—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which ...
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Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effectiv...
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A novel non-geometrical stochastic model(NGSM)for non-wide sense station ary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)channels is *** model is based on a conventional NGSM and employs a more accurate m...
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A novel non-geometrical stochastic model(NGSM)for non-wide sense station ary uncorrelated scattering(non-WSSUS)vehicle-to-vehicle(V2V)channels is *** model is based on a conventional NGSM and employs a more accurate method to reproduce the realistic characteristics of V2V channels,which successfully extends the existing NGSM to include the line-of-sight(LoS)***,the statistical properties of the proposed model in different scenarios,including Doppler power spectral density(PSD),power delay profile(PDP),and the tap correlation coefficient matrix are simulated and compared with those of the existing ***,the simulation results dem onstrate not only the utility of the proposed model,but also the correctness of our theoreti cal derivations.
In this paper, we propose a comprehensive strategy to detect the windows from scene point clouds. First, the planar points are extracted after computing the dimensionality structure of each point. The planar points ar...
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ISBN:
(纸本)9781538684986;9781538684979
In this paper, we propose a comprehensive strategy to detect the windows from scene point clouds. First, the planar points are extracted after computing the dimensionality structure of each point. The planar points are then grouped into clusters based on the normal direction and distance between points. Next, the building walls are extracted from the scene point clouds according to a collection of characteristics such as surface area, normal direction, and topological relationship. Finally, the building facade is sliced both horizontally and vertically. A slice-based algorithm based on the point number is proposed to locate the windows. The experimental results demonstrate that our method can detect the window location quickly and effectively.
The convolution neural network for image classification is an application of deep learning on image processing. Convolutional neural networks have the advantage of being able to convolve directly with image pixels and...
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Ant Colony Optimization (AGO) has the character of positive feedback, distributed searching, and greedy searching. It is applicable to optimization grouping problems. Traditional cryptographic research is mainly bas...
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Ant Colony Optimization (AGO) has the character of positive feedback, distributed searching, and greedy searching. It is applicable to optimization grouping problems. Traditional cryptographic research is mainly based on pure mathematical methods which have complicated theories and algorithm. It seems that there is no relationship between cryptography and ACO. Actually, some problems in cryptography are due to optimization grouping problems that could be improved using an evolutionary algorithm. Therefore, this paper presents a new method of solving secure curve selection problems using ACO. We improved Complex Multiplication (CM) by combining Evolutionary Cryptography Theory with Weber polynomial solutions. We found that ACO makes full use of valid information generated from factorization and allocates computing resource reasonably. It greatly increases the performance of Weber polynomial solutions. Compared with traditional CM, which can only search one root once time, our new method searches all roots of the polynomial once, and the average time needed to search for one root reduces rapidly. The more roots are searched, the more ECs are obtained.
How to quickly compute the number of points on an Elliptic Curve (EC) has been a longstanding challenge. The computational complexity of the algorithm usually employed makes it highly inefficient. Unlike the general...
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How to quickly compute the number of points on an Elliptic Curve (EC) has been a longstanding challenge. The computational complexity of the algorithm usually employed makes it highly inefficient. Unlike the general EC, a simple method called the Weil theorem can be used to compute the order of an EC characterized by a small prime number, such as the Kobltiz EC characterized by two. The fifteen secure ECs recommended by the National Institute of Standards and Technology (NIST) Digital Signature Standard contain five Koblitz ECs whose maximum base domain reaches 571 bits. Experimental results show that the computation speed decreases for base domains exceeding 600 bits. In this paper, we propose a simple method that combines the Weil theorem with Pascals triangle, which greatly reduces the computational complexity. We have validated the performance of this method for base fields ranging from 2l^100 to 2^1000. Furthermore, this new method can be generalized to any ECs characterized by any small prime number.
In order to effectively detect phishing attacks, this paper designed a new detection system for phishing websites using LSTM Recurrent neural networks. LSTM has the advantage of capturing data timing and long-term dep...
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Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection...
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ISBN:
(数字)9781728137261
ISBN:
(纸本)9781728137278
Loop closure detection is a crucial module in simultaneous localization and mapping (SLAM), which reduces accumulative error in building environment map. Traditional appearance-based methods for loop closure detection are vulnerable to environmental variations as they mainly rely on hand-crafted features. The convolutional neural networks (ConvNets) can automatically learn feature representation from original image, and it is more robust to illumination changes. However, the ConvNets methods may fail when the viewpoint changes significantly due to it extract global features. In order to solve the problem mentioned above, in this paper, we design an unsupervised network which combines the advantage of the traditional and ConvNets methods, and propose a new module named spatial pyramid pooling based convolution autoencoder (SPP-CAE). We evaluate the performance of the proposed method on several open datasets using precision-recall metric. The results show that our method is feasible for detecting loops and is more robust than state-of-the-art methods.
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