In this paper, we propose an analysis of broadcasting in the IEEE 802.11p MAC protocol. We consider multi-channel operation which is specifically designed for VANET (Vehicular Ad hoc Networks) applications. This protocol supports channel switching; the device alternates between the CCH (Control Channel) and the SCH (Service Channel) during the fixed synchronization interval. It helps vehicles with a single transceiver to access the CCH periodically during which time they acquire or broadcast safety-related messages. Confining the broadcasting opportunity to the deterministic CCH interval entails a non-typical approach to the analysis. Our analysis is carried out considering 1) the time dependency of the system behavior caused by the channel switching, 2) the mutual influence among the vehicles using a multi-dimensional stochastic process, and 3) the generation of messages distributed over the CCH interval. The proposed analysis enables the prediction of the successful delivery ratio and the delay of the broadcast messages. Furthermore, we propose a refinement of the analysis to take account of the effects of hidden nodes on the system performance. The simulation results show that the proposed analysis is quite accurate in describing both the delivery ratio and delay, as well as in reflecting the hidden node effects. The benefits derived from the distributed generation of traffic are also evidenced by the results of experiments.

In the traditional time delay estimation methods, it is usually implicitly assumed that the observed signals are either only direct path propagate or coherently received. In practice, the multipath propagation and incoherent reception always exist simultaneously. In response to this situation, the joint maximum likelihood (ML) estimation of multipath delays and system error is proposed, and the estimation of the number of multipath is considered as well for the specific incoherent signal model. Furthermore, an algorithm based Gibbs sampling is developed to solve the multi-dimensional nonlinear ML estimation. The efficiency of the proposed estimator is demonstrated by simulation results.

Assume that Alice, Bob, and Carol, each of whom privately holds a one-bit input, want to learn the output of some Boolean function, say the majority function, of their inputs without revealing more of their own secret inputs than necessary. In this paper, we show that such a secure three-input function evaluation can be performed with a deck of real cards; specifically, the three players can learn only the output of the function using eight physical cards — four black and four red cards — with identical backs.

Support Vector Machine (SVM) is one of the most widely used classifiers to categorize observations. This classifier deterministically selects a class that has the largest score for a classification output. In this letter, we propose a multiclass probabilistic classification method that reflects the degree of confidence. We apply the proposed method to age group classification and verify the performance.

This report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

Developing a practical and accurate statistical parser for low-resourced languages is a hard problem, because it requires large-scale treebanks, which are expensive and labor-intensive to build from scratch. Unsupervised grammar induction theoretically offers a way to overcome this hurdle by learning hidden syntactic structures from raw text automatically. The accuracy of grammar induction is still impractically low because frequent collocations of non-linguistically associable units are commonly found, resulting in dependency attachment errors. We introduce a novel approach to building a statistical parser for low-resourced languages by using language parameters as a guide for grammar induction. The intuition of this paper is: most dependency attachment errors are frequently used word orders which can be captured by a small prescribed set of linguistic constraints, while the rest of the language can be learned statistically by grammar induction. We then show that covering the most frequent grammar rules via our language parameters has a strong impact on the parsing accuracy in 12 languages.

In this paper, we propose a zero-forcing (ZF) Tomlinson-Harashima precoding (THP) with substream permutations based on the bit rate maximization for single-user MIMO (SU-MIMO) systems. We study the effect of substream permutations on the ZF-THP SU-MIMO systems, when the mean squared error (MSE) and the bit rate are adopted for the selection of the permutation matrix as criteria. Based on our analysis, we propose a method to increase the bit rate by substream permutations, and derive QR and Cholesky decomposition-based algorithms which realize the proposed method. Furthermore, to improve the error rate performance, we apply zero transmission to subchannels with low signal-to-noise ratios. Numerical examples are provided to demonstrate the effectiveness of the proposed THP MIMO system.

Multi-hop cooperative communication has been investigated in order to overcome disadvantages such as fading, obstruction and low power. In addition, with the goal of increasing access capacity, the orthogonal frequency division multiplexing (OFDM) modulation is being advanced as a solution. In this paper, we propose the approach of relay ordering in a Decode-and-Forward OFDM scheme. Combining techniques such as maximal ratio combining and selection combining are employed at receivers and approximate outage capacity probabilities are derived for evaluating system performance over frequency selective Rayleigh fading channels. Final, the expressions are validated by Monte-Carlo simulations, and are used to compare with the same scheme based relay selection.

Recently, the ratio of probability density functions was demonstrated to be useful in solving various machine learning tasks such as outlier detection, non-stationarity adaptation, feature selection, and clustering. The key idea of this density ratio approach is that the ratio is directly estimated so that difficult density estimation is avoided. So far, parametric and non-parametric direct density ratio estimators with various loss functions have been developed, and the kernel least-squares method was demonstrated to be highly useful both in terms of accuracy and computational efficiency. On the other hand, recent study in pattern recognition exhibited that deep architectures such as a convolutional neural network can significantly outperform kernel methods. In this paper, we propose to use the convolutional neural network in density ratio estimation, and experimentally show that the proposed method tends to outperform the kernel-based method in outlying image detection.

