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This dissertation is mainly focused on the analysis, design and optimization of Low-density parity-check (LDPC) codes over channels with large alphabet sets and the applications on compressed sensing (CS) and flash memories. Compared to belief-propagation (BP) decoding, verification-based (VB) decoding has significantly lower complexity and near optimal performance when the channel alphabet set is large. We analyze the verification-based decoding of LDPC codes over the q-ary symmetric channel (q-SC) and propose the list-message-passing (LMP) decoding which off ers a good tradeoff between complexity and decoding threshold. We prove that LDPC codes with LMP decoding achieve the capacity of the q-SC when q and the block length go to infinity. CS is a newly emerging area which is closely related to coding theory and information theory. CS deals with the sparse signal recovery problem with small number of linear measurements. One big challenge in CS literature is to reduce the number of measurements required to reconstruct the sparse signal. In this dissertation, we show that LDPC codes with verification-based decoding can be applied to CS system with surprisingly good performance and low complexity. We also discuss modulation codes and error correcting codes (ECC’s) design for flash memories. We design asymptotically optimal modulation codes and discuss their improvement by using the idea from load-balancing theory. We also design LDPC codes over integer rings and fields with large alphabet sets for flash memories.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xiii, 148 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 90-95).

Academisch proefschrift--Amsterdam. / "Summary": p. 141-142. "Stellingen": [2] p. inserted. "Literatuur": p. 143-145.

With the increasing need for highway maintenance and the decreasingly available funds, the Texas Department of Transportation (TxDOT) Austin District has sought to better utilize maintenance section employee resources by implementing a compressed workweek. The primary goal of this thesis is to establish a framework and methodology to study the effects of a compressed workweek on maintenance crews in both rural and urban environments. Secondly, this thesis will provide a background of best practices of popular flexible work arrangements, including compressed workweeks, telework, and flextime. The compressed workweek is a type of flexible work arrangement that allows employees to work longer days during a part of the week in exchange for a partial or full day off later in the week. Compressed workweeks can offer numerous benefits to maintenance crews, including increased production, less set up and shut down time per week, decreased operating costs, reduced overhead, and increased employee morale. TxDOT will implement a six-month pilot project in two rural maintenance sections and one urban section. Compressed workweeks have proven to be successful in other districts with rural maintenance sections, but application of a compressed workweek in an urban maintenance section has yet to be studied. Therefore, maintenance crew activity data from previous years will be compared to data collected during the trial period to understand changes in productivity and to estimate vehicle operating costs. Lastly, surveys of maintenance employees will be conducted throughout the study to solve any personal issues that arise as well as determine employee satisfaction with the new schedule. / text

In the Electric Reliability Council of Texas (ERCOT) compressed air energy storage (CAES) is currently viewed as the most promising energy storage technology due to Texas having suitable geology for CAES and few locations suitable for pumped-hydro storage. CAES is a proven technology but the economics for new facilities are uncertain. This work quantified the economic prospects for CAES in ERCOT as a function of installed wind capacity, natural gas price, and CAES capital cost. Two types of models were developed and used in this work. The first type of model was a CAES dispatch optimization model, which determined the maximum operating profits a CAES facility could earn given a set of electricity and ancillary services market prices. These models were used to examine several separate research questions relating to the maximum potential for CAES and the impact of uncertainty and other real-world complications. The models determined that the maximum operating profit from 2002-2010 varied widely from year to year and averaged $120-140/kW-year, which is likely below the operating profits required to justify investing in CAES. The models also determined that current price forecasting methods are sufficient to earn approximately 95% of the operating profits achievable with perfect knowledge of all prices in the year. The second type of model was a unit commitment model of ERCOT, which determined the least-cost operation of all the generators in the generation fleet to meet given load. The unit commitment model was used to determine electricity and ancillary service market prices under different assumptions about natural gas price, installed wind capacity, and installed CAES capacity. The CAES dispatch optimization model was then used to determine the operating profits of a CAES facility under these scenarios. CAES operating profits were found to increase with increasing natural gas price and installed wind capacity and to decrease with increasing installed CAES capacity. CAES operating profits were estimated to support installed CAES capacities from zero to more than 6 GW, depending on the natural gas price, installed wind capacity, installed CAES capacity, and the CAES capital costs. The strongest determinant of the maximum CAES capacity that would be profitable is the natural gas price, followed by the CAES capital costs. / text

MP3, or MPEG-1 Layer 3, is the most widely-used format for storing compressed audio. MP3 is more advantageous than uncompressed audio (PCM), offering a much smaller size but without a noticeable loss in audio quality. This report will demonstrate decoding and playback of MP3 audio using a TLL5000 FPGA board. / text

