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Our goal of this paper is to find a construction of all ℓ-quasi-cyclic self-dual codes over a finite field $ {\mathbb F}_q $ F q of length $ m\ell $ mℓ for every positive even integer ℓ. In this paper, we study the case where $ x^m-1 $ x m − 1 has an arbitrary number of irreducible factors in $ {\mathbb F}_q [x] $ F q [ x ] ; in the previous studies, only some special cases where $ x^m-1 $ x m − 1 has exactly two or three irreducible factors in $ {\mathbb F}_q [x] $ F q [ x ] , were studied. Firstly, the binary code case is completed: for any even positive integer ℓ, every binary ℓ-quasi-cyclic self-dual code can be obtained by our construction. Secondly, we work on the q-ary code cases for an odd prime power q. We find an explicit method for construction of all ℓ-quasi-cyclic self-dual codes over $ {\mathbb F}_q $ F q of length $ m\ell $ mℓ for any even positive integer ℓ, where we require that $ q \equiv 1 \pmod {4} $ q ≡ 1 (mod 4) if the index $ \ell \ge 6 $ ℓ ≥ 6. By implementation of our method, we obtain a new optimal binary self-dual code $ [172, 86, 24] $ [ 172 , 86 , 24 ] , which is also a quasi-cyclic code of index 4. [ABSTRACT FROM AUTHOR]

Pulse compression techniques enable the radar designers to transmit long duration pulse to cater the need of high power at the transmitter and achieve the range resolution of short duration pulse at the radar receiver simultaneously. Due to easy generation and processing, binary codes are widely used as pulse compression codes in radars, sonars and spread-spectrum communication systems. But binary codes have high unwanted pulse compression sidelobes at the output of pulse compression receivers. In this paper, optimum binary codes reported in the literatures are overlaid on Discrete frequency sequences to reduce unwanted peaks sidelobes of optimum binary codes. The proposed approach not only reduced peak sidelobes of optimum binary codes but also increased the pulse compression ratio. In this paper, optimum discrete frequency sequences for overlaying with binary codes are optimized using the Modified Simulated Annealing Algorithm (MSAA). A Discrete Frequency Coded binary signal has a complex signal structure which will be difficult to detect and analyze by enemy Electronics Support Measures (ESM). In this paper, a unified adaptive radar model is also proposed to cater the needs of today's radar signal design requirements. The proposed model will help to improve range resolution, the detection capability of radar, reduce the peak sidelobes significantly, and also improve the ESM capability of radar systems. [ABSTRACT FROM AUTHOR]

In this paper, we used the definition of fuzzy vector spaces and fuzzy subsets in combination in order to define fuzzy codes over a fuzzy vector space. We found that the combined definitions did not satisfy the definition of fuzzy vector spaces over a Galois field F2, and gave the conditions in which the combined definitions hold the definition of fuzzy vector spaces in order to define binary fuzzy codes. By defining binary fuzzy codes over fuzzy vector space in relation to the probability of binary symmetric channels (BSC) sending a codeword incorrectly and the weight of error pattern between codewords, we updated properties of Hamming distance of binary fuzzy codes over fuzzy vector space. From the updated properties, we found that the properties of Hamming distance stated over a vector space also satisfy in binary fuzzy codes defined over a fuzzy vector space. Furthermore, we found some interesting results on the decoding of fuzzy codewords sent over a BSC using minimum nonzero Hamming distance of binary fuzzy codes. [ABSTRACT FROM AUTHOR]

In this this paper all binary codes of length 552 which admit the sporadic simple group Co 3 as an imprimitive transitive permutation group are determined. Our aim is to understand the results also by using theoretical arguments and to discuss the combinatorial properties of the codes as well as their relation to some special properties of the Leech lattice group Co 3 . For all codes (with two exceptions) we obtain the weight enumerators and in many interesting cases the classification of codewords under the action of the group of code automorphisms Co 3 . The exceptional codes are both self-dual and have minimum weight 12. [ABSTRACT FROM AUTHOR]

In this paper, we discussed binary fuzzy codes over a vector space Fn2 by relating classical codes with the probability of a binary symmetric channel (BSC) for receiving a sent codeword correctly. We used the weight of error patterns between a received word and the possible sent codewords to define fuzzy words over n-dimensional vector space Fn2, and used it to define binary fuzzy codes. We also added some properties of Hamming distance of binary fuzzy codes, and the bounds of a Hamming distance of binary fuzzy codes for p=1/r, where r⩾3, and r∈Z+, are determined. Finding Hamming distance of binary fuzzy codes is used for decoding sent messages on a BSC. [ABSTRACT FROM AUTHOR]

