The discrete Pascal transform and its applications

We introduce a new discrete polynomial transform constructed from the rows of Pascal’s triangle. The forward and inverse transforms are computed the same way in both the oneand two-dimensional cases, and the transform matrix can be factored into binary matrices for efficient hardware implementation. We conclude by discussing applications of the transform in

Parallel vector processing of multidimensional orthogonal transforms for digital signal processing applications

The performance of the parallel vector implementation of the one- and two-dimensional orthogonal transforms is evaluated. The orthogonal transforms are computed using actual or modified fast Fourier transform (FFT) kernels. The factors considered in comparing the speed-up of these vectorized digital signal processing algorithms are discussed and it is shown that the traditional way of comparing th execution speed of digital signal processing algorithms by the ratios of the number of multiplications and additions is no longer effective for vector implementation; the structure of the algorithm must also be considered as a factor when comparing the execution speed of vectorized digital signal processing algorithms. Simulation results on the Cray X/MP with the following orthogonal transforms are presented: discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete sine transform (DST), discrete Hartley transform (DHT), discrete Walsh transform (DWHT), and discrete Hadamard transform (DHDT). A comparison between the DHT and the fast Hartley transform is also included.(34 refs)

Scale Up of PLA Nanoparticle Synthesis by Solvent Displacement and Functionalization of the Particles with a Silica Shell for Targeted Drug Delivery Applications

Biodegradable polymer nanoparticles have the properties necessary to address many of the issues associated with current drug delivery techniques including targeted and controlled delivery. A novel drug delivery vehicle is proposed consisting of a poly(lactic acid) nanoparticle core, with a functionalized, mesoporous silica shell. In this study, the production of PLA nanoparticles is investigated using solvent displacement in both a batch and continuous manner, and the effects of various system parameters are examined. Using Pluronic F-127 as the stabilization agent throughout the study, PLA nanoparticles are produced through solvent displacement with diameters ranging from 200 to 250 nm using two different methods: dropwise addition and in an impinging jet mixer. The impinging jet mixer allows for easy scale-up of particle production. The concentration of surfactant and volume of quench solution is found to have minimal impact on particle diameter; however, the concentration of PLA is found to significantly impact the diameter mean and polydispersity. In addition, the stability of the PLA nanoparticles is observed to increase as residual THF is evaporated. Lastly, the isolated PLA nanoparticles are coated with a silica shell using the Stöber Process. It is found that functionalizing the silica with a phosphonic silane in the presence of excess Pluronic F-127 decreases coalescence of the particles during the coating process. Future work should be conducted to fine-tune the PLA nanoparticle synthesis process by understanding the effect of other system parameters and in synthesizing mesoporous silica shells.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.