The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

In this letter, a novel collaborative representation graph based on the local and global consistency label propagation method, denoted as CRLGC, is proposed. The collaborative representation graph is used to reduce the cost time in obtaining the graph which evaluates the similarity of samples. Considering the lacking of labeled samples in real applications, a semi-supervised label propagation method is utilized to transmit the labels from the labeled samples to the unlabeled samples. Experimental results on three image data sets have demonstrated that the proposed method provides the best accuracies in most times when compared with other traditional graph-based semi-supervised classification methods.

Artificial blurring is a typical operation in image forging. Most existing image forgery detection methods consider only one single feature of artificial blurring operation. In this manuscript, we propose to adopt feature fusion, with multifeatures for artificial blurring operation in image tampering, to improve the accuracy of forgery detection. First, three feature vectors that address the singular values of the gray image matrix, correlation coefficients for double blurring operation, and image quality metrics (IQM) are extracted and fused using principal component analysis (PCA), and then a support vector machine (SVM) classifier is trained using the fused feature extracted from training images or image patches containing artificial blurring operations. Finally, the same procedures of feature extraction and feature fusion are carried out on the suspected image or suspected image patch which is then classified, using the trained SVM, into forged or non-forged classes. Experimental results show the feasibility of the proposed method for image tampering feature fusion and forgery detection.

Delay Tolerant Networks (DTNs) are able to provide communication services in challenged networks where the end-to-end path between the source and destination does not exist. In order to increase the probability of message delivery, DTN routing mechanisms require nodes in the network to store and carry messages in their local buffer and to replicate many copies. When the limited buffer is consumed, choosing appropriate messages to discard is critical to maximizing the system performance. Current approaches for this are sub-optimal or assumed unrealistic conditions. In this paper, we propose an optimal buffer management scheme for the realistic situations where the bandwidth is limited and messages vary in size. In our scheme, we design a message discard policy that maximizes the message delivery rate. Simulation results demonstrate the efficiency of our proposal.

To deal with the increasing number of mobile devices accessing the Internet and the increasing demands of mobility management, IETF has proposed Mobile IPv6 and its fast handover protocol FMIPv6. In FMIPv6, the possibility of Care-of Address (CoA) collision and the time for Return Routability (RR) procedure result in long handover delay, which makes it unsuitable for real-time applications. In this paper, we propose an improved handover scheme for FMIPv6, which reduces the handover delay by using proactive CoA acquisition, configuration and test method. In our proposal, collision-free CoA is proactively prepared, and the time for RR procedure does not contribute to the handover delay. Furthermore, we analyze our proposal's benefits and overhead tradeoff. The numerical results demonstrate that it outperforms the current schemes, such as FMIPv6 and enhanced FMIPv6, on the aspect of handover delay and packet transmission delay.

The Hidden Markov Model (HMM) is a popular statistical framework for modeling and analyzing stochastic signals. In this paper, a novel strategy is proposed that makes use of level-building algorithm with a chain of AdaBoost HMM classifiers to model long stochastic processes. AdaBoost HMM classifier belongs to the class of multiple-HMM classifier. It is specially trained to identify samples with erratic distributions. By connecting the AdaBoost HMM classifiers, processes of arbitrary length can be modeled. A probability trellis is created to store the accumulated probabilities, starting frames and indices of each reference model. By backtracking the trellis, a sequence of best-matched AdaBoost HMM classifiers can be decoded. The proposed method is applied to visual speech processing. A selected number of words and phrases are decomposed into sequences of visual speech units using both the proposed strategy and the conventional level-building on HMM method. Experimental results show that the proposed strategy is able to more accurately decompose words/phrases in visual speech than the conventional approach.

After exploring the classic Lambertian reflectance model, we proposed an effective illumination estimation model to extract illumination invariants for face recognition under complex illumination conditions in this paper. The estimated illumination by our method not only meets the actual lighting conditions of facial images, but also conforms to the imaging principle. Experimental results on the combined Yale B database show that the proposed method can extract more robust illumination invariants, which improves face recognition rate.

