Browsing by Subject "Data compression (Telecommunication)"

Now showing 1 - 3 of 3

Open Access
The design of finite-state machines for quantization using simulated annealing
(1993) Kuruoğlu, Ercan Engin
In this thesis, the combinatorial optimization algorithm known as simulated annealing (SA) is applied to the solution of the next-state map design problem of data compression systems based on finite-state machine decoders. These data compression systems which include finite-state vector ciuantization (FSVQ), trellis waveform coding (TWC), predictive trellis waveform coding (PTWC), and trellis coded quantization (TCQ) are studied in depth. Incorporating generalized Lloyd algorithm for the optimization of output map to SA, a finite-state machine decoder design algorithm for the joint optimization of output map and next-state map is constructed. Simulation results on several discrete-time sources for FSVQ, TWC and PTWC show that decoders with higher performance are obtained by the SA-I-CLA algorithm, when compared to other related work in the literature. In TCQ, simulation results are obtained for sources with memory and new observations are made.
Open Access
Lossless data compression with polar codes
(2013) Çaycı, Semih
In this study, lossless polar compression schemes are proposed for finite source alphabets in the noiseless setting. In the first part, lossless polar source coding scheme for binary memoryless sources introduced by Arıkan is extended to general prime-size alphabets. In addition to the conventional successive cancellation decoding (SC-D), successive cancellation list decoding (SCL-D) is utilized for improved performance at practical block-lengths. For code construction, greedy approximation method for density evolution, proposed by Tal and Vardy, is adapted to non-binary alphabets. In the second part, a variable-length, zero-error polar compression scheme for prime-size alphabets based on the work of Cronie and Korada is developed. It is shown numerically that this scheme provides rates close to minimum source coding rate at practical block-lengths under SC-D, while achieving the minimum source coding rate asymptotically in the block-length. For improved performance at practical block-lengths, a scheme based on SCL-D is developed. The proposed schemes are generalized to arbitrary finite source alphabets by using a multi-level approach. For practical applications, robustness of the zero-error source coding scheme with respect to uncertainty in source distribution is investigated. Based on this robustness investigation, it is shown that a class of prebuilt information sets can be used at practical block-lengths instead of constructing a specific information set for every source distribution. Since the compression schemes proposed in this thesis are not universal, probability distribution of a source must be known at the receiver for reconstruction. In the presence of source uncertainty, this requires the transmitter to inform the receiver about the source distribution. As a solution to this problem, a sequential quantization with scaling algorithm is proposed to transmit the probability distribution of the source together with the compressed word in an efficient way.
Open Access
Robust compressive sensing techniques
(2014) Teke, Oğuzhan
Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed from an underdetermined linear measurements. However, in reality there is a mismatch between the assumed and the actual dictionary due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes signifi- cant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this thesis presents two novel robust algorithm and an adaptive discretization framework that can obtain successful sparse representations. In the proposed techniques, the selected dictionary atoms are perturbed towards directions to decrease the orthogonal residual norm. The first algorithm named as Parameter Perturbed Orthogonal Matching Pursuit (PPOMP) targets the off-grid problem and the parameters of the selected dictionary atoms are perturbed. The second algorithm named as Perturbed Orthogonal Matching Pursuit (POMP) targets the unstructured basis mismatch problem and performs controlled rotation based perturbation of selected dictionary atoms. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of both parametric and unstructured basis mismatch problem can be obtained as compared to standard sparse reconstruction techniques. Different from the proposed perturbation approaches, the proposed adaptive framework discretizes the continuous parameter space depending on the estimated sparsity level. Once a provisional solution is obtained with a sparse solver, the framework recursively splits the problem into sparser sub-problems so that each sub-problem is exposed to less severe off-grid problem. In the presented recursive framework, any sparse reconstruction technique can be used. As illustrated over commonly used applications, the error in the estimated parameters of sparse signal components almost achieve the Cram´er-Rao lower bound in the proposed framework.