Lossless image compression has many applications, for example, in medical imaging, space photograph and film industry. In this thesis, we propose an efficient lossless image compression scheme for both binary images and gray-scale images. The scheme first decomposes images into a set of progressively refined binary sequences and then uses the context-based, adaptive arithmetic coding algorithm to encode these sequences. In order to deal with the context dilution problem in arithmetic coding, we propose a Lloyd-like iterative algorithm to quantize contexts. Fixing the set of input contexts and the number of quantized contexts, our context quantization algorithm iteratively finds the optimum context mapping in the sense of minimizing the compression rate. Experimental results show that by combining image decomposition and context quantization, our scheme can achieve competitive lossless compression performance compared to the JBIG algorithm for binary images, and the CALIC algorithm for gray-scale images. In contrast to CALIC, our scheme provides the additional feature of allowing progressive transmission of gray-scale images, which is very appealing in applications such as web browsing.

Dithered quantization and noise shaping is well known in the audio community. The image processing community seems to be aware of this same theory only in bits and pieces, and frequently under conflicting terminology. This thesis attempts to show that dithered quantization of images is an extension of dithered quantization of audio signals to higher dimensions. Dithered quantization, or ``threshold modulation'', is investigated as a means of suppressing undesirable visual artifacts during the digital quantization, or requantization, of an image. Special attention is given to the statistical moments of the resulting error signal. Afterwards, noise shaping, or ``error diffusion'' methods are considered to try to improve on the dithered quantization technique. We also take time to develop the minimum-phase property for two-dimensional systems. This leads to a natural extension of Jensen's Inequality and the Hilbert transform relationship between the log-magnitude and phase of a two-dimensional system. We then describe how these developments are relevant to image processing.

Shannon's classical separation result holds only in the limit of infinite source code dimension and infinite channel code block length. In addition, Shannon theory does not address the design of good source codes when the probability of channel error is nonzero, which is inevitable for finite-length channel codes. Thus, for practical systems, a joint source and channel code design could improve performance for finite dimension source code and finite block length channel code, as well as complexity and delay. Consider a multicast system over a broadcast channel, where different end users typically have different capacities. To support such user or capacity diversity, it is desirable to encode the source to be broadcasted into a scalable bit stream along which multiple resolutions of the source can be reconstructed progressively from left to right. Such source coding technique is called multiresolution source coding. In wireless communications, joint source channel coding (JSCC) has attracted wide attention due to its adaptivity to time-varying channels. However, there are few works on joint source channel coding for network multicast, especially for the optimal source coding over broadcast channels. In this work, we aim at designing and analyzing the optimal multiresolution vector quantization (MRVQ) in conjunction with the subsequent broadcast channel over which the coded scalable bit stream would be transmitted. By adopting random index assignment (RIA) to link MRVQ for the source with superposition coding for the broadcast channel, we establish a closed-form formula of end-to-end distortion for a tandem system of MRVQ and a broadcast channel. From this formula we analyze the intrinsic structure of end-to-end distortion (EED) in a communication system and derive two necessary conditions for optimal multiresolution vector quantization over broadcast channels with random index assignment. According to the two necessary conditions, we propose a greedy iterative algorithm for jointly designed MRVQ with channel conditions, which depends on the channel only through several types of average channel error probabilities rather than the complete knowledge of the channel. Experiments show that MRVQ designed by the proposed algorithm significantly outperforms conventional MRVQ designed without channel information. By building an closed-form formula for the weighted EED with RIA, it also makes the computational complexity incurred during the performance analysis feasible. In comparison with MRVQ design for a fixed index assignment, the computation complexity for quantization design is significantly reduced by using random index assignment. In addition, simulations indicate that our proposed algorithm shows better robustness against channel mismatch than MRVQ design with a fixed index assignment, simply due to the nature of using only the average channel information. Therefore, we conclude that our proposed algorithm is more appropriate in both wireless communications and applications where the complete knowledge of the channel is hard to obtain. Furthermore, we propose two novel algorithms for MRVQ over broadcast channels. One aims to optimize the two corresponding quantizers at two layers alternatively and iteratively, and the other applies under the constraint that each encoding cell is convex and contains the reconstruction point. Finally, we analyze the asymptotic performance of weighted EED for the optimal joint MRVQ. The asymptotic result provides a theoretically achievable quantizer performance level and sheds light on the design of the optimal MRVQ over broadcast channel from a different aspect.

In this thesis, we study the concept of scalable video coding as implemented in the extension to the H.264 video coding standard. Specifically, for the spatial and quality scalability scenarios, we propose an optimization algorithm based on the Soft Decision Quantization (SDQ) concept, which aims at jointly optimizing all layers being encoded. The performance of the algorithm was assessed in an SVC implementation. Experimental results show, that the proposed method significantly improves the coding efficiency when compared to an unmodified SVC encoder.

