Presented by: Elham Hosseini Toodeshki

Date: Tuesday, May 7, 2024
Time: 2:30 pm
Place: ZOOM - Please see below.

Date & Time: May 7, 2024, 02:30 PM Pacific Time (US and Canada)

Location: Remote via Zoom

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Abstract

Erbium-doped nanostructures are explored for their superior optical properties and innovative fabrication techniques, particularly through precise stoichiometric adjustments. We highlight nano thermometric applications where ratiometric temperature measurements are achieved at the nanoscale. Significant advancements are demonstrated as single erbium-doped nanocrystals are optically trapped using sophisticated techniques, establishing their utility

Presented by: Joel Sol

Date: Thursday, May 9, 2024
Time: 9:00 am
Place: ZOOM - Please see below.

Location: Remote via Zoom

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Meeting ID: 890 9121 1412

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Abstract: 

Geometrical quality assurance is critical for improving manufacturing time and cost.
This is more inhibiting when the visual or haptic assessment of human operators is
necessary. Modern machine learning (ML) methods can solve this problem but require
large datasets including diverse deformations. However, preparing those deformations
using physical objects can be difficult and costly. This thesis proposes to use Blender,
an open-source simulation tool, to imitate object deformities and automate the preparation of synthetic datasets. The utility of these datasets can be further improved
using data augmentation techniques such as background randomization or domain
adaptation networks. The background randomization approach provides a way to
generalize the image distribution to a variety of environments, whereas the domain
adapted approach provides a better targeted distribution. This thesis showcases that
discrepancies between real and simulated environments can be mitigated to create
models effective at sim-to-real deformation classification.

Presented by: Faryal Ali

Date: Friday, May 31, 2024
Time: 11:30 am
Place: ZOOM - Please see below.

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Meeting ID: 826 4121 7828

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Abstract

The impact of fog on visibility is a major factor affecting traffic congestion and safety. This paper proposes a microscopic traffic model that captures the features of traffic in foggy weather and characterizes it based on visibility. The intelligent driver (ID) model is based on a constant acceleration exponent and produces similar traffic behaviour for all conditions, which is unrealistic. The performance of the ID and proposed models is evaluated on a 2.2 km ring road for 250 s with a platoon of 51 vehicles. Results are presented which show that the proposed model characterizes traffic realistically with lower acceleration and deceleration compared to the ID model. Further, it does not create stop-and-go waves and is stable even during foggy weather. The proposed model can be used to reduce fuel consumption and pollution resulting from traffic congestion.