We develop an effective algorithm, based on the filtered backprojection (FBP) approach, for the imaging of vegetation. Under the FBP scheme, the reconstruction amounts at a non-trivial Fourier inversion, since the data are Fourier samples arranged on a non-Cartesian grid. The computational issue is efficiently tackled by Non-Uniform Fast Fourier Transforms (NUFFTs), whose complexity grows asymptotically as that of a standard FFT. Furthermore, significant speed-ups, as compared to fast CPU implementations, are obtained by a parallel versions of the NUFFT algorithm, purposely designed to be run on Graphic Processing Units (GPUs) by using the CUDA language. The performance of the parallel algorithm has been assessed in comparison to a CPU-multicore accelerated, Matlab implementation of the same routine, to other CPU-multicore accelerated implementations based on standard FFT and employing linear, cubic, spline and sinc interpolations and to a different, parallel algorithm exploiting a parallel linear interpolation stage. The proposed approach has resulted the most computationally convenient. Furthermore, an indoor, polarimetric experimental setup is developed, capable to isolate and introduce, one at a time, different non-idealities of a real acquisition, as the sources (wind, rain) of temporal decorrelation. Experimental far-field polarimetric measurements on a thuja plicata (western redcedar) tree point out the performance of the set up algorithm, its robustness against data truncation and temporal decorrelation as well as the possibility of discriminating scatterers with different features within the investigated scene.

A Boolean function is said to be correlation immune if its output leaks no information about its input values. Such functions have many applications in computer security practices including the construction of key stream generators from a set of shift registers. Finding methods for easy construction of correlation immune Boolean functions has been an active research area since the introduction of the notion by Siegenthaler. In this paper, we present several constructions of nonpalindromic correlation immune symmetric Boolean functions. Our methods involve finding binomial coefficient identities and obtaining new correlation immune functions from known correlation immune functions. We also consider the construction of higher order correlation immunity symmetric functions and propose a class of third order correlation immune symmetric functions on n variables, where n+1(≥ 9) is a perfect square.

We study nn random symmetric matrices whose entries above the diagonal are iid random variables each of which takes 1 with probability p and 0 with probability 1-p, for a given density parameter p=α/n for sufficiently large α. For a given such matrix A, we consider a matrix A ' that is obtained by removing some rows and corresponding columns with too many value 1 entries. Then for this A', we show that the largest eigenvalue is asymptotically close to α+1 and its eigenvector is almost parallel to all one vector (1,...,1).

In routing, we usually use OSPF with Dijkstra or RIP with Bellman-Ford, but they can only treat single metric routing problem. With multiple metrics, we would use the weighted average of the metrics or techniques from operations research, but they are not suitable for routing because they lack validity and simplicity. Here, we propose a routing algorithm to deal with the three security metrics proposed by I. A. Almerhag and M. E. Woodward, and show an example routing policy. Besides, we make a study on the constraints of the metrics and the routing policies, and come to the precondition of the proposed routing algorithm.

In this paper, we deal with the algebraic immunity of the symmetric Boolean functions. The algebraic immunity is a property which measures the resistance against the algebraic attacks on symmetric ciphers. It is well known that the algebraic immunity of the symmetric Boolean functions is completely determined by a narrow class of annihilators with low degree which is denoted by G(n,). We study and determine the weight support of part of these functions. Basing on this, we obtain some relations between the algebraic immunity of a symmetric Boolean function and its simplified value vector. For applications, we put forward an upper bound on the number of the symmetric Boolean functions with algebraic immunity at least d and prove that the algebraic immunity of the symmetric palindromic functions is not high.

This paper introduces an interactive expression editing system that allows users to design facial expressions easily. Currently, popular example-based methods construct face models based on the examples of target face. The shortcoming of these methods is that they cannot create expressions for novel faces: target faces not previously recorded in the database. We propose a solution to overcome this limitation. We present an interactive facial-geometric-feature animation system for generating expressions of novel faces. Our system is easy to use. By click-dragging control points on the target face, on the computer screen display, unique expressions are generated automatically. To guarantee natural animation results, our animation model employs prior knowledge based on various individuals' expressions. One model prior is learned from motion vector fields to guarantee effective facial motions. Another, different, model prior is learned from facial shape space to ensure the result has a real facial shape. Interactive animation problem is formulated in a maximum a posterior (MAP) framework to search for optimal results by combining the priors with user-defined constraints. We give an extension of the Motion Propagation (MP) algorithm to infer facial motions for novel target faces from a subset of the control points. Experimental results on different facial animations demonstrate the effectiveness of the proposed method. Moreover, one application of our system is exhibited in this paper, where users create expressions for facial sketches interactively.

Linear discriminant analysis (LDA) is one of the well-known schemes for feature extraction and dimensionality reduction of labeled data. Recently, two-dimensional LDA (2DLDA) for matrices such as images has been reformulated into symmetric 2DLDA (S2DLDA), which is solved by an iterative algorithm. In this paper, we propose a non-iterative S2DLDA and experimentally show that the proposed method achieves comparable classification accuracy with the conventional S2DLDA, while the proposed method is computationally more efficient than the conventional S2DLDA.

