Multidirectional and multiscale edge detection via M-band wavelettransform

In this correspondence, the problem of directional and multiscale edge detection is considered. Orthogonal and linear-phase M-band wavelet transform is used to decompose the image into MxM channels. These channels are then combined such that each combination, which we refer to as decomposition filter, results in zero-crossings at the locations of edges corresponding to different directions and resolutions, and inherently performs regularization against noise. By applying a zero-crossing detector on the outputs of the decomposition filters, edge maps of desired resolution and direction are obtained. In addition, with the application of the Teager's energy operator at the analysis stage, it is possible to obtain a reduction in unwanted zero-crossings. Final edge maps of images are obtained through simple combinations of directional edge maps.     

VQ-adaptive block transform coding of images

Two new design techniques for adaptive orthogonal block transforms based on vector quantization (VQ) codebooks are presented. Both techniques start from reference vectors that are adapted to the characteristics of the signal to be coded, while using different methods to create orthogonal bases. The resulting transforms represent a signal coding tool that stands between a pure VQ scheme on one extreme and signal-independent, fixed block transformation-like discrete cosine transform (DCT) on the other. The proposed technique has superior compaction performance as compared to DCT both in the rendition of details of the image and in the peak signal-to-noise ratio (PSNR) figures.     

Scalability and Security Conflict for RFID Authentication Protocols

RFID technology continues to flourish as an inherent part of virtually every ubiquitous environment. However, it became clear that the public—implying the industry—seriously needs mechanisms emerging the security and privacy issues for increasing RFID applications. As the nodes of RFID systems mostly suffer from low computational power and small memory size, various attempts which propose to implement the existing security primitives and protocols, have ignored the realm of the cost limitations and failed. In this study, two recently proposed protocols—SSM and LRMAP—claiming to meet the standard privacy and security requirements are analyzed. The design of both protocols based on defining states where the server authenticates the tag in constant time in a more frequent normal state and needs a linear search in a rare abnormal states. Although both protocols claim to provide untraceability criteria in their design objectives, we outline a generic attack that both protocols failed to fulfill this claim. Moreover, we showed that the SSM protocol is vulnerable to a desynchronization attack which prevents a server from authenticating a legitimate tag. Resultantly, we conclude that defining computationally unbalanced tag states yields to a security/scalability conflict for RFID authentication protocols.     

Auction-Based Throughput Maximization in Cognitive Radio Networks Under Interference Constraint

Cognitive radio is an emerging technique to improve the utilization of radio frequency spectrum in wireless communication networks. That is, spectrum efficiency can be increased significantly by giving opportunistic access of the frequency bands to a group of cognitive users to whom the band has not been licensed. In this paper, as a cross layer application (MAC and physical layers) of graph theory, we consider the problem of throughput maximization of spectrum allocation in cognitive radio networks under interference constraint. We propose a novel auction-based channel allocation mechanism which tries to maximize both total and primary users’ utilities while satisfying signal to interference ratio constraint on primary receivers so that transmitted packets will be successfully received, without controlling secondary user powers. For comparison we discuss a greedy algorithm as well, however, one that does not handle interference issue. In order to compare results of proposed and greedy algorithms, we propose net throughput by taking into account outage probability of primary receiver. Simulation results show that exposing higher SINR (outage) threshold not only decreases total gain and primary users’ utilities but also worsens channel distribution performance. On the other hand adding auction mechanism significantly increases total gain throughput and primary user’ s utility. Particularly, up to SINR threshold values of 20 dBs, auction provides outstanding performance and proposed algorithm has total throughput results close to those of the greedy one even though no interference constraint is applied in the greedy algorithm. Another noticeable point of simulation results is crossover of net throughputs of proposed and greedy algorithms at a SINR threshold level after which results of ABSA-UNIC and NASA-UNIC are much better. This clearly shows superiority of proposed mechanism.     

