Showing 1–3 of 3 results for author: Zarrabi, N

Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning

Authors: Hadi Rezvani, Navid Zarrabi, Ishaan Mehta, Christopher Kolios, Hussein Ali Jaafar, Cheng-Hao Kao, Sajad Saeedi, Nariman Yousefi

Abstract: Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technologies. In response, this paper introduces micro- and nanoplastics (MiNa), a nove… ▽ More Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technologies. In response, this paper introduces micro- and nanoplastics (MiNa), a novel and open-source dataset engineered for the automatic detection and classification of micro and nanoplastics using object detection algorithms. The dataset, comprising scanning electron microscopy images simulated under realistic aquatic conditions, categorizes plastics by polymer type across a broad size spectrum. We demonstrate the application of state-of-the-art detection algorithms on MiNa, assessing their effectiveness and identifying the unique challenges and potential of each method. The dataset not only fills a critical gap in available resources for microplastic research but also provides a robust foundation for future advancements in the field. △ Less

Submitted 29 July, 2026; v1 submitted 20 September, 2024; originally announced September 2024.

Secure Platform for Processing Sensitive Data on Shared HPC Systems

Authors: Michel Scheerman, Narges Zarrabi, Martijn Kruiten, Maxime Mogé, Lykle Voort, Annette Langedijk, Ruurd Schoonhoven, Tom Emery

Abstract: High performance computing clusters operating in shared and batch mode pose challenges for processing sensitive data. In the meantime, the need for secure processing of sensitive data on HPC system is growing. In this work we present a novel method for creating secure computing environments on traditional multi-tenant high-performance computing clusters. Our platform as a service provides a custom… ▽ More High performance computing clusters operating in shared and batch mode pose challenges for processing sensitive data. In the meantime, the need for secure processing of sensitive data on HPC system is growing. In this work we present a novel method for creating secure computing environments on traditional multi-tenant high-performance computing clusters. Our platform as a service provides a customizable, virtualized solution using PCOCC and SLURM to meet strict security requirements without modifying the exist-ing HPC infrastructure. We show how this platform has been used in real-world research applications from different research domains. The solution is scalable by design with low performance overhead and can be generalized for processing sensitive data on shared HPC systems imposing high security criteria △ Less

Submitted 26 March, 2021; originally announced March 2021.

CrowdCam: Dynamic Region Segmentation

Authors: Nir Zarrabi, Shai Avidan, Yael Moses

Abstract: We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. Thi… ▽ More We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. This new type of images raises the need to develop new algorithms tailored specifically for it. We propose a comprehensive solution to the problem. Our solution combines cues that are based on geometry, appearance and proximity. First, geometric reasoning is used to produce rough score maps that determine, for every pixel, how likely it is to be the projection of a static or dynamic scene point. These maps are noisy because CrowdCam images are usually few and far apart both in space and in time. Then, we use similarity in appearance space and proximity in the image plane to encourage neighboring pixels to be labeled similarly as either static or dynamic. We collected a new, and challenging, data set to evaluate our algorithm. Results show that the success score of our algorithm is nearly double that of the current state of the art approach. △ Less

Submitted 23 June, 2019; v1 submitted 28 November, 2018; originally announced November 2018.