The ongoing development of storage devices and technologies for medical image management has led to a growth in the digital archiving of these images. The characteristics of medical x-rays are examined, and a number of digital coding methods are considered. An investigation of several fast cosine transform algorithms is carried out. An adaptive cosine transform coding technique is implemented which produces good quality images using bit rates lower than 0.38 bits per picture element

The past two decades have witnessed rapid growth in the amount of data available to us. Many fields, including physics, biology, and medical studies, generate enormous datasets with a large sample size, a high number of dimensions, or both. For example, some datasets in physics contains millions of records. It is forecasted by Statista Survey that in 2022, there will be over 86 millions users of health apps in United States, which will generate massive mHealth data. In addition, more and more large studies have been carried out, such as the UK Biobank study. This gives us unprecedented access to data and allows us to extract and infer vital information. Meanwhile, it also poses new challenges for statistical methodologies and computational algorithms. For increasingly large datasets, computation can be a big hurdle for valid analysis. Conventional statistical methods lack the scalability to handle such large sample size. In addition, data storage and processing might be beyond usual computer capacity. The UK Biobank genotypes and phenotypes dataset contains about 500,000 individuals and more than 800,000 genotyped single nucleotide polymorphism (SNP) measurements per person, the size of which may well exceed a computer's physical memory. Further, the high dimensionality combined with the large sample size could lead to heavy computational cost and algorithmic instability. The aim of this dissertation is to provide some statistical approaches to address the issues. Chapter 1 provides a review on existing literature. In Chapter 2, a novel perturbation subsampling approach is developed based on independent and identically distributed stochastic weights for the analysis of large scale data. The method is justified based on optimizing convex criterion functions by establishing asymptotic consistency and normality for the resulting estimators. The method can provide consistent point estimator and variance estimator simultaneously. The method is also feasible for a distributed framework. The finite sample performance of the proposed method is examined through simulation studies and real data analysis. In Chapter 3, a repeated block perturbation subsampling is developed for the analysis of large scale longitudinal data using generalized estimating equation (GEE) approach. The GEE approach is a general method for the analysis of longitudinal data by fitting marginal models. The proposed method can provide consistent point estimator and variance estimator simultaneously. The asymptotic properties of the resulting subsample estimators are also studied. The finite sample performances of the proposed methods are evaluated through simulation studies and mHealth data analysis. With the development of technology, large scale high dimensional data is also increasingly prevailing. Conventional statistical methods for high dimensional data such as adaptive lasso (AL) lack the scalability to handle processing of such large sample size. Chapter 4 introduces the repeated perturbation subsampling adaptive lasso (RPAL), a new procedure which incorporates features of both perturbation and subsampling to yield a robust, computationally efficient estimator for variable selection, statistical inference and finite sample false discovery control in the analysis of big data. RPAL is well suited to modern parallel and distributed computing architectures and furthermore retains the generic applicability and statistical efficiency. The theoretical properties of RPAL are studied and simulation studies are carried out by comparing the proposed estimator to the full data estimator and traditional subsampling estimators. The proposed method is also illustrated with the analysis of omics datasets.

Thesis: Elec. E., Massachusetts Institute of Technology, Department of Electrical Engineering, 1977 / Bibliography : leaves 127-132. / Elec. E. / Elec. E. Massachusetts Institute of Technology, Department of Electrical Engineering

Two common goals in computing system design are increasing performance and decreasing power consumption. DRAM-based memory subsystems are a major component of both system performance and power consumption. Memory controllers employ strategies to efficiently schedule DRAM operations to reduce latency and to utilize DRAM low power modes when possible. One of the most important of these is the page policy, which determines when to close pages in DRAM. An effective DRAM memory controller page policy is important to minimizing power consumption and increasing system performance. This thesis explores the impact memory controller page policy has on performance as measured by the number of page-hits minus page-misses and estimated average memory access latency. I captured real-time DDR3 command and address memory traces for the SPEC CPU2006 benchmarks under three memory controller page policies: closed page, fixed open-page, and Intel's adaptive open-page [1]. Traces were captured using a programmable memory traffic analyzer (PMTA), a device interposed between the DIMM slot and DDR3 DIMM on the motherboard. The memory traces for each benchmark were analyzed to determine the absolute number of page-hits and page-misses that occurred. In software post-processing I simulated a theoretically perfect "oracle" page policy for each captured trace to compare the efficiency of existing policies. The SPEC CPU 2006 benchmarks under the oracle page policy for each trace exhibited an average increase in the number of page-hits minus page-misses of 280.3% and an average decrease in the average memory latency of 11.1%. Two new adaptive open-page policies are proposed and simulated using the captured memory traces. These proposed policies result in an average increase of 74.8% and 62.4% in the number of page-hits minus page-misses over Intel's adaptive open-page policy and an average decrease in the average memory latency of 3.8% and 3.4%.

