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- 2018
- Conditional Random Fields (CRF)
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Towards polarisation-encoded quantum key distribution in optical fibre networks
摘要： Quantum key distribution – a process that encodes digital information – often utilises fibre optic technologies for commercial applications. Fibre provides the benefit of a dark channel as well as the convenience of independence of a line-of-sight connection between the sender and receiver. In order to implement quantum key distribution protocols utilising polarisation encoding, the birefringence effects of fibre must be compensated for. Birefringence is caused by manufacturing impurities in the fibre or a change in environmental conditions and results in a rotation of the state of polarisation of light as it is propagated through the fibre. With dynamic environmental conditions, the birefringence effects should be monitored with a test signal at regular time intervals so that the polarisation of each photon can be appropriately compensated to its original state. Orthogonal states are compensated simultaneously, but most protocols, such as BB84 and B92, require non-orthogonal basis sets. Instead of using a compensator for each basis, the presented scheme fixes the polarisation controller onto the plane on the Poincaré that passes through both bases, compensating both non-orthogonal bases simultaneously.
关键词： cryptography,fibre network,QKD,polarisation encoding,single photon
更新于2025-09-23 15:23:52
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[Lecture Notes in Networks and Systems] Renewable Energy for Smart and Sustainable Cities Volume 62 (Artificial Intelligence in Renewable Energetic Systems) || Prediction PV Power Based on Artificial Neural Networks
摘要： The goal of this contribution is to estimate the power delivered by a multicrystals solar photovoltaic module based on artificial neural networks. Two structures of ANNs were tested: multiple-layer perceptron and radial basic function. The results obtained gave good coefficients of correlation, the statistical R2-value obtained is about 0.96 to predict this important parameter.
关键词： Artificial neural network (ANNs),Multiple-layer perceptron (MLP),Radial basic function (RBF),Photovoltaic (PV) power
更新于2025-09-23 15:23:52
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Maximizing the Current Output in Self-Aligned Graphene–InAs–Metal Vertical Transistors
摘要： With finite density of states and electrostatically tunable work function, graphene can function as a tunable contact for semiconductor channel to enable vertical field effect transistors (VFET). However, the overall performance, especially the output current density is still limited by the low conductance of the vertical semiconductor channel, as well as large series resistance of graphene electrode. To overcome these limitations, we construct a VFET by using single crystal InAs film as the high conductance vertical channel and self-aligned metal contact as the source-drain electrodes, resulting a record high current density over 45,000 A/cm2 at a low bias voltage of 1 V. Furthermore, we construct a device-level VFET model using resistor network method, and experimentally validate the impact of each geometry parameter on device performance. Importantly, we found the device performance is not only a function of intrinsic channel material, but also greatly influenced by device geometries and footprint. Our study not only pushes the performance limit of graphene VFETs, but also sheds light on van der Waals integration between two-dimensional material and conventional bulk material for high performance VFETs and circuits.
关键词： resistor network model,high current density,vertical transistor,graphene,van der Waals heterostructure,InAs film
更新于2025-09-23 15:23:52
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Recurrent conditional generative adversarial network for image deblurring
摘要： Nowadays, there is an increasing demand for images with high definition and fine textures, but images captured in natural scenes usually suffer from complicated blurry artifacts, caused mostly by object motion or camera shaking. Since these annoying artifacts greatly decrease image visual quality, deblurring algorithms have been proposed from various aspects. However, most energy-optimization-based algorithms rely heavily on blur kernel priors, and some learning-based methods either adopt pixel-wise loss function or ignore global structural information. Therefore, we propose an image deblurring algorithm based on recurrent conditional generative adversarial network (RCGAN), in which the scale-recurrent generator extracts sequence spatio-temporal features and reconstructs sharp images in a coarse-to-fine scheme. To thoroughly evaluate the global and local generator performance, we further propose a receptive field recurrent discriminator. Besides, the discriminator takes blurry images as conditions, which help to differentiate reconstructed images from real sharp ones. Last but not least, since the gradients are vanishing when training generator with the output of discriminator, a progressive loss function is proposed to enhance the gradients in back-propagation and to take full advantages of discriminative features. Extensive experiments prove the superiority of RCGAN over state-of-the-art algorithms both qualitatively and quantitatively.
关键词： coarse-to-fine,Image deblurring,receptive field recurrent,conditional generative adversarial network
更新于2025-09-23 15:23:52
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An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network
摘要： The objective of this study is to propose an alternative, hybrid solution method for diagnosing diabetic retinopathy from retinal fundus images. In detail, the hybrid method is based on using both image processing and deep learning for improved results. In medical image processing, reliable diabetic retinopathy detection from digital fundus images is known as an open problem and needs alternative solutions to be developed. In this context, manual interpretation of retinal fundus images requires the magnitude of work, expertise, and over-processing time. So, doctors need support from imaging and computer vision systems and the next step is widely associated with use of intelligent diagnosis systems. The solution method proposed in this study includes employment of image processing with histogram equalization, and the contrast limited adaptive histogram equalization techniques. Next, the diagnosis is performed by the classification of a convolutional neural network. The method was validated using 400 retinal fundus images within the MESSIDOR database, and average values for different performance evaluation parameters were obtained as accuracy 97%, sensitivity (recall) 94%, specificity 98%, precision 94%, FScore 94%, and GMean 95%. In addition to those results, a general comparison of with some previously carried out studies has also shown that the introduced method is efficient and successful enough at diagnosing diabetic retinopathy from retinal fundus images. By employing the related image processing techniques and deep learning for diagnosing diabetic retinopathy, the proposed method and the research results are valuable contributions to the associated literature.
