pessimistic predicate/transform model for long running business processes

Many business processes in enterprise applications are both long running and transactional in nature. However, no current transaction model can provide full transaction support for such long running business processes. This paper proposes a new transaction model, the pessimistic predicate/transform (PP/T) model, which can provide full transaction support for long running business processes. A framework was proposed on the enterprise JavaBeans platform to implement the PP/T model. The framework enables application developers to focus on the business logic, with the underlying platform providing the required transactional semantics. The development and maintenance effort are therefore greatly reduced. Simulations show that the model has a sound concurrency management ability for long running business processes.

Improved digital processing method used for image motion measurement based on hybrid opto-digital joint transform correlator

Hybrid opto-digital joint transform correlator (HODJTC) is effective for image motion measurement, but it is different from the traditional joint transform correlator because it only has one optical transform and the joint power spectrum is directly input into a digital processing unit to compute the image shift. The local cross-correlation image can be directly obtained by adopting a local Fourier transform operator. After the pixel-level location of cross-correlation peak is initially obtained, the up-sampling technique is introduced to relocate the peak in even higher accuracy. With signal-to-noise ratio >= 20 dB, up-sampling factor k >= 10 and the maximum image shift <= 60 pixels, the root-mean-square error of motion measurement accuracy can be controlled below 0.05 pixels.

Carbon monoxide adsorption on molybdenum phosphides: Fourier transform infrared spectroscopic and density functional theory studies

Molybdenum phosphide (MoP) and supported molybdenum phosphide (MoP/gamma-Al2O3) have been prepared by the temperature-programmed reduction method. The surface sites of the MoP/gamma-Al2O3 catalyst were characterized by carbon monoxide (CO) adsorption with in situ Fourier transform infrared (FT-IR) spectroscopy. A characteristic IR band at 2037 cm(-1) was observed on the MoP/gamma-Al2O3 that was reduced at 973 K. This band is attributed to linearly adsorbed CO on Mo atoms of the MoP surface and is similar to IR bands at 2040-2060 cm(-1), which correspond to CO that has been adsorbed on some noble metals, such as platinum, palladium, and rhodium. Density functional calculations of the structure of molybdenum phosphides, as well as CO chemisorption on the MoP(001) surface, have also been studied on periodic surface models, using the generalized gradient approximation (GGA) for the exchange-correlation functional. The results show that the chemisorption of CO on MoP occurred mainly on top of molybdenum, because the bonding of CO requires a localized mininum potential energy. The adsorption energy obtained is DeltaH(ads) approximate to -2.18 eV, and the vibrational frequency of CO is 2047 cm-1, which is in good agreement with the IR result of CO chernisorption on MoP/gamma-Al2O3.

Fast Haar Transform Based Feature Extraction for Face Representation and Recognition

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.