In this paper, we propose a randomized Bayesian optimal phase II (RBOP2) design with a binary endpoint (e.g., response rate). A beta-binomial distribution is used to model the binary endpoint for a two-arm phase II trial. Posterior probabilities of the endpoint of interest are evaluated at each interim look and used in the decision to stop the trial due to futility. Compared with other Bayesian designs, the proposed RBOP2 design has the following merits: (i) strongly controls the type I error rate at a pre-defined level; (ii) optimizes the stopping boundaries, thus maximizing the power to detect treatment effects and minimizing the expected sample size for futile treatment; (iii) does not limit the number of interim looks, thus enabling frequent trial monitoring; and (iv) allows the stopping boundaries to be pre-defined in the protocol and is easy to implement. We conduct simulation studies to compare the proposed design with a group sequential design and other Bayesian randomized designs and evaluate its operating characteristics under different scenarios. [ABSTRACT FROM AUTHOR]

In a category with a proper ()-factorization system, we study the notions of strict, co-strict, initial and final morphisms with respect to a topogenous order. Besides showing that they allow simultaneous study of four classes of morphisms obtained separately with respect to closure, interior and neighbourhood operators, the initial and final morphisms lead us to the study of topogenous orders induced by pointed and co-pointed endofunctors. We also lift the topogenous orders along an -fibration. This permits one to obtain the lifting of interior and neighbourhood operators along an -fibration and includes the lifting of closure operators found in the literature. A number of examples presented at the end of the paper demonstrates our results. [ABSTRACT FROM AUTHOR]

In this paper, we construct some designs invariant under the alternating groups, using a method that has been recently introduced by Moori. We consider the action of An on the 2-subsets of a set of size n. The blocks of the designs are the conjugates of the set of fixed-points of the permutation with respect to this action. For every element g ∈ A n , we give explicit formulae to compute the parameters of the designs based on the cycle representation of g. We also prove that the binary codes of these designs have minimum distances at most 4. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing (HRRS) images contain complex visual contents and rich detailed information. This paper proposes a multi-level feature fusion method to improve the feature representation for HRRS image retrieval. Firstly, in order to obtain the multi-scale information of HRRS images, mid-level features and high-level features of VGG16 and GoogLeNet are extracted with different input sizes. Then a feature transformation method is proposed to adjust the size and the number of different feature maps, so that the distinct mid-level features and distinct high-level features can be fused separately using element-wise addition. There is a large amount of redundancy in the fusion features, thus small-region max-pooling method is adopted to aggregate the mid-level fusion feature, and global max-pooling method is used to aggregate the high-level fusion feature. Finally, an adaptive weight allocation method based on similarity is proposed to combine mid-level feature and high-level feature. Experimental results show that the multi-level feature fusion is an effective method to enhance the feature representation, thereby improving the retrieval performance. [ABSTRACT FROM AUTHOR]

In order to achieve fast and accurate retrieval of video copies, this paper proposes a compact video fingerprinting based on quadruplet convolutional neural network. The algorithm consists of four branch networks with shared weights, each branch network contains feature extraction and quantization coding. The projection and excitation network is combined with 3D convolution for feature extraction, which mainly learns feature weights to improve useful features and suppresses valueless features. The deep features learned are mapped into approximate real vectors in a fully connected form and quantized to generate binary codes. The model employs an improved quadruplet loss to divide the feature distance between the copied videos and the non-copied, and a quantization error term is added to ensure that the fingerprint codes contains as much similar information as the original data. The experimental results performed on the public dataset show that the algorithm can effectively improve the robustness and distinctiveness, and the average detection accuracy under multiple compound attacks is better than the compared algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new feature-level information fusion mechanism based on shuffle coding, called shuffle coding-based feature-level fusion (SC-FLF), for personal authentication. Our approach (SC-FLF) aims at constructing an information fusion mechanism to integrate features from the same or different feature spaces in which the ranges of feature values from different traits differ largely. In this mechanism, the shuffle-coding operator includes dimension adjustment, feature standardization, and fusion coding. This paper addresses two distinct methods, such as feature scaling and hashing, to standardize the range of independent features of data. The shuffle encoder of the SC-FLF in Method 1 uses a feature scaling and the resulting binary code represents the distance between a set of normalized feature values with 2’s complement. On the other hand, in Method 2, the shuffle encoder of the SC-FLF with hashing uses a projection framework for maximizing the features on a hyperplane and then quantizes the hash values as a sequence of binary codes. A XOR operation works as the fusion coding to produce the resulting fusion code. Three different types of fusion are designed to evaluate the fusion performance. Experimental validation illustrates that the proposed fusion methods for combining features in multimodal biometrics advances the recognition performance significantly. [ABSTRACT FROM AUTHOR]

