VisLunch/Fall2010 - Revision history

Kpotter: /* December 10: Harsh Bhatia and Sreeraj Jadhav */

2010-12-07T20:13:11Z

December 10: Harsh Bhatia and Sreeraj Jadhav

← Older revision		Revision as of 20:13, 7 December 2010
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	Abtract: Three dimensional visualizations of c. Elegans neuronal structure data can provide unique opportunities for analysis of cell structure and function. With our software, we can parse large amounts of data in raw text files to create interactive visual representations of an array of neuronal vesicles from c. Elegans. We first had to learn how to use Java3D in order to visualize the data, and then come up with ways to convert the data into images. Vesicles, being simple spheres, were fairly easy to create; however, creating the membrane hull proved to be a much larger challenge.		Abtract: Three dimensional visualizations of c. Elegans neuronal structure data can provide unique opportunities for analysis of cell structure and function. With our software, we can parse large amounts of data in raw text files to create interactive visual representations of an array of neuronal vesicles from c. Elegans. We first had to learn how to use Java3D in order to visualize the data, and then come up with ways to convert the data into images. Vesicles, being simple spheres, were fairly easy to create; however, creating the membrane hull proved to be a much larger challenge.

	=== December 10: Harsh Bhatia ~~and Sreeraj Jadhav~~ ===		=== December 10: Sreeraj Jadhav and Harsh Bhatia ===
	'''''Speaker: ~~Harsh Bhatia and~~ Sreeraj Jadhav'''''		'''''Speaker: Sreeraj Jadhav'''''

	Title: Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps		Title: Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps
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	notion of equivalency between edge maps, and show that there exist 23 equivalence classes describing all		notion of equivalency between edge maps, and show that there exist 23 equivalence classes describing all
	possible behaviors of piecewise linear flow within a triangle.		possible behaviors of piecewise linear flow within a triangle.

			'''''Speaker: Harsh Bhatia'''''

			Title: Edge Maps: Representing Flow with Bounded Error (accepted in IEEE Pacific Visualization 2011)

			Abstract: Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques.

Kpotter: /* Sessions */

2010-12-07T20:11:58Z

Sessions

← Older revision		Revision as of 20:11, 7 December 2010
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	! Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles		! Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles
	\|-		\|-
	! December 10 \|\| ~~Harsh Bhatia and~~ Shreeraj Jadhav \|\| Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps		! rowspan=2\| December 10 \|\| Shreeraj Jadhav \|\| Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps
	\|-		\|-
			! Harsh Bhatia \|\| Edge Maps: Representing Flow with Bounded Error
	\|}		\|}

Kpotter: /* Sessions */

2010-12-07T16:17:09Z

Sessions

← Older revision		Revision as of 16:17, 7 December 2010
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	! Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles		! Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles
	\|-		\|-
	! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| ~~TBA~~		! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps
	\|-		\|-
	\|}		\|}

Kpotter: /* December 10: Harsh Bhatia and Sreeraj Jadhav */

2010-12-07T16:16:39Z

December 10: Harsh Bhatia and Sreeraj Jadhav

← Older revision		Revision as of 16:16, 7 December 2010
Line 265:		Line 265:

	=== December 10: Harsh Bhatia and Sreeraj Jadhav ===		=== December 10: Harsh Bhatia and Sreeraj Jadhav ===
	'''''Speaker:'''''		'''''Speaker: Harsh Bhatia and Sreeraj Jadhav'''''

			Title: Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps

			Abstract:Vector fields, represented as vector values sampled on the vertices of a triangulation, are commonly
			used to model physical phenomena. To analyze and understand vector fields, practitioners use derived prop-
			erties such as the paths of massless particles advected by the flow, called streamlines. However, computing
			streamlines requires numerical methods not guaranteed to preserve fundamental invariants such as the fact
			that streamlines cannot cross. The resulting inconsistencies can cause errors in the analysis, e.g. invalid
			topological skeletons, and thus lead to misinterpretations of the data.
			We propose an alternate representation for triangulated vector fields that exchanges vector values with an
			encoding of the transversal flow behavior of each triangle. We call this representation edge maps. This work
			focuses on the mathematical properties of edge maps. Edge maps allow for a multi-resolution approximation
			of flow by merging adjacent streamlines into an interval based mapping. Consistency is enforced at any
			resolution if the merged sets maintain an order-preserving property. At the coarsest resolution, we define a
			notion of equivalency between edge maps, and show that there exist 23 equivalence classes describing all
			possible behaviors of piecewise linear flow within a triangle.

