Robust nonlinear controller design for three-phase grid-connected photovoltaic systems under structured uncertainties

This paper presents a robust nonlinear controller design for a three-phase grid-connected photovoltaic (PV) system to control the current injected into the grid and the dc-link voltage for extracting maximum power from PV units. The controller is designed based on the partial feedback linearization approach, and the robustness of the proposed control scheme is ensured by considering structured uncertainties within the PV system model. An approach for modeling the uncertainties through the satisfaction of matching conditions is provided. The superiority of the proposed robust controller is demonstrated on a test system through simulation results under different system contingencies along with changes in atmospheric conditions. From the simulation results, it is evident that the robust controller provides excellent performance under various operating conditions. © 2014 IEEE.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

A robust grouping proof protocol for RFID EPC C1G2 tags

Several grouping proof protocols for RFID systems have been proposed over the years but they are either found to be vulnerable to certain attacks or do not comply with the EPC class-1 gen-2 (C1G2) standard because they use hash functions or other complex encryption schemes. Among other requirements, synchronization of keys, simultaneity, dependence, detecting illegitimate tags, eliminating unwanted tag processing, and denial-of-proof attacks have not been fully addressed by many. Our protocol addresses these important gaps by taking a holistic approach to grouping proofs and provides forward security, which is an open research issue. The protocol is based on simple (XOR) encryption and 128-bit pseudorandom number generators, operations that can be easily implemented on low-cost passive tags. Thus, our protocol enables large-scale implementations and achieves EPC C1G2 compliance while meeting the security requirements.

Hallucinating optimal high-dimensional subspaces

Linear subspace representations of appearance variation are pervasive in computer vision. This paper addresses the problem of robustly matching such subspaces (computing the similarity between them) when they are used to describe the scope of variations within sets of images of different (possibly greatly so) scales. A naïve solution of projecting the low-scale subspace into the high-scale image space is described first and subsequently shown to be inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed "downsampling constraint". The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The method is evaluated on the problem of matching sets of (i) face appearances under varying illumination and (ii) object appearances under varying viewpoint, using two large data sets. In comparison to the naïve matching, the proposed algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based. © 2014 Elsevier Ltd.

On the multicast lifetime of WANETs with multibeam antennas : Formulation, algorithms, and analysis

We explore the multicast lifetime capacity of energy-limited wireless ad hoc networks using directional multibeam antennas by formulating and solving the corresponding optimization problem. In such networks, each node is equipped with a practical smart antenna array that can be configured to support multiple beams with adjustable orientation and beamwidth. The special case of this optimization problem in networks with single beams have been extensively studied and shown to be NP-hard. In this paper, we provide a globally optimal solution to this problem by developing a general MILP formulation that can apply to various configurable antenna models, many of which are not supported by the existing formulations. In order to study the multicast lifetime capacity of large-scale networks, we also propose an efficient heuristic algorithm with guaranteed theoretical performance. In particular, we provide a sufficient condition to determine if its performance reaches optimum based on the analysis of its approximation ratio. These results are validated by experiments as well. The multicast lifetime capacity is then quantitatively studied by evaluating the proposed exact and heuristic algorithms using simulations. The experimental results also show that using two-beam antennas can exploit most lifetime capacity of the networks for multicast communications. © 2013 IEEE.

On the throughput of two-way relay networks using network coding

 Network coding has shown the promise of significant throughput improvement. In this paper, we study the network throughput using network coding and explore how the maximum throughput can be achieved in a two-way relay wireless network. Unlike previous studies, we consider a more general network with arbitrary structure of overhearing status between receivers and transmitters. To efficiently utilize the coding opportunities, we invent the concept of network coding cliques (NCCs), upon which a formal analysis on the network throughput using network coding is elaborated. In particular, we derive the closed-form expression of the network throughput under certain traffic load in a slotted ALOHA network with basic medium access control. Furthermore, the maximum throughput as well as optimal medium access probability at each node is studied under various network settings. Our theoretical findings have been validated by simulation as well.

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition; (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies; (iii) a description of the main databases of infrared facial images available to the researcher; and lastly (iv) a discussion of the most promising avenues for future research. © 2014 Elsevier Ltd.

