arXiv subjects
Publications and source records attributed to Arshad Khan.
The objective of this article is to study nearly invariant subspaces of the backward shift operator on the real Hardy space. We also investigate nearly invariant subspaces with finite defect, and as a consequence, provide a characterization for almost invariant subspaces of the backward shift operator.
In this article, we completely classify invariant subspaces of finite-rank perturbations of a class of Toeplitz operators on vector-valued Hardy spaces. As a consequence, in the vector-valued setting, we characterize invariant and almost invariant subspaces of a class of Toeplitz operators, as well as nearly invariant subspaces associated with certain Blaschke-based operators. We further treat the finite defect case for these nearly invariant subspaces.
Selective harvesting by autonomous robots will be a critical enabling technology for future farming. Increases in inflation and shortages of skilled labour are driving factors that can help encourage user acceptability of robotic harvesting. For example, robotic strawberry harvesting requires real-time high-precision fruit localisation, 3D mapping and path planning for 3-D cluster manipulation. Whilst industry and academia have developed multiple strawberry harvesting robots, none have yet achieved human-cost parity. Achieving this goal requires increased picking speed (perception, control and movement), accuracy and the development of low-cost robotic system designs. We propose the edge-server over 5G for Selective Harvesting (E5SH) system, which is an integration of high bandwidth and low latency Fifth Generation (5G) mobile network into a crop harvesting robotic platform, which we view as an enabler for future robotic harvesting systems. We also consider processing scale and speed in conjunction with system environmental and energy costs. A system architecture is presented and evaluated with support from quantitative results from a series of experiments that compare the performance of the system in response to different architecture choices, including image segmentation models, network infrastructure (5G vs WiFi) and messaging protocols such as Message Queuing Telemetry Transport (MQTT) and Transport Control Protocol Robot Operating System (TCPROS). Our results demonstrate that the E5SH system delivers step-change peak processing performance speedup of above 18-fold than a stand-alone embedded computing Nvidia Jetson Xavier NX (NJXN) system.
This article describes Hilbert spaces contractively contained in certain reproducing kernel Hilbert spaces of analytic functions on the open unit disc which are nearly invariant under division by an inner function. We extend Hitt's theorem on nearly invariant subspaces of the backward shift operator on $H^2(\bb D)$ as well as its many generalizations to the setting of de Branges spaces.
COVID-19 has been detrimental in terms of the number of fatalities and rising number of critical patients across the world. According to the UNDP (United National Development Programme) Socio-Economic programme, aimed at the COVID-19 crisis, the pandemic is far more than a health crisis: it is affecting societies and economies at their core. There has been greater developments recently in the chest X-ray-based imaging technique as part of the COVID-19 diagnosis especially using Convolution Neural Networks (CNN) for recognising and classifying images. However, given the limitation of supervised labelled imaging data, the classification and predictive risk modelling of medical diagnosis tend to compromise. This paper aims to identify and monitor the effects of COVID-19 on the human lungs by employing Deep Neural Networks on axial CT (Chest Computed Tomography) scan of lungs. We have adopted Mask RCNN, with ResNet50 and ResNet101 as its backbone, to segment the regions, affected by COVID-19 coronavirus. Using the regions of human lungs, where symptoms have manifested, the model classifies condition of the patient as either "Mild" or "Alarming". Moreover, the model is deployed on the Google Cloud Platform (GCP) to simulate the online usage of the model for performance evaluation and accuracy improvement. The ResNet101 backbone model produces an F1 score of 0.85 and faster prediction scores with an average time of 9.04 seconds per inference.
TlSr2I5: Eu2+ is a newly discovered scintillator and show promising scintillation properties for X- and γ-rays spectroscopy applications. Two zones vertical Bridgman technique is used for the growth of this scintillator. Luminescence properties of the grown crystals are measured under X-ray excitation at room temperature. Pure and Eu2+ doped crystals contained broad emission bands between 445-670 nm peaking at 528 nm and 430-600 nm peaking at 463 nm, respectively. Energy resolution, light yield and decay time profiles are studied under 662 keV γ-ray excitation using 137Cs radioactive source. Energy resolution of 4.2 % (FWHM) is obtained for 3 mol% Eu2+ doped crystal. For the same sample, light yield of 70,000 ph/MeV is also obtained. Three and two decay constants are observed for the pure and Eu2+ doped samples, respectively at room temperature. Effective Z-number and density are found to be 61 and 5.30 g/cm3, respectively.
In this study we present our preliminary report on the scintillation properties of new Ce-doped Tl2LaBr5 single crystal. Two zones vertical Bridgman technique is used for the growth of this compound. Pure and Ce-doped samples showed maximum emission peaks at 435 nm and 415 nm, respectively. Best light yield of 43,000+-4300 ph/MeV with 6.3% (FWHM) energy resolution is obtained for 5% Ce-doped sample under γ-ray excitation. Single exponential decay time constant of 25 ns is observed for 5% Ce doped sample. Effective Z-number is found to be 67, therefore efficient detection of X- and γ-ray will be possible. Preliminary results revealed that this compound will be an ideal candidate for the medical imaging techniques. Further investigations are under way for the determination of optimized conditions of this compound.