Designing the MPC105 PCI bridge/memory controller

The MPC105 peripheral component interconnection bridge/memory controller provides a platform-specification-compliant bridge between Power PC microprocessors and the PCI bus. With it, designers can create systems using peripherals already designed for a variety of standard PC interfaces. This bridge chip also integrates a secondary cache controller and high-performance memory controller that supports DRAM or synchronous DRAM and ROM or flash ROM     

植被散射实验数据的特性分析

本文根据电波所获取的各种樾 射实测数据，分析了它们对不同极化波的响应特性。     

工业配料中的自控

本文介绍了微机在工业配料过程中的应用情况，对上料-计量-下料的全过程即“动态运行-静态计量-动态运行”进行控制．     

PRMT 3, a type I protein arginine N-methyltransferase that differs from PRMT1 in its oligomerization, subcellular localization, substrate specificity, and regulation

Methylation is one of the many post-translational modifications that modulate protein function. Although asymmetric NG,NG-dimethylation of arginine residues in glycine-arginine-rich domains of eucaryotic proteins, catalyzed by type I protein arginine N-methyltransferases (PRMT), has been known for some time, members of this enzyme class have only recently been cloned. The first example of this type of enzyme, designated PRMT1, cloned because of its ability to interact with the mammalian TIS21 immediate-early protein, was then shown to have protein arginine methyltransferase activity. We have now isolated rat and human cDNA orthologues that encode proteins with substantial sequence similarity to PRMT1. A recombinant glutathione S-transferase (GST) fusion product of this new rat protein, named PRMT3, asymmetrically dimethylates arginine residues present both in the designed substrate GST-GAR and in substrate proteins present in hypomethylated extracts of a yeast rmt1 mutant that lacks type I arginine methyltransferase activity; PRMT3 is thus a functional type I protein arginine N-methyltransferase. However, rat PRMT1 and PRMT3 glutathione S-transferase fusion proteins have distinct enzyme specificities for substrates present in both hypomethylated rmt1 yeast extract and hypomethylated RAT1 embryo cell extract. TIS21 protein modulates the enzymatic activity of recombinant GST-PRMT1 fusion protein but not the activity of GST-PRMT3. Western blot analysis of gel filtration fractions suggests that PRMT3 is present as a monomer in RAT1 cell extracts. In contrast, PRMT1 is present in an oligomeric complex. Immunofluorescence analysis localized PRMT1 predominantly to the nucleus of RAT1 cells. In contrast, PRMT3 is predominantly cytoplasmic.     

A new parallel particle filter face tracking method based on heterogeneous system

This paper proposed a multi-cue-based face-tracking algorithm with the supporting framework using parallel multi-core and one Graphic Processing Unit (GPU). Due to illumination and partial-occlusion problems, face tracking usually cannot stably work based on a single cue. Focusing on the above-mentioned problems, we first combined three different visual cues??color histogram, edge orientation histogram, and wavelet feature??under the framework of particle filters to considerably improve tracking performance. Furthermore, an online updating strategy made the algorithm adaptive to illumination changes and slight face rotations. Subsequently, attempting two parallel approaches resulted in real-time responses. However, the computational efficiency decreased considerably with the increase of particles and visual cues. In order to handle the large amount of computation costs resulting from the introduced multi-cue strategy, we explored two parallel computing techniques to speed up the tracking process, especially the most computation-intensive observational steps. One is a multi-core-based parallel algorithm with a MapReduce thread model, and the other is a GPU-based speedup approach. The GPU-based technique uses features-matching and particle weight computations, which have been put into the GPU kernel. The results demonstrate that the proposed face-tracking algorithm can work robustly with cluttered backgrounds and differing illuminations; the multi-core parallel scheme can increase the speed by 2?C6 times compared with that of the corresponding sequential algorithms. Furthermore, a GPU parallel scheme and co-processing scheme can achieve a greater increase in speed (8×?C12×) compared with the corresponding sequential algorithms.     

Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (BP) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering are used to form feature vectors. The BP neural network is input to segment blood vessels. Experiments on the color fundus image libraries DRIVE and STARE show that this algorithm can obtain complete retinal blood vessel segmentation as well as connected vessel stems and terminals. When segmenting most small blood vessels, the average accuracy on the DRIVE library reaches 0.9477, and the average accuracy on the STARE library reaches 0.9498, which has a good segmentation effect. Through verification, the algorithm is feasible and effective for blood vessel segmentation of color fundus images and can detect more capillaries.