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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Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting n...
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Community structure is one of the most important features in real networks and reveals the internal organization of the vertices. Uncovering accurate community structure is effective for understanding and exploiting networks. Tolerance Granulation based Community Detection Algorithm(TGCDA) is proposed in this paper, which uses tolerance relation(namely tolerance granulation) to granulate a network hierarchically. Firstly, TGCDA relies on the tolerance relation among vertices to form an initial granule set. Then granules in this set which satisfied granulation coefficient are hierarchically merged by tolerance granulation operation. The process is finished till the granule set includes one granule. Finally, select a granule set with maximum granulation criterion to handle overlapping vertices among some granules. The overlapping vertices are merged into corresponding granules based on their degrees of affiliation to realize the community partition of complex networks. The final granules are regarded as communities so that the granulation for a network is actually the community partition of the *** on several datasets show our algorithm is effective and it can identify the community structure more accurately. On real world networks, TGCDA achieves Normalized Mutual Information(NMI) accuracy 17.55% higher than NFA averagely and on synthetic random networks, the NMI accuracy is also improved. For some networks which have a clear community structure, TGCDA is more effective and can detect more accurate community structure than other algorithms.
Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to ...
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Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.
The subwavelength metal grating is designed and simulated by using the finite difference time domain (FDTD) algorithm. The transmission characteristics of subwavelength metal grating structure are studied based on the...
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