The number of immigrants moving to and settling in Europe has increased over the past decade, making migration one of the most topical and pressing issues in European politics. It is without a doubt that immigration has multiple impacts, in terms of economy, society and culture, on the European Union. It is fundamental to policy-makers to correctly evaluate people's attitudes towards immigration when designing integration policies. Of critical interest is to properly discriminate between subjects who are favourable towards immigration from those who are against it. Public opinions on migration are typically coded as binary responses in surveys. However, traditional methods, such as the standard logistic regression, may suffer from computational issues and are often not able to accurately model survey information. In this paper we propose an efficient Bayesian approach for modelling binary response data based on the generalized logistic regression. We show how the proposed approach provides an increased flexibility compared to traditional methods, due to its ability to capture heavy and light tails. The power of our methodology is tested through simulation studies and is illustrated using European Social Survey data on immigration collected in different European countries in 2016–2017. [ABSTRACT FROM AUTHOR]

This paper proposes a new hyperspectral classification method that combines the joint sparse representation classifier (JSRC) and local binary pattern (LBP) (JSR-LBP). The proposed method uses a statistical histogram of the LBP feature spectrum as a feature vector for classification and recognition. Using the LBP method let us extract the local texture feature of an image more accurately and effectively because of the forceful robustness to light. The proposed method JSR-LBP mainly includes the following steps: First, the JSRC is used to obtain the representation residuals of different pixels. Then, the LBP value is calculated for every pixel in the whole image to generate the LBP histogram; the LBP histogram of the test sample can be obtained from a series of binary codes that are generated using statistics. Next, the Bhattacharyya coefficient is employed to measure the similarity between the test and training samples unlike the traditional classifiers as -nearest neighbor that usually employ the Euclidean distance as similarity metric, and a regularization parameter is then introduced to achieve the balance between the JSRC and LBP. Finally, the test pixel's label is determined by applying the final residual. Experimental results performed on three real hyperspectral images data demonstrate the outstanding performance of the proposed approach compared to other broader classifiers. [ABSTRACT FROM AUTHOR]

Quantum dot-cellular automata (QCA) is an emerging nanotechnological archetype and has widespread applications in designing nanoscale reversible circuit for nanocomputing. The low power consumption, faster operating speed, and low circuit area of QCA pledge the energy efficient design of logic circuit having high device complexity. This paper demonstrates the design of reversible binary to grey and grey to binary code converter based on Feynman gate using QCA for the first time. Both the proposed reversible code converter circuits have quantum cost as 2. The conversion of binary to grey code is achieved with only one garbage value and the grey to binary code conversion is achieved without any garbage value. The proposed Feynman gate circuit required only 38,880 nm2 areas and three clocking zones. The reversible binary to grey circuit required only 92,664 nm2 areas and three clocking zones whereas the reversible grey to binary circuit required only 139,968 nm2 areas and four clocking zones. The proposed design can be used to realise the nanoarchitecture in computer communication having low power consumption. The evaluation of simulation outcomes of proposed circuit with theoretical knowledge established the functional efficiency of the circuits. The circuits are designed and simulated by QCA Designer-2.0.3. [ABSTRACT FROM AUTHOR]

Reversible logic circuits have received emerging attentions in recent years. Reversible logic is widely applied in some new technical fields, such as quantum computing, nanocomputing and optical computing and so on. In this paper, three fault tolerant gates are proposed, ZPL gate, ZQC gate and ZC gate. By using the proposed gates, fault tolerant quantum and reversible BCD adder and skip carry BCD adder are designed, which overcome the limitations of the existing methods. The proposed reversible BCD adders have also parity-preserving property. They are better than the existing counterparts, especially in the quantum cost. Proposed designs have been compared with existing designs with respect to the number of gates, number of garbage outputs and quantum cost. [ABSTRACT FROM AUTHOR]

