We propose a novel algorithm for the recovery of non-sparse, but compressible signals from linear undersampled measurements. The algorithm proposed in this paper consists of two steps. The first step recovers the signal by the l₁-norm minimization. Then, the second step decomposes the l₁ reconstruction into major and minor components. By using the major components, measurements for the minor components of the target signal are estimated. The minor components are further estimated using the estimated measurements exploiting a maximum a posterior (MAP) estimation, which leads to a ridge regression with the regularization parameter determined using the error bound for the estimated measurements. After a slight modification to the major components, the final estimate is obtained by combining the two estimates. Computational cost of the proposed algorithm is mostly the same as the l₁-nom minimization. Simulation results for one-dimensional computer generated signals show that the proposed algorithm gives 11.8% better results on average than the l₁-norm minimization and the lasso estimator. Simulations using standard images also show that the proposed algorithm outperforms those conventional methods.

A new algorithm for separating mass spectra into individual substances for explosives detection is proposed. In the field of mass spectrometry, separation methods, such as principal-component analysis (PCA) and independent-component analysis (ICA), are widely used. All components, however, have no negative values, and the orthogonality condition imposed on components also does not necessarily hold in the case of mass spectra. Because these methods allow negative values and PCA imposes an orthogonality condition, they are not suitable for separation of mass spectra. The proposed algorithm is based on probabilistic latent-component analysis (PLCA). PLCA is a statistical formulation of non-negative matrix factorization (NMF) using KL divergence. Because PLCA imposes the constraint of non-negativity but not orthogonality, the algorithm is effective for separating components of mass spectra. In addition, to estimate the components more accurately, a sparsity constraint is applied to PLCA for explosives detection. The main contribution is industrial application of the algorithm into an explosives-detection system. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms PCA and ICA. Also, results of calculation time demonstrate that the algorithm can work in real time.

A new algorithm for separating mass spectra into individual substances is proposed for explosives detection. The conventional algorithm based on probabilistic latent component analysis (PLCA) is effective in many cases because it makes use of the fact that non-negativity and sparsity hold for mass spectra in explosives detection. The algorithm, however, fails to separate mass spectra in some cases because uncertainty can not be resolved only by non-negativity and sparsity constraints. To resolve the uncertainty, an algorithm based on shift-invariant PLCA (SIPLCA) utilizing temporal correlation of mass spectra is proposed in this paper. In addition, to prevent overfitting, the temporal correlation is modeled with a function representing attenuation by focusing on the fact that the amount of a substance is attenuated continuously and slowly with time. Results of an experimental evaluation of the algorithm with data obtained in a real railway station demonstrate that the proposed algorithm outperforms the PLCA-based conventional algorithm and the simple SIPLCA-based one. The main novelty of this paper is that an evaluation of the detection performance of explosives detection is demonstrated. Results of the evaluation indicate that the proposed separation algorithm can improve the detection performance.

A signal-model-based SAR image formation algorithm is proposed in this paper. A model is used to describe the received signal, and each scatterer can be characterized by a set of its parameters. Two parameter estimation methods via atomic decomposition are presented: (1) applying 1-D matching pursuit to azimuthal projection data; (2) applying 2-D matching pursuit to raw data. The estimated parameters are mapped to form a SAR image, and the mapping procedure can be implemented under application guidelines. This algorithm requires no prior information about the relative motion between the platform and the target. The Cramer-Rao bounds of parameter estimation are derived, and the root mean square errors of the estimates are close to the bounds. Experimental results are given to validate the algorithm and indicate its potential applications.

The theoretically minimum length of a signal for fundamental frequency estimation in a noisy environment is discussed. Assuming that the noise is additive white Gaussian, it is known that a Cramér-Rao lower bound (CRLB) is given by the length and other parameters of the signal. In this paper, we define the minimum length as the length whose CRLB is less than or equal to the specific variance for any parameters of the signal. The specific variance is allowable variance of the estimate within an application of fundamental frequency estimation. By reformulating the CRLB with respect to the initial phase of the signal, the algorithms for determining the minimum length are proposed. In addition, we develop the methods of deciding the specific variance for general fundamental frequency estimation and pitch estimation. Simulation results in terms of both the fundamental frequency estimation and the pitch estimation show the validity of our approach.

