Presented by: Yazhini Venkatraman

Date: Monday, March 16, 2026
Time: 12:30 pm
Place: Online via Zoom

Meeting link: https://uvic.zoom.us/j/83107655614?pwd=UXxZdNOzlUk5tNluZCjPrfQh4jQAJF.1

Meeting ID: 831 0765 5614
Password: 565656
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Abstract: This project presents a real-time face mask classification system designed to support safety monitoring in environments such as workplaces, institutions, and healthcare facilities. The system detects faces using a Caffe based Single Shot Detector and classifies mask usage into four categories: with mask, without mask, with N95 mask, and improper mask. A curated dataset containing 14,857 images across four classes was prepared and preprocessed through face detection, cropping, resizing to 224×224, normalization, and augmentation techniques such as brightness / contrast variation, geometric transformations and blur to improve robustness under different lighting and orientation conditions.

The classification model is built using MobileNet pretrained on the ImageNet dataset as a feature extractor with a custom classifier head. Training uses the Adam optimizer and early stopping with patience values between 1 - 7 is applied to prevent overfitting. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results show that the proposed system achieves an overall classification accuracy of approximately 97.55% (patience = 4) on the test dataset. A browser based webcam interface with a FastAPI backend is implemented to demonstrate real-time face detection and mask classification on standard webcam.

Presented by: Philip Baback Alipour

Date: Tuesday, March 31, 2026
Time: 9:15 am
Place: Zoom - see below.

Zoom Link: https://uvic.zoom.us/j/89140535483?pwd=6PJdbataQzENWRIadlua3RUskMzWfB.1

Meeting ID: 891 4053 5483

Passcode: 040612

Summary:
Imagine a specialized computer program stored on a specialized hardware acts like a powerful quantum lens. Just as a physical lens focuses light to see an object clearly, Quantum Field Lens Coding (QF-LC) is a new framework designed to focus and analyze the energy states of particles. The lens is a set of QF-LC algorithm steps that transform a Single Field of a particle state into a Quantum Double-Filed, projecting future state transitions (STs) as events with a success probability of ≥ 2/3, from a lower probability of 1/3 between 3 to N-entangled particles.  This technology helps scientists predict how systems—ranging from microscopic energy states to macroscopic states, bodies, biological cells, societies, environments, the globe, and the universe—will change, evolve, and reduce entropy to near 0, or near 100% certainty. 

Research Synthesis: From QF-LC Theory to Global Impact

This seminar focuses on the Quantum Double-Field (QDF) model developed within the speaker's PhD dissertation.

The Four Pillars of the QF-LC Project

1.      The QDF Model: A mathematical blueprint of quantum double fields.

2.      The What, the How, and the Key Effect/Output of the Project:

o   The What?  - The Algorithm (QF-LCA): Instructions (QF-LC algorithm steps) that allow quantum/hybrid computers to predict system state transitions (STs) as events.

o   The How? - QDF Circuit: A QDF circuit transforms a single field of a particle state into a QDF as a strong prediction, which is doubling the probability value of the single field (SF) within an ST or a phase transition (PT).

o   The Key Part of Technology:  Transform an SF into a more detailed QDF as a heat engine. 

3.      The Simulator (QF-LCS): Software that mimics a QDF system.

4.      The Dataset: A set of recorded data points used for algorithm training and state analysis of a system.

The Quantum Power-Up: Scalar k (Kappa) and Doubled Probability

Scalar k (Kappa): This acts as a multiplier, scaling the particle's quantum field during particle interactions.

·       Doubling the Probability: This field transformation significantly improves accuracy and gains greater certainty to the point of 100%. While a standard prediction can have a probability of about  ≥ 1/3 (lower threshold of an expected measurement outcome)  for a particle via an entangled particle (three in total, at least).

·       Entanglement from the QDF circuit:  This is developed by focusing the energy through the quantum lenses and qubit pairs which double the probability to 2/3 or higher.

·       Energy Efficiency: The system uses these high-probability predictions to suggest the most efficient energy paths for a user to choose, as an intelligent (or quantum AI) decision support system. This minimizes system entropy  near absolute zero temperatures. 

The Secret Sauce: The Three-Way Connection

To gather data, the system creates a unique information network using three particles:

1.      The Sample: A particle taken from the system being observed.

2.      The Partner: A "trapped" particle that pairs up (entangles) with the sample.

3.      The Decoder: A third particle that acts as a bridge, unlocking hidden information between the first two to maximize their connection (entanglement). 

