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ECE GRAD Seminar:  Label-Free Study of Bovine Serum Albumin Conformational Dynamics & Electrostatic Interaction between Proteins and Gold Surface using Optical Nanotweezers
Department of Electrical and Computer Engineering

Presented by: Sherin George

Date: Monday, November 18, 2024
Time: 11:30 am
Place: Zoom, link below.

Abstract: 

Single molecule analysis of proteins in an aqueous environment without modification (e.g., labels or tethers) elucidates their biophysics and interactions relevant to drug discovery. Forces of radiation pressure from a laser beam can be used to accelerate and trap freely suspended particles as discovered by Ashkin in the 1970s. Optical traps are formed by tightly focusing a laser beam using a high numerical aperture objective lens. The conformational dynamics of a single unmodified protein have been largely unexplored, and this study intends to investigate these dynamics in a single unmodified BSA protein utilizing DNH optical tweezers. In addition , the electrostatic interactions of different unmodified proteins with gold surface is studied using DNH optical tweezers to explore its influence on the trapping efficiency.

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https://uvic.zoom.us/j/82079361352?pwd=VPK7QufK8bUtE561mzM4tjwkbNM4aM.1

Meeting ID: 820 7936 1352
Password: 124364

Note: Please log in to Zoom via SSO and your UVic Netlink ID

ECE GRAD Seminar:  Whispering-Gallery-Mode Simulation Using Recurrent Neural Network
Department of Electrical and Computer Engineering

Presented by: Haobin Luo

Date: Tuesday, November 26, 2024
Time: 11:00 am
Place: Remote Via Zoom

Abstract:

This project outlines whispering gallery mode (WGM) microcavity simulation using recurrent neural networks (RNNs). Firstly, the perturbed WGM mode field distributions are predicted with RNN. Secondly, overlap integrals of WGM fields at two consecutive cavity cross-sections are also predicted using RNN. Compared to traditional numerical simulation methods, which are typically resource-intensive and time-consuming, this RNN approach significantly reduces both computational power and processing time. This RNN framework is ready to be integrated to the cylindrical mode match method to simulate wave propagating in microcavities at high efficiency.

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https://uvic.zoom.us/j/3269213685?pwd=S0JWNnIzdVpTRVRmelRxYU5sbm9pQT09&omn=87637855530

Meeting ID: 326 921 3685

Passcode: 057719

Note: Please log in to Zoom via SSO and your UVic Netlink ID

ECE GRAD Seminar:  ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Misuse Cases in the Era of Generative AI and Cloud-based Health Information
Department of Electrical and Computer Engineering

Presented by: Khalid Al-hammuri

Date: Thursday, November 28, 2024
Time: 3:00 pm
Place: Zoom - see below.

Zoom Link: https://uvic.zoom.us/j/81350467865

Abstract: 

Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also becoming increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90\% of vulnerabilities emerged from misuse cases and human errors. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate misuse cases while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring schemas to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native micro-services of authentication, encryption, logging, and authorizations. Second, creating a bond trust scoring to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model to generate the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent to create a scoring system. The advantage of this method is that it is applicable to any language and adapts to all attributes as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting misuse cases. 

 

Khalid Al-Hammuri is inviting you to a scheduled Zoom meeting.

Topic: Khalid Al-hammuri PhD Graduate Seminar: ZTCloudGuard

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https://uvic.zoom.us/j/81350467865

Meeting ID: 813 5046 7865

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October 2024 seminars...
 
 
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