An analytical model for optimal spectrum leasing under constraints of quality of service in CRNs

Cognitive radio improves spectrum efficiency and mitigates spectrum scarcity by allowing cognitive users to opportunistically access idle chunks of the spectrum owned by licensed users. In long-term spectrum leasing markets, secondary network operators make a decision about how much spectrum is optimal to fulfill their users' data transmission requirements. We study this optimization problem in multiple channel scenarios. Under the constrains of expected user admission rate and quality of service, we model the secondary network into a dynamic data transportation system. In this system, the spectrum accesses of both primary users and secondary users are in accordance with stochastic processes, respectively. The main metrics of quality of service we are concerned with include user admission rate, average transmission delay and stability of the delay. To quantify the relationship between spectrum provisioning and quality of service, we propose an approximate analytical model. We use the model to estimate the lower and upper bounds of the optimal amount of the spectrum. The distance between the bounds is relatively narrow. In addition, we design a simple algorithm to compute the optimum by using the bounds. We conduct numerical simulations on a slotted multiple channel dynamic spectrum access network model. Simulation results demonstrate the preciseness of the proposed model. Our work sheds light on the design of game and auction based dynamic spectrum sharing mechanisms in cognitive radio networks.

Chameleon hashing for secure and privacy-preserving vehicular communications

Many services and applications in vehicular ad-hoc networks (VANETs) require preserving and secure data communications. To improve driving safety and comfort, the traffic-related status information will be broadcasted regularly and shared among drivers. Without the security and privacy guarantees, attackers could track their interested vehicles by collecting and analyzing their traffic messages. Hence, anonymous message authentication is an essential requirement of VANETs. On the other hand, when a vehicle is involved in a dispute event of warning message, the certificate authority should be able to recover the real identity of this vehicle. To deal with this issue, we propose a new privacy-preserving authentication protocol with authority traceability using elliptic curve based chameleon hashing. Compared with existing schemes, our approach possesses the following features: 1) mutual and anonymous authentication for both vehicle-to-vehicle and vehicle-to-roadside communications, 2) vehicle unlinkability, 3) authority tracking capability, and 4) high computational efficiency. We also demonstrate the merits of our proposed scheme through security analysis and extensive performance evaluation.

Secure outsourced attribute-based signatures

Attribute-based signature (ABS) enables users to sign messages over attributes without revealing any information other than the fact that they have attested to the messages. However, heavy computational cost is required during signing in existing work of ABS, which grows linearly with the size of the predicate formula. As a result, this presents a significant challenge for resource-constrained devices (such as mobile devices or RFID tags) to perform such heavy computations independently. Aiming at tackling the challenge above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing-cloud service provider (S-CSP). Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the complexity. As a result, we present two concrete OABS schemes: i) in the first OABS scheme, the number of exponentiations involving in signing is reduced from O(d) to O(1) (nearly three), where d is the upper bound of threshold value defined in the predicate; ii) our second scheme is built on Herranz et al.'s construction with constant-size signatures. The number of exponentiations in signing is reduced from O(d2) to O(d) and the communication overhead is O(1). Security analysis demonstrates that both OABS schemes are secure in terms of the unforgeability and attribute-signer privacy definitions specified in the proposed security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of OABS and show how to achieve accountability as well.

New variable step-sizes minimizing mean-square deviation for the lms-type algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms. © 2014 Springer Science+Business Media New York.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

A comparison of mobile learning attitudes among university students in four nations

This paper contains findings based on administering a Likert-type mobile learning attitude survey to 261 university students from four nations, China, Lebanon, the UAE and the USA. Students were asked to provide attitudes and perceptions toward the use of mobile technologies in education. The results of the survey indicate that students in different regions of the world tend to agree that mobile learning could empower informal learning and could enhance teaching and learning. Lebanon students were most similar to those from the USA, while students from China were more similar to those from the UAE. Similarities and differences in results among nations and implications of these findings are discussed.

Internet traffic classification using constrained clustering

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.

Representing images using curvilinear feature driven subdivision surfaces.

This paper presents a subdivision-based vector graphics for image representation and creation. The graphics representation is a subdivision surface defined by a triangular mesh augmented with color attribute at vertices and feature attribute at edges. Special cubic B-splines are proposed to describe curvilinear features of an image. New subdivision rules are then designed accordingly, which are applied to the mesh and the color attribute to define the spatial distribution and piecewise-smoothly varying colors of the image. A sharpness factor is introduced to control the color transition across the curvilinear edges. In addition, an automatic algorithm is developed to convert a raster image into such a vector graphics representation. The algorithm first detects the curvilinear features of the image, then constructs a triangulation based on the curvilinear edges and feature attributes, and finally iteratively optimizes the vertex color attributes and updates the triangulation. Compared with existing vector-based image representations, the proposed representation and algorithm have the following advantages in addition to the common merits (such as editability and scalability): 1) they allow flexible mesh topology and handle images or objects with complicated boundaries or features effectively; 2) they are able to faithfully reconstruct curvilinear features, especially in modeling subtle shading effects around feature curves; and 3) they offer a simple way for the user to create images in a freehand style. The effectiveness of the proposed method has been demonstrated in experiments.