Recent optimistic revisions to projections for recoverable natural gas resources in the United States have generated renewed interest in the possibility of greater utilization of natural gas as a transportation fuel. Against a backdrop of significant policy challenges for the United States, including air quality concerns in urban areas, slow economic growth and high unemployment, and a rising unease with regard to an increasing dependence on foreign oil; natural gas offers the nation’s transportation sector an opportunity to reduce mobile emissions, lower fuel costs, create jobs and reduce dependence on imported oil. While the current focus for expanded use of natural gas in the transportation sector emphasizes heavy duty and fleet vehicles, there may also be potential for increased use for passenger vehicles. Inconvenience, with regard to refueling, and high incremental vehicle costs, however, are seen as major obstacles to greater adaptation. This analysis examines the benefits and drawbacks of natural gas vehicles from the passenger vehicle perspective and includes data from a cross-country road trip. The report includes a review of market trends and possible development scenarios and concludes with recommendations to minimize the potential challenges of greater adaptation of natural gas vehicles in the passenger vehicle market. / text

Problems at the intersection of compression and statistical inference recur frequently due to the concurrent use of signal and image compression and classification algorithms in many applications. This dissertation addresses two such problems: statistical inference on compressed data, and rate-allocation for joint compression and classification.Features of the JPEG2000 standard make possible the development of computationally efficient algorithms to achieve such a goal for imagery compressed using this standard. We propose the use of the information content (IC) of wavelet subbands, defined as the number of bytes that the JPEG2000 encoder spends to compress the subbands, for content analysis. Applying statistical learning frameworks for detection and classification, we present experimental results for compressed-domain texture image classification and cut detection in video. Our results indicate that reasonable performance can be achieved, while saving computational and bandwidth resources. IC features can also be used for preliminary analysis in the compressed domain to identify candidates for further analysis in the decompressed domain.In many applications of image compression, the compressed image is to be presented to human observers and statistical decision-making systems. In such applications, the fidelity criterion with respect to which the image is compressed must be selected to strike an appropriate compromise between the (possibly conflicting) image quality criteria for the human and machine observers. We present tractable distortion measures based on the Bhattacharyya distance (BD) and a new upper bound on the quantized probability of error that make possible closed form expressions for rate allocation to image subbands and show their efficacy in maintaining the aforementioned balance between compression and classification. The new bound offers two advantages over the BD in that it yields closed-form solutions for rate-allocation in problems involving correlated sources and more than two classes.

Rapid advances in sensor technologies have fueled massive torrents of data streaming across networks. Such large volume of information, indeed, restricts the operational performance of data processing, causing inefficiency in sensing, computation, communication and control. Hence, classical data processing techniques need to be re-analyzed and re-designed prior to be applied to modern networked data systems. This thesis aims to understand and characterize fundamental principles and interactions in and among sensing, compression, communication, computation and control, involved in networked data systems. In this regard, the thesis investigates four problems. The common theme is the design and analysis of optimized low-delay transmission strategies with affordable complexity for reliable communication of acquired data over networks with the objective of providing high quality of service for users. In the first three problems considered in the thesis, an emerging framework for data acquisition, namely, compressed sensing, is used which performs acquisition and compression simultaneously. The first problem considers the design of iterative encoding schemes, based on scalar quantization, for transmission of compressed sensing measurements over rate-limited links. Our approach is based on an analysis-by-synthesis principle, where the motivation is to reflect non-linearity in reconstruction, raised by compressed sensing, via synthesis, on choosing the best quantized value for encoding, via analysis. Our design shows significant reconstruction performance compared to schemes that only consider direct quantization of compressed sensing measurements. In the second problem, we investigate the design and analysis of encoding--decoding schemes, based on vector quantization, for transmission of compressed sensing measurements over rate-limited noisy links. In so realizing, we take an approach adapted from joint source-channel coding framework. We show that the performance of the studied system can approach the fundamental theoretical bound by optimizing the encoder-decoder pair. The price, however, is increased complexity at the encoder. To address the encoding complexity of the vector quantizer, we propose to use low-complexity multi-stage vector quantizer whose optimized design shows promising performance. In the third problem considered in the thesis, we take one step forward, and study joint source-channel coding schemes, based on vector quantization, for distributed transmission of compressed sensing measurements over noisy rate-limited links. We design optimized distributed coding schemes, and analyze theoretical bounds for such topology. Under certain conditions, our results reveal that the performance of the optimized schemes approaches the analytical bounds. In the last problem and in the context of control under communication constraints, we bring the notion of system dynamicity into the picture. Particularly, we study relations among stability in dynamical networked control systems, performance of real-time coding schemes and the coding complexity. For this purpose, we take approaches adapted from separate source-channel coding, and derive theoretical bounds on the performance of two types of coding schemes: dynamic repetition codes, and dynamic Fountain codes. We analytically and numerically show that the dynamic Fountain codes, over binary-input symmetric channels, with belief propagation decoding, are able to provide system stability in a networked control system. The results in the thesis evidently demonstrate that impressive performance gain is feasible by employing tools from communication and information theory to control and sensing. The insights offered through the design and analysis will also reveal fundamental pieces for understanding real-world networked data puzzle. / <p>QC 20140407</p>