DBS电视接收机低中频与直接转换调谐器的比较

CMOS技术是把L波段卫星调谐器、解调器和其它功能整合至低成本、高效能单芯片的关键。本文将探讨新型低中频和传统零中频DBS调谐器架构在单芯片DBS射频前端设计中的优缺点。     

hTERT-Driven Immortalization of RDEB Fibroblast and Keratinocyte Cell Lines Followed by Cre-Mediated Transgene Elimination

The recessive form of dystrophic epidermolysis bullosa (RDEB) is a crippling disease caused by impairments in the junctions of the dermis and the basement membrane of the epidermis. Using ectopic expression of hTERT/hTERT + BMI-1 in primary cells, we developed expansible cultures of RDEB fibroblasts and keratinocytes. We showed that they display the properties of their founders, including morphology, contraction ability and expression of the respective specific markers including reduced secretion of type VII collagen (C7). The immortalized keratinocytes retained normal stratification in 3D skin equivalents. The comparison of secreted protein patterns from immortalized RDEB and healthy keratinocytes revealed the differences in the contents of the extracellular matrix that were earlier observed specifically for RDEB. We demonstrated the possibility to reverse the genotype of immortalized cells to the state closer to the progenitors by the Cre-dependent hTERT switch off. Increased β-galactosidase activity and reduced proliferation of fibroblasts were shown after splitting out of transgenes. We anticipate our cell lines to be tractable models for studying RDEB from the level of single-cell changes to the evaluation of 3D skin equivalents. Our approach permits the creation of standardized and expandable models of RDEB that can be compared with the models based on primary cell cultures.     

Catalytic combustion over platinum group catalysts: fuel-lean versus fuel-rich operation

Performance data are presented for methane oxidation on alumina-supported Pd, Pt, and Rh catalysts under both fuel-rich and fuel-lean conditions. Catalyst activity was measured in a micro-scale isothermal reactor at temperatures between 300 and 800 °C. Non-isothermal (near adiabatic) temperature and reaction data were obtained in a full-length (non-differential) sub-scale reactor operating at high pressure (0.9 MPa) and constant inlet temperature, simulating actual reactor operation in catalytic combustion applications.

Under fuel-lean conditions, Pd catalyst was the most active, although deactivation occurred above 650 °C, with reactivation upon cooling. Rh catalyst also deactivated above 750 °C, but did not reactivate. Pt catalyst was active above 600 °C. Fuel-lean reaction products were CO₂ and H₂O for all three catalysts.

The same catalysts tested under fuel-rich conditions demonstrated much higher activity. In addition, a ‘lightoff’ temperature was found (between 450 and 600 °C), where a stepwise increase in reaction rate was observed. Following ‘lightoff’ partial oxidation products (CO, H₂) appeared in the mixture, and their concentration increased with increasing temperature. All three catalysts exhibited this behavior.

High-pressure (0.9 MPa) sub-scale reactor and combustor data are shown, demonstrating the benefits of fuel-rich operation over the catalyst for ultra-low emissions combustion.     

Visual search tree profiling

Understanding how the search space is explored for a given constraint problem – and how it changes for different models, solvers or search strategies – is crucial for efficient solving. Yet programmers often have to rely on the crude aggregate measures of the search that are provided by solvers, or on visualisation tools that can show the search tree, but do not offer sophisticated ways to navigate and analyse it, particularly for large trees. We present an architecture for profiling a constraint programming search that is based on a lightweight instrumentation of the solver. The architecture combines a visualisation of the search tree with various tools for convenient navigation and analysis of the search. These include identifying repeated subtrees, high-level abstraction and navigation of the tree, and the comparison of two search trees. The resulting system is akin to a traditional program profiler, which helps the user to focus on the parts of the execution where an improvement to their program would have the greatest effect.     

Evaluation of hydrogen bonding ability of liquids and solids by C-13 NMR. Silica gel as a strong hydrogen bond donor

The chemical shift difference between signals of C() and C() of unsaturated ketones, , which we used before to measure acid strengths, has now been used to evaluate the hydrogen bond donor ability of solvents which are not acidic enough to hydronate the indicator. For such solvents there is no general correlation between H-bond donor ability and acid strength: hexa-fluoroisopropanol is a much weaker acid than acetic acid, but it is a stronger H-bond donor. The method can be applied to evaluate the H-bonding properties of solid surfaces, and it was thus found that silica gel has a much stronger H-bond donor ability than methanol or acetic acid.     

Accuracy estimate and optimization techniques for SimRank computation

The measure of similarity between objects is a very useful tool in many areas of computer science, including information retrieval. SimRank is a simple and intuitive measure of this kind, based on a graph-theoretic model. SimRank is typically computed iteratively, in the spirit of PageRank. However, existing work on SimRank lacks accuracy estimation of iterative computation and has discouraging time complexity. In this paper, we present a technique to estimate the accuracy of computing SimRank iteratively. This technique provides a way to find out the number of iterations required to achieve a desired accuracy when computing SimRank. We also present optimization techniques that improve the computational complexity of the iterative algorithm from O(n ⁴) in the worst case to min(O(nl), O(n ³/ log₂ n)), with n denoting the number of objects, and l denoting the number object-to-object relationships. We also introduce a threshold sieving heuristic and its accuracy estimation that further improves the efficiency of the method. As a practical illustration of our techniques, we computed SimRank scores on a subset of English Wikipedia corpus, consisting of the complete set of articles and category links.     

Supervised Learning of Quantizer Codebooks by Information Loss Minimization

This paper proposes a technique for jointly quantizing continuous features and the posterior distributions of their class labels based on minimizing empirical information loss such that the quantizer index of a given feature vector approximates a sufficient statistic for its class label. Informally, the quantized representation retains as much information as possible for classifying the feature vector correctly. We derive an alternating minimization procedure for simultaneously learning codebooks in the euclidean feature space and in the simplex of posterior class distributions. The resulting quantizer can be used to encode unlabeled points outside the training set and to predict their posterior class distributions, and has an elegant interpretation in terms of lossless source coding. The proposed method is validated on synthetic and real data sets and is applied to two diverse problems: learning discriminative visual vocabularies for bag-of-features image classification and image segmentation.     

An Efficient Scaling Algorithm for the Minimum Weight Bibranching Problem

Let G=(VG,AG) be a digraph and let S ⊔ T be a bipartition of VG. A bibranching is a subset B⊆AG such that for each node s∈S there exists a directed s–T path in B and, vice versa, for each node t∈T there exists a directed S–t path in B.     

Verifying Temporal Properties of Reactive Systems: A STeP Tutorial

We review a number of formal verification techniques supported by STeP, the Stanford Temporal Prover, describing how the tool can be used to verify properties of several versions of the Bakery Mutual exclusion algorithm for mutual exclusion. We verify the classic two-process algorithm and simple variants, as well as an atomic parameterized version. The methods used include deductive verification rules, verification diagrams, automatic invariant generation, and finite-state model checking and abstraction.     

Analyzing the efficacy of using digital ink devices in a learning environment

There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby facilitating high test scores.