Signature Vector Research Articles

Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

The minimum variance distortionless response (MVDR) beamforming algorithm is used in smart antenna design for wireless communication. The operation of MVDR is based on finding the optimum weight to direct the main lobe beam to the desired user location with a unity gain. MVDR is very sensitive to signature vector mismatch. This mismatch occurs due to waveform deformation, local scattering, imperfect array element calibration and element shape distortion, which leads to errors in finding the direction of arrival (DOA) of the signal. In this paper, a new technique to modify the MVDR is presented, modelled and evaluated. The proposed algorithm is named modified MVDR (MMVDR) and is dependent on reconstructing the signature vector (steering vector) and the covariance matrix to introduce accurate beamformer weight by re-localization the reference element to be in the middle of ULA, rather than at one end side. The new reference position partitions the array’s elements into two groups around this reference, which leads to treat received signals with identical phase along the array’s elements, as well as increasing the degree of freedom to deals with different types of uniform arrays. The evaluation results show that MMVDR outperforms MVDR with respect to beamformer accuracy, system cost, processing time and signal classification to overcome the errors in DOA estimation which occur due to fabrication and calibration errors.

The Kruskal-Katona Theorem and a Characterization of System Signatures

We show how to determine if a given vector can be the signature of a system on a finite number of components and, if so, exhibit such a system in terms of its structure function. The method employs combinatorial results from the theory of (finite) simplicial complexes, and provides a full characterization of signature vectors using a theorem of Kruskal (1963) and Katona (1968). We also show how the same approach can provide new combinatorial proofs of further results, e.g. that the signature vector of a system cannot have isolated zeroes. Finally, we prove that a signature with all nonzero entries must be a uniform distribution.

The Kruskal-Katona Theorem and a Characterization of System Signatures

We show how to determine if a given vector can be the signature of a system on a finite number of components and, if so, exhibit such a system in terms of its structure function. The method employs combinatorial results from the theory of (finite) simplicial complexes, and provides a full characterization of signature vectors using a theorem of Kruskal (1963) and Katona (1968). We also show how the same approach can provide new combinatorial proofs of further results, e.g. that the signature vector of a system cannot have isolated zeroes. Finally, we prove that a signature with all nonzero entries must be a uniform distribution.

The Kruskal-Katona Theorem and a Characterization of System Signatures

We show how to determine if a given vector can be the signature of a system on a finite number of components and, if so, exhibit such a system in terms of its structure function. The method employs combinatorial results from the theory of (finite) simplicial complexes, and provides a full characterization of signature vectors using a theorem of Kruskal (1963) and Katona (1968). We also show how the same approach can provide new combinatorial proofs of further results, e.g. that the signature vector of a system cannot have isolated zeroes. Finally, we prove that a signature with all nonzero entries must be a uniform distribution.

An attribute-based signature scheme from lattice assumption

Inspired by the framework of Boyen, in this paper, an attribute-based signature (ABS) scheme from lattice assumption is proposed. In this attribute-based signature scheme, an entity’s attributes set corresponds to the concatenation of a lattice matrix with the sum of some random matrices, and the signature vector is generated by using the Preimage Sampling algorithm. Compared with current attribute-based signature schemes, this scheme can resist quantum attacks and enjoy shorter public-key, smaller signature size and higher efficiency.

Semi-Distributed Backoff: Collision-Aware Migration from Random to Deterministic Backoff

Collision is imminent in wireless networks (WNs) capacitated with randomized distributed channel access. In such networks, the probability of frame drop (Pdrop) is greater than zero due to successive collisions with a finite retry limit m. The IEEE 802.11 DCF is one such widely accepted protocol used in wireless local area networks (WLANs). The objective of the work reported in this paper is to provide guaranteed channel access to colliding frames for avoiding successive collisions for any loss-sensitive application, and thus, increase network throughput under finite m. We propose the semi-distributed backoff (SDB) algorithm, which operates in two modes: S-mode and R-mode. The key idea of the SDB scheme is to perform receiver-side backoff, if a mobile station encounters collision even after performing the existing sender-side backoff procedures. Using the SDB scheme, we design Semi-DCF, a MAC protocol for WLANs, which performs opportunistic migration from random to deterministic backoff. Semi-DCF functions independent of m. This protocol exploits the collision detection capability of receivers for disseminating information on optimal backoffs to the contenders using signature vectors. An analysis of Semi-DCF using the 2D Markov chain, coupled with results of network simulation establishes its superior performance in WLANs.

