Difference Frequency Generation of 5-18 mum in a AgGaSe(2) Crystal

Mid-infrared radiation in the 5-18-mum range has been obtained by difference frequency generation in a AgGaSe(2) crystal by pumping with the output of a type I LiNbO(3) optical parametric oscillator (OPO). Here we suggest the use of a LiTaO(3) retarder to achieve an orthogonal state of polarization between OPO outputs that are necessary for efficient pumping of a AgGaSe(2) crystal. Several tens of kilowatts of peak power near 8 mum and continuously tunable operation in the above range have been obtained.     

Exponential stability of singular systems with multiple time-varying delays

This paper deals with the class of continuous-time singular linear systems with multiple time-varying delays in a range. The global exponential stability problem of this class of systems is addressed. Delay-range-dependent sufficient conditions such that the system is regular, impulse-free and α-stable are developed in the linear matrix inequality (LMI) setting. Moreover, an estimate of the convergence rate of such stable systems is presented. A numerical example is employed to show the usefulness of the proposed results.     

Improving utilization of reconfigurable resources using two-dimensional compaction

Partial reconfiguration allows parts of the reconfigurable chip area to be configured without affecting the rest of the chip. This allows placement of tasks at run time on the reconfigurable chip. Area management is a very important issue which highly affect the utilization of the chip and hence the performance. This paper focuses on a major aspect of moving running tasks to free space for new incoming tasks (compaction). We study the effect of compacting running tasks to free more contiguous space on the system performance. First, we introduce a straightforward compaction strategy called Blind compaction. We use its performance as a reference to measure the performance of other compaction algorithms. Then we propose a two-dimensional compaction algorithm called one-corner compaction. This algorithm runs with respect to one chip corner. We further extend this algorithm to the four corners of the chip and introduce the four-corners compaction algorithm. Finally, we compare the performance of these algorithms with some existing compaction strategies: Brebner, G. and Diessel, O. (Proceedings of the 11th international workshop on field programmable gate arrays (FPL), pp. 182–191, 2001); Diesel, O. and ElGindy, H. (Proceedings of the 5th Australasian conference on parallel and real-time systems (PART), pp. 191–200, 1998); Diesel, O., et al. (IEE proceedings on computers and digital techniques, vol. 147, pp. 181–188, 2000). The simulation results show improvement in average task allocation time when using the four-corners compaction algorithm by 15% and in chip utilization by 16% over the Blind compaction. These results outperform the existing strategies.

Facet-Dependent Intrinsic Activity of Single Co3O4 Nanoparticles for Oxygen Evolution Reaction

Deciphering the influence of nanocatalyst morphology on their catalytic activity in the oxygen evolution reaction (OER), the limiting reaction in water splitting process, is essential to develop highly active precious metal-free catalysts, yet poorly understood. The intrinsic OER activity of Co₃O₄ nanocubes and spheroids is probed at the single particle level to unravel the correlation between exposed facets, (001) vs. (111), and activity. Single cubes with predominant (001) facets show higher activity than multi-faceted spheroids. Density functional theory calculations of different terminations and reaction sites at (001) and (111) surfaces confirm the higher activity of the former, expressed in lower overpotentials. This is rationalized by a change in the active site from octahedral to tetrahedral Co and the potential-determining step from *OH to *O for the cases with lowest overpotentials at the (001) and (111) surfaces, respectively. This approach enables the identification of highly active facets to guide shape-selective syntheses of improved metal oxide nanocatalysts for water oxidation.     

Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics

This paper documents a systematic investigation on the predictability of short-term trends of crude oil prices on a daily basis. In stark contrast with longer-term predictions of crude oil prices, short-term prediction with time horizons of 1–3 days posits an important problem that is quite different from what has been studied in the literature. The problem of such short-term predicability is tackled through two aspects. The first is to examine the existence of linear or nonlinear dynamic processes in crude oil prices. This sub-problem is addressed with statistical analysis involving the Brock-Dechert-Scheinkman test for nonlinearity. The second aspect is to test the capability of artificial neural networks (ANN) for modeling the implicit nonlinearity for prediction. Four experimental models are designed and tested with historical data: (1) using only the lagged returns of filtered crude oil prices as input to predict the returns of the next days; this is used as the benchmark, (2) using only the information set of filtered crude oil futures price as input, (3) combining the inputs from the benchmark and second models, and (4) combing the inputs from the benchmark model and the intermarket information. In order to filter out the noise in the original price data, the moving averages of prices are used for all the experiments. The results provided sufficient evidence to the predictability of crude oil prices using ANN with an out-of-sample hit rate of 80%, 70%, and 61% for each of the next three days’ trends.     

