Robust Image Watermarking Using QR Factorization In Wavelet Domain

A robust blind image watermarking algorithm in wavelet transform domain (WT) based on QR factorization, and quantization index modulation (QIM) technique is presented for legal protection of digital images. The host image is decomposed into wavelet subbands, and then the approximation subband is QR factorized. The secret watermark bit is embedded into the R vector in QR using QIM. The experimental results show that the proposed algorithm preserves the high perceptual quality. It also sustains against JPEG compression, and other image processing attacks. The comparison analysis demonstrates the proposed scheme has better performance in imperceptibility and robustness than the previously reported watermarking algorithms

Shannon Meets Nyquist: Capacity of Sampled Gaussian Channels

We explore two fundamental questions at the intersection of sampling theory and information theory: how channel capacity is affected by sampling below the channel's Nyquist rate, and what sub-Nyquist sampling strategy should be employed to maximize capacity. In particular, we derive the capacity of sampled analog channels for three prevalent sampling strategies: sampling with filtering, sampling with filter banks, and sampling with modulation and filter banks. These sampling mechanisms subsume most nonuniform sampling techniques applied in practice. Our analyses illuminate interesting connections between under-sampled channels and multiple-input multiple-output channels. The optimal sampling structures are shown to extract out the frequencies with the highest SNR from each aliased frequency set, while suppressing aliasing and out-of-band noise. We also highlight connections between undersampled channel capacity and minimum mean-squared error (MSE) estimation from sampled data. In particular, we show that the filters maximizing capacity and the ones minimizing MSE are equivalent under both filtering and filter-bank sampling strategies. These results demonstrate the effect upon channel capacity of sub-Nyquist sampling techniques, and characterize the tradeoff between information rate and sampling rate.Comment: accepted to IEEE Transactions on Information Theory, 201

Nonlinear filtering for narrow-band time delay estimation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 101-103).This thesis presents a method for improving passive acoustic tracking. A large family of acoustic tracking systems combine estimates of the time difference of arrival (TDoA) between pairs of spatially separated sensors - this work improves those estimates by independently tracking each TDoA using a Bayesian filter. This tracking is particularly useful for overcoming spatial aliasing, which results from tracking narrowband, high frequency sources. I develop a theoretical model for the evolution of each TDoA from a bound placed on the velocity of the target being tracked. This model enables an efficient form of exact marginalization. I then present simulation and experimental results demonstrating improved performance over a simpler nonlinear preprocessor and Kalman filtering, so long as this bound is chosen appropriately.by Mark M. Tobenkin.M.Eng

Image enhancements for low-bitrate videocoding

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 71).by Brian C. Davison.M.Eng

Discrete multitone modulation with principal component filter banks

Discrete multitone (DMT) modulation is an attractive method for communication over a nonflat channel with possibly colored noise. The uniform discrete Fourier transform (DFT) filter bank and cosine modulated filter bank have in the past been used in this system because of low complexity. We show in this paper that principal component filter banks (PCFB) which are known to be optimal for data compression and denoising applications, are also optimal for a number of criteria in DMT modulation communication. For example, the PCFB of the effective channel noise power spectrum (noise psd weighted by the inverse of the channel gain) is optimal for DMT modulation in the sense of maximizing bit rate for fixed power and error probabilities. We also establish an optimality property of the PCFB when scalar prefilters and postfilters are used around the channel. The difference between the PCFB and a traditional filter bank such as the brickwall filter bank or DFT filter bank is significant for effective power spectra which depart considerably from monotonicity. The twisted pair channel with its bridged taps, next and fext noises, and AM interference, therefore appears to be a good candidate for the application of a PCFB. This is demonstrated with the help of numerical results for the case of the ADSL channel