In this paper, we address adaptive link switching over heterogeneous wireless access networks including IEEE 802.11. When an IEEE 802.11 link is congested, the transmission link of a terminal with multi-RATs (radio access technologies) is switched to another radio access systems. To this end, we propose link-level metrics of LC (link cost) and AC (access cost) for quantifying TCP congestion over IEEE 802.11 networks. The proposed metric can be easily measured at a local wireless terminal, and that enables each multi-RAT terminal to work in a distributed way. Through various indoor and outdoor experiments using a test-bed system, we verify that the proposed link level metrics are good indicators of TCP traffic congestion. Experimental results show that the proposed metrics can detect congestion occurrence quickly, and avoid the TCP throughput degradation of other neighboring terminals, when they are used for transmission link switching.

This paper proposes a new method to reduce the cost of nearest neighbor searches in metric spaces. Many similarity search indexes recursively divide a region into subregions by using pivots, and construct a tree-structured index. Most of recently developed indexes focus on pruning objects and do not pay much attention to the tree balancing. As a result, indexes having imbalanced tree-structure may be constructed and the search cost is degraded. We propose a similarity search index called the Partitioning Capacity (PC) Tree. It selects the optimal pivot in terms of the PC that quantifies the balance of the regions partitioned by a pivot as well as the estimated effectiveness of the search pruning by the pivot. As a result, PCTree reduces the search cost for various data distributions. We experimentally compared PCTree with four indexes using synthetic data and five real datasets. The experimental results shows that the PCTree successfully reduces the search cost.

It is theoretically impossible to restore the original fingerprint image from a sequence of line images captured by a line sensor. However, in this paper we propose a unique fingerprint-image-generation algorithm, which derives fingerprint images from sequences of line images captured at different swipe speeds by the line sensor. A continuous image representation, called trajectory, is used in modeling distortion of raw fingerprint images. Sequences of line images captured from the same finger are considered as sequences of points, which are sampled on the same trajectory in N-dimensional vector space. The key point here is not to reconstruct the original image, but to generate identical images from the trajectory, which are independent of the swipe speed of the finger. The method for applying the algorithm in a practical application is also presented. Experimental results on a raw fingerprint image database from a line sensor show that the generated fingerprint images are independent of swipe speed, and can achieve remarkable matching performance with a conventional minutiae matcher.

A new image watermarking scheme is presented to achieve high capacity information hiding and geometric invariance simultaneously. Visually salient region is introduced into watermark synchronization. The saliency value of a region is used as the quantitative measure of robustness, based on which the idea of locally most salient region (LMSR) is proposed to generate the disjoint invariant regions. A meaningful binary watermark is then encoded using Chinese Remainder Theorem (CRT) in transform domain. Simulation results and comparisons demonstrate the effectiveness of the proposed scheme.

Blood pressure is the measurement of the force exerted by blood against the walls of the arteries. Hypertension is a major risk factor of cardiovascular diseases. The systolic and diastolic blood pressures obtained from the oscillometric method could carry clues about hypertension. However, blood pressure is influenced by individual traits such as physiology, the geometry of the heart, body figure, gender and age. Therefore, consideration of individual traits is a requisite for reliable hypertension monitoring. The oscillation waveforms extracted from the cuff pressure reflect individual traits in terms of oscillation patterns that vary in size and amplitude over time. Thus, uniform features for individual traits from the oscillation patterns were extracted, and they were applied to evaluate systolic and diastolic blood pressures using two feedforward neural networks. The measurements of systolic and diastolic blood pressures from two neural networks were compared with the average values of systolic and diastolic blood pressures obtained by two nurses using the auscultatory method. The recognition performance was based on the difference between the blood pressures measured by the auscultation method and the proposed method with two neural networks. The recognition performance for systolic blood pressure was found to be 98.2% for 20 mmHg, 93.5% for 15 mmHg, and 82.3% for 10 mmHg, based on maximum negative amplitude. The recognition performance for diastolic blood pressure was found to be 100% for 20 mmHg, 98.8% for 15 mmHg, and 88.2% for 10 mmHg based on maximum positive amplitude. In our results, systolic blood pressure showed more fluctuation than diastolic blood pressure in terms of individual traits, and subjects with prehypertension or hypertension (systolic blood pressure) showed a stronger steep-slope pattern in 1/3 section of the feature windows than normal subjects. The other side, subjects with prehypertension or hypertension (diastolic blood pressure) showed a steep-slope pattern in front of the feature windows (2/3 section) than normal subjects. This paper presented a novel blood pressure measurement system that can monitor hypertension using personalized traits. Our study can serve as a foundation for reliable hypertension diagnosis and management based on consideration of individual traits.

