In this letter, we address the issue of estimating the temporal dependence characteristic of link loss by using network tomography. We use a k-th order Markov chain (k > 1) to model the packet loss process, and estimate the state transition probabilities of the link loss model using a constrained optimization-based method. Analytical and simulation results indicate that our method yields more accurate packet loss probability estimates than existing loss inference methods.

In this paper, a low-complexity symbol-spaced turbo frequency domain equalization (FDE) algorithm based on Laurent decomposition is proposed for precoded binary continuous phase modulation (CPM) with modulation index h=1/2. At the transmitter, a precoder is utilized to eliminate the inherent memory of the CPM signal. At the receiver, a matched filter based on Laurent decomposition is utilized to make the detection symbol-spaced. As a result, the symbol-spaced iteration can be taken between the equalizer and the decoder directly without a CPM demodulator, and we derive a symbol-spaced soft interference cancellation frequency domain equalization (SSIC-FDE) algorithm for binary CPM with h=1/2. A new data block structure for FDE of partial response CPM is also presented. The computational complexity analysis and simulations show that this approach provides a complexity reduction and an impressive performance improvement over previously proposed turbo FDE algorithm for binary CPM with h=1/2 in multi-path fading channels.

A design of dual band amplifier using composite right/left handed (CRLH) transmission line structure is described. First, two single-band matching networks are designed for two frequencies, and they are synthesized into one dual band matching network. It is shown that CRLH transmission lines with arbitrary dual frequencies and dual electrical lengths can be designed. The CRLH transmission line section for the dual band matching network is implemented by lumped inductors and capacitors as the left handed (LH) section, and normal transmission line elements as the right handed (RH) section. As an example, a dual band amplifier for 1800 MHz and 2300 MHz is designed and measured. The simulated and measured performances well verify the proposed design by showing good matching and gain responses at the desired frequencies.

This study shows a fast simulation method of turbo codes over slow Rayleigh fading channels. The reduction of the simulation time is achieved by applying importance sampling (IS). The conventional IS method of turbo codes over Rayleigh fading channels focuses only on modification of additive white Gaussian noise (AWGN) sequences. The proposed IS method biases not only AWGNs but also channel gains of the Rayleigh fading channels. The computer runtime of the proposed method is about 1/5 of that of the conventional IS method on the evaluation of a frame error rate of 10-6. When we compare with the Monte Carlo simulation method, the proposed method needs only 1/100 simulation runtime under the condition of the same accuracy of the estimator.

Visually saliency detection provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. One of the main aims of visual attention in computer vision is to detect and segment the salient regions in an image. In this paper, we employ matrix decomposition to detect salient object in nature images. To efficiently eliminate high contrast noise regions in the background, we integrate global context information into saliency detection. Therefore, the most salient region can be easily selected as the one which is globally most isolated. The proposed approach intrinsically provides an alternative methodology to model attention with low implementation complexity. Experiments show that our approach achieves much better performance than that from the existing state-of-art methods.

In this letter, we propose a novel method for change detection in multitemporal optical satellite images. Unlike the tradition methods, the proposed method is able to detect changed region even from unregistered images. In order to obtain the change detection map from the unregistered images, we first compute the sum of the color difference (SCD) of a pixel to all pixels in an input image. Then we calculate the SCD of this pixel to all pixels in the other input image. Finally, we use the difference of the two SCDs to represent the change detection map. Experiments on the multitemporal images demonstrates the good performance of the proposed method on the unregistered images.

In this paper, we propose a method for supervised single-channel speech separation through sparse decomposition using periodic signal models. The proposed separation method employs sparse decomposition, which decomposes a signal into a set of periodic signals under a sparsity penalty. In order to achieve separation through sparse decomposition, the decomposed periodic signals have to be assigned to the corresponding sources. For the assignment of the periodic signal, we introduce clustering using a K-means algorithm to group the decomposed periodic signals into as many clusters as the number of speakers. After the clustering, each cluster is assigned to its corresponding speaker using preliminarily learnt codebooks. Through separation experiments, we compare our method with MaxVQ, which performs separation on the frequency spectrum domain. The experimental results in terms of signal-to-distortion ratio show that the proposed sparse decomposition method is comparable to the frequency domain approach and has less computational costs for assignment of speech components.

Reversible color component transforms derived by the LU factorization are briefly described. It is possible to obtain an reversible implementation to a given component transform, even if the original transform is irreversible. Some examples are presented and their performances are compared in image compression.