This paper presents a nonlinear model of human brain activity in response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal often contains a low frequency signal component (trend), which is usually removed by detrending because it is considered a part of noise. However, such detrending could destroy the dynamics of the BOLD signal and ignore an essential component in the response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. The presented model also has low Schwarz information criterion, which implies that it will be less likely to overfit the experimental data. Comparison between the various types of artificial trends suggests that the trends are not merely the result of noise in the BOLD signal.

Several models of feed-forward complex-valued neural networks have been proposed, and those with split and polar-represented activation functions have been mainly studied. Neural networks with split activation functions are relatively easy to analyze, but complex-valued neural networks with polar-represented functions have many applications but are difficult to analyze. In previous research, Nitta proved the uniqueness theorem of complex-valued neural networks with split activation functions. Subsequently, he studied their critical points, which caused plateaus and local minima in their learning processes. Thus, the uniqueness theorem is closely related to the learning process. In the present work, we first define three types of reducibility for feed-forward complex-valued neural networks with polar-represented activation functions and prove that we can easily transform reducible complex-valued neural networks into irreducible ones. We then prove the uniqueness theorem of complex-valued neural networks with polar-represented activation functions.

An integral attack is one of the most powerful attacks against block ciphers. We propose a new technique for the integral attack called the Fast Fourier Transform (FFT) key recovery. When N chosen plaintexts are required for the integral characteristic and the guessed key is k bits, a straightforward key recovery requires the time complexity of O(N2^k). However, the FFT key recovery only requires the time complexity of O(N+k2^k). As a previous result using FFT, at ICISC 2007, Collard etal proposed that FFT can reduce the time complexity of a linear attack. We show that FFT can also reduce the complexity of the integral attack. Moreover, the estimation of the complexity is very simple. We first show the complexity of the FFT key recovery against three structures, the Even-Mansour scheme, a key-alternating cipher, and the Feistel structure. As examples of these structures, we show integral attacks against Prøst, AES, PRESENT, and CLEFIA. As a result, an 8-round Prøst P_128,K can be attacked with about an approximate time complexity of 2^79.6. For the key-alternating cipher, a 6-round AES and a 10-round PRESENT can be attacked with approximate time complexities of 2^51.7 and 2^97.4, respectively. For the Feistel structure, a 12-round CLEFIA can be attacked with approximate time complexities of 2^87.5.

A K-user parallel concatenated code (PCC) is proposed for a Gaussian multiple-access channel with symbol synchronization, equal-power, and equal-rate users. In this code, each user employs a PCC with M+1 component codes, where the first component code is a rate-1/q repetition code and the other M component codes are the same rate-1 recursive convolutional (RC) codes. By designing the repetition coding rate and the RC component code, the K-user PCC achieve reliable transmission for a given number of users and noise level. Two decoding schemes are considered: low-density parity-check (LDPC)-like decoding and Turbo-like decoding. For each decoding scheme, a fixed point analysis is given to optimize the parameters: the rate of repetition component code 1/q, the number of RC component codes M, or the RC component codes themselves. The analysis shows that an accumulate code is the optimal RC component code for a K-user PCC, in the sense of achieving the maximum sum rate. The K-user PCC with an accumulate component code achieves a larger sum rate in the high rate region than the conventional scheme of an error correction code serially concatenated with spreading under similar encoding and decoding complexity.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l₀-norm minimization by l₁-norm minimization, our method designs a reweighted l₁ norm to substitute the l₀ norm. The capability of the reweighted l₁ norm to bridge the gap between the l₀- and l₁-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l₁-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

The sparse Fourier transform (SFT) seeks to recover k non-negligible Fourier coefficients from a k-sparse signal of length N (k«N). A single frequency signal can be recovered via the Chinese remainder theorem (CRT) with sub-sampled discrete Fourier transforms (DFTs). However, when there are multiple non-negligible coefficients, more of them may collide, and multiple stages of sub-sampled DFTs are needed to deal with such collisions. In this paper, we propose a combinatorial aliasing-based SFT (CASFT) algorithm that is robust to noise and greatly reduces the number of stages by iteratively recovering coefficients. First, CASFT detects collisions and recovers coefficients via the CRT in a single stage. These coefficients are then subtracted from each stage, and the process iterates through the other stages. With a computational complexity of O(klog klog ²N) and sample complexity of O(klog ²N), CASFT is a novel and efficient SFT algorithm.

