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.

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.

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.

We investigate non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) in the cellular multiple-input multiple-output (MIMO) downlink for systems beyond LTE-Advanced. Taking into account the overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal multiuser multiplexing and the applicability of the SIC receiver in the MIMO downlink, we propose intra-beam superposition coding of a multiuser signal at the transmitter and the spatial filtering of inter-beam interference followed by the intra-beam SIC at the user terminal receiver. The intra-beam SIC cancels out the inter-user interference within a beam. Regarding the transmitter beamforming (precoding), in general, any kind of beamforming matrix determination criteria can be applied to the proposed NOMA method. In the paper, we assume open loop-type random beamforming, which is very efficient in terms of the amount of feedback information from the user terminal. Furthermore, we employ a weighted proportional fair (PF)-based resource (beam of each frequency block and power) allocation for the proposed method. Simulation results show that the proposed NOMA method using the intra-beam superposition coding and SIC simultaneously achieves better sum and cell-edge user throughput compared to orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

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.

In indoor localization using sensor networks, performance improvements are required for non-line-of-sight (NLOS) environments in which the estimation error is high. NLOS mitigation schemes involve the detection and elimination of the NLOS measurements. The iterative minimum residual (IMR) scheme, which is often applied to the localization scheme using the time of arrival (TOA), is commonly employed for this purpose. The IMR scheme is a low-complexity scheme and its NLOS detection performance is relatively high. However, when there are many NLOS nodes in a sensor field, the NLOS detection error of the IMR scheme increases and the estimation accuracy deteriorates. Therefore, we propose a new scheme that exploits coarse NLOS detection based on stochastic characteristics prior to the application of the IMR scheme to improve the localization accuracy. Improved performances were confirmed in two NLOS channel models by performing numerical simulations.

High-availability seamless redundancy (HSR) is a fault-tolerant protocol for Ethernet networks that provides two frame copies for each frame sent. Each copy is forwarded on a separate physical path. HSR is a potential candidate for several fault-tolerant Ethernet applications including smart grid communications. However, one of the drawbacks of the HSR protocol is that it generates and circulates unnecessary frames within connected rings regardless of the presence of a destination node in the ring. This downside will degrade network performance and may deplete network resources. Previously, we proposed a simple but efficient approach to solving the above problem, namely, port locking (PL), which is based on the media access control address. The PL approach enables the network to learn the locations of the source and destination nodes gradually for each connection pair without using network control frames; the PL then prunes all the rings that do not contain the destination node by locking the corresponding ring's entrance ports at its QuadBox node. In this paper, we present an enhanced port-locking (EPL) approach that increases the number of pruned unused HSR rings. The analysis and corresponding simulation results show that the network traffic volume is significantly reduced for a large-sized HSR connected-rings network and consequently, network performance is greatly improved compared to the standard HSR protocol, and even PL.

This paper studies a cascade system of dynamic binary neural networks. The system is characterized by signum activation function, ternary connection parameters, and integer threshold parameters. As a fundamental learning problem, we consider storage and stabilization of one desired binary periodic orbit that corresponds to control signals of switching circuits. For the storage, we present a simple method based on the correlation learning. For the stabilization, we present a sparsification method based on the mutation operation in the genetic algorithm. Using the Gray-code-based return map, the storage and stability can be investigated. Performing numerical experiments, effectiveness of the learning method is confirmed.

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(N2k). However, the FFT key recovery only requires the time complexity of O(N+k2k). 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 P128,K can be attacked with about an approximate time complexity of 279.6. For the key-alternating cipher, a 6-round AES and a 10-round PRESENT can be attacked with approximate time complexities of 251.7 and 297.4, respectively. For the Feistel structure, a 12-round CLEFIA can be attacked with approximate time complexities of 287.5.

This paper presents an inter-satellite link (ISL) reassignment method to optimize the snapshot routing performance for polar-orbit LEO satellite networks. When the snapshot routing tables are switching simultaneously in all satellites, we propose to reassign the inter-plane ISLs with regularity to improve the quality of the next snapshot, such as snapshot duration, on-board transceiver utilization ratio and end to end delay. Evaluations indicate that our method can attain equal-length snapshots regardless of the latitude of the polar area border, and so is superior to the natural partition method. Meanwhile, compared with the equal partition method which is used in the Iridium system, our method can prolong 82.87% snapshot duration, increase 8.68% on-board transceiver utilization ratio and reduce 5.30% average end to end delay of the whole network. Therefore, we believe that the ISL reassignment method can be efficiently applied in all practical polar-orbit LEO satellite networks.