Reconstruction of undersampled signals and alignment in the frequency domain

Imperial Users onl

High Performance Techniques for Face Recognition

The identification of individuals using face recognition techniques is a challenging task. This is due to the variations resulting from facial expressions, makeup, rotations, illuminations, gestures, etc. Also, facial images contain a great deal of redundant information, which negatively affects the performance of the recognition system. The dimensionality and the redundancy of the facial features have a direct effect on the face recognition accuracy. Not all the features in the feature vector space are useful. For example, non-discriminating features in the feature vector space not only degrade the recognition accuracy but also increase the computational complexity. In the field of computer vision, pattern recognition, and image processing, face recognition has become a popular research topic. This is due to its wide spread applications in security and control, which allow the identified individual to access secure areas, personal information, etc. The performance of any recognition system depends on three factors: 1) the storage requirements, 2) the computational complexity, and 3) the recognition rates. Two different recognition system families are presented and developed in this dissertation. Each family consists of several face recognition systems. Each system contains three main steps, namely, preprocessing, feature extraction, and classification. Several preprocessing steps, such as cropping, facial detection, dividing the facial image into sub-images, etc. are applied to the facial images. This reduces the effect of the irrelevant information (background) and improves the system performance. In this dissertation, either a Neural Network (NN) based classifier or Euclidean distance is used for classification purposes. Five widely used databases, namely, ORL, YALE, FERET, FEI, and LFW, each containing different facial variations, such as light condition, rotations, facial expressions, facial details, etc., are used to evaluate the proposed systems. The experimental results of the proposed systems are analyzed using K-folds Cross Validation (CV). In the family-1, Several systems are proposed for face recognition. Each system employs different integrated tools in the feature extraction step. These tools, Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), 2D or 3D DWT, and Fast Independent Component Analysis (FastICA), are applied to the processed facial images to reduce the dimensionality and to obtain discriminating features. Each proposed system produces a unique representation, and achieves less storage requirements and better performance than the existing methods. For further facial compression, there are three face recognition systems in the second family. Each system uses different integrated tools to obtain better facial representation. The integrated tools, Vector Quantization (VQ), Discrete cosine Transform (DCT), and 2D DWT, are applied to the facial images for further facial compression and better facial representation. In the systems using the tools VQ/2D DCT and VQ/ 2D DWT, each pose in the databases is represented by one centroid with 4*4*16 dimensions. In the third system, VQ/ Facial Part Detection (FPD), each person in the databases is represented by four centroids with 4*Centroids (4*4*16) dimensions. The systems in the family-2 are proposed to further reduce the dimensions of the data compared to the systems in the family-1 while attaining comparable results. For example, in family-1, the integrated tools, FastICA/ 2D DMWT, applied to different combinations of sub-images in the FERET database with K-fold=5 (9 different poses used in the training mode), reduce the dimensions of the database by 97.22% and achieve 99% accuracy. In contrast, the integrated tools, VQ/ FPD, in the family-2 reduce the dimensions of the data by 99.31% and achieve 97.98% accuracy. In this example, the integrated tools, VQ/ FPD, accomplished further data compression and less accuracy compared to those reported by FastICA/ 2D DMWT tools. Various experiments and simulations using MATLAB are applied. The experimental results of both families confirm the improvements in the storage requirements, as well as the recognition rates as compared to some recently reported methods

Attitude control system for the high energy transient experiment small satellite

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1991.Includes bibliographical references (p. 207-208).by Daniel H. Chang.M.S