Approximate algorithms for static and continuous range queries in mobile navigation

For many years, spatial range search has been applied to computational geometry and multimedia problems to find interest objects within a given radius. Range search query has traditionally been used to return all objects within a given radius. However, having all objects is not necessary, especially when there are already enough objects closer to the query point. Furthermore, expanding the radius may give users better results, especially when there are a lot of objects just outside the search boundary. Therefore, in this paper, we focus on approximate range search, where the query results are approximate, rather than exact. We propose approximate static range search (ARS) which combines two approaches, namely (i) lowerbound approximate range search, and (ii) upperbound approximate range search. Using ARS, we are able to deliver a better performance, together with low false hit and reasonable false miss. We also extend ARS in the context of a continuous query setting, in which the query moves. This is particularly important in spatial databases as a mobile user who invokes the query is moving. In terms of continuous range search, the intention is to find split points—the locations where the query results will be updated. Accordingly, we propose two methods for approximate continuous range search, namely (i) range search minimization, and (ii) split points minimization. Our performance evaluation which compares our methods with the traditional continuous range search shows that our methods considerably reduce the number of split points, thereby improving overall performance.     

An entropy approach for abnormal activities detection in video streams

Detection of aberration in video surveillance is an important task for public safety. This paper puts forward a simple but effective framework to detect aberrations in video streams using Entropy, which is estimated on the statistical treatments of the spatiotemporal information of a set of interest points within a region of interest by measuring their degree of randomness of both directions and displacements. Entropy is a measure of the disorder/randomness in video frame. It has been showed that degree of randomness of the directions (circular variance) changes markedly in abnormal state of affairs and does change only direction variation but does not change with displacement variation of the interest point. Degree of randomness of the displacements has been put in for to counterbalance this deficiency. Simple simulations have been exercised to see the characteristics of these crude elements of entropy. Normalized entropy measure provides the knowledge of the state of anomalousness. Experiments have been conducted on various real world video datasets. Both simulation and experimental results report that entropy measures of the frames over time is an outstanding way to characterize anomalies in videos.     

Towards an effective automatic query expansion process using an association rule mining approach

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon?test.     

High productive machining of C/SiC preceramics

High productive machining of C/SiC preceramics is investigated in relation to the fabrication of complex-shaped reaction-bonded silicon carbide ceramics. Machinability is analyzed at different manufacturing steps of ceramics preparation. Machining after the carbonization step is shown to be the most efficient. Great value of material removal rate of 360 mm³/s is achieved by high productive CNC-milling of carbonized preceramics at a feed rate of 50 mm/s without any defects upon the processed surfaces, edges, and corners. Diamond tool wears approximately .01% (weight loss per mass of material removed) in the process of green CNC-milling is two orders lower compared with the milling of sintered ceramics (2.8%). Specifics of surface processing are investigated depending on carbon content in preceramics. The increase of bonding carbon from 8 to 16 vol.% decelerates loose abrasive grinding three times, improves the accuracy of surface leveling, and leads to a change of fracture mechanism. The obtained results can be helpful for the advantageous manufacture of complex-shaped silicon carbide ceramics.     

AZ31B镁合金板材中晶界滑移的晶体塑性仿真

建立一种耦合滑移、动态再结晶以及晶界滑移的晶体塑性模型以仿真镁合金的高温变形行为及织构演化.首先,通过实验测量单轴拉伸、压缩后的织构以及显微组织演化,研究AZ31B镁合金在300°C的变形机制.结果发现,动态再结晶在应变小于0.2时起到细化晶粒的作用,之后晶界滑移在变形过程中起显著作用.此外,建立晶界滑移模型来评估由晶...