摘要：
Encryption plays an important role in protecting data, especially data transferred on the Internet. However, encryption is computationally expensive and this leads to high energy costs. Parallel encryption solutions using more CPU/GPU cores can achieve high performance. If we consider energy efficiency to be cost effective using parallel encryption solutions at the same time, this problem can be alleviated effectively. Because many CPU/GPU cores and encryption are pervasive currently, saving energy cost by parallel encrypting has become an unavoidable problem. In this paper, we propose an energy-efficient parallel Advance Encryption Standard (AES) algorithm for CPU-GPU heterogeneous platforms. These platforms, such as the Green 500 computers, are popular in both high performance and general computing. Parallelizing AES algorithm, using both GPUs and CPUs, balances the workload between CPUs and GPUs based on their computing capacities. This approach also uses the Nvidia Management Library (NVML) to adjust GPU frequencies, overlaps data transfers and computation, and fully utilizes GPU computing resources to reduce energy consumption as much as possible. Experiments conducted on a platform with one K20M GPU and two Xeon E5-2640 v2 CPUs show that this approach can reduce energy consumption by 74% compared to CPU-only parallel AES algorithm and 21% compared to GPU-only parallel AES algorithm on the same platform. Its energy efficiency is 4.66 MB/Joule on average higher than both CPU-only parallel AES algorithm (1.15 MB/Joule) and GPU-only parallel AES algorithm (3.65 MB/Joule). As an energy-efficient parallel AES algorithm solution, it can be used to encrypt data on heterogeneous platforms to save energy, especially for the computers with thousands of heterogeneous nodes. (C) 2020 Elsevier B.V. All rights reserved.
摘要：
With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.
摘要：
We introduce the concept of the photonic crystal (PC) into the waveguide system and propose a metal-dielectric-metal (MDM) waveguide system coupled with three periodic stubs and a multimode cavity. This system can realize multiple ultra-narrow plasmon-induced transparency (PIT) effects with a minimum linewidth of about 4 nm. These PIT peaks exhibit tunable evolution characteristics of two groups. The wavelengths and linewidths of one group of PIT peaks in the transmission spectra only can be changed slightly, while the wavelengths of another group of PIT peaks can be changed significantly by adjusting one of the geometric parameters of the cavity. This research may open up new design ideas for ultra-narrowband multichannel filters and switches.
摘要：
Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
期刊：
Journal of Materials Science: Materials in Electronics,2020年31(17):14421-14425 ISSN：0957-4522
通讯作者：
Wang, Haiou;Tan, Weishi
作者机构：
[Su, Kunpeng; Wang, Haiou; Zhang, Hui; Huang, Shuai; Huo, Dexuan] Hangzhou Dianzi Univ, Inst Mat Phys, Hangzhou 310018, Peoples R China.;[Tan, Weishi] Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;[Tan, Weishi] Nanjing Univ Sci & Technol, Dept Appl Phys, Key Lab Soft Chem & Funct Mat, Minist Educ, Nanjing 210094, Peoples R China.
通讯机构：
[Wang, Haiou; Tan, Weishi] H;[Tan, Weishi] N;Hangzhou Dianzi Univ, Inst Mat Phys, Hangzhou 310018, Peoples R China.;Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;Nanjing Univ Sci & Technol, Dept Appl Phys, Key Lab Soft Chem & Funct Mat, Minist Educ, Nanjing 210094, Peoples R China.
摘要：
Half-doped perovskite Manganese oxide has been widely studied because of its excellent properties such as colossal magnetoresistance (CMR) effect and charge-ordered (CO) phase separation. In this work, four Sm(0.5)Ca(0.5)MnO(3)samples with different particle sizes are prepared by high-temperature solid-state reaction and ball milling. The crystal structure of the samples is studied by X-ray diffraction (XRD). The Sm(0.5)Ca(0.5)MnO(3)sample is single phase, which belongs to orthorhombic structure. The surface morphology and particle size of the samples are examined by scanning electron microscope (SEM). The average particle size of the sample without ball milling is about 4 mu m. With ball milling time for 12 h, 24 h, and 36 h, the particle size decreases, and finally it reaches hundreds to tens of nanometers. This shows that ball milling is an effective way to control the particle size. The M-T curves and M-H hysteresis loops of the samples are measured by physical properties measurements systems (PPMS). The two M-T curves measured in the warming and cooling processes do not overlap for Sm(0.5)Ca(0.5)MnO(3)without ball milling, and the phenomenon of thermal hysteresis appears. Meanwhile, the M-T curve has a significant protuberance peak near 270 K. All of these indicate the CO behavior, whereas the particle size of Sm(0.5)Ca(0.5)MnO(3)decreases with different milling times (12-36 h) and the CO phase is suppressed gradually, which leads to the decrease of CO temperature, magnetization, remanence, and coercivity.
