When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.

Recent research has shown that the class of rotation symmetric Boolean functions is beneficial to cryptographics. In this paper, for an odd prime p, two sufficient conditions for p-variable rotation symmetric Boolean functions to be 1-resilient are obtained, and then several concrete constructions satisfying the conditions are presented. This is the first time that resilient rotation symmetric Boolean functions have been systematically constructed. In particular, we construct a class of 2-resilient rotation symmetric Boolean functions when p=2m+1 for m ≥ 4. Moreover, several classes of 1-order correlation immune rotation symmetric Boolean functions are also got.

Building detection from high resolution remote sensing images is challenging due to the high intraclass variability and the difficulty in describing buildings. To address the above difficulties, a novel approach is proposed based on the combination of shape-specific feature extraction and discriminative feature classification. Shape-specific feature can capture complex shapes and structures of buildings. Discriminative feature classification is effective in reflecting similarities among buildings and differences between buildings and backgrounds. Experiments demonstrate the effectiveness of the proposed approach.

In this paper, we explore the relationship between Geometric Dilution of Precision (GDOP) and Cramer-Rao Bound (CRB) by tracing back to the original motivations for deriving these two indexes. In addition, the GDOP is served as a sensor-target geometric uncertainty analysis tool whilst the CRB is served as a statistical performance evaluation tool based on the sensor observations originated from target. And CRB is the inverse matrix of Fisher information matrix (FIM). Based on the original derivations for a same positioning application, we interpret their difference in a mathematical view to show that.

In this letter, we propose a novel method for face hallucination by learning a new distance metric in the low-resolution (LR) patch space (source space). Local patch-based face hallucination methods usually assume that the two manifolds formed by LR and high-resolution (HR) image patches have similar local geometry. However, this assumption does not hold well in practice. Motivated by metric learning in machine learning, we propose to learn a new distance metric in the source space, under the supervision of the true local geometry in the target space (HR patch space). The learned new metric gives more freedom to the presentation of local geometry in the source space, and thus the local geometries of source and target space turn to be more consistent. Experiments conducted on two datasets demonstrate that the proposed method is superior to the state-of-the-art face hallucination and image super-resolution (SR) methods.

IDMs are getting more effective and secure with biometric recognition and more privacy-preserving with advanced cryptosystems. In order to meet privacy and security needs of an IDM, the cryptographic background should rely on reliable random number generation. In this study, a Biometric Random Number Generator (BRNG) is proposed which plays a crucial role in a typical cryptosystem. The proposed novel approach extracts the high-frequency information in biometric signal which is associated with uncertainty existing in nature of biometrics. This bio-uncertainty, utilized as an entropy source, may be caused by sensory noise, environmental changes, position of the biometric trait, accessories worn, etc. The filtered nondeterministic information is then utilized by a postprocessing technique to obtain a random number set fulfilling the NIST 800-22 statistical randomness criteria. The proposed technique presents random number sequences without need of an additional hardware.

Video data mining based on topic models as an emerging technique recently has become a very popular research topic. In this paper, we present a novel topic model named sequential correspondence hierarchical Dirichlet processes (Seq-cHDP) to learn the hidden structure within video data. The Seq-cHDP model can be deemed as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the time-dependency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the correspondence mechanism that provides a solution for dealing with the multimodal data such as the mixture of visual words and speech words extracted from video files. A cascaded Gibbs sampling method is applied for implementing the inference task of Seq-cHDP. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that Seq-cHDP outperforms other baseline models.

Speaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

Write once memory (WOM) codes allow reuse of a write-once medium. This paper focuses on applying WOM codes to the binary symmetric asymmetric multiple access channel (BS-AMAC). At one specific rate pair, WOM codes can achieve the BS-AMAC maximum sum-rate. Further, any achievable rate pair for a two-write WOM code is also an achievable rate pair for the BS-AMAC. Compared to the uniform input distribution of linear codes, the non-uniform WOM input distribution is helpful for a BS-AMAC. In addition, WOM codes enable “symbol-wise estimation”, resulting in the decomposition to two distinct channels. This scheme does not achieve the BS-AMAC maximum sum-rate if the channel has errors, however leads to reduced-complexity decoding by enabling independent decoding of two codewords. Achievable rates for this decomposed system are also given. The AMAC has practical application to the relay channel and we briefly discuss the relay channel with block Markov encoding using WOM codes. This scheme may be effective for cooperative wireless communications despite the fact that WOM codes are designed for data storage.