Adaptive and Robust Digital Harmonic-Reject Mixer With Optimized Local Oscillator Spacing

Harmonic-reject mixers (HRM) are commonly used in wideband receivers to suppress the effects of spurious harmonic interferers. Both analog and digital HRMs have been proposed in the literature. In existing works, the local oscillator (LO) phases are assumed to be equally spaced. When viewed as a multiuser access system, the drawback of equally-spaced LO phases is that the signature vectors of the desired signal and some harmonic interferers become aligned, limiting the number of interferers that can be attenuated. In this paper, a digital HRM with non-uniform phase spacing that can suppress additional harmonics while being robust to gain and phase mismatches is described. This paper also presents a low-complexity, adaptive and robust digital HRM receiver that enables the fewest LOs and analog-to-digital converters necessary depending on the interference environment for low power. The performance of the adaptive non-uniformly spaced HRM is shown to be significantly superior to existing HRMs with equal LO phase spacing.

A low dimensional approach on network characterization.

In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.

Reliability Properties of $(n,f,k)$ Systems

In the present article we study several reliability properties of (n, f, k) systems. More specifically, for the special case k = 2, we deduce the generating function of a system's reliability, and discuss techniques for evaluating its signature vector and reliability function. In addition, for the general case (k ≥ 2), we establish some stochastic comparisons between reliability structures' lifetimes using their signatures, and present several results and applications pertaining to the lifetimes of (n, f, k) systems.

Discrete Fourier transform based pattern classifiers

Abstract A technique for pattern classification using the Fourier transform combined with the nearest neighbor classifier is proposed. The multidimensional fast Fourier transform (FFT) is applied to the patterns in the data base. Then the magnitudes of the Fourier coefficients are sorted in descending order and the first P coefficients with largest magnitudes are selected, where P is a design parameter. These coefficients are then used in further processing rather than the original patterns. When a noisy pattern is presented for classification, the pattern’s P Fourier coefficients with largest magnitude are extracted. The coefficients are arranged in a vector in the descending order of their magnitudes. The obtained vector is referred to as the signature vector of the corresponding pattern. Then the distance between the signature vector of the pattern to be classified and the signature vectors of the patterns in the data base are computed and the pattern to be classified is matched with a pattern in the data base whose signature vector is closest to the signature vector of the pattern being classified.

Symmetry Feature Extraction Based on Quaternionic Local Phase

It has been shown that lines and edges are important for biological visual systems and this information can be described in terms of symmetric relations (even and odd) which permits a compact data representation. In order to define symmetry, we need two basic concepts, an object definition and transforms definitions. The main aim of this work is the feature symmetry extraction for computer vision applications using the local phase. In order to compute the local phase, we use the quaternion analytical signal and the monogenic with the atomic function up(x) a novel window which is easy to derivate and has compact support in space domain. In addition, we use the up(x) and Plop(x, y) to compute the Hilbert and the Riesz transform in terms of them first derivative. As an illustration, we apply the atomic function based Riesz transform using a multiscale approach to obtain characteristics of symmetry of object shapes in images to build feature signature vectors which in turn can be used for object classification.

Mixture Representations for the Joint Distribution of Lifetimes of two Coherent Systems with Shared Components

The signature of a system is defined as the vector whoseith element is the probability that the system fails concurrently with theith component failure. The signature vector is known to be a distribution-free measure and a representation of the system's survival function has been developed in terms of the system's signature. The present work is devoted to the study of the joint distribution of lifetimes of pairs of systems with shared components. Here, a new distribution-free measure, the ‘joint bivariate signature’, of a pair of systems with shared components is defined, and a new representation theorem for the joint survival function of the system lifetimes is established. The theorem is shown to facilitate the study of the dependence between systems and the comparative performance of two pairs of such systems.

Mixture Representations for the Joint Distribution of Lifetimes of two Coherent Systems with Shared Components

The signature of a system is defined as the vector whoseith element is the probability that the system fails concurrently with theith component failure. The signature vector is known to be a distribution-free measure and a representation of the system's survival function has been developed in terms of the system's signature. The present work is devoted to the study of the joint distribution of lifetimes of pairs of systems with shared components. Here, a new distribution-free measure, the ‘joint bivariate signature’, of a pair of systems with shared components is defined, and a new representation theorem for the joint survival function of the system lifetimes is established. The theorem is shown to facilitate the study of the dependence between systems and the comparative performance of two pairs of such systems.

On Signatures of Series and Parallel Systems Consisting of Modules with Arbitrary Structures

The signature of a system is a useful concept not only in the analysis of binary coherent systems but also in network reliability. Computation of system signature is a well-defined combinatorial problem. This article is concerned with the computation of signature vectors of series and parallel systems consisting of modules. We derive simple formulas for the signature and minimal signature of series and parallel systems based on signatures and minimal signatures of modules with given structures. We present computational results to illustrate the findings.

