Skin color-based video segmentation under time-varying illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling, and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24 percent is observed in 17 out of 21 test sequences. In all but one case, the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.     

FloatBoost learning and statistical face detection

A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.     

Alloyed Branch History: Combining Global and Local Branch History for Robust Performance

This paper introduces alloyed prediction, a new hardware-based two-level branch predictor organization that combines global and local history in the same structure, combining the advantages of current two-level predictors with those of hybrid predictors. The alloyed organization is motivated by measurements showing that wrong-history mispredictions are even more important than conflict-induced mispredictions. Wrong-history mispredictions arise because current two-level, history-based predictors provide only global or only local history. The contribution of wrong history to the overall misprediction rate is substantial because most programs have some branches that require global history and others that require local history. This paper explores several ways to implement alloyed prediction, including the previously proposed bi-mode organization. Simulations show that mshare is the best alloyed organization among those we examine, and that mshare gives reliably good prediction compared to bimodal (two-bit), two-level, and hybrid predictors. The robust performance of alloying across a range of predictor sizes stems from its ability to attack wrong-history mispredictions at even very small sizes without subdividing the branch prediction hardware into smaller and less effective components.     

Laser-induced breakdown spectroscopy for the environmental determination of total carbon and nitrogen in soils

Soils from various sites have been analysed with the laser-induced breakdown spectroscopy (LIBS) technique for total elemental determination of carbon and nitrogen. Results from LIBS have been correlated to a standard laboratory-based technique (sample combustion), and strong linear correlations were obtained for determination of carbon concentrations. The LIBES technique was used on soils before and after acid washing, and the technique appears to be useful for the determination of both organic and inorganic soil carbon. The LIBS technique has the potential to be packaged into a field-deployable instrument.     

Aurora: a new model and architecture for data stream management

This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.Received: 12 September 2002, Accepted: 26 March 2003, Published online: 21 July 2003Edited by Y. Ioannidis     

无敌小强：奥林巴斯uTOUGH-8010七防相机

作为三防相机的大力推动者，奥林巴斯（OLYMPUS）通过技术的不停积累，在2010年更是推出了拥有7防标准的“无敌小强”。让人咋舌的10m防水、2m防震和防尘的性能数据，完全傲视如今的三防相机市场。     

IMT-2000无线通信标准

国际无线通信联盟（ＩＴＵ）于去年 １１月在赫尔辛基会议上最终确立了ＩＭＴ－２０００标准。在不偏袒现有任何一个协议的情况下，这一协议为所有主要的无线通信协议提供了统一的３Ｇ（第三代）无线通信平台。有了ＩＭＴ－２０００标准，互相竞争的无线通信技术将在３Ｇ世界里共存，并可在一个框架下做到互相连接。 两年多来，无线系统供应商一直在为适用于第三代（３Ｇ）无线通信网络的最好平台争吵。一个无线平台的主要结构层包括一个基础的核心网络和相应编码、处理、传输的无线通信协议。为满足目前手机、ＰＤＡ（个人数字助理）和其它…     

Benefits of Residual Aluminum in Ductile Iron

Two main chemistry systems of micro-inclusions can be identified in ductile iron: Ca-S-X and Mg-Si-O-X with the majority of the inclusions regardless of treatment type and location being of the second type, silicates. Laboratory investigations have shown that simple silicates were present in the matrix, while more complex silicates were present in conjunction with graphite, probably acting as graphite nucleation sites. In these more complex silicates, elevated levels of Al, Ca, Ce and La were typical. Comparing micro-particles embedded in iron matrix and graphite nodules of iron treated with pure Mg-metal and iron treated with MgFeSi alloy showed a higher amount of complex silicates with elevated Al-levels in the iron treated with MgFeSi. Further laboratory investigation was undertaken to explore which source of Al and which range of residual Al would have a favorable impact on the graphite nucleation in ductile iron. The work showed that a residual aluminum of 0.005 to 0.020 wt.% appears to be beneficial for improving ductile iron solidification characteristics without the incidence of pinholes. Greatest benefits were achieved when introducing the Al into the iron via an inoculant late during processing or via a pre-conditioner to the base iron. Al added via the MgFeSi provided less benefit. Some case studies illustrating the effect of Al in ductile iron are also presented, as Al-containing pre-conditioner or/and Al-bearing, FeSi inoculant application.     

Multi-stage modeling using fuzzy multi-criteria feature selection to improve survival prediction of ICU septic shock patients

In many binary medical classification problems, the cost of misclassifying one category is higher than the other, and in these applications it is desirable to employ a classifier with selective sensitivity or specificity. This work explores the utility of a fuzzy multi–criteria function for performance evaluation during knowledge–based medical classification and prediction. The method presented here uses fuzzy optimization to combine the sensitivity, specificity, and accuracy of classification as goals in a single objective function. This approach is used to assign flexible goals, which can be used to maximize the outcome in terms of each one of the goals. The proposed approach significantly increases the sensitivity and the specificity while maintaining or increasing accuracy. The versatility of the method is further exploited in a multi-model approach, using individual structures of multi-objective optimization of sensitivity and specificity separately, and then combining their outcomes through a decision-making module. Among various medical benefits derived from applying this technique, the divergent feature sets selected by high sensitivity and specificity models lend insight into factors more integrally connected to what causes risk of death for patients.