Hardware Acceleration of the Embedded Zerotree Wavelet Algorithm

The goal of this project was to gain experience in designing and implementing a microelectronic system to acclerate the execution of a time-consuming software algorithm, the Embedded Zerotree Wavelet (EZW), which is used in multimedia applications. The algorithm was implemented using MATLAB to be certain it was fully understood and to serve as a validation reference. Then, the algorithm was mapped into a hardware description language, VHDL, and its resulting implementation verified with the golden reference. The hardware description was then targeted to a field-programmable gate array (FPGA). Significant acceleration was achieved since the hardware implementation in a FPGA (Xilinx Virtex-1000E using a 8.315 MHz clock) ran 10,000 times faster than the MATLAB implementation on a SUN-220 workstation. Additional speedup exploiting the parallel capabilities of the FPGA was not achieved since the EZW algorithm utilizes only sequential operations

A Distributed Parameter Model for a Solid Oxide Fuel Cell: Simulating Realistic Operating Conditions

We present a detailed multiphysics model capable of simulating the dyn amic behavior of a solid oxide fuel cell (SOFC). This model includes a description of a ll the important physical and chemical processes in a fuel cell: fluid flow, mass and heat trans fer, electronic and ionic potential fields, as well as the chemical and electrochemical react ions. The resulting highly nonlinear, coupled system of differential equations is solved using a fi nite volume discretization. Our interest lies in simulating realistic operating conditions with the obj ective of high efficiency operation at high fuel utilization. While there are a number of studies in the literature that present multiphysics models for SOFCs, few have focused on simulat ing operating conditions that are necessary if SOFC systems are to realize their promise of h igh efficiency conversion of chemical energy to electrical energy. In this report we present s imulation results at operating conditions that approach the required ranges of power density an d overall efficiency. Our results include a) the temperature and composition profiles along a typical f uel cell in a SOFC stack, b) the dynamic response of the cell to step changes in the available inpu t variables. Since models such as the one presented here are fairly expensive computationa lly and cannot be directly used for online model predictive control, one generally looks to use simplifie d reduced order models for control. We briefly discuss the implications of our model results o n the validity of using reduced models for the control of SOFC stacks to show that avoid ing operating regions where well-known degradation modes are activated is non-trivial without u sing detailed multiphysics models

Non-antibiotic selection systems for soybean somatic embryos: the lysine analog aminoethyl-cysteine as a selection agent

<p>Abstract</p> <p>Background</p> <p>In soybean somatic embryo transformation, the standard selection agent currently used is hygromycin. It may be preferable to avoid use of antibiotic resistance genes in foods. The objective of these experiments was to develop a selection system for producing transgenic soybean somatic embryos without the use of antibiotics such as hygromycin.</p> <p>Results</p> <p>When tested against different alternate selection agents our studies show that 0.16 μg/mL glufosinate, 40 mg/L isopropylamine-glyphosate, 0.5 mg/mL (S-(2 aminoethyl)-L-cysteine) (AEC) and the acetolactate synthase (ALS) inhibitors Exceed^®and Synchrony^®both at 150 μg/mL inhibited soybean somatic embryo growth. Even at the concentration of 2 mg/mL, lysine+threonine (LT) were poor selection agents. The use of AEC may be preferable since it is a natural compound. Unlike the plant enzyme, dihydrodipicolinate synthase (DHPS) from <it>E. coli </it>is not feed-back inhibited by physiological concentrations of lysine. The <it>dapA </it>gene which codes for <it>E. coli </it>DHPS was expressed in soybean somatic embryos under the control of the CaMV 35S promoter. Following introduction of the construct into embryogenic tissue of soybean, transgenic events were recovered by incubating the tissue in liquid medium containing AEC at a concentration of 5 mM. Only transgenic soybeans were able to grow at this concentration of AEC; no escapes were observed.</p> <p>Conclusion</p> <p>Genetically engineered soybeans expressing a lysine insensitive DHPS gene can be selected with the non-antibiotic selection agent AEC. We also report here the inhibitory effects of glufosinate, (isopropylamine-glyphosate) (Roundup^®), AEC and the ALS inhibitors Exceed^®and Synchrony^®against different tissues of soybean</p

An iterative identification procedure for dynamic modeling of biochemical networks

<p>Abstract</p> <p>Background</p> <p>Mathematical models provide abstract representations of the information gained from experimental observations on the structure and function of a particular biological system. Conferring a predictive character on a given mathematical formulation often relies on determining a number of non-measurable parameters that largely condition the model's response. These parameters can be identified by fitting the model to experimental data. However, this fit can only be accomplished when identifiability can be guaranteed.</p> <p>Results</p> <p>We propose a novel iterative identification procedure for detecting and dealing with the lack of identifiability. The procedure involves the following steps: 1) performing a structural identifiability analysis to detect identifiable parameters; 2) globally ranking the parameters to assist in the selection of the most relevant parameters; 3) calibrating the model using global optimization methods; 4) conducting a practical identifiability analysis consisting of two (<it>a priori </it>and <it>a posteriori</it>) phases aimed at evaluating the quality of given experimental designs and of the parameter estimates, respectively and 5) optimal experimental design so as to compute the scheme of experiments that maximizes the quality and quantity of information for fitting the model.</p> <p>Conclusions</p> <p>The presented procedure was used to iteratively identify a mathematical model that describes the NF-<it>κ</it>B regulatory module involving several unknown parameters. We demonstrated the lack of identifiability of the model under typical experimental conditions and computed optimal dynamic experiments that largely improved identifiability properties.</p

Biochemical systems identification by a random drift particle swarm optimization approach

BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study

Optimization of Time-Course Experiments for Kinetic Model Discrimination

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction