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A high-accuracy unsupervised classification model is developed in a multimode self-interference microring resonator (SIMRR). For the SIMRR, there are many whispering gallery modes (WGMs) present. Each of these resonance modes supported by the SIMRR has a different response to different target parameters, so that the SIMRR-based sensor has the super capability to distinguish between multiple components. In the classification model, principal component analysis (PCA) is firstly used to reduce the dimensionality of this multimode sensing information from the SIMRR-based sensor. When the original higher-dimensional data points are projected onto the lower-dimensional data with only the first few principal components, they can be easily categorized into several different types by using density-based spatial clustering of application with noise (DBSCAN) algorithm. As an example, the unsupervised classification method is numerically validated based on a designed three-gas SIMRR-based sensor. The numerical results prove that the classification model can achieve an ultra high classification accuracy for the designed three-gas sensor with more than 60 dB in signal-to-noise ratio.

A wideband transmission frequency selective surface (FSS) rasorber with low insertion loss for stealthy radome in antenna systems is presented. In order to obtain the wideband transmission with low insertion loss, a choked structure and a 2-order bandpass FSS are adopted. The choke is just a parallel LC resonator or deformation. The passband below the absorption band by using the lumped inductor loaded in the element gap. As a validation, the FSS rasorber is fabricated and measured. The measurement result shows that the transmission above −1 dB covers a bandwidth of 4.1-5.8 GHz and the reflection below −10 dB covers a bandwidth of 7.4-13.4 GHz.

We present a wideband frequency selective surface (FSS) absorber for oblique incidence. The absorber structure with anisotropic elements exhibits different impedances in two polarizations to match free space characteristic impedance at oblique incidence, thus resulting in a low microwave reflectivity over a wideband range. The grating lobe is suppressed by the use of skewed grid. Simulation and measurement results all illustrate that the reflectivity covers a broad band of 1.35-4.0 GHz below −10 dB for both TE and TM polarizations at an incident angle of 45°.

In the field of fast and large-scale image retrieval, the binary hash code based on convolutional features enjoys powerful advantages. CNN model's leading recognition ability shows that its high-level features distribute intensively and have stable between-class distances. On the basis of the distribution characteristic of convolutional features, this paper presents a new method of improving retrieval performance by means of using the category centers of high-level features to enhance the common semantic content of high-level features of the query image and making the consequent binary hash code gain better retrieval results. Experimental results indicate that (i) the category centers of CNN feature offer comparable discriminability with the initial model when identifying the semantic information of images; (ii) before calculating binary hash code, the integration of the CNN feature of a query image with category centers weighted by predicted probabilities can boost performance in similar image retrieval. State-of-the-art retrieval performance is obtained on the Cifar-10 dataset in this paper among all hash methods.

In recent years, deep convolutional networks have made a milestone progress in the field of image recognition. However, the recognition ability of the deep convolution network declines in the case of unbalanced training data, in terms of categories with fewer training images, the recognition ability of the deep convolution network declines more seriously. Aiming at this kind of problem, this paper presents a new classification method which recognizes a query image by comparing distances between category centers of CNN features of the whole training dataset and the corresponding CNN feature of this query image. The experimental results on Cifar-10 and Cifar-100 show that the claimed method can more accurately identify images whose categories have only a few training samples and that the mean precision of the recognition can be improved effectively.

Checkpoint/restart has been an effective mechanism to achieve fault tolerance for many scientific applications. However, as GPU becomes a much bigger role in high performance computing, there is no effective checkpoint/restart scheme yet due to GPU's batch-mode execution manner. The paper proposes an application-level checkpoint/restart scheme to save and restore GPU computation states. A precompiler and run-time support module are developed to construct and save states in CPU system memory dynamically. Secondary storage can be utilized for scalability and long-term fault tolerance. CUDA applications with complicated memory use are support as well. Experimental results have demonstrated the effectiveness of the proposed scheme.

Computation state construction is an indispensable step to achieve fault tolerance and computation mobility for scientific applications by saving and restoring the state during program execution. However, there is no effective state construction scheme yet due to the GPU's batch-mode execution manner as the GPU takes on a larger role in high performance computing. The GPU's complex memory hierarchy means the states are scattered in different memory locations that are difficult to fetch. Programs that are running in parallel make the states difficult to construct for each thread. The paper proposes an application-level computation state construction scheme to support GPU programs. A precompiler and run-time support module are developed to construct and save states in the CPU system memory dynamically. Memory blocks are registered, and new data structures are proposed to save and restore the computation states represented by variables and pointers in the GPU. Secondary storage can be utilized for scalability and long-term fault tolerance.