We propose a framework to perform auto-radiometric calibration in photometric stereo methods to estimate surface orientations of an object from a sequence of images taken using a radiometrically uncalibrated camera under varying illumination conditions. Our proposed framework allows the simultaneous estimation of surface normals and radiometric responses, and as a result can avoid cumbersome and time-consuming radiometric calibration. The key idea of our framework is to use the consistency between the irradiance values converted from pixel values by using the inverse response function and those computed from the surface normals. Consequently, a linear optimization problem is formulated to estimate the surface normals and the response function simultaneously. Finally, experiments on both synthetic and real images demonstrate that our framework enables photometric stereo methods to accurately estimate surface normals even when the images are captured using cameras with unknown and nonlinear response functions.

In this paper, we aim to investigate the potential usefulness of machine learning in image quality assessment (IQA). Most previous studies have focused on designing effective image quality metrics (IQMs), and significant advances have been made in the development of IQMs over the last decade. Here, our goal is to improve prediction outcomes of “any” given image quality metric. We call this the “IQM's Outcome Improvement” problem, in order to distinguish the proposed approach from the existing IQA approaches. We propose a method that focuses on the underlying IQM and improves its prediction results by using machine learning techniques. Extensive experiments have been conducted on three different publicly available image databases. Particularly, through both 1) in-database and 2) cross-database validations, the generality and technological feasibility (in real-world applications) of our machine-learning-based algorithm have been evaluated. Our results demonstrate that the proposed framework improves prediction outcomes of various existing commonly used IQMs (e.g., MSE, PSNR, SSIM-based IQMs, etc.) in terms of not only prediction accuracy, but also prediction monotonicity.

In a previous work [1], Wang et al. proposed a privacy-preserving outsourcing scheme for biometric identification in cloud computing, namely CloudBI. The author claimed that it can resist against various known attacks. However, there exist serious security flaws in their scheme, and it can be completely broken through a small number of constructed identification requests. In this letter, we modify the encryption scheme and propose an improved version of the privacy-preserving biometric identification design which can resist such attack and can provide a much higher level of security.

Automated biometrics identification using finger vein images has increasingly generated interest among researchers with emerging applications in human biometrics. The traditional feature-level fusion strategy is limited and expensive. To solve the problem, this paper investigates the possible use of infrared hybrid finger patterns on the back side of a finger, which includes both the information of finger vein and finger dorsal textures in original image, and a database using the proposed hybrid pattern is established. Accordingly, an Intersection enhanced Gabor based Direction Coding (IGDC) method is proposed. The Experiment achieves a recognition ratio of 98.4127% and an equal error rate of 0.00819 on our newly established database, which is fairly competitive.

In this paper, we address the problem of non-parametric template matching which does not assume any specific deformation models. In real-world matching scenarios, deformation between a template and a matching result usually appears to be non-rigid and non-linear. We propose a novel approach called local rigidity constraints (LRC). LRC is built based on an assumption that the local rigidity, which is referred to as structural persistence between image patches, can help the algorithm to achieve better performance. A spatial relation test is proposed to weight the rigidity between two image patches. When estimating visual similarity under an unconstrained environment, high-level similarity (e.g. with complex geometry transformations) can then be estimated by investigating the number of LRC. In the searching step, exhaustive matching is possible because of the simplicity of the algorithm. Global maximum is given out as the final matching result. To evaluate our method, we carry out a comprehensive comparison on a publicly available benchmark and show that our method can outperform the state-of-the-art method.

Information-Centric Networking (ICN) treats contents as first class citizens and adopts name-based routing for content distribution and retrieval. Content names rather than IP addresses are directly used for routing. However, due to the location-independent naming and the huge namespace, name-based routing faces scalability and efficiency issues including large routing tables and high path stretches. This paper proposes a universal Scalable Name-based Geometric Routing scheme (SNGR), which is a careful synthesis of geometric routing and name resolution. To provide scalable and efficient underlying routing, a universal geometric routing framework (GRF) is proposed. Any geometric routing scheme can be used directly for name resolution based on GRF. To implement an overlay name resolution system, SNGR utilizes a bi-level grouping design. With this design, a resolution node that is close to the consumer can always be found. Our theoretical analyses guarantee the performance of SNGR, and experiments show that SNGR outperforms similar routing schemes in terms of node state, path stretch, and reliability.

