Band selection for hyperspectral images using probabilistic memetic algorithm

Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two recently proposed state-of-the-art MAs for band selection are presented.     

Image inpainting algorithm based on TV model and evolutionary algorithm

With the development of modern image processing techniques, the numbers of images increase at a high speed in network. As a new form of visual communication, image is widely used in network transmission. However, the image information would be lost after transmission. In view of this, we are motivated to restore the image to make it complete in an effective and efficient way in order to save the network bandwidth. At present, there are two main methods for digital image restoration, texture-based method and non-textured-based method. In the texture-based method, Criminisi algorithm is a widely used algorithm. However, the inaccurate completion order and the inefficiency in searching matching patches are two main limitations of Criminisi algorithm. To overcome these shortcomings, in this paper, an exemplar image completion based on evolutionary algorithm is proposed. In the non-textured-based method, total variation method is a typical algorithm. An improved total variation algorithm is proposed in this paper. In the improved algorithm, the diffusion coefficients are defined according to the distance and direction between the damaged pixel and its neighborhood pixel. Experimental results show that the proposed algorithms have better general performance in image completion. And these two new algorithms could improve the experience of network surfing and reduce the network communication cost.     

Hierarchical classification in text mining for sentiment analysis of online news

Sentiment analysis in text mining is a challenging task. Sentiment is subtly reflected by the tone and affective content of a writer’s words. Conventional text mining techniques, which are based on keyword frequencies, usually run short of accurately detecting such subjective information implied in the text. In this paper, we evaluate several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset with respect to the contextual polarity and frequent terms of a document. We call this approach “hierarchical classification”. The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied. Meanwhile we use two methods to test this hierarchical classification model, and also have a comparison of the two methods.     

Statistical shape modelling for expression-invariant face analysis and recognition

Paper introduces a 3-D shape representation scheme for automatic face analysis and identification, and demonstrates its invariance to facial expression. The core of this scheme lies on the combination of statistical shape modelling and non-rigid deformation matching. While the former matches 3-D faces with facial expression, the latter provides a low-dimensional feature vector that controls the deformation of model for matching the shape of new input, thereby enabling robust identification of 3-D faces. The proposed scheme is also able to handle the pose variation without large part of missing data. To assist the establishment of dense point correspondences, a modified free-form-deformation based on B-spline warping is applied with the help of extracted landmarks. The hybrid iterative closest point method is introduced for matching the models and new data. The feasibility and effectiveness of the proposed method was investigated using standard publicly available Gavab and BU-3DFE datasets, which contain faces with expression and pose changes. The performance of the system was compared with that of nine benchmark approaches. The experimental results demonstrate that the proposed scheme provides a competitive solution for face recognition.     

Quantum state transfer between an optomechanical cavity and a diamond nuclear spin ensemble

We explore an efficient scheme for transferring quantum state between an optomechanical cavity and nuclear spins of nitrogen-vacancy centers in diamond, where quantum information can be efficiently stored (retrieved) into (from) the nuclear spin ensemble assisted by a mechanical resonator in a dispersive regime. Our scheme works for a broad range of cavity frequencies and might have potential applications in employing the nuclear spin ensemble as a memory in quantum information processing. The feasibility of our protocol is analyzed using currently available parameters.     

Lower bound on concurrence for arbitrary-dimensional tripartite quantum states

In this paper, we study the concurrence of arbitrary-dimensional tripartite quantum states. An explicit operational lower bound of concurrence is obtained in terms of the concurrence of substates. A given example shows that our lower bound may improve the well-known existing lower bounds of concurrence. The significance of our result is to get a lower bound when we study the concurrence of arbitrary \(m\otimes n\otimes l\)-dimensional tripartite quantum states.     

