A novel Position-Invariant Robust Feature, designated as PIRF, is presented to address the problem of highly dynamic scene recognition. The PIRF is obtained by identifying existing local features (i.e. SIFT) that have a wide baseline visibility within a place (one place contains more than one sequential images). These wide-baseline visible features are then represented as a single PIRF, which is computed as an average of all descriptors associated with the PIRF. Particularly, PIRFs are robust against highly dynamical changes in scene: a single PIRF can be matched correctly against many features from many dynamical images. This paper also describes an approach to using these features for scene recognition. Recognition proceeds by matching an individual PIRF to a set of features from test images, with subsequent majority voting to identify a place with the highest matched PIRF. The PIRF system is trained and tested on 2000+ outdoor omnidirectional images and on COLD datasets. Despite its simplicity, PIRF offers a markedly better rate of recognition for dynamic outdoor scenes (ca. 90%) than the use of other features. Additionally, a robot navigation system based on PIRF (PIRF-Nav) can outperform other incremental topological mapping methods in terms of time (70% less) and memory. The number of PIRFs can be reduced further to reduce the time while retaining high accuracy, which makes it suitable for long-term recognition and localization.

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

DTDs are continuously updated according to changes in the real world. Let t be an XML document valid against a DTD D, and suppose that D is updated by an update script s. In general, we cannot uniquely "infer" a transformation of t from s, i.e., we cannot uniquely determine the elements in t that should be deleted and/or the positions in t that new elements should be inserted into. In this paper, we consider inferring K optimum transformations of t from s so that a user finds the most desirable transformation more easily. We first show that the problem of inferring K optimum transformations of an XML document from an update script is NP-hard even if K = 1. Then, assuming that an update script is of length one, we show an algorithm for solving the problem, which runs in time polynomial of |D|, |t|, and K.

This letter presents a novel pre-transformed interleaved frequency division multiple access (IFDMA) transmission structure that improves system performance without the desire of channel information at the transmitter. Simulation results show that the proposed structure can provide improved system performance while only moderately increasing the complexity, and keeping the advantage of a low peak-to-average power ratio (PAPR) of the transmitted signal for SISO and MISO channels.

Chiba et al. (2006) proposed a framework of program transformation of term rewriting systems by developed templates. Contrast to the previous framework of program transformation by templates based on lambda calculus, this framework provides a method to verify the correctness of transformation automatically. Tupling (Bird, 1980) is a well-known technique to eliminate redundant recursive calls for improving efficiency of programs. In Chiba et al.'s framework, however, one can not use tuple symbols to construct developed templates. Thus their framework is not capable of tupling transformations. In this paper, we propose a more flexible notion of templates so that a wider variety of transformations, including tupling transformations, can be handled.

In this paper, we propose four new general constructions of LCZ/ZCZ sequence sets based on interleaving technique and affine transformations. A larger family of LCZ/ZCZ sequence sets with longer period are generated by these constructions, which are more flexible among the selection of the alphabet size, the period of the sequences and the length of LCZ/ZCZ, compared with those generated by the known constructions. Especially, two families of the newly constructed sequences can achieve or almost achieve the theoretic bound.

Topological structure of peer-to-peer (P2P) networks affects their operating performance. Thus, various models have been proposed to construct an efficient topology for the P2P networks. However, due to the simultaneous failures of peers and other disastrous events, it is difficult to maintain the originally designed topological structure that provides the network with some performance benefits. For this reason, in this paper we propose a simple local rewiring method that changes the network topology to have small diameter as well as highly clustered structure. Moreover, the presented evaluation study shows how these topological properties are involved with the performance of P2P networks.

This paper proposes closed form solutions to the L2-sensitivity minimization subject to L2-scaling constraints for second-order state-space digital filters with real poles. We consider two cases of second-order digital filters: distinct real poles and multiple real poles. The proposed approach reduces the constrained optimization problem to an unconstrained optimization problem by appropriate variable transformation. We can express the L2-sensitivity by a simple linear combination of exponential functions and formulate the L2-sensitivity minimization problem by a simple polynomial equation. As a result, L2-sensitivity is expressed in closed form, and its minimization subject to L2-scaling constraints is achieved without iterative calculations.

Model-Driven Engineering and Domain-Specific Modeling Languages are encouraging an increased used of metamodels for the definition of languages and tools. Although the Meta Object Facility language is the standard for metamodeling, there are alternative metamodeling languages that are aimed at satisfying specific requirements. In this context, sharing information throughout different domains and tools requires not only being able to translate models between modeling languages defined with the same metamodeling language, but also between different metamodeling languages. This paper addresses this latter need describing a general technique to define transformations that perform this translation. In this work, two case studies illustrate the application of this process.

A hidden Markov model (HMM)-based parameter estimation scheme is proposed for wideband speech recovery. In each Markov state, the estimation efficiency is improved using a new mapping function derived from the weighted least squares of vector deviations. The experimental results reveal that the performance of the proposed scheme is superior to that combining the HMM and Gaussian mixture model (GMM).

