Sieve algorithms are regarded as the best algorithms to solve the shortest vector problem (SVP) on account of its good asymptotical quality, which could make it outperform enumeration algorithms in solving SVP of high dimension. However, due to its large memory requirement, sieve algorithms are not practical as expected, especially on high dimension lattice. To overcome this bottleneck, TupleSieve algorithm was proposed to reduce memory consumption by a trade-off between time and memory. In this work, aiming to make TupleSieve algorithm more practical, we combine TupleSieve algorithm with SubSieve technique and obtain a sub-exponential gain in running time. For 2-tuple sieve, 3-tuple sieve and arbitrary k-tuple sieve, when selecting projection index d appropriately, the time complexity of our algorithm is O(20.415(n-d)), O(20.566(n-d)) and $O(2^{rac{kmathrm{log}_2p}{1-k}(n-d)})$ respectively. In practice, we propose a practical variant of our algorithm based on GaussSieve algorithm. Experimental results show that our algorithm implementation is about two order of magnitude faster than FPLLL's GuassSieve algorithm. Moreover, techniques such as XOR-POPCNT trick, progressive sieving and appropriate projection index selection can be exploited to obtain a further acceleration.

In this letter, we derive a lower bound on the diversity multiplexing tradeoff (DMT) in multiple-input multiple-output (MIMO) nonorthogonal amplify-and-forward (NAF) cooperative channels with resolution-constrained channel state feedback. It is shown that power control based on the feedback improves the DMT performance significantly in contrast to the no-feedback case. For instance, the maximum diversity increase is exponential in K with K-level feedback.

We report on a novel micromechanical photonic integrated circuits (PIC) for integrating free-space optical systems on a chip. Using polysilicon surface-micromachining technique, micro-optical elements, three-dimensional optomechanical structures, and microactuators are monolithically integrated on silicon substrate. We will discuss the basic building blocks of the micromechanical PIC, including XYZ micropositioners, 2-axis tilting micromirrors, scanning microlenses, and their integration with vertical cavity surface-emitting lasers. We will also discuss their applications in reconfigurable optical interconnect and active alignment in parallel free-space optical interconnect systems.

The fast handover protocol adopted in a IPv6 hierarchical structure provides a seamless handover in wireless IP networks by minimizing the handover latency. To reduce the handover latency, the fast handover uses anticipation based on layer 2 trigger. Nonetheless, a mobile node can still lose its connection with the old link during the fast handover procedures. Accordingly, this paper analyzes the handover latency and packet delivery costs associated with fast handover failure cases based on a timing diagram.

In this paper, we define an attributed random graph, which can be considered as a generalization of conventional ones, to include multiple attributes as well as numeric attribute instead of a single nominal attribute in random vertices and edges. Then we derive the probability equations for an attributed graph to be an outcome graph of the attributed random graph, and the equations for the entropy calculation of the attributed random graph. Finally, we propose the application areas to computer vision and machine learning using these concepts.

In this paper we design a fast fabric defect detection framework (Fast-DDF) based on gray histogram back-projection, which adopts end to end multi-convoluted network model to realize defect classification. First, the back-projection image is established through the gray histogram on fabric image, and the closing operation and adaptive threshold segmentation method are performed to screen the impurity information and extract the defect regions. Then, the defect images segmented by the Fast-DDF are marked and normalized into the multi-layer convolutional neural network for training. Finally, in order to solve the problem of difficult adjustment of network model parameters and long training time, some strategies such as batch normalization of samples and network fine tuning are proposed. The experimental results on the TILDA database show that our method can deal with various defect types of textile fabrics. The average detection accuracy with a higher rate of 96.12% in the database of five different defects, and the single image detection speed only needs 0.72s.

It is generally believed that one major function of the immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in the immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model (here, we call it the binary immune network) based on the biological immune response network was proposed in our previous work. However, there are some problems like that input and memory were all binary and it did not consider the antigen diversity of immune system. To improve these problems, in this paper we propose a fuzzy immune network model by considering the antigen diversity of immune system that is the most important property to be exhibited in the immune system. As an application, the proposed fuzzy immune network is applied to pattern recognition problem. Computer simulations illustrate that the proposed fuzzy immune network model not only can improve the problems existing in the binary immune network but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As one example of the latter, we propose an artificial immune system architecture inspired by the human immune system and apply it to pattern recognition. We test the proposed architecture by the simulations on arbitrary sequences of analog input pattern classification and binary input pattern recognition. The simulation results illustrate that the proposed architecture is effective at clustering arbitrary sequences of analog input patterns into stable categories and it can produce stronger noise immunity than the binary network .

The purpose of this paper is to present a case study of the development, implementation and performance analysis of an autonomous flight control strategy for a 1-meter small-sized unmanned aerial vehicle. Firstly, a learning algorithm based open-loop control is proposed by simulating a skilled human operator's manipulation of the aircraft. This is aimed to generate a set of command data inputs and investigate the multi-channel control characteristics with the open-loop control. Secondly, a feedforward plus a proportional and derivative (PD) feedback control is employed to control the vehicle in following the command data to complete the loitering flight. The PD control gains are tuned automatically according to the attitude of the vehicle using the fuzzy logic theory. Thirdly, autonomous flight experiments conducted on a 1-meter small-sized aerial vehicle demonstrated the effectiveness of the proposed method.