Field Induced Positional Shift of Bloch Electrons and its Dynamical Implications

We derive the field correction to the Berry curvature of Bloch electrons, which can be traced back to a positional shift due to the interband mixing induced by external electromagnetic fields. The resulting semiclassical dynamics is accurate to second order in the fields, in the same form as before, provided that the wave packet energy is derived up to the same order. As applications, we discuss the orbital magnetoelectric polarizability and predict nonlinear anomalous Hall effects

SCMA with Low Complexity Symmetric Codebook Design for Visible Light Communication

Sparse code multiple access (SCMA) is attracting significant research interests currently, which is considered as a promising multiple access technique for 5G systems. It serves as a good candidate for the future communication network with massive nodes due to its capability of handling user overloading. Introducing SCMA to visible light communication (VLC) can provide another opportunity on design of transmission protocols for the communication network with massive nodes due to the limited communication range of VLC, which reduces the interference intensity. However, when applying SCMA in VLC systems, we need to modify the SCMA codebook to accommodate the real and positive signal requirement for VLC.We apply multidimensional constellation design methods to SCMA codebook. To reduce the design complexity, we also propose a symmetric codebook design. For all the proposed design approaches, the minimum Euclidean distance aims to be maximized. Our symmetric codebook design can reduce design and detection complexity simultaneously. Simulation results show that our design implies fast convergence with respect to the number of iterations, and outperforms the design that simply modifies the existing approaches to VLC signal requirements

Application of deep learning neural network for classification of TB lung CT images based on patches

In this work, convolutional neural network (CNN) is applied to classify the five types of Tuberculosis (TB) lung CT images. In doing so, each image has been segmented into rectangular patches with side width and high varying between 20 and 55 pixels, which are later normalised into 30x30 pixels. While classifying TB types, six instead of five categories are distinguished. Group 6 houses those patches/segments that are common to most of the other types, or background. In this way, while each 3D dataset only has less than 10% distinguishable volumes that are applied to perform the training, the rest remains part of the learning cycle by participating to the classification, leading to an automated process to differentiation of five types of TB. When tested against 300 datasets, the Kappa value is 0.2187, ranking 5 among 23 submissions. However, the accuracy value of ACC is 0.4067, the highest in this competition of classification of TB types

Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques

While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of top 10 causes of death and has shown signs of increasing. To complement conventional diagnostic procedure of applying microbiological culture that takes several weeks and remains expensive, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administrating antibiotic drugs. This research undertakes the investigation of predicting multi-drug resistant (MDR) patients from drug sensitive (DS) ones based on CT lung images to monitor the effectiveness of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images with limited regions capturing abnormities, patch-based deep convolutional neural network (CNN) allied to support vector machine (SVM) classifier is implemented on a collection of datasets from 230 patients obtained from ImageCLEF 2017 competition. As a result, the proposed architecture of CNN+SVM+patch performs the best with classification accuracy rate at 91.11% (79.80% in terms of patches). In addition, hand-crafted SIFT based approach accomplishes 88.88% in terms of subject and 83.56% with reference to patches, the highest in this study, which can be explained away by the fact that the datasets are in small numbers. Significantly, during the Tuberculosis Competition at ImageCLEF 2017, the authors took part in the task of classification of 5 types of TB disease and achieved top one with regard to averaged classification accuracy (i.e. ACC = 0.4067), which is also premised on the approach of CNN+SVM+patch. On the other hand, when the whole slices of 3D TB datasets are applied to train a CNN network, the best result is achieved through the application of CNN coupled with orderless pooling and SVM at 64.71% accuracy rate

Segmentation of brain lesions from CT images based on deep learning techniques

While Computerised Tomography (CT) may have been the first clinical tool to study human brains when any suspected abnormality related to the brain occurs, the volumes of CT lesions usually are usually disregarded due to variations among inter-subject measurements. This research responds to this challenge by applying the state of the art deep learning techniques to automatically delineate the boundaries of abnormal features, including tumour, associated edema, head injury, leading to benefiting both patients and clinicians in making timely accurate clinical decisions. The challenge with the application of deep leaning based techniques in medical domain remains that it requires datasets in great abundance, whilst medical data tend to be in small numbers. This work, built on the large field of view of DeepLab convolutional neural network for semantic segmentation, highlights the approaches of both semantics-based and patch-based segmentation to differentiate tumour, lesion and background of the brain. In addition, fusions with a number of other methods to fine tune regional borders are also explored, including conditional random fields (CRF) and multiple scales (MS). With regard to pixel level accuracy, the averaged accuracy rates for segmentation of tumour, lesion and background amount to 82.9%, 85.7%, 85.3% and 81.3% while applying the approaches of DeepLab, DeepLab with MS, DeepLab with MS and CRF, and patch-based pixel-wise classification respectively. In terms of the measurement of intersection over union of two regions, the accuracy rates are of 70.3%, 75.1%, 77.2%, and 63.6% respectively, implying overall DeepLab fused with MS and CRF performs the best