In many engineering problems it is required to convert a polynomial into another polynomial through a transformation. Due to its wide range of applications, the polynomial transformation has received much attention and many techniques have been developed to compute the coefficients of a transformed polynomial from those of an original polynomial. In this letter a new result is presented concerning the transformation matrix for arbitrary polynomial transformation. A simple algorithm is obtained which enables one to successively compute transformation matrices of various order.

Finding an appropriate data transformation between two schemas has been an important problem. In this paper, assuming that an update script between original and updated DTDs is available, we consider inferring a transformation algorithm from the original DTD and the update script such that the algorithm transforms each document valid against the original DTD into a document valid against the updated DTD. We first show a transformation algorithm inferred from a DTD and an update script. We next show a sufficient condition under which the transformation algorithm inferred from a DTD d and an update script is unambiguous, i.e., for any document t valid against d, elements to be deleted/inserted can unambiguously be determined. Finally, we show a polynomial-time algorithm for testing the sufficient condition.

Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.

Role-based access control (RBAC) is widely used as an access control mechanism in various computer systems. Since an organization's lines of authority influence the authorized privileges of jobs, roles also form a hierarchical structure. A role graph is a model that represents role hierarchies and is suitable for the runtime phase of RBAC deployment. Since a role graph cannot take various forms for given roles and cannot handle abstraction of roles well, however, it is not suitable for the design phase of RBAC deployment. Hence, an extended role graph, which can take a more flexible form than that of a role graph, is proposed. The extended role graph improves diversity and clarifies abstraction of roles, making it suitable for the design phase. An equivalent transformation algorithm (ETA), for transforming an extended role graph into an equivalent role graph, is also proposed. Using the ETA, system administrators can deploy efficiently RBAC by using an extended role graph in the design phase and a standard role graph in the runtime phase.

Combining transmission of ultra wideband pulses, organized in blocks, with the inclusion of cyclic prefixing pulses yields a pulsewidth periodic signal at the receiver. Although unknown, this signal fits perfectly the diversity exploitive architecture of a RAKE receiver. Aiming to profit from this signal arrangement, we propose a pulse shape modulation system employing a RAKE receiver that estimates this periodic signal during a training interval and uses the estimated values for detection of data symbols. Our proposal relies on the invariability of the multipath propagation channel during the transmission of a UWB packet, the adequate application of the cyclic prefix, and the fact that different transmitted pulses result in different periodic signals at the receiver. This system is equivalent to transforming the multipath nature of the UWB propagation channel into a multichannel digital communications affected solely by additive noise. Our proposal is important because it ameliorates the performance of a pulse shape modulation RAKE receiver. On the other hand, the cost of our proposed system resides in the inefficiencies product of the cyclic prefix inclusion.

Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

This paper proposes the Gramian-preserving frequency transformation for linear discrete-time state-space systems. In this frequency transformation, we replace each delay element of a discrete-time system with an allpass system that has a balanced realization. This approach can generate transformed systems that have the same controllability/observability Gramians as those of the original system. From this result, we show that the Gramian-preserving frequency transformation gives us transformed systems with different magnitude characteristics, but with the same structural property with respect to the Gramians as that of the original system. This paper also presents a simple method for realization of the Gramian-preserving frequency transformation. This method makes use of the cascaded normalized lattice structure of allpass systems.

In this paper we show some new look at large deviation theorems from the viewpoint of the information-spectrum (IS) methods, which has been first exploited in information theory, and also demonstrate a new basic formula for the large deviation rate function in general, which is expressed as a pair of the lower and upper IS rate functions. In particular, we are interested in establishing the general large deviation rate functions that are derivable as the Fenchel-Legendre transform of the cumulant generating function. The final goal is to show, under some mild condition, a necessary and sufficient condition for the IS rate function to be derivable as the Fenchel-Legendre transform of the cumulant generating function, i.e., to be a rate function of Gartner-Ellis type.

This paper presents the near-field to far-field transformation for an outdoor radar cross section (RCS) range. Direct measurement of the large actual target requires quite a long measurement range. The near-field to far-field RCS transformation method achieves the reduction of measurement range. However the non-uniformity of the incident electric field distribution on the target causes some errors in RCS prediction. We propose a novel near-field to far-field RCS transformation method that can be applied to an outdoor RCS measurement. The non-uniformity of the incident electric field distribution is successfully resolved by introducing the correction term of the ground bounce. We investigate the validity of the proposed method by the simulation and measurement.

We point out that the interval algorithm can be expressed in the form of a shift on the sequence space. Then we clarify that, by using a Bernoulli process, the interval algorithm can generate only a block of Markov chains or a sequence of independent blocks of Markov chains but not a stationary Markov process. By virtue of the finitary coding constructed by Hamachi and Keane, we obtain the procedure, called the finitary interval algorithm, to generate a Markov process by using the interval algorithm. The finitary interval algorithm also gives maps, defined almost everywhere, which transform a Markov measure to a Bernoulli measure.