CSCE 411H: Data Modeling for Systems Development—A Peer Review of Teaching Project Inquiry Portfolio

A new course CSCE411H has been developed in 2015-2016. The course tackles the learning of traditional and emerging data modeling techniques in big data related areas from the system and application perspectives. The students have mixed background in Business, Engineering, and Art and Science with different levels. These have introduced a unique set of challenges in the development of this new course. In this inquiry portfolio, I investigated if the adjustment of assignments can benefit the team work of the students with a variety of background. Through the data collection and analysis, the investigation showed that the new assignment design can facilitate the students to reach the learning goals. It also suggested that more effort would be desired to design assignments to help business students in team work with increasing complexities. Although this inquiry portfolio targets a specific question, the general methodology can help us systematically investigate and address other issues in teaching and learning activities

CSCE 411H: Data Modeling for Systems Development—A Peer Review of Teaching Project Inquiry Portfolio

A new course CSCE411H has been developed in 2015-2016. The course tackles the learning of traditional and emerging data modeling techniques in big data related areas from the system and application perspectives. The students have mixed background in Business, Engineering, and Art and Science with different levels. These have introduced a unique set of challenges in the development of this new course. In this inquiry portfolio, I investigated if the adjustment of assignments can benefit the team work of the students with a variety of background. Through the data collection and analysis, the investigation showed that the new assignment design can facilitate the students to reach the learning goals. It also suggested that more effort would be desired to design assignments to help business students in team work with increasing complexities. Although this inquiry portfolio targets a specific question, the general methodology can help us systematically investigate and address other issues in teaching and learning activities

An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented

MFA-DVR: Direct Volume Rendering of MFA Models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community

A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Recently, imaged-based approaches have developed rapidly for high-throughput plant phenotyping (HTPP). Imaging reduces a 3D plant into 2D images, which makes the retrieval of plant morphological traits challenging. We developed a novel LiDAR-based phenotyping instrument to generate 3D point clouds of single plants. The instrument combined a LiDAR scanner with a precision rotation stage on which an individual plant was placed. A LabVIEW program was developed to control the scanning and rotation motion, synchronize the measurements from both devices, and capture a 360◦ view point cloud. A data processing pipeline was developed for noise removal, voxelization, triangulation, and plant leaf surface reconstruction. Once the leaf digital surfaces were reconstructed, plant morphological traits, including individual and total leaf area, leaf inclination angle, and leaf angular distribution, were derived. The system was tested with maize and sorghum plants. The results showed that leaf area measurements by the instrument were highly correlated with the reference methods (R2 \u3e 0.91 for individual leaf area; R2 \u3e 0.95 for total leaf area of each plant). Leaf angular distributions of the two species were also derived. This instrument could fill a critical technological gap for indoor HTPP of plant morphological traits in 3D

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

HyperSeed: An End-to-End Method to Process Hyperspectral Images of Seeds

High-throughput, nondestructive, and precise measurement of seeds is critical for the evaluation of seed quality and the improvement of agricultural productions. To this end, we have developed a novel end-to-end platform named HyperSeed to provide hyperspectral information for seeds. As a test case, the hyperspectral images of rice seeds are obtained from a high-performance line-scan image spectrograph covering the spectral range from 600 to 1700 nm. The acquired images are processed via a graphical user interface (GUI)-based open-source software for background removal and seed segmentation. The output is generated in the form of a hyperspectral cube and curve for each seed. In our experiment, we presented the visual results of seed segmentation on different seed species. Moreover, we conducted a classification of seeds raised in heat stress and control environments using both traditional machine learning models and neural network models. The results show that the proposed 3D convolutional neural network (3D CNN) model has the highest accuracy, which is 97.5% in seed-based classification and 94.21% in pixel-based classification, compared to 80.0% in seed-based classification and 85.67% in seed-based classification from the support vector machine (SVM) model. Moreover, our pipeline enables systematic analysis of spectral curves and identification of wavelengths of biological interest

Determination of four sulfonamides residues in prawn by ultrasound-assisted matrix solid phase dispersive extraction combined with pre-column derivation high performance liquid chromatography-fluorescence

Objective To establish a method for the simultaneous determination of four sulfonamides residues in prawn, including sulfadiazine, sulfathiazole, sulfamerazine and sulfamethazine, by ultrasound-assisted matrix solid phase dispersion extraction coupled with pre - column derivation high performance liquid chromatography(HPLC). Methods Through the optimization of extraction conditions, ethyl acetate was selected as the extraction agent and florisil as the solid dispersion agent, and the sulfonamides in prawn were extracted with the method ultrasound-assisted matrix solid phase dispersion. The sulfonamides were pre-column derived by fluorescamine and detected by HPLC-fluorescence method. Results All sulfonamides showed good linearity in the concentration range of 2-100μg/L, with the correlation coefficient>0.999. The limit of detection and limit of quantification was 0.5 and 2μg/kg, respectively. The spike recoveries of blank prawn samples were 84.4%-93.9% at two levels of 2 and 20μg/kg, with the relative standard deviation(n =3)less than 7.7%. Conclusion The method is simple, time-consuming and high precision, which meets the requirements of residue analysis