ORCID Profile
0000-0002-3417-8590
Current Organisation
University of New South Wales
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Elsevier BV
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: ACM
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Association for Computing Machinery (ACM)
Date: 27-03-2023
DOI: 10.1145/3580878
Abstract: For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way into the latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room, and a large office space, demonstrate that the use of light spectral information significantly reduces the localization error for the different percentiles.
Publisher: Wiley
Date: 19-03-2021
Abstract: Endometrial extracellular vesicles (EVs) are emerging as important players in reproductive biology. However, how their proteome is regulated throughout the menstrual cycle is not known. Such information can provide novel insights into biological processes critical for embryo development, implantation, and successful pregnancy. Using mass spectrometry‐based quantitative proteomics, we show that small EVs (sEVs) isolated from uterine lavage of fertile women (UL‐sEV), compared to infertile women, are laden with proteins implicated in antioxidant activity (SOD1, GSTO1, MPO, CAT). Functionally, sEVs derived from endometrial cells enhance antioxidant function in trophectoderm cells. Moreover, there was striking enrichment of invasion‐related proteins (LGALS1/3, S100A4/11) in fertile UL‐sEVs in the secretory (estrogen plus progesterone‐driven, EP) versus proliferative (estrogen‐driven, E) phase, with several players downregulated in infertile UL‐sEVs. Consistent with this, sEVs from EP‐ versus E‐primed endometrial epithelial cells promote invasion of trophectoderm cells. Interestingly, UL‐sEVs from fertile versus infertile women carry known players redictors of embryo implantation (PRDX2, IDHC), endometrial receptivity (S100A4, FGB, SERPING1, CLU, ANXA2), and implantation success (CAT, YWHAE, PPIA), highlighting their potential to inform regarding endometrial status regnancy outcomes. Thus, this study provides novel insights into proteome reprograming of sEVs and soluble secretome in uterine fluid, with potential to enhance embryo implantation and hence fertility.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 25-09-2021
DOI: 10.36227/TECHRXIV.16674127.V1
Abstract: In this paper we consider such HAPS-based cellular systems to provide IoT coverage for protected area. br
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 25-09-2021
DOI: 10.36227/TECHRXIV.16674127
Abstract: In this paper we consider such HAPS-based cellular systems to provide IoT coverage for protected area. br
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: MDPI AG
Date: 23-10-2019
DOI: 10.3390/S19214609
Abstract: This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth’s magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.
Publisher: Association for Computing Machinery (ACM)
Date: 27-09-2023
DOI: 10.1145/3610913
Publisher: Elsevier BV
Date: 12-2023
No related grants have been discovered for Mahbub Hassan.