Practical chosen-ciphertext secure Hierarchical Identity-Based Broadcast Encryption

We focus on practical Hierarchical Identity-Based Broadcast Encryption (HIBBE) with semantic security against adaptively chosen-ciphertext attacks (CCA2) in the standard model. We achieve this goal in two steps. First, we propose a new HIBBE scheme that is secure against chosen-plaintext attacks (CPA). Compared with the existing HIBBE scheme that is built from composite-order bilinear groups, our construction is based on prime-order bilinear groups. The much better efficiency of group operations in prime-order bilinear groups makes our proposed HIBBE scheme more practical. Then, we convert it into a CCA2-secure scheme at the cost of a one-time signature. Instead of extending one user hierarchy in the Canetti–Halevi–Katz approach from CPA-secure (\(l+1\))-Hierarchical Identity-Based Encryption [(\(l+1\))-HIBE] to CCA2-secure \(l\)-HIBE, our construction merely adds one on-the-fly dummy user in the basic scheme. We formally prove the security of these two schemes in the standard model. Comprehensive theoretical analyses and experimental results demonstrate that the proposed HIBBE schemes achieve desirable performance.     

A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers

Internet Data Center (IDC) is one of important emerging cyber-physical systems. To guarantee the quality of service for their worldwide users, large Internet service providers usually operate multiple geographically distributed IDCs. The enormous power consumption of these data centers may lead to both huge electricity bills and considerable carbon emissions. To mitigate these problems, on-site renewable energy plants are emerging in recent years. Since the renewable energy is intermittent, greening geographical load balancing (GGLB for short) has been proposed to reduce both the electricity bills and carbon emissions by following the renewables. However, GGLB is not able to well deal with the wildly fluctuating wind power when applied into IDCs with on-site wind energy plants. It may either fail to minimize the total electricity bills or incur the costly frequent on–off switching of servers. In order to minimize the total electricity bills of geographically distributed IDCs with on-site wind energy plants, we formulate the total electricity bills minimization problem and propose a novel two-time-scale load balancing framework TLB to solve it. First, TLB models the runtime cooling efficiency for each IDC. Then it predicts the future fine-grained (e.g., 10-min) on-site wind power output at the beginning of each scheduling period (e.g., an hour). After that, TLB transforms the primal optimization problem into a typical mixed-integer linear programming problem and solves it to finally obtain the optimal scheduling policy including the open server number as well as the request routing policy. It is worth noting that the open server number of each IDC will remain the same during each scheduling period. As an application instance of TLB, we also design a two-time-scale load balancing algorithm TLB-ARMA for our experimental scenario. Evaluation results based on real-life traces show that TLB-ARMA is able to reduce the total electricity bills by as much as 12.58 % compared with the hourly executed GGLB without incurring the costly repeated on–off switching of servers.     

Node localization algorithm for wireless sensor networks using compressive sensing theory

In recent years, localization has been recognized as an important supporting technology for wireless sensor networks (WSNs). Along with the increase in WSN indoor applications, indoor localization has become a hot research topic and many localization algorithms have been studied. Among these algorithms, the localization method based on compressive sensing theory emerges as a popular approach to indoor localization. In this approach, the nodes are sparse when compared to the number of grids utilized to represent the locations of the nodes, so the locations are considered as sparse signal and can be reconstructed using the compressive sensing techniques. The localization problem is formulated as the sparse reconstruction of sparsifying matrix which is comprised of measurement of received signal at grids. In order to improve the localization accuracy and meet the real-time requirement of localization applications in large indoor area, an indoor localization algorithm based on dynamic measurement compressive sensing for wireless sensor networks is proposed. Using the bounding-box method, we firstly identify a potential area that possesses the independent features. Instead of using the entire node deployment region as the measurement area, our method can decrease the number of meshing and also the dimension of measurement matrix. Meanwhile, we assume that only the anchor nodes which have communication relationship with the unknown nodes can be used as the measuring nodes; the measurement matrix of unknown nodes which need to be localized can be dynamically constructed according to the potential area and the received anchor node information, and the maximum number of measurement is decided by the number of grids of potential area. The proposed algorithm can mitigate the measurement redundancy and improve the real-time feature. Simulation results indicate that the proposed algorithm can reduce the time complexity and also maintain good localization accuracy and localization efficiency.