Browsing by Author "Dr. Mahdy Rahman Chowdhury"

Now showing 1 - 6 of 6
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    Open Access
    All object on-chip tractor beam using SPP
    (North South University, 2022) Masudur Rahim; Md. Mohaimanul Masud Sunny; Fahim Al Ifran Rahim; Dr. Mahdy Rahman Chowdhury; 1320251043; 1220446043; 1320650043
    In this paper we designed an on-chip configuration where implementation of an all object optical tractor beam was made possible. By all object, we are referring to spherical nanoparticles of dielectric, plasmonic and chiral material. This tractor beam was composed of the superposition of two evanescently confined surface waves called SPP (Surface Plasmon Polariton), which was excited over the on-chip base by the incidence of a plane polarized laser light propagating vertically upwards. Arrays of nanoantenna like structures called metasurfaces has been used in this setup to manipulate the evanescent surface wave. Exerting pulling force is only possible when the beam incident over the particle has a non-paraxial nature. In reality, non-paraxiality of a beam is a somewhat hypothetical requirement. However, this issue is resolved by creating an interference field formed by the superposition of the SPP beam along with the induced effect within the particle and the substrate which led to the emergence of this backward pulling force. Pulling force was realized within certain diameter range (Mie Range). To the best of our knowledge, this article stands as the first proposal of realizing all object tractor beam in an on-chip configuration where the configuration itself is mitigating the paraxiality issue for all objects and using the same setup, pulling of such particles can be realized. Our work can be extended to sorting of particles in an inhomogeneous mixture, such as analysis of biomolecules.
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    Open Access
    Deep learning based visual pollutant detection system
    (North South University, 2020) Ishaq Ali; Md. Sabbir Hossain; Dr. Mahdy Rahman Chowdhury; 1530869042; 1610661042
    Most of us are familiar with mainly four types of pollutions. They are 1) air pollution 2) water pollution 3) sound pollution and 4) soil pollution. But a very few of us know about ‘visual pollution’ since it is relatively a new concept. But it is gaining people’s attention slowly. Determining which object should be considered as a visual pollutant is quite a challenge because an object which is visually disturbing to one person may not be visually disturbing to another person. In this study, there are four classes 1) billboards and signage 2) telephone and communication wires 3) network towers and 4) street litter. A deep learning model has been used to detect visual pollutants in an image that was trained and tested on images that we collected using the Google search engine. Our study has a lot of applications in real life like image and video analysis, visual pollutant management, visual pollution index generation, server deployment, and many more. Our study suggests that higher levels of accuracy can be achieved by increasing the size of the dataset.
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    Electron Matter-wave Tractor Beams: Study & Simulation of Quantum-Mechanical Stress and Force
    (North South University, 2022) Md. Arifur Rahman; Md. Mahabub Alam Arafin; Jahidul Islam; Dr. Mahdy Rahman Chowdhury; 1822038043; 1813241043; 1812555043
    Classical force equations describing the macroscopic universe have been studied and applied extensively throughout the past three centuries. However, the study & simulation of quantum-mechanical stress & force in the microscopic domain remain scantily explored. We attempt to accelerate the research of microscopic scenarios involving quantum-mechanical forces and successfully devise a COMSOL Multiphysics simulation setup and derive all the corresponding mathematical formulations to observe a wide array of quantum-mechanical phenomena, in particular, the simulation of electron matter-wave tractor beams in COMSOL Multiphysics 6.0.
