68 research outputs found

    Randomized ancillary qubit overcomes detector-control and intercept-resend hacking of quantum key distribution

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    Practical implementations of quantum key distribution (QKD) have been shown to be subject to various detector side-channel attacks that compromise the promised unconditional security. Most notable is a general class of attacks adopting the use of faked-state photons as in the detector-control and, more broadly, the intercept-resend attacks. In this paper, we present a simple scheme to overcome such class of attacks: A legitimate user, Bob, uses a polarization randomizer at his gateway to distort an ancillary polarization of a phase-encoded photon in a bidirectional QKD configuration. Passing through the randomizer once on the way to his partner, Alice, and again in the opposite direction, the polarization qubit of the genuine photon is immune to randomization. However, the polarization state of a photon from an intruder, Eve, to Bob is randomized and hence directed to a detector in a different path, whereupon it triggers an alert. We demonstrate theoretically and experimentally that, using commercial off-the-shelf detectors, it can be made impossible for Eve to avoid triggering the alert, no matter what faked-state of light she uses.Comment: Quantum encryption, bidirectional quantum key distribution, detector control, intercept and resend attacks, faked state photon

    Recent Trends in Plasmonic Nanowire Solar Cells

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    Light trapping is crucial for low-cost and highly efficient nanowire (NW) solar cells (SCs). In order to increase the light absorption through the NWSCs, plasmonic materials can be incorporated inside or above the NW design. In this regard, two novel designs of plasmonic NWSCs are reported and analyzed using 3D finite difference time domain method. The geometrical parameters of the reported designs are studied to improve their electrical and optical efficiencies. The ultimate and power conversion efficiencies (PCE) are used to quantify the conversion efficiency of the light into electricity. The first design relies on funnel shaped SiNWs with plasmonic core while the cylindrical NWs of the second design are decorated by Ag diamond shaped. The calculated ultimate efficiency and PCE of the plasmonic funnel design are equal to 44% and 18.9%, respectively with an enhancement of 43.3 % over its cylindrical NWs counterpart. This enhancement can be explained by the coupling between the three optical modes, supported by the upper cylinder, lower cone and plasmonic material. Moreover, the cylindrical SiNWs decorated by Ag diamond offer an ultimate efficiency and short-circuit current density of 25.7%, and 21.03 mA∕cm2, respectively with an improvement of 63% over the conventional cylindrical SiNWs

    Light absorption enhancement in thin film hydrgenated amorphus Si solar cells

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    In this paper, light absorption enhancement in thin film solar cell (SC) is reported and analyzed. The suggested design is based on a nanostructured pattern that increases the diffuse scattered component of radiation and hence the absorption through the active layer. An ion beam sputtering (lBS) approach is used to texture large areas of the glass substrate with high aspect-ratio ripples in order to increase light scattering. Then, thin film SC supported on the textured glass is simulated and analyzed using 3D finite difference time domain (FDTD) method. The suggested SC can offer an ultimate efficiency of 19.26% with short circuit current of 15.76 mA/cm2 with an enhancement of 31.435% over the SC without texturing surface

    Electrical performance of efficient quad-crescent-shaped Si nanowire solar cell

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    The electrical characteristics of quad-crescent-shaped silicon nanowire (NW) solar cells (SCs) are numerically analyzed and as a result their performance optimized. The structure discussed consists of four crescents, forming a cavity that permits multiple light scattering with high trapping between the NWs. Additionally, new modes strongly coupled to the incident light are generated along the NWs. As a result, the optical absorption has been increased over a large portion of light wavelengths and hence the power conversion efficiency (PCE) has been improved. The electron–hole (e–h) generation rate in the design reported has been calculated using the 3D finite difference time domain method. Further, the electrical performance of the SC reported has been investigated through the finite element method, using the Lumerical charge software package. In this investigation, the axial and core–shell junctions were analyzed looking at the reported crescent and, as well, conventional NW designs. Additionally, the doping concentration and NW-junction position were studied in this design proposed, as well as the carrier-recombination-and-lifetime effects. This study has revealed that the high back surface field layer used improves the conversion efficiency by ∼ 80%. Moreover, conserving the NW radial shell as a low thickness layer can efficiently reduce the NW sidewall recombination effect. The PCE and short circuit current were determined to be equal to 18.5% and 33.8 mA/cm2^{2} for the axial junction proposed. However, the core–shell junction shows figures of 19% and 34.9 mA/cm2^{2}. The suggested crescent design offers an enhancement of 23% compared to the conventional NW, for both junctions. For a practical surface recombination velocity of 102^{2} cm/s, the PCE of the proposed design, in the axial junction, has been reduced to 16.6%, with a reduction of 11%. However, the core–shell junction achieves PCE of 18.7%, with a slight reduction of 1.6%. Therefore, the optoelectronic performance of the core–shell junction was marginally affected by the NW surface recombination, compared to the axial junction

    Accelerating biomedical image segmentation using equilibrium optimization with a deep learning approach

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    Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches
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