Maximum likelihood block signal detection employing QR decomposition and M-algorithm (QRM-MLBD) can significantly improve the bit error rate (BER) performance of single-carrier (SC) transmission while significantly reducing the computational complexity compared to maximum likelihood detection (MLD). However, its computational complexity is still high. In this paper, we propose the computationally efficient 2-step QRM-MLBD. Compared to conventional QRM-MLBD, the number of symbol candidates can be reduced by using preliminary decision made by minimum mean square error based frequency-domain equalization (MMSE-FDE). The BER performance achievable by 2-step QRM-MLBD is evaluated by computer simulation. It is shown that it can significantly reduce the computational complexity while achieving almost the same BER performance as the conventional QRM-MLBD.

In the task of face recognition, a challenging issue is the one sample problem, namely, there is only one training sample per person. Principal component analysis (PCA) seeks a low-dimensional representation that maximizes the global scatter of the training samples, and thus is suitable for one sample problem. However, standard PCA is sensitive to the outliers and emphasizes more on the relatively distant sample pairs, which implies that the close samples belonging to different classes tend to be merged together. In this paper, we propose two-stage block-based whitened PCA (TS-BWPCA) to address this problem. For a specific probe image, in the first stage, we seek the K-Nearest Neighbors (K-NNs) in the whitened PCA space and thus exclude most of samples which are distant to the probe. In the second stage, we maximize the “local” scatter by performing whitened PCA on the K nearest samples, which could explore the most discriminative information for similar classes. Moreover, block-based scheme is incorporated to address the small sample problem. This two-stage process is actually a coarse-to-fine scheme that can maximize both global and local scatter, and thus overcomes the aforementioned shortcomings of PCA. Experimental results on FERET face database show that our proposed algorithm is better than several representative approaches.

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

In the conventional iterative superimposed pilot-assisted channel estimation (SI-PACE), simple averaging of the instantaneous channel estimates obtained by using the pilot over several single-carrier (SC) blocks (called the frame in this paper) is taken in order to reduce the interference from data symbols. Therefore, the conventional SI-PACE has low tracking ability against fading time variations. To solve the tracking problem, Wiener filtering (WF)-based averaging can be used instead of simple averaging. However, WF incurs high computational complexity. Furthermore, the estimation error of the fading autocorrelation function significantly degrades the channel estimation accuracy. In order to improve the channel estimation accuracy while keeping the computational complexity low, a new iterative SI-PACE using sliding WF (called iterative SWFSI-PACE) is proposed. The channel estimation is done by sliding a WF having a shorter filter size than the measurement interval. The bit error rate (BER) and throughput performances of SC-FDE using iterative SWFSI-PACE are investigated by computer simulation to show that the proposed scheme achieves good BER and throughput performances while keeping the computational complexity low irrespective of the fading rate (or maximum Doppler frequency).

As a giant in open source community, OpenOffice.org has become the most popular office suite within Linux community. But OpenOffice.org is relatively slow while loading documents. Research shows that the most time consuming part is importing one page of whole document. If there are many pages in a document, the accumulation of time consumed can be astonishing. Therefore, this paper proposes a solution, which has improved the speed of loading documents through asynchronous importing mechanism: a document is not imported as a whole, but only part of the document is imported at first for display, then mechanism in the background is started to asynchronously import the remaining parts, and insert it into the drawing queue of OpenOffice.org for display. In this way, the problem can be solved and users don't have to wait for a long time. Application start-up time testing tool has been used to test the time consumed in loading different pages of documents before and after optimization of OpenOffice.org, then, we adopt the regression theory to analyse the correlation between the page number of documents and the loading time. In addition, visual modeling of the experimental data are acquired with the aid of matlab. An obvious increase in loading speed can be seen after a comparison of the time consumed to load a document before and after the solution is adopted. And then, using Microsoft Office compared with the optimized OpenOffice.org, their loading speeds are almost same. The results of the experiments show the effectiveness of this solution.

This paper proposes a diversity scheme for Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) based on Radio Frequency (RF) signal processing. Although a 22 MIMO-OFDM system can double the capacity without expanding the occupied frequency bandwidth, we cannot get additional diversity gain using the linear MIMO decomposition method. The proposed method improves the bit error rate (BER) performance by making efficient use of RF signal processing. Computer simulation results show that the proposed scheme gives additional diversity gain.

