An Economic Analysis of Electron Accelerators and Cobalt-60 for Irradiating Food

Average costs per pound of irradiating food are similar for the electron accelerator and cobalt-60 irradiators analyzed in this study, but initial investment costs can vary by $1 million. Irradiation costs range from 0.5 to 7 cents per pound and decrease as annual volumes treated increase. Cobalt-60 is less expensive than electron beams for annual volumes below 50 million pounds. For radiation source requirements above the equivalent of 1 million curies of cobalt-60, electron beams are more economical.food irradiation, electron accelerators, cobalt-60, cost comparison, economies of size, Food Consumption/Nutrition/Food Safety,

Horn antenna with v-shaped corrugated surface

Corrugated shape is easily machined for millimeter wave application and is better suited for folding antenna designs. Measured performance showed ""V'' corrugations and rectangular corrugations have nearly the same pattern beamwidth, gain, and impedance. Also, ""V'' corrugations have higher relative power loss

High School Student Information Access and Engineering Design Performance

Developing solutions to engineering design problems requires access to information. Research has shown that appropriately accessing and using information in the design process improves solution quality. This quasi-experimental study provides two groups of high school students with a design problem in a three hour design experience. One group has access to the internet while the other does not. Quality of design solution was measured and the two groups were compared. Solution quality did not change significantly. Student information requests were categorized and the most commonly requested piece of information related to cost of materials. Students spent substantially more time in the design process with internet access

Transforming the Core Course in the College of Technology

During the summer of 2012, a team of four faculty members from the College of Technology redesigned Tech 12000 (Design Thinking in Technology). This course, after its first year of implementation as a traditional course, was flipped and blended. In addition, the content related to achieving the learning outcomes was drastically remodeled. Faculty threw out the paper-based textbooks, lecture approaches and large class sizes. The new course embraced a distributed model of resources including web based text and multimedia created by our faculty and others accessed by students asynchronously in preparation for class. Classes are small (40 students) and feel like workshops where the instructor is a guide and facilitator. Students experience flexibility and autonomy within a guided sequence of learning experiences. The first half of the semester students experience short duration small group work with structure and guidance as they work collaboratively to solve problems. The problems and approaches are structured to allow students to apply concepts focused on design thinking in a technological context. During the second half of the semester, students work in groups of 4-5 and begin with identifying a problem in their environment, researching the issue, benchmarking, brainstorming, evaluating, prototyping and presenting their work

Team Based Engineering Design Thinking

The objective of this research was to explore design thinking among teams of high school students. This objective is encompassed in the research question driving this inquiry: How do teams of high school students allocate time across stages of design? Design thinking on the professional level typically occurs in a team environment. Many individuals contribute in a variety of ways to facilitate the successful development of a solution to a problem. Teachers often require students to work in groups, but little is known about how the group functions in the context of design and the potential interaction between group performance and authentic design challenges. Few research results are available to guide teachers in developing successful design teams and encouraging them in their efforts

Deep Structured Features for Semantic Segmentation

We propose a highly structured neural network architecture for semantic segmentation with an extremely small model size, suitable for low-power embedded and mobile platforms. Specifically, our architecture combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear classifier. While stages i) and ii) are completely pre-specified, only the linear classifier is learned from data. We apply the proposed architecture to outdoor scene and aerial image semantic segmentation and show that the accuracy of our architecture is competitive with conventional pixel classification CNNs. Furthermore, we demonstrate that the proposed architecture is data efficient in the sense of matching the accuracy of pixel classification CNNs when trained on a much smaller data set.Comment: EUSIPCO 2017, 5 pages, 2 figure

Engineering Design Thinking and Information Gathering Final Report

The objective of this research was to explore the relationship between information access and design solution quality of high school students presented with an engineering design problem. This objective is encompassed in the research question driving this inquiry: How does information access impact the design process? This question has emerged in the context of an exploratory DR-K12 grant project titled, Exploring Engineering Design Knowing and Thinking as an Innovation in STEM Learning. The research work presented here has expanded the data set developed in the DR-K12 and examined the larger data set with a focus on how information access impacts design thinking. The opportunity to explore the impact of information gathering was not afforded in the DR-K12, but emerged as an area of interest during the pilot phase

The Possibility of Transfer(?): A Comprehensive Approach to the International Criminal Tribunal for Rwanda’s Rule 11bis To Permit Transfer to Rwandan Domestic Courts

We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.Comment: E. Agustsson, M. Tschannen, and F. Mentzer contributed equally to this work. ICCV 2019 camera ready versio

Practical Full Resolution Learned Lossless Image Compression

We propose the first practical learned lossless image compression system, L3C, and show that it outperforms the popular engineered codecs, PNG, WebP and JPEG 2000. At the core of our method is a fully parallelizable hierarchical probabilistic model for adaptive entropy coding which is optimized end-to-end for the compression task. In contrast to recent autoregressive discrete probabilistic models such as PixelCNN, our method i) models the image distribution jointly with learned auxiliary representations instead of exclusively modeling the image distribution in RGB space, and ii) only requires three forward-passes to predict all pixel probabilities instead of one for each pixel. As a result, L3C obtains over two orders of magnitude speedups when sampling compared to the fastest PixelCNN variant (Multiscale-PixelCNN). Furthermore, we find that learning the auxiliary representation is crucial and outperforms predefined auxiliary representations such as an RGB pyramid significantly.Comment: Updated preprocessing and Table 1, see A.1 in supplementary. Code and models: https://github.com/fab-jul/L3C-PyTorc