School of Mechanical Engineering Seminar
Wednesday, July 10, 2024, at 14:00
Wolfson Building of Mechanical Engineering, Room 206

Integrative Tool for Water Quality Assessment in Patiala's Wastewater Ponds: Field, Visual, and Satellite Approaches

A thesis submitted toward the degree of Master of Science in Engineering

Ronny Erdan

under the supervision of Prof. Hadas Mamane.

This study analyzed the water quality of 100 sewage ponds around Patiala, Punjab, combining field sensor measurements, satellite imagery, and visual analysis. Key water parameters such as dissolved oxygen (DO), oxidation-reduction potential (ORP), chlorophyll, pH, temperature, nitrate, and ammonia were measured on-site. Positive correlations were found between pond temperature to ORP, pH, and DO, and negative correlations to chlorophyll, ammonium, and nitrate. The Modified Normalized Difference Water Index (MNDWI) was used to confirm the identification of sewage ponds while the Normalized Difference Vegetation Index (NDVI) and Visual Assessment were employed to quantitatively assess the pond’s vegetation cover and pond quality, use and maintenance. For example, visual analysis identified gas release and visible rubbish as the most prevalent characteristics of ponds. Ponds with vegetation features were termed "green ponds," compared to lesser vegetation referred to as "nongreen ponds." Green ponds showed higher DO, chlorophyll concentrations and NDVI values compared to non-green ponds. The study underlines the importance of this multi-method approach to improve environmental assessments of sewage ponds health.

Electrical Engineering Systems Seminar

Speaker: Bar Ettedgui

M.Sc. student under the supervision of Prof. Shai Avidan and Prof. Yitzhak Yitzhaky

Wednesday, 8^th May 2024, at 15:00

Room 011, Kitot Building, Faculty of Engineering

Atmospheric Turbulence Degraded Video with Recurrent GAN (ATVR-GAN)

Abstract

Atmospheric turbulence (AT) can change the path and direction of light during video capturing of a target in space due to the random motion of the turbulemient medium, a phenomenon that is most noticeable when shooting videos at long ranges, resulting in severe video dynamic distortion and blur. To mitigate geometric distortion and reduce spatially and temporally varying blur, we pro-pose a novel Atmospheric Turbulence Video Restoration Generative Adversarial Network (ATVR-GAN) with a specialized Recurrent Neural Network (RNN) generator, which is trained to predict the scene’s turbulent optical flow (OF) field and utilizes a recurrent structure to catch both spatial and temporal dependencies. The new architecture is trained using a newly combined loss function that counts for the spatiotemporal distortions, specifically tailored to the AT problem. Our network was tested on synthetic and real imaging data and compared against leading algorithms in the field of AT mitigation and image restoration. The proposed method outperformed these methods for both synthetic and real data examined.

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז. בדף הנוכחות שיועבר באולם במהלך הסמינר