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.

To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy c-means clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods.

This paper presents a method of imaging a two-dimensional section of a walking person using multiple Doppler radar systems. Although each simple radar system consists of only two receivers, different radial speeds allow target positions to be separated and located. The signal received using each antenna is processed employing time-frequency analysis, which separates targets in the time-range-velocity space. This process is followed by a direction-of-arrival estimation employing interferometry. The data obtained using the multiple radar systems are integrated using a clustering algorithm and a target-tracking algorithm. Through realistic simulations, we demonstrate the remarkable performance of the proposed imaging method in generating a clear outline image of a human target in unknown motion.

In a previous paper, we proposed a rescue support system for victims buried in an earthquake disaster by constructing an ad-hoc network using home-server based smart homes. However, this system has the following two problems: i) it cannot ensure sufficient density of home servers to realize adequate WLAN coverage, ii) the system does not consider areas in which home servers cannot be used such as parks and factories, for example. In this paper, we propose a new method using a delay tolerant network (DTN) technique. In this method, rescuers (such as rescue teams) with mobile devices relay information between disconnected networks by walking around during rescue activities. For a performance evaluation, we performed simulation experiments using a map of Abeno-ku, Osaka. From our results, we show that the proposed method increases the information acquisition rate, and that the network can be maintained. We also quantitatively show the penetration rate of smart homes needed for our system. In addition, we show that the rescue request system is more effective than other systems, and the method with the mobile device relay is better than without this method.

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 mobile ad hoc network (MANET) consists of mobile wireless terminals without using base stations. MANETs are expected to be utilized for various purposes such as traffic jam information announcements for vehicles and safety confirmation systems in disaster. Each MANET uses unique routing protocols that have been adapted for particular applications. Therefore, utilizing a common routing protocol for multiple MANETs is difficult. In this paper, we propose an Autonomous Clustering-based Inter-Domain Routing protocol to communicate between MANETs. Using the autonomous clustering, the proposed inter-domain routing scheme can change the network gateways between MANETs adaptively according to the network topology changes.

To eliminate casting shadows of moving objects, which cause difficulties in vision applications, a novel method is proposed based on Visual background extractor by altering its updating mechanism using relevant spatiotemporal information. An adaptive threshold and a spatial adjustment are also employed. Experiments on typical surveillance scenes validate this scheme.

This paper describes 0.8-/1.5-GHz-band GaAs-HBT power amplifier modules with a newly designed analog bias control scheme. This scheme has two features. One is to achieve approximately linear quiescent current control using not a BiFET process but only the usual HBT process. The other is to help improve linearity under reduced supply voltage and lower quiescent current operation. The following two key techniques are incorporated into the bias scheme. The first is to employ two different kinds of bias circuits: emitter follower bias and current injection bias. The second is the unique current injection bias block, based on the successful combination of an input buffer with an emitter resistance load and a current mirror. These techniques allow quiescent current control that is almost proportional to an externally applied analog control voltage. To confirm the effectiveness of the scheme, 0.8-GHz-band and 1.5-GHz-band power amplifier modules were designed and fabricated using the usual HBT process. Measurements conducted under the conditions of a 3.4V supply voltage and an HSDPA WCDMA modulated signal are as follows. The 0.8-GHz-band amplifier can deliver a 28-dBm output power (Pout), a 28.4-dB power gain (Gp), and 42% PAE while restricting the ACLR to less than -40dBc. For the 1.5-GHz-band amplifier, 28dBm of Pout, 29dB of Gp, and 41% of PAE are obtained with the same ACLR levels. The measurements also confirm that the quiescent current for the second stage in the amplifiers is approximately linearly changed from 14mA to 58mA over a control voltage ranging from 1.1V to 2.2V. In addition, our measured DG.09-based current dissipation with both supply voltage and analog bias controls is as low as 16.9mA, showing that the analog bias control scheme enables an average current reduction of more than 20%, as compared to a conventional supply voltage and two-step quiescent current control.

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.