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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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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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.
Acquisition is a key technology in DSSS *** differential correlation is usually employed to eliminate the effect of frequency ***,as the length of pseudo-code grows and the decrease of the SNR,the traditional differen...
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Acquisition is a key technology in DSSS *** differential correlation is usually employed to eliminate the effect of frequency ***,as the length of pseudo-code grows and the decrease of the SNR,the traditional differential acquisition algorithms will result in great loss of *** paper presents an improved differential acquisition method,named as M-orders Auto-Correlation based on Differential correlation（MAC-DF）.In the proposed method,the input signal is multiplied by the complex conjugate of the pseudo-code to eliminate its *** that,the product is applied to the M-orders Auto-Correlation to compensate the SNR loss caused by the differential *** means of mathematical model to analyze its acquisition *** compare MAC-DF acquisition algorithm with the differential coherent and non-coherent acquisition algorithms through *** simulation results indicate that this algorithm is approximately 5-6dB superior to the traditional differential acquisition algorithms in improving acquisition sensitivity and more adaptive to work under low SNR.
Partial AUC is a popular performance metric used in many applications such as bioinformatics and biometrics. Optimizing this measure directly is often challenging since no close form solution exists. In recent work, N...
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Differential evolution (DE) is a population-based random optimisation algorithm, which has been used to solve benchmark functions and real-world optimisation problems. The DE has three important operators: mutation, c...
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