Causal and semicausal AR image model identification using the EMalgorithm

The method presented by T. Katayama and T. Hirai (1990), who considered the problem of semicausal autoregressive (AR) parameter identification for images degraded by observation noise, is extended. In particular, an approach to identifying both the causal and semicausal AR parameters without a priori knowledge of the observation noise power is proposed. The image is decomposed into 1-D independent complex scalar subsystems resulting from the vector state-space model, using the unitary discrete Fourier transform (DFT). Then the expectation-maximization algorithm is applied to each subsystem to identify the AR parameters of the transformed image. The AR parameters of the original image are then identified using the least-square method. The restored image is obtained as a byproduct of the EM algorithm.     

Identification of image and blur parameters in frequency domainusing the EM algorithm

We extend a method presented previously, which considers the problem of the semicausal autoregressive (AR) parameter identification for images degraded by observation noise only. We propose a new approach to identify both the causal and semicausal AR parameters and blur parameters without a priori knowledge of the observation noise power and the PSF of the degradation. We decompose the image into 1-D independent complex scalar subsystems resulting from the vector state-space model by using the unitary discrete Fourier transform (DFT). Then, by applying the expectation-maximization (EM) algorithm to each subsystem, we identify the AR model and blur parameters of the transformed image. The AR parameters of the original image are then identified by using the least squares (LS) method. The restored image is obtained as a byproduct of the EM algorithm.     

A Note on the Periodicity and the Output Rate of Bit Search Type Generators

In this paper, the bit-search type irregular decimation algorithms, that are used within linear-feedback shift register (LFSR)-based stream ciphers, are investigated. In particular, bit-search generator (BSG) and and its variant ABSG are concentrated on and two different setups are considered for the analysis. In the first case, the input is assumed to be an m-sequence; it is shown that all possible output sequences can be classified into two sets, each of which is characterized by the equivalence of their elements up to shifts. Furthermore, it is proved that the cardinality of each of these sets is equal to the period of one of its elements and subsequently the (upper and lower) bounds on the expected output period (assuming that no subperiods exist) are derived. In the second setup, we work in a probabilistic framework and assume that the input sequence is evenly distributed (i.e., independent and identically distributed (i.i.d.) Bernoulli process with probability 1/2). Under these assumptions, closed-form expressions are derived for the distribution of the output length and the output rate, which is shown to be asymptotically Gaussian-distributed and concentrated around the mean with exponential tightness.     

Permutation Based Design of Orthogonal Block Transforms and Filter Banks

In this work, a new efficient design techniquefor orthogonal block transforms, lapped orthogonal transformsand 4-channel perfect reconstruction subband filter banks isdeveloped. The technique consists of permutation and sign changeoperations on a reference vector. This approach can be thoughtof as a generalization of the Hadamard transform in the sensethat the reference vector h ₀ (which will be a prototype low-pass filteralso forming one of the basis functions of the transform) willin general have components that are not identically 1's. Thedesign technique, a constructive method based on Hadamard arrays,provides a convenient means to explore new transforms. The meritof our method is that the number of unknowns and equality constraintsare both reduced significantly which render the design proceduremuch more feasible while guaranteeing at the same time linearphase.     

Motion estimation in the frequency domain using fuzzy c-planesclustering

A previous work explicitly models the discontinuous motion estimation problem in the frequency domain where the motion parameters are estimated using a harmonic retrieval approach. The vertical and horizontal components of the motion are independently estimated from the locations of the peaks of respective periodogram analyses and they are paired to obtain the motion vectors using a procedure proposed by Chen, Giannakis, Nandhakumar (see IEEE Trans. Image Processing, vol.7, p.1242-56, 1998). In this paper, we present a more efficient method that replaces the motion component pairing task and hence eliminates the problems of the pairing method described by Chen et al. The method described in this paper uses the fuzzy c-planes (FCP) clustering approach to fit planes to three-dimensional (3-D) frequency domain data obtained from the peaks of the periodograms. Experimental results are provided to demonstrate the effectiveness of the proposed method.