Recently, the transmission of vector quantization (VQ) over a code-division multiple access (CDMA) channel has received a considerable attention in research community. The complexity of the optimal decoding for VQ in CDMA communications is prohibitive for implementation, especially for systems with a medium or large number of users. A suboptimal approach to VQ decoding over a CDMA channel, disturbed by additive white Gaussian noise (AWGN), was recently developed. Such a suboptimal decoder is built from a soft-output multiuser detector (MUD), a soft bit estimator and the optimal soft VQ decoders of individual users. <p>Due to its lower complexity and good performance, such a decoding scheme is an attractive alternative to the complicated optimal decoder. It is necessary to extend this decoding scheme for a frequency-selective Rayleigh fading CDMA channel, a channel model typically seen in mobile wireless communications. This is precisely the objective of this thesis. <p>Furthermore, the suboptimal decoders are obtained not only for binary phase shift keying (BPSK), but also for M-ary pulse amplitude modulation (M-PAM). This extension offers a flexible trade-off between spectrum efficiency and performance of the systems. In addition, two algorithms based on distance measure and reliability processing are introduced as other alternatives to the suboptimal decoder. <p>Simulation results indicate that the suboptimal decoders studied in this thesis also performs very well over a frequency-selective Rayleigh fading CDMA channel.

We study the spin polarization and conductance in infinitely long quasi one-dimensionalquantum wires under various conditions in an attempt to reproduce and to explain some of theanomalous conductance features as seen in various experiments. In order to accomplish thistask we create an idealized model of a quantum wire in a split-gate semiconductorheterostructure and we perform self-consistent Hartree-Fock calculations to determine theelectron occupation and spin polarization. Based on those results we calculate the currentthrough the wire as well as the direct and differential conductances. In the frame of theproposed model the results show a high degree of similarity to some of the experimentallyobserved conductance features, particularly the 0.25- and 0.85-plateaus. These results lead usto the conclusion that those conductance anomalies are in fact caused by the electronsspontaneously polarizing due to electron-electron interactions when an applied potentialdrives a current through the wire.

A novel residual prediction algorithm is proposed for high-bit-rate video coding in this work. We analysis the relationship between the residual data and different quantization parameters, according to the comparison results, we observe that the residual data is raised rapidly when the quality increases. Consequently, in order to reduce the bitrate, we propose a new residual prediction algorithm, it mainly reduce the residual data when the quantization parameter is finer. The proposed algorithm not only reduces the bitrate but also improves the video quality for high-bit-rate coding. Experimental results show that the proposed algorithm outperforms H.264/AVC. Compared to H.264/AVC, the proposed method decreases about 9.66% bitrate in average. The experimental results demonstrated that the second-order-residual prediction algorithm is efficiency for high-bit-rate coding.

Hole-doped maganite with middle to narrow bandwidth La1-xCaxMnO3 was extensively studied because of its colossal magnetoresistance (CMR) characteristic under a magnetic field. These kind of materials show un- common magnetic and electric properties. The charge order phase only happens to the region x> 0.5, and along with decreasing temperature, its phase goes from para-insulator to charge-ordered then to antiferromagne- tism. In our studies, we apply correlation function of Green¡¦s function to LCMO and get susceptibility of charge and spin. Then we can get the cri- tical value of Coulomb repulsion inside the material by substituting the experimental values of phase transition temperature. This critical values is the key point of charge-ordered. Then we can also get the size of char- ge gap which decides the stability of charge-ordered phase. After know- ing the Coulomb repulsion and charge gap, we can picture the relation of inside and on-site Coulomb repulsion qualitatively while the transition happens. Here the on-site Coulomb repulsion means to the Hund¡¦s coupl- ing between d electrons. And by this we¡¦ll understand the physics inside CMR materials.

A four-session text independent, TV-recorded audio-video database for speaker recognition is collected in this thesis. The speaker data is used to verify the applicability of a design methodology based on Mel-frequency cepstrum coefficients and Gaussian mixture model. Both single-session and multi-session problems are discussed in the thesis. Experimental results indicate that 90% correct rate can be achieved for a single-session 3000-speaker corpus while only 67% correct rate can be obtained for a two-session 800-speaker dataset. The performance of a multi-session speaker recognition system is greatly reduced due to the variability incurred in the recording environment, speakers¡¦ recording mood and other unknown factors. How to increase the system performance under multi-session conditions becomes a challenging task in the future. And the establishment of such a multi-session large-scale speaker database does indeed play an indispensable role in this task.

In this work, Schr&ouml / dinger and Dirac equations will be examined in geometries that confine the particles to hypersurfaces. For this purpose, two methods will be considered. The first method is the thin layer method which relies on explicit use of geometrical relations and the squeezing of a certain coordinate of space (or spacetime). The second is Dirac&rsquo / s quantization procedure involving the modification of canonical quantization making use of the geometrical constraints. For the Dirac equation, only the first method will be considered. Lastly, the results of the two methods will be compared and some notes on the differences between the results will be included.