Conical microlens arrays that flatten optical-irradiance profiles of nonuniform sources

One can create conical microlens arrays on a glass plate by simply spin coating spin-on-glass film on the glass, patterning the photoresist, and then etching the glass in a HF solution. These arrays can be used to obtain more-uniform irradiance profiles from nonuniform incoherent sources.     

在Palm上阅读和编辑文件

你是否想过要在一个Palm设备上阅读或更新文档和电子表格?那么就安装来自Data Viz的Documents To GoProfessional Edition 3.0(市场价50美元)吧。在每次HotSync过程中,下载的文件将会与桌面系统进行同步,这样不管是在桌面系统还是在Palm设备上进行的编辑,都会反映到另一个文件拷贝上。Documents To Go的前一个版本只提供     

Modeling and predicting face recognition system performance based on analysis of similarity scores

This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition", based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods     

Dynamic view selection for time-varying volumes

Animation is an effective way to show how time-varying phenomena evolve over time. A key issue of generating a good animation is to select ideal views through which the user can perceive the maximum amount of information from the time-varying dataset. In this paper, we first propose an improved view selection method for static data. The method measures the quality of a static view by analyzing the opacity, color and curvature distributions of the corresponding volume rendering images from the given view. Our view selection metric prefers an even opacity distribution with a larger projection area, a larger area of salient features' colors with an even distribution among the salient features, and more perceived curvatures. We use this static view selection method and a dynamic programming approach to select time-varying views. The time-varying view selection maximizes the information perceived from the time-varying dataset based on the constraints that the time-varying view should show smooth changes of direction and near-constant speed. We also introduce a method that allows the user to generate a smooth transition between any two views in a given time step, with the perceived information maximized as well. By combining the static and dynamic view selection methods, the users are able to generate a time-varying view that shows the maximum amount of information from a time-varying data set.     

Exploring context and content links in social media: a latent space method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.     

Comparison of Skidding Performance of Small Track-type Experimental Prototype Skidder and J-50 Skidding Tractor

In this paper, field experimental comparison is made between a small track-type experimental prototype skidder and a J-50 skidding tractor. Experimental data, including skidding productivity, soil compaction on skidding trails, and damage rate of the residual trees, are analyzed. The results indicate that with the condition of scattered skidding area and low skidding volume per cycle, small track-type experimental prototype skidder has advantage on working and a higher skidding productivity. It makes lower soil compaction to the skidding trails in the depth of 0-5 cm, 5-10 cm, and 10-15 cm. Under the same work conditions, the damage rate of the residual trees made by small track-type experimental prototype skidder is only 1/5 of those made by J-50 type skidding tractor. The damage rate is reduced by 80%.     

Effect of Rare Earth （Niobium） on Costed and Hot-Rolled Microstructure and Properties of Heavy Rail Steels

Effect of Rare Earth (Niobium) on Costed and Hot-Rolled Microstructure and Properties of Heavy Rail Steels     

Pulse Signal Recovery Method Based on Sparse Representation

Pulse signal recovery is to extract useful amplitude and time information from the pulse signal contaminated by noise.It is a great challenge to precisely recover the pulse signal in loud background noise.The conventional approaches,which are mostly based on the distribution of the pulse energy spectrum,do not well determine the locations and shapes of the pulses.In this paper,we propose a time domain method to reconstruct pulse signals.In the proposed approach,a sparse representation model is established to deal with the issue of the pulse signal recovery under noise conditions.The corresponding problem based on the sparse optimization model is solved by a matching pursuit algorithm.Simulations and experiments validate the effectiveness of the proposed approach on pulse signal recovery.     

Enhanced-performance self-powered photodetector based on multi-layer MoS2 sandwiched between two asymmetric graphene contacts

Self-powered photodetectors can convert light into electrical signals without external power input and are widely used in applications such as imaging,sensing,communication,and security.The most popular approach for constructing a self-powered photodetector is typically based on the fabrication of an asymmetric metal-semiconductor (MS) contact;however,this technique is seriously limited by the Fermi-level pinning effect.Here,we report a room-temperature photodetector based on multi-layer MoS     

改进型重复Wiener滤波/PUM模型--实现抗噪连续语音识别

众所周知,抗噪问题是现在语音识别研究中的重点。文章描述了一种新的抗噪语音识别方法,即通过改进型重复Wiener滤波结合后验概率联合模型PUM(PosteriorUnionModel)[3]实现在噪声环境下连续字语音识别的方法。这种方法先采用改进型重复Wiener滤波器对语音信号进行语音增强预处理,消除已知噪声,为PUM模型提供只有局部频带被噪声污染的语音信号,再利用PUM模型进行抗噪语音识别。试验表明在各种不同的噪声环境下新方法有更高的平均识别率。