The diffraction by a composite wedge composed of a perfect conductor and a lossy dielectric is investigated using the hidden rays of diffraction (HRD) method. The usual principle of geometrical optics is employed to trace not only ordinary rays incident on the lit boundary but also hidden rays incident on the shadow boundary. The modified propagation constants are adopted to represent the non-uniform plane wave transmission through the lossy dielectric. The HRD diffraction coefficients are constructed routinely by the sum of the cotangent functions, which have one-to-one correspondence with both ordinary and hidden rays. The angular period of the cotangent functions is adjusted to satisfy the edge condition at the tip of the composite wedge. The accuracy of the HRD diffraction coefficients in the physical region is checked by showing how closely the diffraction coefficients in the complementary region satisfy the null-field condition.

A path diversity is an effective technique to get highly reliable communications in the sensor network. In this paper, the path diversity is examined for a tree network composed of binary symmetric channels (BSC) from the view point of bit error probability (BEP). End-nodes of the network are connected to a fusion center, which sums up the received data. The probability density function (pdf) of decision variable conditioned on a source node data is derived by an iterative algorithm to obtain BEP. Numerical results show that in the case of a majority decision, BEP at the fusion center is almost the same as the BSC crossover probability due to the path diversity effects, even if the number of relay links increases.

In this paper, we explicitly construct a large class of symmetric Boolean functions on 2k variables with algebraic immunity not less than d, where integer k is given arbitrarily and d is a given suffix of k in binary representation. If let d = k, our constructed functions achieve the maximum algebraic immunity. Remarkably, 2⌊ log2k ⌋ + 2 symmetric Boolean functions on 2k variables with maximum algebraic immunity are constructed, which are much more than the previous constructions. Based on our construction, a lower bound of symmetric Boolean functions with algebraic immunity not less than d is derived, which is 2⌊ log2d ⌋ + 2(k-d+1). As far as we know, this is the first lower bound of this kind.

This letter presents integrating algorithms for affine constraints defined on a manifold. We first explain definition and geometric representation of affine constraints. Next, we derive integrating algorithms to calculate independent first integrals of affine constraints for the two cases where the they are completely integrable and partially nonintegrable. Moreover, we prove the existence of inverse functions in the algorithms. Some examples are also shown to verify our results.

Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. In this paper, we propose a novel and fundamental algorithm for cancelable biometrics called correlation-invariant random filtering (CIRF) with provable security. Then we construct a method for generating cancelable fingerprint templates based on the chip matching algorithm and the CIRF. Experimental evaluation shows that our method has almost the same accuracy as the conventional fingerprint verification based on the chip matching algorithm.

Locality of high frequency electromagnetic scattering phenomena is embodied and imported to the Method of Moments (MoM) to reduce computational load. The proposed method solves currents on small areas only around inner and edge stationary phase points (SPPs) on the scatterer surfaces. The range of MoM area is explicitly specified in terms of Fresnel zone number as a function of frequency, source and observer positions. Based upon this criterion, scatterer of arbitrary size and shape can be solved with almost frequency independent number of unknowns. In some special cases like focusing systems, locality disappears and the method reduces to the standard MoM. The hybrid method called PO-MoM is complementarily introduced to cope with these cases, where Fresnel zone number with analogous but different definition is used. The selective use of Local-MoM and PO-MoM provides frequency insensitive number of unknowns for general combination of source and observation points. Numerical examples of RCS calculation for two dimensional flat and curved surfaces are presented to demonstrate the accuracy and reduction of unknowns of this method. The Fresnel zone, introduced in the scattering analysis for the first time, is a useful indicator of the locality or the boundary for MoM areas.

We present a new method that can represent the reflectance of metallic paints accurately using a two-layer reflectance model with sampled microfacet distribution functions. We model the structure of metallic paints simplified by two layers: a binder surface that follows a microfacet distribution and a sub-layer that also follows a facet distribution. In the sub-layer, the diffuse and the specular reflectance represent color pigments and metallic flakes respectively. We use an iterative method based on the principle of Gauss-Seidel relaxation that stably fits the measured data to our highly non-linear model. We optimize the model by handling the microfacet distribution terms as a piecewise linear non-parametric form in order to increase its degree of freedom. The proposed model is validated by applying it to various metallic paints. The results show that our model has better fitting performance compared to the models used in other studies. Our model provides better accuracy due to the non-parametric terms employed in the model, and also gives efficiency in analyzing the characteristics of metallic paints by the analytical form embedded in the model. The non-parametric terms for the microfacet distribution in our model require densely measured data but not for the entire BRDF(bidirectional reflectance distribution function) domain, so that our method can reduce the burden of data acquisition during measurement. Especially, it becomes efficient for a system that uses a curved-sample based measurement system which allows us to obtain dense data in microfacet domain by a single measurement.

We study the use of the additive white Gaussian noise channel to achieve a cryptographic primitive that is important in secure multiparty computation. A protocol for unconditionally secure oblivious transfer is presented. We show that channel input alphabets with a certain algebraic structure and their partitions are useful in achieving the requirements on the primitive. Signal design for a protocol with high information rate is discussed.