use of these techniques, together with more conventional MRI modalities (such as T1, and T2 imaging) in the investigation of cerebral ischaemia. The work was performed both in a paediatric population in a whole-body clinical MR system (1.5 T) and in an animal model of focal ischaemia at high magnetic field strength (8.5 T). For the paediatric studies, a single shot echo planar imaging (EPI) sequence was developed to enable the on-line calculation of maps of the trace of the diffusion tensor. In the process of this development, it was necessary to address two different imaging artefacts in these maps: eddy current induced image shifts, and residual Nyquist ghost artefacts. Perfusion imaging was implemented using an EPI sequence to follow the passage through the brain of a bolus of a paramagnetic contrast agent. Computer simulations were performed to evaluate the limitations of this technique in the quantification of cerebral blood flow when delay in the arrival and dispersion of the bolus of contrast agent are not accounted for. These MRI techniques were applied to paediatric patients to identify acute ischaemic events, as well as to differentiate between multiple acute events, or between acute and chronic events. Furthermore, the diffusion and perfusion findings were shown to contribute significantly to the management of patients with high risk of stroke, and in the evaluation of treatment outcome. In the animal experiments, permanent middle cerebral artery occlusion was performed in rats to investigate longitudinally the acute MRI changes (first 4-6 hours) following an ischaemic event. This longitudinal analysis contributed to the understanding of the evolution of the ischaemic lesion. Furthermore, the findings allowed the acute identification of tissue 'at risk' of infarction

이동 물체 감지 및 분진 영상 복원의 연구

학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 수리과학부, 2021. 2. 강명주.Robust principal component analysis(RPCA), a method used to decom- pose a matrix into the sum of a low-rank matrix and a sparse matrix, has been proven effective in modeling the static background of videos. However, because a dynamic background cannot be represented by a low-rank matrix, measures additional to the RPCA are required. In this thesis, we propose masked RPCA to process backgrounds containing moving textures. First- order Marcov random field (MRF) is used to generate a mask that roughly labels moving objects and backgrounds. To estimate the background, the rank minimization process is then applied with the mask multiplied. During the iteration, the background rank increases as the object mask expands, and the weight of the rank constraint term decreases, which increases the accuracy of the background. We compared the proposed method with state- of-art, end-to-end methods to demonstrate its advantages. Subsequently, we suggest novel dedusting method based on dust-optimized transmission map and deep image prior. This method consists of estimating atmospheric light and transmission in that order, which is similar to dark channel prior-based dehazing methods. However, existing atmospheric light estimating methods widely used in dehazing schemes give an overly bright estimation, which results in unrealistically dark dedusting results. To ad- dress this problem, we propose a segmentation-based method that gives new estimation in atmospheric light. Dark channel prior based transmission map with new atmospheric light gives unnatural intensity ordering and zero value at low transmission regions. Therefore, the transmission map is refined by scattering model based transformation and dark channel adaptive non-local total variation (NLTV) regularization. Parameter optimizing steps with deep image prior(DIP) gives the final dedusting result.강건 주성분 분석은 배경 감산을 통한 동영상의 전경 추출의 방법으로 이 용되어왔으나, 동적배경은저계수행렬로표현될수없기때문에동적배경 감산에성능적한계를가지고있었다. 우리는전경과배경을구분하는일계마 르코프연쇄를도입해정적배경을나타내는항과곱하고이것을이용한새로 운형태의강건주성분분석을제안하여동적배경감산문제를해결한다. 해당 최소화문제는반복적인교차최적화를통하여해결한다. 이어서대기중의미세 먼지에의해오염된영상을복원한다. 영상분할과암흑채널가정에기반하여 깊이지도를구하고, 비국소총변동최소화를통하여정제한다. 이후깊은영상 가정에기반한영상생성기를통하여최종적으로복원된영상을구한다. 실험을 통하여제안된방법을다른방법들과비교하고질적인측면과양적인측면모 두에서우수함을확인한다.Abstract i 1 Introduction 1 1.1 Moving Object Detection In Dynamic Backgrounds 1 1.2 Image Dedusting 2 2 Preliminaries 4 2.1 Moving Object Detection In Dynamic Backgrounds 4 2.1.1 Literature review 5 2.1.2 Robust principal component analysis(RPCA) and their application status 7 2.1.3 Graph cuts and α-expansion algorithm 14 2.2 Image Dedusting 16 2.2.1 Image dehazing methods 16 2.2.2 Dust model 18 2.2.3 Non-local total variation(NLTV) 19 3 Dynamic Background Subtraction With Masked RPCA 21 3.1 Motivation 21 3.1.1 Motivation of background modeling 21 3.1.2 Mask formulation 23 3.1.3 Model 24 3.2 Optimization 25 3.2.1 L-Subproblem 25 3.2.2 L˜-Subproblem 26 3.2.3 M-Subproblem 27 3.2.4 p-Subproblem 28 3.2.5 Adaptive parameter control 28 3.2.6 Convergence 29 3.3 Experimental results 31 3.3.1 Benchmark Algorithms And Videos 31 3.3.2 Implementation 32 3.3.3 Evaluation 32 4 Deep Image Dedusting With Dust-Optimized Transmission Map 41 4.1 Transmission estimation 41 4.1.1 Atmospheric light estimation 41 4.1.2 Transmission estimation 43 4.2 Scene radiance recovery 47 4.3 Experimental results 51 4.3.1 Implementation 51 4.3.2 Evaluation 52 5 Conclusion 58 Abstract (in Korean) 69 Acknowledgement (in Korean) 70Docto

Adaptive Background Modeling with Temporal Feature Update for Dynamic Foreground Object Removal

In the study of computer vision, background modeling is a fundamental and critical task in many conventional applications. This thesis presents an introduction to background modeling and various computer vision techniques for estimating the background model to achieve the goal of removing dynamic objects in a video sequence. The process of estimating the background model with temporal changes in the absence of foreground moving objects is called adaptive background modeling. In this thesis, three adaptive background modeling approaches were presented for the purpose of developing \teacher removal algorithms. First, an adaptive background modeling algorithm based on linear adaptive prediction is presented. Second, an adaptive background modeling algorithm based on statistical dispersion is presented. Third, a novel adaptive background modeling algorithm based on low rank and sparsity constraints is presented. The design and implementation of these algorithms are discussed in detail, and the experimental results produced by each algorithm are presented. Lastly, the results of this research are generalized and potential future research is discussed

Students’ view of Quantum Information Technologies, part 2

The aim of the paper is to show how graduatedengineering students in classical ICT view practically the advent ofthe QIT. The students do their theses in El.Eng. and ICT and wereasked how to implement now or in the future the QIT in theircurrent or future work. Most of them have strictly defined researchtopics and in some cases the realization stage is advanced. Thus,most of the potential QIT application areas are defined and quitenarrow. In such a case, the issue to be considered is theincorporation of QIT components and interfaces into the existingICT infrastructure, software and hardware alike, and propose asolution as a reasonable functional hybrid system. The QITcomponents or circuits are not standalone in most cases, theyshould be somehow incorporated into existing environment, with ameasurable added value. Not an easy task indeed. We have toexcuse the students if the proposed solutions are not ripe enough.The exercise was proposed as an on-purpose publicationworkshop, related strictly to the fast and fascinating developmentof the QIT. The paper is a continuation of publishing exercises withprevious groups of students participating in QIT lectures