Modern fault injection techniques allow an adversary to attack cryptographic devices by inducing errors of any multiplicity. The induced errors can affect any part of the circuit, including the Finite State Machine (FSM) that controls the execution of cryptographic algorithms. One of the most efficient countermeasures against these attacks is robust error detecting codes. Robust codes are usually designed under the assumption that the code words occur with equal probability. However, in most FSMs, some states (which are encoded as binary code words) are much more probable than others. In this paper, we show that when the probability distribution of the states is highly skewed, the state assignment, that is, the mapping between the states and the code words, determines the attack detection capability. A worst-case scenario is analyzed, and a method that allows the designer to avoid this scenario with a relatively low cost is presented. The proposed security-oriented method and random state assignment are analyzed with benchmark FSMs. [ABSTRACT FROM AUTHOR]

The multi-dimensional knapsack problems (MKP) have a landscape called a rugged landscape, which may lead to local optima without any progress to optimal solution. Optimization requirement often involves searching amongst various solutions under multi-objective situations. Maintaining diversity and avoiding premature convergence while keeping the population size small and unique is one of the prime approaches to meet the requirements. In this paper, we propose a practical solution to the duplicity as well as premature convergence problem. We have introduced the concept of virtually compressed binary trie (VCBT) and tried to show that the VCBT can be naturally integrated with the genetic algorithm (GA) so that duplicates are completely eliminated while the trie size is kept reasonably small and practically feasible. Our binary trie coding scheme (BTCS) relies on problem-specific knowledge in fragmenting the search space into feasible and infeasible regions, and thus pruning the infeasible areas. Pruning of the trie occurs frequently and is dependent upon many parameters (other than the infeasibility) and the trie size is kept small throughout the whole process. Comparison tables are given for the performance of the BTCS and other good performing evolutionary algorithms found in literature for the MKP. Here, the optimization ability of the BTCS is compared against the GA given by Chu and Beasley; in particular, on a suite of standard MKP test instances from the OR library. The simulation results show that the proposed strategy significantly improves the computational efficiency of GA and generates robust and near-optimal solutions. [ABSTRACT FROM AUTHOR]

This paper deals with the ways in which young educated Israeli lesbians construct their bodies, disregarding the traditional categories based on binary criteria such as Butch/Femme. The “lack of codes” is deconstructed in order to bring forward their complexity and their adherence to the postmodern era. The results, based on 50 in-depth interviews, point to the young lesbians' tendency to unify within their bodies two trends: one that allows the mingling of the male with the female body; and another that eliminates the option to choose to be either femme or butch. This study points to means of social control that assure the acceptance of the dressing code as well as bodily codes by the lesbian community. Their acceptance allows drawing boundaries not only between the lesbian and the heterosexual women but also between the young lesbians and the former generation of lesbians who lived out of the closet. [ABSTRACT FROM AUTHOR]

In this paper, we construct two self-similar groups factorizable by their locally finite 2-subgroups, generators of which are constructed from the generators of the first Grigorchuk group. Both groups contain the first Grigorchuk group, the second also contains the binary adding machine. The first group is a 2-group but not locally finite and the second group is neither locally finite nor a 2-group. Therefore each of them is a new example providing a negative answer to well-known solved problems. Surprisingly, definitions of both groups involve the Gray code. [ABSTRACT FROM PUBLISHER]

For n ≥ 4, the complement of the triangular graph denoted by T(n) is the graph of which the vertex-set is Ω{2}, the set of all 2-subsets of Ω={1,2,...,n}, and any two vertices u and v constitute an edge [u, v] if and only if u⊓v=φ. In this paper the binary codes generated by the rows of the adjacency matrix of T(n) and their corresponding duals are studied. These codes are viewed as subspaces of vector spaces defined on the edge-sets of graphs of order n. Automorphism groups are obtained for these codes, and in the non-trivial cases permutation decoding (PD)-sets of the order of n when n ≡ 1 (mod 4), and of the order of n2 when n ≡ 3 (mod 4) are obtained for the dual code. [ABSTRACT FROM AUTHOR]

Bayesian regression models have been widely studied and adopted in the statistical literature. Many studies consider the development of reliable priors to select the relevant variables and derive accurate posterior predictive distributions. Moreover in the context of small high-dimensional data, where the number of observations is very small with respect to the number of predictors, sparsity is assumed and many parameters can be set to values close to zero without affecting the fit of the model. Aim of this work is to develop a comparative analysis to empirically evaluate the performances of several Bayesian regression approaches in these contexts. In this study we assume that the predictors can be expressed only as binary variables coding the presence or the absence of a particular characteristic of the system. This binary structure is often present in many real studies, in particular in laboratory experimentation and in very high-dimension genome wide association studies. [ABSTRACT FROM AUTHOR]

