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Recently, several array radar structures combined with sub-Nyquist techniques and corresponding algorithms have been extensively studied. Carrier frequency and direction-of-arrival (DOA) estimations of multiple narrow-band signals received by array radars at the sub-Nyquist rates are considered in this paper. We propose a new sub-Nyquist array radar architecture (a binary array radar separately connected to a multi-coset structure with M branches) and an efficient joint estimation algorithm which can match frequencies up with corresponding DOAs. We further come up with a delay pattern augmenting method, by which the capability of the number of identifiable signals can increase from M-1 to Q-1 (Q is extended degrees of freedom). We further conclude that the minimum total sampling rate 2MB is sufficient to identify $ {K \leq Q-1}$ narrow-band signals of maximum bandwidth $B$ inside. The effectiveness and performance of the estimation algorithm together with the augmenting method have been verified by simulations., Comment: 6 pages, 2 figures, conference

Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information (e.g., age, HPV results), and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. Results: An 88.3 plus or minus 4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task (low-risk [normal, ectropion and LSIL] vs. high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve (AUC) value of 0.959 with an 86.7 plus or minus 11.4% sensitivity and 93.5 plus or minus 3.8% specificity. Conclusion: The proposed deep-learning based CADx method outperformed three human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. Significance: Label-free OCM imaging, combined with deep-learning based CADx methods, hold a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases., Comment: 9 pages, 5 figures, and 2 tables

The interfacial structure of SrZr$_{x}$Ti$_{1-x}$O$_3$ films grown on semiconducting Ge substrates are investigated by synchrotron X-ray diffraction and first-principles density functional theory. By systematically tuning Zr content x, the effects of bonding at the interface and epitaxial strain on the physical structure of the film can be distinguished. The interfacial perovskite layers are found to be polarized as a result of cation-anion ionic displacements perpendicular to the perovskite/semiconductor interface. We find a correlation between the observed buckling and valence band offsets at the SrZr$_{x}$Ti$_{1-x}$O$_3$/Ge interface. The theoretical valence band offsets for the polar structures are in agreement with reported X-ray photoelectron spectroscopy measurements. These results have important implications for the integration of functional oxide materials with established semiconductor based technologies.

Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated, however, few of these methods have focused on private clouds. This paper proposes a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes using additional information on the arrival time and duration of virtual machines to reduce the datacenter scale and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. One state-of-the-art and several classic VMP methods are also modified to adapt to the proposed model to observe their performance and compare with our proposed algorithm. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a convergence rate that is much faster than those of the other search-based algorithms evaluated. In particular, DCBB yields the optimal solution for a real-world workload with an execution time that is an order of magnitude shorter than that required by the original branch-and-bound (BB) algorithm., Comment: Submitted to Journal of Supercomputing on 27th June, 2018, Revised on 12th December, 2018, Accepted on 15th December, 2018