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Yes / Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG. / Iran National Science Foundation; Royal Academy of Engineering Distinguished Visiting Fellowship under Grant DVF1617\6\45

A new form of organic energy storage devices (organic capacitors) is presented in the first part of this dissertation. The storage devices are made out of an organic semiconductor material and charge storage elements from synthesized nanoparticles. The semiconducting polymer is obtained by blending poly (vinyl alcohol) and poly (acrylic acid) in crystal state polymers with a known plasticizer; glycerol or sorbitol. Synthesized nanoparticles namely, zinc-oxide (ZnO), erbium (Er), cadmium sulfide (CdS), palladium (Pd) and silver-platinum (AgPt) were used as charge storage elements in fabrication of metal-insulator-semiconductor (MIS) structure. The organic semiconductor and synthesized nanoparticles are tested to evaluate and characterize their electrical performance and properties. Fabrication of the organic capacitors consisted of layer-by-layer deposition and thermal evaporation of the electrode terminals. Capacitance versus voltage (C-V) measurement tests were carried out to observe hysteresis loops with a window gate that would indicate the charging, discharging and storage characteristics. Experimental investigation of various integrated energy harvesting techniques combined with these organic based novel energy storage devices are performed in the second part of this dissertation. The source of the energy is the wind and is harvested by means of miniature wind turbines and vibrations, using piezoelectric transduction. In both cases, the generated electric charge is stored in these capacitors. The performance of the organic capacitors are evaluated through their comparison with commercial capacitors. The results show that the voltage produced from the two energy harvesters was high enough to store the harvested energy in the organic capacitors. The charge and energy levels of the organic capacitors are also reported. The third part of this dissertation focuses on harvesting energy from a self-induced flutter of a thin composite beam. The composite beam consisted of an MFC patch bonded near the clamped end and placed vertically in the center of a wind tunnel test section. The self sustaining energy harvesting from the unimorph composite beam is exploited. The effects of different operational parameters including the optimum angle of attack, wind speed and load resistance are determined. / Ph. D.

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Yes / Cerium (Ce3+) substituted Ni-Co ferrites with composition Ni0.3Co0.7CexFe2−xO4 (x = 0.0–0.20, with step size 0.05) were synthesized by sol-gel method. Face-centered cubic (FCC) spinel structure was revealed by X-ray analysis. The crystalline size was calculated ranging between 17.1 and 18.8 nm, lattice constant showed a decreasing trend with increase of Ce3+ contents, furthermore, X-ray density was calculated between 5.30 and 5.69 g/cm3. The two characteristic spinel ferrites absorption bands were seen around 550 (cm−1) and 415 (cm−1) in Fourier transform infra-red (FTIR) spectroscopy. The microstructural and elemental studies were carried out by field emission transmission electron microscopy (FE-TEM) and energy dispersive X-ray (EDX) respectively, the average particle size was calculated around 21.83 nm. Magnetic studies were per- formed by vibrating sample magnetometer (VSM), which showed that saturation magnetization Ms and remanence Mr decreased with substitution up to x = 0.10 due to small magnetic moment of Ce3+ than Fe3+. The coercivity Hc increased with substitution up to 908.93 Oe at x = 0.05, then it decreased following the trend of anisotropy constant. The dielectric studies exhibited decrease in dielectric parameters with fre- quency due to decreasing polarization in material. The dielectric loss was significantly decreased in material at high frequency. The Cole-Cole interpretation exhibited conduction mechanism being caused by grain boundary density. These attributes of Ce3+ substituted Ni-Co ferrites suggest their possible use in memory storage, switching and high frequency devices like antenna and satellite systems. / The authors would like to acknowledge the Researcher's Supporting Project Number (RSP-2021/269) King Saud University, Riyadh, Saudi Arabia, for their support in this work.

Memory design is a crucial component of VLSI system design from area, power and performance perspectives. To meet the increasingly challenging system specifications, architecture, circuit and device level innovations are required for existing memory technologies. Emerging memory solutions are widely explored to cater to strict budgets. This thesis presents design methodologies for custom memory design with the objective of power-performance benefits across specific applications. Taking example of STTRAM (spin transfer torque random access memory) as an emerging memory candidate, the design space is explored to find optimal energy design solution. A thorough thermal reliability study is performed to estimate detection reliability challenges and circuit solutions are proposed to ensure reliable operation. Adoption of the application-specific optimal energy solution is shown to yield considerable energy benefits in a read-heavy application called MBC (memory based computing). Circuit level customizations are studied for the volatile SRAM (static random access memory) memory, which will provide improved energy-delay product (EDP) for the same MBC application. Memory design has to be aware of upcoming challenges from not only the application nature but also from the packaging front. Taking 3D die-folding as an example, SRAM performance shift under die-folding is illustrated. Overall the thesis demonstrates how knowledge of the system and packaging can help in achieving power efficient and high performance memory design.