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2,192 results on '"Haque, Mohammad"'

2. Low-noise parametric microwave amplifier based on self-heated nonlinear impedance with sub-nanosecond thermal response

Author

Will, Marco, Haque, Mohammad Tasnimul, Chaudhry, Yuvraj, Golubev, Dmitry, and Hakonen, Pertti

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Low-noise amplifiers are of great importance in the field of quantum technologies. We study a thermally driven parametric amplifier based on a superconductor-insulator-graphene-insulator-superconductor (SIGIS) junction coupled to a superconducting microwave cavity. The strong non-linearity in the temperature dependence of our device leads to thermal self-modulation that produces impedance oscillations at frequencies around twice the angular cavity resonance frequency $\omega_\mathrm{r}$. In particular, reactance modulation of the effective capacitance yields a gain of 18.6 dB over a frequency span of 125 kHz with a minimum noise temperature of $T_\mathrm{N} = 1.4$ K. Our theoretical modelling gives insight into the exact mixing processes, confirmation of the electron-phonon coupling parameter and possible improvements of the studied system., Comment: 9 pages, 5 figures, v2 changes to fig 2 and fig.2 caption

Published
2024

3. An autoencoder for compressing angle-resolved photoemission spectroscopy data

Author

Agustsson, Steinn Ymir, Haque, Mohammad Ahsanul, Truong, Thi Tam, Bianchi, Marco, Klyuchnikov, Nikita, Mottin, Davide, Karras, Panagiotis, and Hofmann, Philip

Subjects
Condensed Matter - Materials Science, Computer Science - Machine Learning
Abstract

Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$. To test the data representation capacity of ARPESNet, we compare $k$-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

Published
2024

7. DNA liquid biopsy-based prediction of cancer-associated venous thromboembolism

Author

Jee, Justin, Brannon, A. Rose, Singh, Rohan, Derkach, Andriy, Fong, Christopher, Lee, Adrian, Gray, Lauren, Pichotta, Karl, Luthra, Anisha, Diosdado, Monica, Haque, Mohammad, Guo, Jiannan, Hernandez, Jennifer, Garg, Kavita, Wilhelm, Clare, Arcila, Maria E., Pavlakis, Nick, Clarke, Stephen, Shah, Sohrab P., Razavi, Pedram, Reis-Filho, Jorge S., Ladanyi, Marc, Schultz, Nikolaus, Zwicker, Jeffrey, Berger, Michael F., Li, Bob T., and Mantha, Simon

Published
2024
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27. Perceptions of University Faculty of Saudi Arabia towards Online Classes Conducted during the COVID-19 Pandemic

Author

Chatta, Bala Swamy, Haque, Mohammad Imdadul, and Rao, M. Madhu Sudhan

Abstract

The study aims to find out the perceptions of university faculty who taught the courses online during the COVID-19 pandemic in March 2020. The purpose is to highlight the issues of students and faculty involved in the new context of teaching and learning and prepare them to face the technical and academic challenges. A questionnaire is circulated among the faculty of universities in the Kingdom of Saudi Arabia. The majority of the university faculty who taught the courses online from the middle of the semester are satisfied with their teaching. However, they prefer face-to-face instruction to online instruction. A few of them are in favor of blended learning. The faculty accepts technical and academic issues and issues related to the trust and the value of online instruction. The study is helpful for all the educational institutions, teachers, and students across the world to understand how to conduct online education during crises and calamities to eradicate the technical and academic problems present in the online mode of teaching, and to update and accept the changing trends and developments in the field of education. The article is original because it studies the perceptions of the regular university faculty who taught half of the course face-to-face and the other half online. The novelty lies in finding out how they participated in online teaching during the lockdown period of the COVID-19 pandemic.

Published
2022

34. Thermal self-oscillations in monolayer graphene coupled to a superconducting microwave cavity

Author

Haque, Mohammad Tasnimul, Will, Marco, Zyuzin, Alexander, Golubev, Dmitry, and Hakonen, Pertti

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Quantum Physics
Abstract

Nonlinear phenomena in superconducting resonator circuits are of great significance in the field of quantum technology. We observe thermal self-oscillations in a monolayer graphene flake coupled to Molybdenum-Rhenium superconducting resonator. The graphene flake forms a SINIS junction coupled to the resonator with strong temperature dependent resistance. In certain conditions of pump power and frequency, this nonlinearity leads to thermal self-oscillations appearing as sidebands in cavity transmission measurements with strong temperature dependence and gate tunability. The experimental observations fit well with theoretical model based on thermal instability. The modelling of the oscillation sidebands provides a method to evaluate electron phonon coupling in disordered graphene sample at low energies., Comment: 12 pages, 14 figures

Published
2022

46. Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark

Author

Afshar, Parnian, Mohammadi, Arash, Plataniotis, Konstantinos N., Farahani, Keyvan, Kirby, Justin, Oikonomou, Anastasia, Asif, Amir, Wee, Leonard, Dekker, Andre, Wu, Xin, Haque, Mohammad Ariful, Hossain, Shahruk, Hasan, Md. Kamrul, Kamal, Uday, Hsu, Winston, Lin, Jhih-Yuan, Rahman, M. Sohel, Ibtehaz, Nabil, Foisol, Sh. M. Amir, Lam, Kin-Man, Guang, Zhong, Zhang, Runze, Channappayya, Sumohana S., Gupta, Shashank, and Dev, Chander

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability, and is also time-consuming, considering the fact that only experts are capable of providing annotations. Automatic and semi-automatic tumor segmentation methods have recently shown promising results. However, as different researchers have validated their algorithms using various datasets and performance metrics, reliably evaluating these methods is still an open challenge. The goal of the Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark created through 2018 IEEE Video and Image Processing (VIP) Cup competition, is to provide a unique dataset and pre-defined metrics, so that different researchers can develop and evaluate their methods in a unified fashion. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. At the registration stage, there were 129 members clustered into 28 teams from 10 countries, out of which 9 teams made it to the final stage and 6 teams successfully completed all the required tasks. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique. Methods developed by the three finalists show promising results in tumor segmentation, however, more effort should be put into reducing the false positive rate. This competition manuscript presents an overview of the VIP-Cup challenge, along with the proposed algorithms and results.

Published
2022

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