Dai-Yuan (DY) conjugate gradient method is an effective method for solving large-scale unconstrained optimization problems. In this paper, a new DY method, possessing a spectral conjugate parameter βk, is presented. An attractive property of the proposed method is that the search direction generated at each iteration is descent, which is independent of the line search. Global convergence of the proposed method is also established when strong Wolfe conditions are employed. Finally, comparison experiments on impulse noise removal are reported to demonstrate the effectiveness of the proposed method.

Recently, a high dimensional classification framework has been proposed to introduce spatial structure information in classical single kernel support vector machine optimization scheme for brain image analysis. However, during the construction of spatial kernel in this framework, a huge adjacency matrix is adopted to determine the adjacency relation between each pair of voxels and thus it leads to very high computational complexity in the spatial kernel calculation. The method is improved in this manuscript by a new construction of tensorial kernel wherein a 3-order tensor is adopted to preserve the adjacency relation so that calculation of the above huge matrix is avoided, and hence the computational complexity is significantly reduced. The improvement is verified by experimental results on classification of Alzheimer patients and cognitively normal controls.

A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.

There has been an explosion in wireless devices and mobile data traffic, and cellular network alone is unable to support such fast growing demand on data transmission. Therefore, it is reasonable to add another network to the cellular network to augment the capacity. In fact, the dilemma of cellular network is mainly caused by that the same content is repeatedly transmitted in the network, since many people are interested in the same content. A broadcast network, however, could mitigate this problem and save wireless bandwidth by delivering popular content to multiple clients simultaneously. This paper presents a content dissemination system that combines broadcast and cellular networks. Using the model of Markov Decision Process (MDP), we propose an online optimal scheme to maximize the expected number of clients receiving their interested content, which takes clients' interests and queuing length at broadcast and cellular base stations into full consideration. Simulations demonstrate that the proposed scheme effectively decreases item drop rate at base stations and enhances the average number of clients who receive their interested content.

In network tomography, most work to date is based on exploiting probe packet level correlations to infer the link loss rates and delay distributions. Some other work focuses on identifying the congested links using uncorrelated end-to-end measurements and link prior probability of being congested. In their work, the prior probabilities are identified by the matrix inversion with a number of measurement snapshots, and the algorithm to find the congested links is heuristic and not optimal. In this letter, we present a new estimator for the prior probabilities that is computationally simple, being an explicit function of the measurement snapshots. With these prior probabilities, the identification of the congested link set is equivalent to finding the solution for a probability maximization problem. We propose a fast bottom-up approach named FBA to find the solution for this problem. The FBA optimizes the solution step by step from the bottom up. We prove that the solution by the FBA is optimal.

A generalized covariance matrix taper (GCMT) model is proposed to enhance the performance of knowledge-aided space-time adaptive processing (KA-STAP) under sea clutter environments. In KA-STAP, improving the accuracy degree of the a priori clutter covariance matrix is a fundamental issue. As a crucial component in the a priori clutter covariance matrix, the taper matrix is employed to describe the internal clutter motion (ICM) or other subspace leakage effects, and commonly constructed by the classical covariance matrix taper (CMT) model. This work extents the CMT model into a generalized CMT (GCMT) model with a greater degree of freedom. Comparing it with the CMT model, the proposed GCMT model is more suitable for sea clutter background applications for its improved flexibility. Simulation results illustrate the efficiency of the GCMT model under different sea clutter environments.