In this paper, a new scheme is presented for ground moving target indication for multichannel high-resolution wide-swath (HRWS) SAR systems with modified reconstruction filters. The conventional steering vector is generalized for moving targets through taking into account the additional Doppler centroid shift caused by the across-track velocity. Two modified steering vectors with symmetric velocity information are utilized to produce two images for the same scene. Due to the unmatched steering vectors, the stationary backgrounds are defocused but they still hold the same intensities in both images but moving targets are blurred to different extents. The ambiguous components of the moving targets can also be suppressed due to the beamforming in the reconstruction procedure. Therefore, ground moving target indication can be carried out via intensity comparison between the two images. The effectiveness of the proposed method is verified by both simulated and real airborne SAR data.

An asymmetric left-handed coupled-line is presented to implement the tight forward coupler. Two left-handed transmission lines are coupled through its shunt inductors. The numerical procedures based on the generalized four-port scattering parameters combined with the periodical boundary conditions are applied to extract the modal characteristics of the asymmetric coupled-line, and theoretically predict that the proposed coupled-line can make a normalized phase constant of c mode 1.57 times larger than π mode for the forward coupler miniaturization. The design curves based on different overlapping length of the shunt inductors are reported for the coupler design. The procedures, so-called the port-reduction-method (PRM), are applied to experimentally characterize the coupler prototype using the two-port instruments. The measured results confirm that prototype uses 0.21 λg at 430 GHz to achieve -4.55 dB forward coupling with 13% 1-dB operating bandwidth.

The likelihood-ratio based score level fusion (LR-based fusion) scheme has attracted much attention, since it maximizes accuracy if a log-likelihood ratio (LLR) is accurately estimated. In reality, it can happen that a user cannot input some query samples due to temporary physical conditions such as injuries and illness. It can also happen that some modalities tend to cause false rejection (i.e. the user is a “goat” for these modalities). The LR-based fusion scheme can handle these situations by setting LLRs corresponding to missing query samples to 0. In this paper, we refer to such a mode as a “modality selection mode”, and address an issue of accuracy in this mode. Specifically, we provide the following contributions: (1) We firstly propose a “modality selection attack”, in which an impostor inputs only query samples whose LLRs are more than 0 (i.e. takes an optimal strategy) to impersonate others. We also show that the impostor can perform this attack against the SPRT (Sequential Probability Ratio Test)-based fusion scheme, which is an extension of the LR-based fusion scheme to a sequential fusion scenario. (2) We secondly consider the case when both genuine users and impostors take this optimal strategy, and show that the overall accuracy in this case is “worse” than the case when they input all query samples. More specifically, we prove that the KL (Kullback-Leibler) divergence between a genuine distribution of integrated scores and an impostor's one, which can be compared with password entropy, is smaller in the former case. We also show to what extent the KL divergence losses for each modality. (3) We finally evaluate to what extent the overall accuracy becomes worse using the NIST BSSR1 Set 2 and Set 3 datasets, and discuss directions of multibiometric applications based on the experimental results.

This paper proposes a new class of Hilbert pairs of almost symmetric orthogonal wavelet bases. For two wavelet bases to form a Hilbert pair, the corresponding scaling lowpass filters are required to satisfy the half-sample delay condition. In this paper, we design simultaneously two scaling lowpass filters with the arbitrarily specified flat group delay responses at ω=0, which satisfy the half-sample delay condition. In addition to specifying the number of vanishing moments, we apply the Remez exchange algorithm to minimize the difference of frequency responses between two scaling lowpass filters, in order to improve the analyticity of complex wavelets. The equiripple behavior of the error function can be obtained through a few iterations. Therefore, the resulting complex wavelets are orthogonal and almost symmetric, and have the improved analyticity. Finally, some examples are presented to demonstrate the effectiveness of the proposed design method.

Rotation symmetric Boolean functions (RSBFs) that are invariant under circular translation of indices have been used as components of different cryptosystems. In this paper, odd-variable balanced RSBFs with maximum algebraic immunity (AI) are investigated. We provide a construction of n-variable (n=2k+1 odd and n ≥ 13) RSBFs with maximum AI and nonlinearity ≥ 2n-1-¥binom{n-1}{k}+2k+2k-2-k, which have nonlinearities significantly higher than the previous nonlinearity of RSBFs with maximum AI.