We introduce a new model for sums of exchangeable binary random variables. The proposed distribution is an approximation to the exact distributional form, and relies on the theory of completely monotone functions and the Laplace transform of a gamma distribution function. Using Monte Carlo methods, we show that this new model compares favorably to the beta-binomial model with respect to estimating the success probability of the Bernoulli trials and the correlation between any two variables in the exchangeable set. We apply the new methodology to two classic data sets and the results are summarized. [ABSTRACT FROM AUTHOR]

Suppose an important continuous quality characteristic with specification x ≥ c is expensive to measure with a gold standard measurement system. A 100% pass/fail inspection scheme uses a binary measurement system such as a no-go gauge to avoid the expensive gold standard measurements. The inspection scheme makes some errors and we are interested in estimating both the probability of passing a bad part and of failing a good part. We assume that the inspection system is not destructive so we can inspect parts multiple times if we so choose. A part is verified if we use the gold standard system to determine if the part is within specification or not. We propose and quantify the benefits of a new cost-effective assessment plan that verifies only a small fraction of the parts selected for the study. [ABSTRACT FROM AUTHOR]

Linear complementary dual (LCD) codes are linear codes satisfying C ∩ C ⊥ = { 0 }. Researchers have proved to construct linear codes over finite fields F q , q>3, is equivalent to construct LCD codes. This means that the investigation of binary and ternary LCD codes is the only remaining open problem. In this work, we propose new constructions of binary and ternary LCD codes by using matrix-product codes and prove that binary and ternary LCD codes from matrix-product codes are asymptotically good. Then we construct some new and good binary and ternary LCD codes using matrix-product matrices. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus on discriminative features of important objects, resulting in unsatisfactory retrieval performance with complex backgrounds and small objects. We therefore propose a multi-attention fusion network with dilated convolution and label smoothing for CBRSIR. First, a dilated convolutional layer is used to replace the fifth convolutional layer in the network to obtain a large receptive field. Then, a contextual transformer attention (CoT) and an efficient channel attention (ECA) are fused together for spatial-wise and channel-wise discriminative features, respectively. The multi-attention module is embedded between the newly added dilated convolutional layer and the followed average pooling layer. Besides, in order to enhance the differences between the discriminative features of those correct and incorrect classes, label smoothing is used to replace the cross-entropy loss function. Some ablation experiments are conducted on six benchmark datasets. Compared to the baseline AlextNet with the above different modules, the mAP values of the proposed network improve by 14.84% to 24.21%. The results indicate that our network can significantly improve the retrieval performance. In addition, we have also conducted some experiments for network migration and comparison with some recent methods (e.g. a triplet deep metric learning and deep feature learning with latent relationship embedding network). Experimental results illustrate that our network can be effectively migrated to other similar CNN models and can achieve state-of-the-art or competitive results. [ABSTRACT FROM AUTHOR]

Exploratory Data Analysis (EDA) approaches are adopted to address the difficult extreme-K categorical sample problem. Due to observed data's categorical nature, all comparisons among populations are performed by comparing their distributions in the form of a histogram with symbolic bins. A distance measure is designed to evaluate the discrepancy between two symbol-based histograms to facilitate Hierarchical Clustering (HC) algorithms. The resultant binary HC-tree then serves as the basis for our EDA task of discovering tree-patterns of interest. Since each population-leaf's location within a binary HC-tree's geometry is expressed through a binary code sequence, a binary code segment characterizes all commonly shared tree-patterns for all members. We then generate a large ensemble of mimicries of the observed dataset based on multinomial distributions and construct a large ensemble of binary HC-trees. Upon each identified tree-pattern which we determined based on the observed dataset, we evaluate its reliability and uncertainty through two histograms. [ABSTRACT FROM AUTHOR]