Kpotter: /* Sessions */

2010-12-03T16:41:12Z

Sessions

← Older revision		Revision as of 16:41, 3 December 2010
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	! bgcolor="#c97d81" \| November 26 \|\| bgcolor="#c97d81"\|Thanksgiving \|\| bgcolor="#c97d81"\|NO VisLunch		! bgcolor="#c97d81" \| November 26 \|\| bgcolor="#c97d81"\|Thanksgiving \|\| bgcolor="#c97d81"\|NO VisLunch
	\|-		\|-
	! December 3 \|\| Alan Cannaday, Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles		! rowspan=2\| December 3 \|\| Alan Cannaday \|\| Regularization methods for the inverse Laplace transform
			\|-
			! Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles
	\|-		\|-
	! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| TBA		! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| TBA

Kpotter: /* December 03: Alan Cannaday, Jason Thummel and Wilson Batemann */

2010-12-03T16:39:40Z

December 03: Alan Cannaday, Jason Thummel and Wilson Batemann

← Older revision		Revision as of 16:39, 3 December 2010
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	'''''Speaker: Jason Thummel and Wilson Batemann'''''		'''''Speaker: Jason Thummel and Wilson Batemann'''''

	Title: ~~TBD~~		Title: 3D Visualization of c. Elegans Neuron Vesicles

	Abtract: ~~TBD~~		Abtract: Three dimensional visualizations of c. Elegans neuronal structure data can provide unique opportunities for analysis of cell structure and function. With our software, we can parse large amounts of data in raw text files to create interactive visual representations of an array of neuronal vesicles from c. Elegans. We first had to learn how to use Java3D in order to visualize the data, and then come up with ways to convert the data into images. Vesicles, being simple spheres, were fairly easy to create; however, creating the membrane hull proved to be a much larger challenge.

	=== December 10: Harsh Bhatia and Sreeraj Jadhav ===		=== December 10: Harsh Bhatia and Sreeraj Jadhav ===
	'''''Speaker:'''''		'''''Speaker:'''''

Kpotter: /* Sessions */

2010-12-03T16:39:09Z

Sessions

← Older revision		Revision as of 16:39, 3 December 2010
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	! bgcolor="#c97d81" \| November 26 \|\| bgcolor="#c97d81"\|Thanksgiving \|\| bgcolor="#c97d81"\|NO VisLunch		! bgcolor="#c97d81" \| November 26 \|\| bgcolor="#c97d81"\|Thanksgiving \|\| bgcolor="#c97d81"\|NO VisLunch
	\|-		\|-
	! December 3 \|\| Alan Cannaday, Jason Thummel and Wilson Batemann \|\| ~~TBA~~		! December 3 \|\| Alan Cannaday, Jason Thummel and Wilson Batemann \|\| 3D Visualization of c. Elegans Neuron Vesicles
	\|-		\|-
	! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| TBA		! December 10 \|\| Harsh Bhatia and Shreeraj Jadhav \|\| TBA