A simple robust finite-time convergent observer is presented in the presence of unknown input disturbance and measurement noise. In order to achieve the robust estimation and ensure the finite-time convergence, the proposed observer is constructed by using a multiple integral observer scheme in a hybrid system framework. Comparative computer simulations and laboratory experiments have been performed to test the effectiveness of the proposed observer.

This letter deals with the consensus problem of multi-agent systems, which are composed of feedforward nonlinear systems under a directed network with a communication time delay. In order to solve this problem, a new consensus protocol with a low gain parameter is proposed. Moreover, it is shown that under some sufficient conditions, the proposed protocol can solve the consensus problem of nonlinear multi-agent systems even in the presence of an arbitrarily large communication delay. An illustrative example is presented to verify the validity of the proposed approach.

A predicate encryption scheme enables the owner of the master key to enforce fine-grained access control on encrypted cloud data through the delegation of predicate tokens to cloud storages. In particular, Blundo et al. proposed a construction where a predicate token reveals partial information of the involved keywords to enable efficient operations on encrypted keywords. However, we found that a predicate token reveals more information than what was claimed because of the encoding scheme. In this letter, we not only analyze this extra information leakage but also present an improved encoding scheme for the Blundo et al's scheme and the other similar schemes to preserve predicate privacy.

Network Coding-based Epidemic Routing (NCER) facilitates the reduction of data delivery delay in Delay Tolerant Networks (DTNs). The intrinsic reason lies in that the network coding paradigm avoids competitions for transmission opportunities between segmented packets of a large data file. In this paper, we focus on the impact of transmission competitions on the delay performance of NCER when multiple data files exist. We prove analytically that when competition occurs, transmitting the least propagated data file is optimal in the sense of minimizing the average data delivery delay. Based on such understanding, we propose a family of competition avoidance policies, namely the Least Propagated First (LPF) policies, which includes a centralized, a distributed, and a modified variants. Numerical results show that LPF policies can achieve at least 20% delay performance gain at different data traffic rates, compared with the policy currently available.

To improve the BER performance of the conventional cooperative communication, this letter proposes an efficient method for the reliability, and it uses hierarchical modulation that has both the high priority (HP) layer and the low priority (LP) layer. To compensate more reliable transmission, the proposed method uses the error correction capability of Reed-Solomon (RS) codes additionally. The simulation results show that the proposed method can transmit data more reliably than the basic RS coded decode-and-forward (DF) method.

In this letter, a robust algorithm for jointly finding an estimate of the start of the frame and transmission mode is proposed in a digital audio broadcasting (DAB) system. In doing so, the use of differential-correlation based joint detection is proposed, which considers not only the height of correlation peak but also its plateau. We show via simulations that the proposed detection algorithm is capable of robustly detecting the start of a frame and its mode against the variation of signal-to-noise ratio, providing a performance advantage over the conventional algorithm.

This paper proposes a foreground segmentation method for indoor environments using depth images only. It uses a morphological operator and histogram analysis to segment the foreground. In order to compare the accuracy for foreground segmentation, we use metric measurements of false positive rate (FPR), false negative rate (FNR), total error (TE), and a similarity measure (S). A series of experimental results using video sequences collected under various circumstances are discussed. The proposed system is also designed in a field-programmable gate array (FPGA) implementation with low hardware resources.

Due to ease of implementation for various user interactive applications, much research on motion recognition has been completed using Kinect. However, one drawback of Kinect is that the skeletal information obtained is provided under the assumption that the user faces Kinect. Thus, the skeletal information is likely incorrect when the user turns his back to Kinect, which may lead to difficulty in motion recognition from the application. In this paper, we implement a highly accurate human motion capture system by installing six Kinect sensors over 360 degrees. The proposed method enables skeleton to be obtained more accurately by assigning higher weights to skeletons captured by Kinect in which the user faces forward. Toward this goal, the front vector of the user is temporally traced to determine whether the user is facing Kinect. Then, more reliable joint information is utilized to construct a skeletal representation of each user.