关键词： Image processing,Deep learning,Convolutional neural network,Diabetic retinopathy
更新于2025-09-23 15:23:52
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Passive Sub-Carrier Multiplexing for Discriminating Overlapped Optical Spectra
摘要： A passive technique for multiplexing spectrally overlapping optical network signals is proposed and experimentally verified. The system utilizes arrayed fiber delay lines for multiplexing signal generation and offers simultaneous plotting of multiple optical spectra. Accurate recovery of individual spectrum of multiplexed long-period gratings spectra is experimentally demonstrated in a refractive index sensing application. This system provides an all-optical platform for economical multiplexing system in optical sensing networks.
关键词： Multiplexing scheme,optical sensor network,temporal shifting
更新于2025-09-23 15:23:52
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Stereo-Matching Network for Structured Light
摘要： Recently, deep learning has been widely applied in binocular stereo matching for depth acquisition, which has led to an immense increase of accuracy. However, little attention has been paid to the structured light ?eld. In this letter, a network for structured light is proposed to extract effective matching features for depth acquisition. The proposed network promotes the Siamese network by considering receptive ?elds of different scales and assigning proper weights to the corresponding features, which is achieved by combining pyramid-pooling structure with the squeeze-and-excitation network into the Siamese network for feature extraction and weight calculations, respectively. For network training and testing, a structured-light dataset with amended ground truths is generated by projecting a random pattern into the existing binocular stereo dataset. Experiments demonstrate that the proposed network is capable of real-time depth acquisition, and it provides superior depth maps using structured light.
关键词： SLNet,stereo matching,Structured light,siamese network
更新于2025-09-23 15:23:52
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Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network
摘要： Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called 'Deep Retina.' Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.
关键词： deep convolutional neural network,mobile app,fractional max-pooling,support vector machine,diabetic retinopathy,fundus images,teaching-learning-based optimization
更新于2025-09-23 15:23:52
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Deep structure tensor graph search framework for automated extraction and characterization of retinal layers and fluid pathology in retinal SD-OCT scans
摘要： Maculopathy is a group of retinal disorders that affect macula and cause severe visual impairment if not treated in time. Many computer-aided diagnostic methods have been proposed over the past that automatically detect macular diseases. However, to our best knowledge, no literature is available that provides an end-to-end solution for analyzing healthy and diseased macular pathology. This paper proposes a vendor-independent deep convolutional neural network and structure tensor graph search-based segmentation framework (CNN-STGS) for the extraction and characterization of retinal layers and fluid pathology, along with 3-D retinal profiling. CNN-STGS works by first extracting nine layers from an optical coherence tomography (OCT) scan. Afterward, the extracted layers, combined with a deep CNN model, are used to automatically segment cyst and serous pathology, followed by the autonomous 3-D retinal profiling. CNN-STGS has been validated on publicly available Duke datasets (containing a cumulative of 42,281 scans from 439 subjects) and Armed Forces Institute of Ophthalmology dataset (containing 4,260 OCT scans of 51 subjects), which are acquired through different OCT machinery. The performance of the CNN-STGS framework is validated through the marked annotations, and it significantly outperforms the existing solutions in various metrics. The proposed CNN-STGS framework achieved a mean Dice coefficient of 0.906 for segmenting retinal fluids, along with an accuracy of 98.75% for characterizing cyst and serous fluid from diseased retinal OCT scans.
关键词： convolutional neural network (CNN),Optical coherence tomography (OCT),maculopathy,ophthalmology,graph search
更新于2025-09-23 15:23:52
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The impact of PVs and EVs on domestic electricity network charges: A case study from Great Britain
摘要： Electric power distribution network charges have become a popular area of study for regulators, industry and academia. Increasing use of photovoltaics (PVs) and electric vehicles (EVs) by domestic customers has created concerns about the fairness of the current tariff structure. Proposing a tariff design, which will be cost reflective, transparent, sustainable, economically efficient is socially desirable. Wealth transfer through electricity distribution tariffs is a major concern for energy regulators. This paper aims to analyse the current distribution network tariffs faced by four main household customer groups in Great Britain (GB) - defined as those who own a PV and an EV, those with EV but no PV, those with PV but no EV and finally those with neither EV nor PV – under various uptake scenarios for EVs and PVs. We illustrate the impact on household tariffs for the most and least expensive GB network operators, namely London Power Networks and Scottish Hydro Electric Power Distribution. The results show that, due to the current network charges calculation structure, as PV penetration increases, the distribution tariffs increase for all customers regardless of whether someone owns a PV or not. On the other hand, as EV penetration increases, the distribution tariffs decrease for all customer groups. Another key finding is that the distribution tariffs in Great Britain are EV dominated and the future EV and PV penetration projections indicate that the distribution tariffs will likely decrease for all customers in Great Britain.
关键词： Tariff,Distribution,Network,EV,PV
更新于2025-09-23 15:23:52