Kpotter: /* December 03: Alan Cannaday, Jason Thummel and Wilson Batemann */

2010-12-02T18:34:38Z

December 03: Alan Cannaday, Jason Thummel and Wilson Batemann

← Older revision		Revision as of 18:34, 2 December 2010
Line 256:		Line 256:
	Abstract: In many applications such as transistors, solar cells, LEDs, and diode lasers it is important to control the spontaneous photon emissions from atoms. One way of quantifying this effect is to embed quantum dots in the photonic crystal. An isolated quantum dot absorbs light at a given frequency and emits it with an exponentially decaying intensity with known decay rate. When many quantum dots are embedded in a photonic crystal and observed while absorbing and emitting light, the sum of their emissions or emission intensity I(t) at time t can be modeled using the Laplace transform, i.e. I(t)/I(0) = L[φ(γ)](t). Where φ(γ) is the distribution of concentration of emitters for a certain decay rate γ. The problem of determining φ(γ) from its Laplace transform I(t) is known to be a severely ill-posed linear inverse problem, which means that small perturbations in I(t) can lead to large errors in the estimation of φ(γ). For my project I explored different regularization methods for the inverse Laplace transform proposed by Epstein and Schotland (2008) that allows us to compute the inverse Laplace transform without requiring prior information about φ(γ).		Abstract: In many applications such as transistors, solar cells, LEDs, and diode lasers it is important to control the spontaneous photon emissions from atoms. One way of quantifying this effect is to embed quantum dots in the photonic crystal. An isolated quantum dot absorbs light at a given frequency and emits it with an exponentially decaying intensity with known decay rate. When many quantum dots are embedded in a photonic crystal and observed while absorbing and emitting light, the sum of their emissions or emission intensity I(t) at time t can be modeled using the Laplace transform, i.e. I(t)/I(0) = L[φ(γ)](t). Where φ(γ) is the distribution of concentration of emitters for a certain decay rate γ. The problem of determining φ(γ) from its Laplace transform I(t) is known to be a severely ill-posed linear inverse problem, which means that small perturbations in I(t) can lead to large errors in the estimation of φ(γ). For my project I explored different regularization methods for the inverse Laplace transform proposed by Epstein and Schotland (2008) that allows us to compute the inverse Laplace transform without requiring prior information about φ(γ).

	'''''Speaker:Jason Thummel and Wilson Batemann'''''		'''''Speaker: Jason Thummel and Wilson Batemann'''''

	Title: TBD		Title: TBD

Kpotter: /* December 03: Alan Cannaday, Jason Thummel and Wilson Batemann */

2010-12-02T18:34:22Z

December 03: Alan Cannaday, Jason Thummel and Wilson Batemann

← Older revision		Revision as of 18:34, 2 December 2010
Line 252:		Line 252:
	'''''Speaker: Alan Cannaday'''''		'''''Speaker: Alan Cannaday'''''

	Title:Regularization methods for the inverse Laplace transform		Title: Regularization methods for the inverse Laplace transform

	Abstract: In many applications such as transistors, solar cells, LEDs, and diode lasers it is important to control the spontaneous photon emissions from atoms. One way of quantifying this effect is to embed quantum dots in the photonic crystal. An isolated quantum dot absorbs light at a given frequency and emits it with an exponentially decaying intensity with known decay rate. When many quantum dots are embedded in a photonic crystal and observed while absorbing and emitting light, the sum of their emissions or emission intensity I(t) at time t can be modeled using the Laplace transform, i.e. I(t)/I(0) = L[φ(γ)](t). Where φ(γ) is the distribution of concentration of emitters for a certain decay rate γ. The problem of determining φ(γ) from its Laplace transform I(t) is known to be a severely ill-posed linear inverse problem, which means that small perturbations in I(t) can lead to large errors in the estimation of φ(γ). For my project I explored different regularization methods for the inverse Laplace transform proposed by Epstein and Schotland (2008) that allows us to compute the inverse Laplace transform without requiring prior information about φ(γ).		Abstract: In many applications such as transistors, solar cells, LEDs, and diode lasers it is important to control the spontaneous photon emissions from atoms. One way of quantifying this effect is to embed quantum dots in the photonic crystal. An isolated quantum dot absorbs light at a given frequency and emits it with an exponentially decaying intensity with known decay rate. When many quantum dots are embedded in a photonic crystal and observed while absorbing and emitting light, the sum of their emissions or emission intensity I(t) at time t can be modeled using the Laplace transform, i.e. I(t)/I(0) = L[φ(γ)](t). Where φ(γ) is the distribution of concentration of emitters for a certain decay rate γ. The problem of determining φ(γ) from its Laplace transform I(t) is known to be a severely ill-posed linear inverse problem, which means that small perturbations in I(t) can lead to large errors in the estimation of φ(γ). For my project I explored different regularization methods for the inverse Laplace transform proposed by Epstein and Schotland (2008) that allows us to compute the inverse Laplace transform without requiring prior information about φ(γ).

Kpotter: /* December 03: Alan Cannaday, Jason Thummel and Wilson Batemann */

2010-12-02T18:25:20Z

December 03: Alan Cannaday, Jason Thummel and Wilson Batemann

← Older revision		Revision as of 18:25, 2 December 2010
Line 257:		Line 257:

	'''''Speaker:Jason Thummel and Wilson Batemann'''''		'''''Speaker:Jason Thummel and Wilson Batemann'''''

	Title: TBD		Title: TBD