Development of Undergraduate Students’ Professional Skills

The development of engineering students’ professional skills has gained considerable national attention from Accreditation Board for Engineering and Technology, the National Academy of Engineering, ASCE, and other constituents. There is little debate that these professional skills are necessary. Engineering programs have tried many approaches to develop these skills in the undergraduate programs. Colorado State University (CSU) has developed a new approach modeled on the type of professional development that occurs in the professional environment. This new Professional Learning Institute (PLI) provides students with a broad array of workshops, presentations, and experiential opportunities addressing the areas of cross cultural communication and teamwork, innovation, leadership, ethics, and public service. This program introduces students to the concept of professional development through required extracurricular activities, includes minimum requirements along with requirements to earn certificates in specialty areas for motivated students. The majority of offerings in the PLI are presented by leaders from the engineering profession who have teamed with CSU to provide high quality programs for our students.     

Synthesis and characterization of zirconia-magnesia inert matrix fuel: Ce homolog studies

X-ray powder diffraction, microscopy, thermal analysis and electron probe microanalysis were used to characterize a ZrO₂-MgO inert matrix containing CeO₂ as a homolog for PuO₂ and Er₂O₃ as a burnable poison. The synthesis was carried out using a precipitation method. A large composition range of MgO to ZrO₂ was evaluated to determine phases present, phase mixing, phase composition, microstructure and thermal properties. It was found that most compositions of the material consist of two phases: MgO (periclase) and ZrO₂ (cubic zirconia). The zirconia phase incorporates 5% (wt/wt) MgO and up to 14% and 12% (wt/wt) CeO₂ and Er₂O₃, respectively. The MgO phase remains pure, which will enable it to retain its heat transfer and solubility properties and will improve the overall thermal conductivity and reprocessing component of the inert matrix fuel. The results with Ce will be used as the basis of future studies with actinides.     

A novel sequence representation for unsupervised analysis of human activities

Formalizing computational models for everyday human activities remains an open challenge. Many previous approaches towards this end assume prior knowledge about the structure of activities, using which explicitly defined models are learned in a completely supervised manner. For a majority of everyday environments however, the structure of the in situ activities is generally not known a priori. In this paper we investigate knowledge representations and manipulation techniques that facilitate learning of human activities in a minimally supervised manner. The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.In particular, we investigate modeling activity sequences in terms of their constituent subsequences that we call event n-grams. Exploiting this representation, we propose a computational framework to automatically discover the various activity-classes taking place in an environment. We model these activity-classes as maximally similar activity-cliques in a completely connected graph of activities, and describe how to discover them efficiently. Moreover, we propose methods for finding characterizations of these discovered classes from a holistic as well as a by-parts perspective. Using such characterizations, we present a method to classify a new activity to one of the discovered activity-classes, and to automatically detect whether it is anomalous with respect to the general characteristics of its membership class. Our results show the efficacy of our approach in a variety of everyday environments.     

A simple background elimination method for Raman spectra

In this paper, we consider a new background elimination method for Raman spectra. The proposed method is based on peak detection, smoothing, and interpolation. Since the background is usually slowly varying with respect to wavelength, we could estimate the background by eliminating significant peaks. For this purpose, we seek the peaks by inspecting the smoothed derivative of a given spectrum. After clipping out the corresponding peak regions, we estimate the background by applying a modified linear interpolation. Then the background is eliminated from the measured Raman spectrum by simple subtraction. The experimental results showed that the proposed method gave satisfactory results for real Raman spectra as well as synthetic data. As the proposed method requires no prior knowledge of spectrum, we expect that the method could be applied to other spectral data as well.     

Evaluation of antimicrobial effects of lauric arginate on reduction of Salmonella spp. in ground chicken

The efficacy of lauric arginate (LAE) on Salmonella survivability in ground chicken containing 9.8% fat was determined under refrigerated storage. The effect of LAE treatments on total aerobes, pH and objective colour were also assessed in ground chicken. LAE was highly effective against Salmonella in 0.1% peptone with no detectable survivors following treatment with 200 ppm and 400 ppm of LAE. No difference existed (P > 0.05) in the growth of Salmonella and total aerobes after treatment with 200 and 400 ppm of LAE in ground chicken inoculated with a four strain mixture of Salmonella. At these concentrations, LAE did not exhibit any treatment effect on the pH and colour of ground chicken (P > 0.05). In conclusion, although LAE possesses strong inhibitory (P < 0.05) effect against Salmonella in suspension in 0.1% peptone water, no inhibitory (P > 0.05) effect on growth of Salmonella was observed in ground chicken at the currently approved levels of 200 ppm of LAE.     

Situvis: A sensor data analysis and abstraction tool for pervasive computing systems

Pervasive systems are large-scale systems consisting of many sensors capturing numerous types of information. As this data is highly voluminous and dimensional, data analysis tasks can be extremely cumbersome and time-consuming. Enabling computers to recognise real-world situations is an even more difficult problem, involving not only data analysis, but also consistency checking. Here we present Situvis, an interactive visualisation tool for representing sensor data and creating higher-level abstractions from the data. This paper builds on previous work, Clear et al. (2009) [8] through evolved tool functionality and an evaluation of Situvis. A user-trial consisting of 10 participants shows that Situvis can be used to complete the key tasks in the development process of situation specifications in over 50% less time than an improvised alternative toolset.     

BGA评估中使用的分析方法

球珊阵列（BGAs）在印刷电路板（PCB）组件上已经相当常见，进而带来了新的故障模式，要求新的分析工具或方法，以便正确评估这些元件。传统式SMT元件，如SOICs或OFPs，允许以可视方式检测焊点，查找异常情况；而BGAs则做不到这一点，因为大多数焊点隐藏在元件下面，人眼是看不到的。本文讨论了STI实验室遇到的部分故障模式，以及进行调查使用的工具和方法。