With the rapid development of technology and economy, the number of cars is increasing rapidly, which brings a series of traffic problems. To solve these traffic problems, the development of intelligent transportation systems are accelerated in many cities. While vehicles and their detailed information detection are great significance to the development of urban intelligent transportation system, the traditional vehicle detection algorithm is not satisfactory in the case of complex environment and high real-time requirement. The vehicle detection algorithm based on motion information is unable to detect the stationary vehicles in video. At present, the application of deep learning method in the task of target detection effectively improves the existing problems in traditional algorithms. However, there are few dataset for vehicles detailed information, i.e. driver, car inspection sign, copilot, plate and vehicle object, which are key information for intelligent transportation. This paper constructs a deep learning dataset containing 10,000 representative images about vehicles and their key information detection. Then, the SSD (Single Shot MultiBox Detector) target detection algorithm is improved and the improved algorithm is applied to the video surveillance system. The detection accuracy of small targets is improved by adding deconvolution modules to the detection network. The experimental results show that the proposed method can detect the vehicle, driver, car inspection sign, copilot and plate, which are vehicle key information, at the same time, and the improved algorithm in this paper has achieved better results in the accuracy and real-time performance of video surveillance than the SSD algorithm.

The ignored side effect reflecting in the introduction of mismatching brought by contrast enhancement in representative SIFT based vein recognition model is investigated. To take advantage of contrast enhancement in increasing keypoints generation, hierarchical keypoints selection and mismatching removal strategy is designed to obtain state-of-the-art recognition result.

Due to the lack of end-to-end paths between the communication source and destination in the Disruption Tolerant Network (DTN), its routing employs the store-carry-and-forward mechanism. In order to provide communication service in the DTN where there is only intermittent connectivity between nodes, a variety of epidemic-style routing algorithms have been proposed to achieve high message delivery probability at the cost of energy consumption. In this contribution, we investigate the problem of optimal multi-frame content transmission. By formulating the optimization problem with a Markov model, we derive the optimal policies under the two conditions of with and without energy constraint. We also investigate the performance of the proposed optimal policies through extensive numerical analyses, and conclude that the optimal policies give the best performance and the energy constraint critically degrades the system performance in the multi-frame content transmission.

An anonymous identity based encryption (anonymous IBE) scheme requires that an adversary can not determine the identity of the recipient from a ciphertext encrypted by the corresponding public key. The anonymity was formalized in previous works [1],[13], and this can be considered under chosen plaintext attack and adaptive chosen ciphertext attack, yielding two notions of security, ID-II-CPA and ID-II-CCA, where II denotes "indistinguishability of identities." However, how to obtain an ID-II-CCA secure anonymous IBE in the random oracle model is still a challenging problem. We firstly propose a new notion of plaintext awareness in the two identities setting, called PATI. Secondly, we prove that the IBE scheme is ID-II-CCA secure if it is PATI secure. Finally, we propose the first generic conversion for anonymous IBE from ID-II-CPA to ID-II-CCA in the random oracle model.

In delay tolerant networks, energy efficient forwarding algorithms are significant to enhance the performance of message transmission probability. In this paper, we focus on the problem of optimal probabilistic epidemic forwarding with energy constraint. By introducing a continuous time model, we obtain the optimal static and dynamic policies for multi-messages forwarding. Extensive numerical results show that the optimal dynamic policy achieves higher transmission probability than the optimal static policy while the number of messages decreases the average transmission probability.

The LC-based Digitally Controlled Oscillator (DCO) is one of the most important components of all digital phase locked loops. The performance of the loops is significantly determined by the DCO's frequency resolution. In order to enhance the frequency resolution, we propose a mismatched capacitor pairs based digitally controlled switched capacitance array, which dramatically reduces the minimum switched varactor capacitance. Furthermore, we implement a DCO based on our proposal in SMIC 0.18 µm and conduct simulation in Spectre. The simulation results show that the frequency resolution is enhanced compared with the existing methods.

Automatic thresholding is an important technique for rail defect detection, but traditional methods are not competent enough to fit the characteristics of this application. This paper proposes the Maximum Weighted Object Correlation (MWOC) thresholding method, fitting the features that rail images are unimodal and defect proportion is small. MWOC selects a threshold by optimizing the product of object correlation and the weight term that expresses the proportion of thresholded defects. Our experimental results demonstrate that MWOC achieves misclassification error of 0.85%, and outperforms the other well-established thresholding methods, including Otsu, maximum correlation thresholding, maximum entropy thresholding and valley-emphasis method, for the application of rail defect detection.