Two novel approaches to triclustering of three-way binary data are proposed. Tricluster is defined as a dense subset of a ternary relationYdefined on sets of objects, attributes, and conditions, or, equivalently, as a dense submatrix of the adjacency matrix of the ternary relationY. This definition is a scalable relaxation of the notion of triconcept in Triadic Concept Analysis, whereas each triconcept of the initial data-set is contained in a certain tricluster. This approach generalizes the one previously introduced for concept-based biclustering. We also propose a hierarchical spectral triclustering algorithm for mining dense submatrices of the adjacency matrix of the initial ternary relationY. Finally, we describe some applications of the proposed techniques, compare proposed approaches and study their performance in a series of experiments with real data-sets. [ABSTRACT FROM AUTHOR]

The entropic measure of distributional spread, representing the total binary code complexity of the probability distribution, is equal to the area under the partition entropy function. A conjugate dual, obtained as a simple internal sign change from the partition entropy function, can be employed to indicate the relative complexity to the right of any given point versus that to the left. The area beneath this function constitutes a dual measure of distribution asymmetry, which can be reconciled with one based on conditional expected values. [ABSTRACT FROM AUTHOR]

Cloud classification from ground-based observations is a challenging task that attracts increasing attention, favoured by the development of all-sky imaging equipment. In this work, we propose a new method for cloud type classification from all-sky images. Appropriate versions of two descriptors, Regional Local Binary Pattern (R-LBP) and Four Patch-Local Binary Pattern (FP-LBP), are employed on the images in order to extract not only global but also local textural information from the observed cloud type patterns. In the classification stage, a linear Support Vector Machine (SVM) scheme and Linear Discriminant Analysis (LDA) classifiers are adopted for the assignment of the corresponding cloud type label. Our method is evaluated against two state-of-the-art methods and their datasets consisting of 5000 and 2500 images, respectively. According to the results, the proposed method outperforms the previous ones. Due to its promising results and the novelty of local pattern information of clouds, the proposed methodology could be considered as the basis for future studies aiming to overcome the basic disadvantage of all-sky imaging algorithms: to provide regional cloud type information instead of one cloud type for the whole sky. [ABSTRACT FROM AUTHOR]

Traditionally, computer programs have been developed using the sequential programming paradigm. With the advent of parallel computing systems, such as multicore processors and distributed environments, the sequential paradigm became a barrier to the utilisation of the available resources, since the program is restricted to a single processing unit. To address this issue, we propose a transparent automatic parallelisation tool with a binary rewriter. The steps of our approach are: the disassembly of theIntel x86application, its transformation into an intermediary language; the analysis of this intermediary representation to obtain the flow and dependency graphs; the partitioning of the application into parallel units, using the obtained graphs; and, finally, the reassembly of the application back into the originalIntel x86architecture. By transforming the compiled application software, we aim at obtaining a program which can better explore the parallel resources, with no extra effort required from users or developers. [ABSTRACT FROM PUBLISHER]

With the increasing needs in security systems, hand vein recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for dorsal hand vein recognition by analysing vein patterns. The methodology involves extraction of dorsal hand vein features based on Gaussian filter. In order to obtain effective pattern of dorsal hand vein vascular, we propose an innovative and robust Gaussian directional filter method to extract the dorsal hand vein patterns and to encode the vein features in binary code. A new coding scheme, namely Gaussian directional binary code, is then proposed for dorsal hand vein recognition. To evaluate the efficacy of the proposed approach, the normalised Hamming distance used in recognition is adopted. A total of 5120 dorsal hand vein images were collected from 256 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) and low equal error rate (0·95%) have been obtained by the proposed method. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition. [ABSTRACT FROM AUTHOR]

To obtain an arbitrary polarization in three dimensions is of significant interest for many applications. Here, we demonstrate a configuration to achieve it. After phase coding by binary phase plates, two cylindrical polarized beams are superposed and focused by a high-numerical aperture lens to obtain transversally and radially polarized components in the focus vicinity. By adjusting their intensity and phase difference, the desired three-dimensional polarization can be achieved. The proportion of the desired polarization is extremely high and the focus spot is smaller than the diffraction-limited spot size. [ABSTRACT FROM AUTHOR]

The local binary pattern (LBP) is a robust but computationally simple approach in texture analysis. However, LBP performs poorly in the presence of noise and large illumination variation. Thus, a local adaptive image descriptor termed as LAID is introduced in this proposal. It is a ternary pattern and is able to generate persistent codes to represent microtextures in a given image, especially in noisy conditions. It can also generate stable texture codes if the pixel intensities change abruptly due to the illumination changes. Experimental results also show the superiority of the proposed method over other state-of-the-art methods. [ABSTRACT FROM PUBLISHER]