We describe a cell-based connected component labeling algorithm to calculate the 0th and 1st moment features as the attributes for labeled regions. These can be used to indicate their sizes and positions for multi-object extraction. Based on the additivity in moment features, the cell-based labeling algorithm can label divided cells of a certain size in an image by scanning the image only once to obtain the moment features of the labeled regions with remarkably reduced computational complexity and memory consumption for labeling. Our algorithm is a simple-one-time-scan cell-based labeling algorithm, which is suitable for hardware and parallel implementation. We also compared it with conventional labeling algorithms. The experimental results showed that our algorithm is faster than conventional raster-scan labeling algorithms.

A novel dual-band magnetic loop antenna is proposed using slot-loaded composite right/left-handed (SL-CRLH) structures. Since each radiating element consists of a symmetrically-array of unit-cells, a dual-band magnetic loop source is obtained with unchanged beam patterns. Simulations and measurements show its good radiation performance with monopole-like radiation patterns in both operating bands.

We quantitatively evaluate how sampling and spatio/temporal granularity in traffic monitoring affect the detectability of anomalous traffic. Those parameters also affect the monitoring burden, so network operators face a trade-off between the monitoring burden and detectability and need to know which are the optimal paramter values. We derive equations to calculate the false positive ratio and false negative ratio for given values of the sampling rate, granularity, statistics of normal traffic, and volume of anomalies to be detected. Specifically, assuming that the normal traffic has a Gaussian distribution, which is parameterized by its mean and standard deviation, we analyze how sampling and monitoring granularity change these distribution parameters. This analysis is based on observation of the backbone traffic, which exhibits spatially uncorrelated and temporally long-range dependence. Then we derive the equations for detectability. With those equations, we can answer the practical questions that arise in actual network operations: what sampling rate to set to find the given volume of anomaly, or, if the sampling is too high for actual operation, what granularity is optimal to find the anomaly for a given lower limit of sampling rate.

A new frequency estimator for a single real-valued sinusoid signal in white noise is proposed. The new estimator uses the Pisarenko Harmonic Decomposer (PHD) estimator to get a coarse frequency estimate and then makes use of multiple correlation lags to obtain an adjustment term. For the limited-length single sinusoid, its correlation has the same frequency as itself but with a non-zero phase. We propose to use Taylor series to expand the correlation at the PHD coarse estimated frequency with amplitude and phase of the correlation into consideration. Simulation results show that this new method improves the estimation performance of the PHD estimator. Moreover, when compared with other existing estimator, the mean square frequency error of the proposed method is closer to the Cramer-Rao Lower Bound (CRLB) for certain SNR range.

This paper presents a new non-interactive string-commitment scheme that achieves universally composable security. Security is proven under the decisional composite residuosity (DCR) assumption (or the decisional Diffie-Hellman (DDH) assumption) in the common reference string (CRS) model. The universal composability (UC) is a very strong security notion. If cryptographic protocols are proven secure in the UC framework, then they remain secure even if they are composed with arbitrary protocols and polynomially many copies of the protocols are run concurrently. Many UC commitment schemes in the CRS model have been proposed, but they are either interactive commitment or bit-commitment (not string-commitment) schemes. We note, however, that although our scheme is the first non-interactive UC string-commitment scheme, a CRS is not reusable. We use an extension of all-but-one trapdoor functions (ABO-TDFs) proposed by Peikert and Waters at STOC 2008 as an essential building block. Our main idea is to extend (original deterministic) ABO-TDFs to probabilistic ones by using the homomorphic properties of their function indices. The function indices of ABO-TDFs consist of ciphertexts of homomorphic encryption schemes (such as ElGamal, and Damgåd-Jurik encryption). Therefore we can re-randomize the output of ABO-TDFs by re-randomization of ciphertexts. This is a new application of ABO-TDFs.

As the fundamental component of dynamic spectrum access, implementing spectrum sensing is one of the most important goals in cognitive radio networks due to its key functions of protecting licensed primary users from harmful interference and identifying spectrum holes for the improvement of spectrum utilization. However, its performance is generally compromised by the interference from adjacent primary channels. To cope with such interference and improve detection performance, this paper proposes a non-coherent power decomposition-based energy detection method for cooperative spectrum sensing. Due to its use of power decomposition, interference cancellation can be applied in energy detection. The proposed power decomposition does not require any prior knowledge of the primary signals. The power detection with its interference cancellation can be implemented indirectly by solving a non-homogeneous linear equation set with a coefficient matrix that involves only the distances between primary transmitters and cognitive secondary users (SUs). The optimal number of SUs for sensing a single channel and the number of channels that can be sensed simultaneously are also derived. The simulation results show that the proposed method is able to cope with the expected interference variation and achieve higher probability of detection and lower probability of false alarm than the conventional method in both hard combining and soft combining scenarios.