Entwicklung einer berührungslosen EEG-Mütze mittels kapazitiver Elektroden

Non-contact capacitive electrodes for bioelectric diagnostics provide an interesting alternative to classical galvanically coupled electrodes. Such a low cost diagnostic system can be applied without preparation time and in mobile wireless environments. For even higher user comfort textile capacitive electrodes are preferable. In this work, a comprehensive model for the electronic noise properties and frequency dependent responses of PCB-based, as well as textile non-contact capacitive electrodes, is presented. A thorough study of the influence of the electrical components on the resulting noise properties of these electrodes, is provided by independently measuring the corresponding noise spectra. The most important low frequency noise source of capacitive electrode is the necessary high input bias resistance. By comparing the noise measurements with the theoretical noise model of the electrode, it is concluded that the surface of the electrode contributes to an additional 1/f-power noise. It is also found that the highest possible coupling capacitance is most favorable for low noise behavior. Therefore, we implemented electrodes with electrically conducting fabric surfaces. With these electrodes, it is possible to enlarge the surface of the electrode while simultaneously maintaining a small distance between the body and the electrode over the whole surface area, thus maximizing the capacitance. We also show that the use of textile capacitive electrodes, reduces the noise considerably. Furthermore, this thesis describes the construction of a capacitive non-contact textile electroencephalography measuring hat (cEEG hat) with seven measuring channels. This hat benefits from the low noise characteristics of the integrated developed textile capacitive electrodes. The measured noise spectrum of this cEEG hat shows low noise characteristics at low frequencies. This fulfills many requirements for measuring brain signals. The implemented cEEG hat is comfortable to wear during very long measurements and even during sleep periods. In contrast to common methods, the cEEG hat provides a possibility of measuring EEG signal during sleep outside laboratories and in the comfort of home. EEG sleep measurements shown in this work, are recorded inside a normal apartment. The possibility of brain computer interface application is also shown by measuring steady state visually evoked potentials (SSVEP) at different frequencies.Berührungslose, kapazitive Elektroden für bioelekrische Untersuchungen stellen eine interessante Alternative zu klassischen galvanisch gekoppelten Elektroden dar. Ein solches preisgünstiges Diagnosesystem kann ohne lange Vorbereitungszeit und in mobilen Umgebungen eingesetzt werden. Für gesteigerten Tragekomfort sind textile Elektroden von Vorteil. In dieser Arbeit wird eine umfassende Beschreibung der elektronischen Rauscheigenschaften und des frequenzabhängigen Verhaltens von sowohl platinenbasierten, als auch textilen kapazitiven Elektroden vorgestellt. Die Einflüsse aller elektronischen Komponenten auf die resultierenden Rauscheigenschaften werden durch Messungen der entsprechenden Rauschspektren untersucht. Die wichtigste niederfrequente Rauschquelle kapazitiver Elektroden stellt der notwendige und zugleich hohe Bias-Eingangswiderstand dar. Durch Vergleich der gemessenen Rauschspektren mit dem theoretischen Modell wird die Oberfläche der Elektroden als eine zusätzliche 1/f-Rauschquelle identifiziert. Dabei ist die größtmögliche Kopplungskapazität vorteilhaft für ein niedriges Rauschen. Deshalb setzen wir im Folgenden Elektroden aus elektrisch leitfähigen Textilien ein. Mit diesen Elektroden ist es möglich, die Oberfläche der Elektrode unter gleichzeitiger Beibehaltung eines kleinen Abstandes zum Körper zu vergrößern. Dies maximiert wiederum die Kapazität. Wir zeigen zudem, dass die Verwendung textiler kapazitiver Elektroden die Rauscheigenschaften deutlich verbessert. Desweiteren wird in dieser Arbeit die Konstruktion eines kapazitiven, berührungslosen EEG-Helmes (cEEG-Mütze) mit sieben Kanälen beschrieben. Dieser Helm profitiert von den guten Rauscheigenschaften der zuvor entwickelten und hier integrierten textilen Elektroden. Die gemessenen Rauschspektren zeigen ein niedriges Rauschen im unteren Frequenzbereich. Dies erfüllt viele Voraussetzungen für die Messung von Gehirnsignalen. Die erstellte cEEG-Mütze lässt sich während langer Messzeiten und Schlafperioden angenehm tragen. Im Gegensatz zu herkömmlichen Methoden ermöglicht sie Messungen außerhalb von Laboratorien und im gewohnten Umfeld. Alle in dieser Arbeit gezeigten Schlafmessungen wurden in einer normalen Wohnung aufgezeichnet. Außerdem wird die Einsatzmöglichkeit für sogenannte ”Gehirn-Computer-Schnittstellen” anhand der Messung von ”steady state visually evoked potentials” (SSVEP) Signalen bei verschiedenen Frequenzen demonstriert

Coding for Relay Networks with Parallel Gaussian Channels

A wireless relay network consists of multiple source nodes, multiple destination nodes, and possibly many relay nodes in between to facilitate its transmission. It is clear that the performance of such networks highly depends on information for- warding strategies adopted at the relay nodes. This dissertation studies a particular information forwarding strategy called compute-and-forward. Compute-and-forward is a novel paradigm that tries to incorporate the idea of network coding within the physical layer and hence is often referred to as physical layer network coding. The main idea is to exploit the superposition nature of the wireless medium to directly compute or decode functions of transmitted signals at intermediate relays in a net- work. Thus, the coding performed at the physical layer serves the purpose of error correction as well as permits recovery of functions of transmitted signals. For the bidirectional relaying problem with Gaussian channels, it has been shown by Wilson et al. and Nam et al. that the compute-and-forward paradigm is asymptotically optimal and achieves the capacity region to within 1 bit; however, similar results beyond the memoryless case are still lacking. This is mainly because channels with memory would destroy the lattice structure that is most crucial for the compute-and-forward paradigm. Hence, how to extend compute-and-forward to such channels has been a challenging issue. This motivates this study of the extension of compute-and-forward to channels with memory, such as inter-symbol interference. The bidirectional relaying problem with parallel Gaussian channels is also studied, which is a relevant model for the Gaussian bidirectional channel with inter-symbol interference and that with multiple-input multiple-output channels. Motivated by the recent success of linear finite-field deterministic model, we first investigate the corresponding deterministic parallel bidirectional relay channel and fully characterize its capacity region. Two compute-and-forward schemes are then proposed for the Gaussian model and the capacity region is approximately characterized to within a constant gap. The design of coding schemes for the compute-and-forward paradigm with low decoding complexity is then considered. Based on the separation-based framework proposed previously by Tunali et al., this study proposes a family of constellations that are suitable for the compute-and-forward paradigm. Moreover, by using Chinese remainder theorem, it is shown that the proposed constellations are isomorphic to product fields and therefore can be put into a multilevel coding framework. This study then proposes multilevel coding for the proposed constellations and uses multistage decoding to further reduce decoding complexity