Weak target detection is a key problem in passive bistatic radar (PBR). Track-before-detect (TBD) is an effective solution which has drawn much attention recently. However, TBD has not been fully developed in PBR. In this paper, the transition function and the selection of parameters in dynamic programming are analyzed in PBR. Then a novel processing scheme of dynamic programming based TBD is proposed to reduce the computation complexity without severely decreasing the detection performance. Discussions including complexity, detection performance, threshold determination, selection of parameters and detection of multitarget, are presented in detail. The new method can provide fast implementation with only a slight performance penalty. In addition, good multitarget detection performance can be achieved by using this method. Simulations are carried out to present the performance of the proposed processing scheme.

Appearance changes conform to certain rules for a same person,while for different individuals the changes are uncontrolled. Hence, this paper studies the age progression rules to tackle face verification task. The age progression rules are discovered in the difference space of facial image pairs. For this, we first represent an image pair as a matrix whose elements are the difference of a set of visual words. Thereafter, the age progression rules are trained using Support Vector Machine (SVM) based on this matrix representation. Finally, we use these rules to accomplish the face verification tasks. The proposed approach is tested on the FGnet dataset and a collection of real-world images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity.

In this paper, we propose a simple model for estimating the effects of human body shadowing (HBS) in high frequency bands. The model includes two factors: the shadowing width (SW), which is the width of the area with shadowing loss values greater than 0dB, and the median shadowing loss value (MSLV), which is obtained by taking the median of the shadowing loss values within the SW. These factors are determined by formulas using parameters, i.e. frequency, distance between the base station (BS) and human body, distance between the terminal and human body, BS antenna height, and direction of the human body. To obtain the formulas, a method for calculating the effects of HBS based on the uniform theory of diffraction (UTD) and a human body model comprising lossy dielectric flat plates is proposed and verified. Then, the general forms of the formulas are predicted using the theory of knife-edge diffraction (KE). A series of computer simulations using the proposed calculation method with random changes in parameters is conducted to verify the general formulas and derive coefficients for these formulas through regression formulas.

To ensure the security of image transmission, this paper presents a new image encryption algorithm based on a genetic algorithm (GA) and a piecewise linear chaotic map (PWLCM), which adopts the classical diffusion-substitution architecture. The GA is used to identify and output the optimal encrypted image that has the highest entropy value, the lowest correlation coefficient among adjacent pixels and the strongest ability to resist differential attack. The PWLCM is used to scramble pixel positions and change pixel values. Experiments and analyses show that the new algorithm possesses a large key space and resists brute-force, statistical and differential attacks. Meanwhile, the comparative analysis also indicates the superiority of our proposed algorithm over a similar, recently published, algorithm.

Parameter variations in nanometer process technology are one of the major design challenges. They cause delay to be increased on the critical path and may change the logic level of internal nodes. The basic concept to solve these problems at the circuit level, design-for-variability (DFV), is to add an error handling circuit to the conventional circuits so that they are robust to nanometer related variations. The state-of-the-art variation-aware flip flops are mainly evolved from aggressive dynamic voltage and frequency scaling (DVFS) -based low-power application systems which handle errors due to the scaled supply voltage. However, they only detect the timing errors and cannot correct the errors. We propose a variation-aware flip flop which can detect and correct the timing error efficiently. The experimental results show that the proposed variation-aware flip flop is more robust and lower power than the existing approaches.

This work investigates the physical layer security for three cooperative automatic-repeat-request (CARQ) protocols, including the decode-and-forward (DF) CARQ, opportunistic DF (ODF) CARQ, and the distributed space-time code (DSTC) CARQ. Assuming that there is no instantaneous channel state information (CSI) of legitimate users' channel and eavesdropper's channel at the transmitter, the connection outage performance and secrecy outage performance are derived to evaluate the reliability and security of each CARQ protocol. Then, we redefine the concept of the secrecy throughput to evaluate the overall efficiency of the system in terms of maintaining both reliable and secure transmission. Furthermore, through an asymptotic analysis in the high signal-to-noise ratio (SNR) regime, the direct relationship between reliability and security is established via the reliability-security tradeoff (RST). Numerical results verify the analysis and show the efficiency of the CARQ protocols in terms of the improvement on the secrecy throughput. More interestingly, increasing the transmit SNR and the maximum number of transmissions of the ARQ protocols may not achieve a security performance gain. In addition, the RST results underline the importance of determining how to balance the reliability vs. security, and show the superiority of ODF CARQ in terms of RST.

Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

Nonprimitive non-narrow-sense BCH codes have been studied by many scholars. In this paper, we utilize nonprimitive non-narrow-sense BCH codes to construct a family of asymmetric quantum codes and two families of quantum convolutional codes. Most quantum codes constructed in this paper are different from the ones in the literature. Moreover, some quantum codes constructed in this paper have good parameters compared with the ones in the literature.

In this paper, we investigate iterative detection and decoding, a.k.a. turbo detection, for multiple-input multiple-output (MIMO) transmission. Specifically, we consider using a low complexity soft-in/soft-out MIMO detector based on belief propagation over a pair-wise graph that accepts a priori information feedback from a channel decoder. Simulation results confirm that considerable performance improvement can be obtained with only a few detection-and-decoding iterations if convolutional channel coding is used. A brief estimate is given of the overall complexity of turbo detectors, to verify the key argument that the performance of a maximum a posteriori (MAP) detector (without turbo iteration) can be achieved, at much lower computation cost, by using the low complexity soft-in/soft-out MIMO detector under consideration.