This process effectively turns the quantum circuit into a "heat engine" that processes information to achieve a desired system  (Hamiltonian) outcome, known as the target state (TS) as real world applications listed below.

Real-World Applications

This research project has practical goals that extend beyond the lab (notable examples):

• ​Medicine: Reconstructing damaged DNA to predict virus spread or cancer growth (a TS).
• Security: Identifying forged documents via microscopic particle signatures (a TS).
• ​Global Sustainability: Providing affordable clean energy by optimizing system efficiencies (a TS).

QF-LCA project origins as peer-reviewed by Software Impacts and Data in Brief Elsevier journals, can be accessed at https://github.com/SoftwareImpacts/SIMPAC-2024-159

Presented by: Philip Baback Alipour

Date: Tuesday, March 31, 2026
Time: 10:30 am - 11:30 am
Place: Zoom - see below.

Zoom Link: https://uvic.zoom.us/j/85614470240?pwd=R5j2kQ0q00m9ZY6ItGl1NuaSXxKflw.1

Meeting ID: 856 1447 0240

Passcode: 726036

Summary:
Imagine a specialized computer program stored on a specialized hardware acts like a powerful quantum lens. Just as a physical lens focuses light to see an object clearly, Quantum Field Lens Coding (QF-LC) is a new framework designed to focus and analyze the energy states of particles. The lens is a set of QF-LC algorithm steps that transform a Single Field of a particle state into a Quantum Double-Filed, projecting future state transitions (STs) as events with a success probability of ≥ 2/3, from a lower probability of 1/3 between 3 to N-entangled particles.  This technology helps scientists predict how systems—ranging from microscopic energy states to macroscopic states, bodies, biological cells, societies, environments, the globe, and the universe—will change, evolve, and reduce entropy to near 0, or near 100% certainty. 

Research Synthesis: From QF-LC Theory to Global Impact

This seminar focuses on the Quantum Double-Field (QDF) model developed within the speaker's PhD dissertation.

The Four Pillars of the QF-LC Project

1.      The QDF Model: A mathematical blueprint of quantum double fields.

2.      The What, the How, and the Key Effect/Output of the Project:

o    The What?  - The Algorithm (QF-LCA): Instructions (QF-LC algorithm steps) that allow quantum/hybrid computers to predict system state transitions (STs) as events.

o    The How? - QDF Circuit: A QDF circuit transforms a single field of a particle state into a QDF as a strong prediction, which is doubling the probability value of the single field (SF) within an ST or a phase transition (PT).

o    The Key Part of Technology:  Transform an SF into a more detailed QDF as a heat engine. 

3.      The Simulator (QF-LCS): Software that mimics a QDF system.

4.      The Dataset: A set of recorded data points used for algorithm training and state analysis of a system.

The Quantum Power-Up: Scalar k (Kappa) and Doubled Probability

Scalar k (Kappa): This acts as a multiplier, scaling the particle's quantum field during particle interactions.

·         Doubling the Probability: This field transformation significantly improves accuracy and gains greater certainty to the point of 100%. While a standard prediction can have a probability of about  ≥ 1/3 (lower threshold of an expected measurement outcome)  for a particle via an entangled particle (three in total, at least).

·         Entanglement from the QDF circuit:  This is developed by focusing the energy through the quantum lenses and qubit pairs which double the probability to 2/3 or higher.

·         Energy Efficiency: The system uses these high-probability predictions to suggest the most efficient energy paths for a user to choose, as an intelligent (or quantum AI) decision support system. This minimizes system entropy  near absolute zero temperatures. 

The Secret Sauce: The Three-Way Connection

To gather data, the system creates a unique information network using three particles:

1.      The Sample: A particle taken from the system being observed.

2.      The Partner: A "trapped" particle that pairs up (entangles) with the sample.

3.      The Decoder: A third particle that acts as a bridge, unlocking hidden information between the first two to maximize their connection (entanglement). 

This process effectively turns the quantum circuit into a "heat engine" that processes information to achieve a desired system  (Hamiltonian) outcome, known as the target state (TS) in real world applications listed below.

Real-World Applications

This research project has practical goals that extend beyond the lab (notable examples):

• ​Medicine: Reconstructing damaged DNA to predict virus spread or cancer growth (a TS).
• Security: Identifying forged documents via microscopic particle signatures (a TS).
• ​Global Sustainability: Providing affordable clean energy by optimizing system efficiencies (a TS).

QF-LCA project origins as peer-reviewed by Software Impacts and Data in Brief Elsevier journals, can be accessed at https://github.com/SoftwareImpacts/SIMPAC-2024-159