Interactive Online Undergraduate Laboratories Using J-DSP

Spanias, Andreas; Atti, Venkatraman

doi:10.1109/te.2005.854569

Cited by 87 publications

(44 citation statements)

References 20 publications

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“…In the remote delivery mode, students off site can control the equipment and collect data using a Web-based interface. Examples have been reported in process control, power electronics, networking, fluid mechanics, photonics, and digital signal processing [2]- [8]. In a virtual laboratory, simulations based on mathematical models implemented on a computer can replace the physical laboratory.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers have concluded that student learning is not detrimentally affected by using Webbased remote laboratories; in fact, students appreciate the flexibility that they offer [2], [4]- [8]. Analogous studies indicate that no significant difference in student learning results from using virtual laboratories versus physical laboratories [15], although feedback from students indicates that the complete removal of physical laboratories from the curriculum would not be welcome [10].…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Student Learning in Experimental Design Using a Virtual Laboratory

et al. 2008

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Abstract-This paper describes the instructional design, implementation, and assessment of a virtual laboratory based on a numerical simulation of a chemical vapor deposition (CVD) process, the virtual CVD laboratory. The virtual CVD laboratory provides a capstone experience in which students synthesize engineering science and statistics principles and have the opportunity to apply experimental design in the context similar to that of a practicing engineer in industry with a wider design space than is typically seen in the undergraduate laboratory. The simulation of the reactor is based on fundamental principles of mass transfer and chemical reaction, obscured by added "noise." The software application contains a 3-D student client that simulates a cleanroom environment, an instructor Web interface with integrated assessment tools, and a database server. As opposed to being constructed as a direct one-to-one replacement, this virtual laboratory is intended to complement the physical laboratories in the curriculum so that certain specific elements of student learning can be enhanced. Implementation in four classes is described. Assessment demonstrates students are using an iterative experimental design process reflective of practicing engineers and correlates success in this project to higher order thinking skills. Student surveys indicate that students perceived the virtual CVD laboratory as the most effective learning medium used, even above physical laboratories.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Student Learning in Experimental Design Using a Virtual Laboratory

et al. 2008

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“…These Java modules have been integrated as a separate toolbox in the Java-DSP package. Java-DSP is an NSF sponsored online programming environment for signal/image processing education and research [21]. Though initially designed to enable students and distance learners to perform laboratories over the internet, more recently toolboxes that support multidisciplinary research have been developed.…”

Section: Development Of Java Toolboxmentioning

confidence: 99%

Automated tumor segmentation using kernel sparse representations

Thiagarajan

Rajan

Ramamurthy

et al. 2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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In this paper, we describe a pixel based approach for automated segmentation of tumor components from MR images. Sparse coding with data-adapted dictionaries has been successfully employed in several image recovery and vision problems. Since it is trivial to obtain sparse codes for pixel values, we propose to consider their non-linear similarities to perform kernel sparse coding in a high dimensional feature space. We develop the kernel K-lines clustering procedure for inferring kernel dictionaries and use the kernel sparse codes to determine if a pixel belongs to a tumorous region. By incorporating spatial locality information of the pixels, contiguous tumor regions can be efficiently identified. A low complexity segmentation approach, which allows the user to initialize the tumor region, is also presented. Results show that both of the proposed approaches lead to accurate tumor identification with a low false positive rate, when compared to manual segmentation by an expert.

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“…Because of the increasing popularity of Web-based education and the advancements in streaming media applications, several web-based DSP Laboratory courses have been designed for distance education [4,5,6,7,8]. An internet-based signal processing laboratory that provides hands-on learning experiences in distributed learning environments has been developed in [6].…”

Section: Introductionmentioning

confidence: 99%