Quasi-twisted codes of fixed index > 1 have been shown recently to be asymptotically good (A. Alahmadi, C. Güneri, H. Shoaib, P. Solé, 2017). We use this result to construct asymptotically good additive c...
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We construct trace codes over Z4 by using Boolean functions and skew sets, respectively. Their Lee weight distribution is studied by using a Galois ring version of the Walsh-Hadamard transform and exponential sums. We...
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In this Letter, we propose a color holographic zoom system based on a liquid lens. We use the spatial multiplexing method to realize color reconstruction. By controlling the focal lengths of the liquid lens and the en...
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In this Letter, we propose a color holographic zoom system based on a liquid lens. We use the spatial multiplexing method to realize color reconstruction. By controlling the focal lengths of the liquid lens and the encoded digital lens on the spatial light modulator panel, we can change the magnification of the reconstructed image very quickly, without mechanical parts and keeping the output plane stationary.
Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we pre...
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Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in ...
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ISBN:
(纸本)9781509016129
Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.
Owing to the shortages of inconvenience, expensive and high professional requirements etc. for conventional recognition devices of wheat leaf diseases, it does not satisfy the requirements of uploadin
Owing to the shortages of inconvenience, expensive and high professional requirements etc. for conventional recognition devices of wheat leaf diseases, it does not satisfy the requirements of uploadin
Cross-modal hashing has received more and more attention because of its fast query speed and low storage cost. In this paper, we propose a flexible yet simple cross-modal hashing method to deal with the problem of cro...
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ISBN:
(纸本)9781467384155
Cross-modal hashing has received more and more attention because of its fast query speed and low storage cost. In this paper, we propose a flexible yet simple cross-modal hashing method to deal with the problem of cross-modal retrieval. The proposed method consists of two steps. In the first phase, we use a kernel canonical correlation analysis method named Anchor kernel canonical correlation analysis (AKCCA) to map data from different modalities into a common kernel space. In the second phase, we use the method named Supervised Hashing with Kernels (KSH) to learn hashing functions bit by bit. These two useful ingredients are combined seamlessly to achieve promising results. Experimental results on a benchmark dataset demonstrate that our method performs better than several state-of-the-art methods.
Cyclic codes of dimension 2 over a finite field are shown to have at most two nonzero weights. This extends a construction of Rao et al (2010). We compute their weight distribution, and give a condition on the roots o...
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Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is propose...
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Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is proposed. Then it is applied to the speech endpoint detection. Furthermore, endpoint detection is carried out with the feature of energy. Experimental results show that both the computational efficiency and the robustness against noise of the proposed algorithm are improved remarkably compared with traditional algorithm. The average prob- ability of correct point detection （Pc-point） of the proposed voice activity detection （VAD） is 6.07% higher than that of G.729b VAD in different noisy at different signal-noise ratios （SNRs） environments.
The calculation of small-scale data is commonly used in scientific computing and application domain, and the high-efficiency method of small calculation can give play to the potency of many calculation and application...
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ISBN:
(纸本)9781467386456
The calculation of small-scale data is commonly used in scientific computing and application domain, and the high-efficiency method of small calculation can give play to the potency of many calculation and application. In this paper, a novel self-adaptive parallel computing method based on the graphics processing unit (GPU) architecture for batches of small scale computing tasks is proposed herein. It also provides two other implementation methods, which are the CPU algorithm and the traditional GPU algorithm, and then compares the efficiency of the three schemes. The experimental results show that the implementation efficiency of this new method on the GPU is better than that of method executed by the CPU and that of the traditional GPU algorithm for batching small-scale computing tasks. And this new approach is furthest utilizing the GPU resources, while a large amount of data is processed.
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