期刊：
Journal of Luminescence,2020年228:117636 ISSN：0022-2313
通讯作者：
Zhou, Weiping;Tan, Weishi
作者机构：
[Chen, Guibin; Cheng, Ju; Zhai, Zhangyin; Ma, Chunlin] Huaiyin Normal Univ, Sch Phys & Elect Elect Engn, Huaian 223001, Peoples R China.;[Zhou, Weiping] Nanchang Univ, Sch Mat Sci & Engn, Nanchang 330031, Jiangxi, Peoples R China.;[Tan, Weishi] Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.;[Wang, Xiaoxiong; Wang, Xingyu; Ma, Chunlin] Nanjing Univ Sci & Technol, Sch Sci, Nanjing 210094, Peoples R China.
通讯机构：
[Zhou, Weiping] N;[Tan, Weishi] H;Nanchang Univ, Sch Mat Sci & Engn, Nanchang 330031, Jiangxi, Peoples R China.;Hunan City Univ, Coll Informat & Elect Engn, All Solid State Energy Storage Mat & Devices Key, Yiyang 413002, Peoples R China.
期刊：
JOURNAL OF COMPUTATIONAL BIOLOGY,2020年28(1):33-42 ISSN：1066-5277
通讯作者：
Cao, Buwen
作者机构：
[Qin, Hua; Cao, Buwen; Deng, Shuguang] Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.;[Cao, Buwen; Luo, Jiawei] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Peoples R China.;[Li, Guanghui] East China Jiaotong Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China.;[Liang, Cheng] Shandong Normal Univ, Coll Informat Sci & Engn, Jinan, Peoples R China.
通讯机构：
[Cao, Buwen] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.
关键词：
clustering;functional module;functional similarity network;miRNA similarity;microRNA-disease association
摘要：
Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations.
摘要：
A fundamental problem facing deep neural networks is that they require a large amount of data to keep the system efficient in complex applications. Promising results of this problem are made possible by using techniques such as data enhancement or transfer learning in large data sets. However, when the application provides limited or unbalanced data, the problem persists. In addition, the number of false positives generated by deep model training has a significant negative impact on system performance. This study aims to solve the problem of false positives and class imbalances by implementing an improved filter library framework for Cole pest identification. The system consists of three main units: First, the primary diagnostic unit (boundary box generator) generates a bounding box containing the location of the infected area and class. Then, the promising box belonging to each category is used as an input to the secondary diagnostic unit (CNN filter bank) for verification. In the second unit, the misclassified samples are filtered by training for each category of independent CNN classifiers. The result of the CNN filter bank is to determine if a target belongs to the category because it is detected (true) or no (false), otherwise. Finally, an integrated unit combines the information of the autonomous unit and the secondary unit in the future while maintaining a true positive sample and eliminating false positives of misclassification in the first unit. By this implementation, the recognition rate of this method is about 96%, which is 13% higher than our previous work in the complex task of Cole disease and pest identification. In addition, our system is able to handle false positives generated by bounding box generators and class imbalances that occur on data sets with limited data.
摘要：
Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.
关键词：
Community detection;Multi-similarity;Vertex feature;Social network;K-means clustering
摘要：
Social network detection and identification constitute an important topic in the field of sociology. Previous graph similarity has focus on either the topological structure of graph or the feature value of vertex. In this work, a multi-similarity measure method for community is described. The approach devised by using multi-similarity properties based on vertex features, relationship density and topology structure, and therefore is can be formulated and extended to practical implementation. The framework of community detection combines K-means clustering, spectral clustering and modularity algorithm-making it an effective basis for the realization of a social network interpretation. With this scheme, three evaluation criteria are proposed for methodology determination. The experimental results show a better working performance of the recommended method than traditional algorithms via statistical analysis.
摘要：
Single-molecule localization methods play a vital role in a localization-based super-resolution fluorescence microscopy. However, it is difficult for conventional localization schemes based on the Gaussian fitting to locate overlapped high-density fluorescent emitters. Currently, in the spatial domain, the compressive-sensing-based algorithm (CSSTORM) can localize high-emitter-density images. However, the computational cost of this approach is extremely high, which limits its practical application. Here, we propose an alternative frequency-domain compressed sensing (FD-CS) technique for fast super-resolution imaging. Unlike the CSSTORM method, which is a measurement matrix based on the point spread function, a Fourier dictionary designed in the frequency domain and orthogonal matching pursuit is used to reliably recover the original signal. The simulation and experimental results prove that the FD-CS is 1000 times faster than CSSTORM with CVX and ten times faster than that with L1-Homotopy with almost the same localization accuracy and recall rate. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to a super-resolution image analysis.