Digital Equalization of a Polyphase Harmonic Mixer

A mathematical framework is developed for the harmonic distortion problem. This formulation has interesting analogies to well-studied problems in array processing and multiuser detection. Based on this framework, a minimum mean squared error equalizer is first derived. By exploiting the unique structure of the signature vectors, a noniterative low-complexity blind equalizer is proposed. Existing harmonic rejection approaches are analyzed within this framework. The proposed equalizer can be shown to significantly outperform existing approaches in the presence of large interferers.

Texture classification using discrete Tchebichef moments

In this paper, a method to characterize texture images based on discrete Tchebichef moments is presented. A global signature vector is derived from the moment matrix by taking into account both the magnitudes of the moments and their order. The performance of our method in several texture classification problems was compared with that achieved through other standard approaches. These include Haralick's gray-level co-occurrence matrices, Gabor filters, and local binary patterns. An extensive texture classification study was carried out by selecting images with different contents from the Brodatz, Outex, and VisTex databases. The results show that the proposed method is able to capture the essential information about texture, showing comparable or even higher performance than conventional procedures. Thus, it can be considered as an effective and competitive technique for texture characterization.

Efficient feature tracking of time-varying surfaces using multi-scale motion flow propagation

This paper presents a framework for efficient feature tracking of time-varying surfaces. The framework can not only capture the dynamic geometry features on time-varying surfaces, but can also compute the accurate boundaries of the geometry features. The basic idea of the proposed approach is using the multi-scale motion flow and surface matching information to propagate the feature frame on time-varying surfaces. We first define an effective multi-scale geometry motion flow for the time-varying surfaces, which efficiently propagates the geometry features along the time direction of the time-varying surfaces. By combining both the approximately invariant signature vectors and geometry motion flow vectors, we also incorporate the shape matching into the system to process feature tracking for time-varying surfaces in large deformation while with low frame sampling rate. Our approach does not depend on the topological connection of the underlying surfaces. Thus, it can process both mesh-based and point-based time-varying surfaces without vertex-to-vertex correspondence across the frames. Feature tracking results on different kinds of time-varying surfaces illustrate the efficiency and effectiveness of the proposed method.

Vehicle matching in smart camera networks using image projection profiles at multiple instances

Tracking vehicles using a network of cameras with non-overlapping views is a challenging problem of great importance in traffic surveillance. One of the main challenges is accurate vehicle matching across the cameras. Even if the cameras have similar views on vehicles, vehicle matching remains a difficult task due to changes of their appearance between observations, and inaccurate detections and occlusions, which often occur in real scenarios. To be executed on smart cameras the matching has also to be efficient in terms of needed data and computations. To address these challenges we present a low complexity method for vehicle matching robust against appearance changes and inaccuracies in vehicle detection. We efficiently represent vehicle appearances using signature vectors composed of Radon transform like projections of the vehicle images and compare them in a coarse-to-fine fashion using a simple combination of 1-D correlations. To deal with appearance changes we include multiple observations in each vehicle appearance model. These observations are automatically collected along the vehicle trajectory. The proposed signature vectors can be calculated in low-complexity smart cameras, by a simple scan-line algorithm of the camera software itself, and transmitted to the other smart cameras or to the central server. Extensive experiments based on real traffic surveillance videos recorded in a tunnel validate our approach.

Invariant correlation to position and rotation using a binary mask applied to binary and gray images

In this paper more alternative ways to generate the binary ring masks are studied and a new methodology is presented when in the analysis the image come with some distortion due to rotation. This new algorithm requires low computational cost. Signature vectors of the target so like signature vectors of the object to be recognized in the problem image are obtained using a binary ring mask constructed in accordance with the real or the imaginary part of their Fourier transform analyzing two different conditions in each one. In this manner, each image target or problem image, will have four unique binary ring masks. The four ways are analyzed and the best is chosen. In addition, due to any image with rotation include some distortion, the best transect is chosen in the Fourier plane in order to obtain the best signature through the different ways to obtain the binary mask. This methodology is applied to two cases: to identify different types of alphabetic letters in Arial font and to identify different fossil diatoms images. Considering the great similarity between diatom images the results obtained are excellent.

On the number of failed components in a coherent operating system

In this paper, we investigate the number of failed components in a coherent system. We consider an (n−m+1)-out-of-n system and we compute the probability that there are exactly i failures, i=0,…,m−1, in the system under the condition that it is operating at time t. Several properties of the proposed time dependent probabilities are studied. The results are then extended to coherent systems consisting of n components with an arbitrary signature vector.