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    On Chip Optical Trapping and Sorting
    (North South University, 2019-05) Syeda Ramisa Masum; Muhammad Zayan Ahsan; Sayed Hasan Salim; MD. Fakrul Hassan; Dr. Mahdy Rahman Chowdhury; 1320584045; 1110905043; 1511806045; 1511034043
    In this paper we investigated the effects of Bessel Beam and plane wave separately on three different types of particle. Our research was mainly with particles of chiral, dielectric and plasmonic material on nanometer scale. The particles were observed to have exhibited different behavioral patterns under the effects of said waves when a slanted substrate was placed under each particle. The different behavior of particles were sufficient to sort these particles and also due to the slanted plasmonic sheet it was possible to trap certain types of particles. The incident light for both cases was projected from above with incident angle of 90 degrees. Our work accentuates the impact of tractor beam on optical chip thus sorting dipolar particles. Under the influence of plane wave, our study shows that all the particles behave similarly as all three di-pole objects are getting pulling force and therefore, they are being trapped. But for Bessel beam, which is a non-diffractive, high intensity light, with a cone angle of 20 degree and incident angle of 90 degree; the particles show different behavior. Chiral and Dielectric particles experiences pulling force therefore they are being trapped but plasmonic particle exerts pushing force. This phenomenon allows us to sort unknown particles. The inside physics can be defined with Poynting vector which represents the instantaneous power flow due to instantaneous electric and magnetic fields
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    Optical pulling of multiple Rayleigh sized particles simultaneously using a Metasurface
    (North South University, 2022) Mridula Rodoshi; Mohammad Ahsanul Haque; Dr. Mahdy Rahman Chowdhury; 1711517043; 1831696643
    Creating pulling force for several sorts of particles simultaneously on a single setup is very difficult and unusual in the literature. In this article, a single nanometer-sized metasurface was used to generate an optical pulling force for multiple particles simultaneously. Initially, three particles were the main focus, but this was eventually expanded to seven particles to strengthen the findings based on multiparticles. An incident beam (varying the wavelength from 640nm to 1200nm) is used to produce surface plasmon polariton waves, which were afterwards responsible for pulling the Rayleigh sized (50nm in our instance) particles. By generating a dual non-paraxial surface plasmon polariton (SPP) energized plasmonic complex field, which induces completely different behaviors in Rayleigh scatterers with dissimilar material properties, the proposed setup promotes effective on-chip material-based optical pulling of silica (a dielectric object), gold (a plasmonic object), and chiral nanospheres.
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    Senior Project Design Deep Fake Detection
    (North South University, 2021-01-01) Asif Faruki; Mahadi Hasan Bhuiyan; Sazzad Alam; Dr. Mahdy Rahman Chowdhury; 1632478042; 1731653642; 1611200642
    Deep fakes are the end result of virtual deception to create convincing motion pictures to mislead the viewer. To accomplish this, high-intensity mastering algorithms based entirely on autoencoders or GANs are used, which can be easily accessible and correct year after year, resulting in fake motion pictures that are difficult to distinguish from real ones. "Seeing is believing" is now not actual, and this has far-accomplishing implications for many aspects of our lives. Deepfakes are getting easier and easier to create as the generation advances. In truth, some of it could be carried out with an app in the palm of your hand. Deepfakes are tough to spot. Deepfakes have grown hard to detect with the naked eye. Deep learning-based video modification tools have grown more widely available in recent years. People can simply learn how to create deep fake videos with victims and target images with little to no effort. This poses a significant social issue for everyone whose images are publicly accessible on the Internet, particularly on social media platforms. According to a recent Google survey conducted from December 2018 to December 2020, the number of online deepfake movies increases every day. In December 2020, there were 85,084 videos online, compared to 7,964 videos in December 2018. As a result, it is rapidly growing. There are several methods to detect deep fakes. The objective of this paper is to expose deep fakes with deep learning techniques. Inception-ResNet-v2 was used to detect deep fakes, which is a deep learning technique. The detection has been done with the use of 3 datasets, which have been taken from Kaggle and GitHub. Deepfake was detected using Python 3, Google Colab, and Keras as the frameworks. We have found 98% accuracy by using Inception-ResNet-v2 with the datasets. Deep learning algorithms have advanced to the point where it's now feasible to create splendid-practical pictures and movies, called "deep fakes." Those deepfakes have the capacity to attain a massive 6 target market and have negative effects on our society. In spite of the fact that a variety of efforts have gone into detecting deep fakes, their performance pales in comparison to ours. In this project, we endorse the use of deep learning to find a residual network architecture for deepfake detection in an adaptable way. This, inception-resnet-v2, is one of the best methods for detecting deepfakes using deep mastering. In comparison to advanced techniques, our proposed approach is significantly less expensive competitive prediction accuracy based totally on our studied search space.