Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Size Effects in Carbon Nanotubes

Abstract: The intershell spacing of multi-walled carbon nanotubes was determined by analyzing the high resolution transmission electron microscopy images of these nanotubes. For the nanotubes that were studied, the intershell spacing ${\stackrel{^}{d}}_{002}$ is found to range from 0.34 to 0.39 nm, increasing with decreasing tube diameter. A model based on the results from real space image analysis is used to explain the variation in intershell spacings obtained from reciprocal space periodicity analysis. The increase in intershell spacing with decreased nanotube diameter is attributed to the high curvature, resulting in an increased repulsive force, associated with the decreased diameter of the nanotube shells.

Multiple kernel tracking with SSD

Abstract: Kernel-based objective functions optimized using the mean shift algorithm have been demonstrated as an effective means of tracking in video sequences. The resulting algorithms combine the robustness and invariance properties afforded by traditional density-based measures of image similarity, while connecting these techniques to continuous optimization algorithms. This paper demonstrates a connection between kernel-based algorithms and more traditional template tracking methods. here is a well known equivalence between the kernel-based objective function and an SSD-like measure on kernel-modulated histograms. It is shown that under suitable conditions, the SSD-like measure can be optimized using Newton-style iterations. This method of optimization is more efficient (requires fewer steps to converge) than mean shift and makes fewer assumptions on the form of the underlying kernel structure. In addition, the methods naturally extend to objective functions optimizing more elaborate parametric motion models based on multiple spatially distributed kernels. We demonstrate multi-kernel methods on a variety of examples ranging from tracking of unstructured objects in image sequences to stereo tracking of structured objects to compute full 3D spatial location.

A contextual examination of new venture performance: entrepreneur leadership behavior, top management team heterogeneity, and environmental dynamism

Abstract: Summary This study examined the relationship of entrepreneur leadership behavior (empowering and directive), top management team heterogeneity (functional, educational specialty, educational level, and skill) and industry environmental dynamism (rate of unpredicted change in number of industry establishments, number of industry employees, industry revenue, and industry research and development intensity) on new venture performance (revenue growth and employment growth) using two different samples—the Inc. 500 list of America’s fastest growing startups and a national (United States) random sample of new ventures. In dynamic industry environments, startups with heterogeneous top management teams were found to perform best when led by directive leaders and those with homogenous top management teams performed best when led by empowering leaders. Conversely in stable industry environments, startups with heterogeneous top management teams were found to perform best when led by empowering leaders and those with homogenous top management teams performed best when led by directive leaders. These findings were consistent across both samples and demonstrate the value in a contextual approach to leadership, which considers adjusting leadership behavior in accordance to factors that are both internal and external to the firm. Copyright # 2007 John Wiley & Sons, Ltd.

Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier

Abstract: Multiobjective optimization is rapidly becoming an invaluable tool in engineering design. A particular class of solutions to the multiobjective optimization problem is said to belong to the Pareto frontier. A Pareto solution, the set of which comprises the Pareto frontier, is optimal in the sense that any improvement in one design objective can only occur with the worsening of at least one other. Accordingly, the Pareto frontier plays an important role in engineering design—it characterizes the tradeoffs between conflicting design objectives. Some optimization methods can be used to automatically generate a set of Pareto solutions from which a final design is subjectively chosen by the designer. For this approach to be successful, the generated Pareto set must be truly representative of the complete optimal design space (Pareto frontier). In other words, the set must not overrepresent one region of the design space, or neglect others. Some commonly used methods comply with this requirement, whereas others do not. This paper offers a new phase in the development of the normal constraint method, which is a simple approach for generating Pareto solutions that are evenly distributed in the design space of an arbitrary number of objectives. The even distribution of the generated Pareto solutions can facilitate the process of developing an analytical expression for the Pareto frontier in n dimension. An even distribution of Pareto solutions also facilitates the task of choosing the most desirable (final) design from among the set of Pareto solutions. The normal constraint method bears some similarities to the normal boundary intersection and � -constraint methods. Importantly, the developments presented in this paper define its critical distinction, namely, the ability to generate a set of evenly distributed Pareto solutions over the complete Pareto frontier. Examples are provided that show the normal constraint method to perform favorably under the new developments when compared with the normal boundary intersection method, as well as with the original normal constraint method.