In this paper, synchronization for uncertain fractional order chaotic systems is investigated. By using the fractional order extension of the Lyapunov stability criterion, a linear feedback controller and an adaptive controller are designed for synchronizing uncertain fractional order chaotic systems without and with unknown external disturbance, respectively. Quadratic Lyapunov functions are used in the stability analysis of fractional-order systems, and fractional order adaptation law is constructed to update design parameter. The proposed methods can guarantee that the synchronization error converges to zero asymptotically. Finally, illustrative examples are given to confirm the theoretical results.

In this paper, we introduce a distributed power allocation strategy for random access, that has the capabilities of multipacket reception (MPR) and successive interference cancellation (SIC). The proposed random access scheme is suitable for machine-to-machine (M2M) communication application in fifth-generation (5G) cellular networks. A previous study optimized the probability distribution for discrete transmission power levels, with implicit limitations on the successful decoding of at most two packets from a single collision. We formulate the optimization problem for the general case, where a base station can decode multiple packets from a single collision, and this depends only on the signal-to-interference-plus-noise ratio (SINR). We also propose a feasible suboptimal iterative per-level optimization process; we do this by introducing relationships among the different discrete power levels. Compared with the conventional power allocation scheme with MPR and SIC, our method significantly improves the system throughput; this is confirmed by computer simulations.

In our previous work [12], [13], we introduced generalized feed-forward shift registers (GF2SR, for short) to apply them to secure and testable scan design, where we considered the security problem from the viewpoint of the complexity of identifying the structure of GF2SRs. Although the proposed scan design is secure in the sense that the structure of a GF2SR cannot be identified only from the primary input/output relation, it may not be secure if part of the contents of the circuit leak out. In this paper, we introduce a more secure concept called strong security such that no internal state of strongly secure circuits leaks out, and present how to design such strongly secure GF2SRs.

Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

In this paper, we study a distributed time-reversal space-time block coded single-carrier (D-TR-STBC-SC) system for amplify-and-forward (AF) half-duplex relaying in frequency-selective Rayleigh fading channels. Under the imperfect channel estimation condition, we analyze the mean-square-error (MSE) performance of the optimal and channel-mismatched frequency domain minimum MSE (FD-MMSE) and least square (LS) equalization. Our analysis results show that, unlike the point-to-point communications, the channel-mismatched FD-MMSE equalization of D-TR-STBC-SC relaying network leads to the ceiling effect that the MSE increases as the signal-to-noise ratio (SNR) of relay-to-destination link increases. Decomposing the MSE, it is found that the primary cause of the ceiling effect is the source-to-destination link in the first time-slot, which makes the covariance matrix of noise vector ill-conditioned. In order to resolve the channel-mismatching problems in the equalization process, we develop optimum relay power control strategies by considering practical channel estimations, i.e., training-based LS and linear minimum MSE (LMMSE) channel estimations. It is shown that the optimum power control resolves the trade-off between MSE performance and relay power consumption, and improves the robustness against the channel-mismatching. Finally, we introduce a performance evaluation to demonstrate the performance of channel equalization combined with the proposed power controls in D-TR-STBC-SC relaying network.

Code-based public-key encryption schemes (PKE) are the candidates for post-quantum cryptography, since they are believed to resist the attacks using quantum algorithms. The most famous such schemes are the McEliece encryption and the Niederreiter encryption. In this paper, we present the zero-knowledge (ZK) proof systems for proving statements about data encrypted using these schemes. Specifically, we present a proof of plaintext knowledge for both PKE's, and also a verifiable McEliece PKE. The main ingredients of our constructions are the ZK identification schemes by Stern from Crypto'93 and by Jain, Krenn, Pietrzak, and Tentes from Asiacrypt'12.

In this paper, we propose a transmit multi-block frequency-domain equalization (MB-FDE) for frequency-domain space-time block coded joint transmit/receive diversity (FD-STBC-JTRD). Noting that a STBC codeword consists of multiple coded blocks, the transmit MB-FDE uses the multiple transmit FDE weight matrices, each associated with each coded block. Both single-carrier (SC) transmission and orthogonal frequency-division multiplexing (OFDM) transmission are considered. For SC transmission, the transmit MB-FDE weight matrices are jointly optimized so as to minimize the mean square error (MSE) between the transmit signal before STBC encoding and the received signal after STBC decoding. For OFDM transmission, they are jointly optimized so as to maximize the received signal-to-noise power ratio (SNR) after STBC decoding. We show by theoretical analysis that the proposed transmit MB-FDE can achieve 1/RSTBC times higher received SNR than the conventional transmit single-block FDE (SB-FDE), where RSTBC represents the code rate of STBC. It is confirmed by computer simulation that, when more than 2 receive antennas are used, MB-FDE can always achieve better BER performance than SB-FDE irrespective of the number of transmit antennas, and the channel frequency-selectivity.

This paper proposes a construction method of binary Z-periodic complementary sequence set (Z-PCSs) based on binary aperiodic complementary sequence pair (Golay pair) and interleaved technique. The constructed set is optimal or almost optimal with respect to the theoretical bound in different conditons. The set can be used in multi-carrier code division multiple access communication systems. The designed sequence has periodic complementary characteristics, which lead to a strong ability to resist multi-path interference and multiple access interference.

OSEK/VDX, a standard for an automobile OS, has been widely adopted by many manufacturers to design and develop a vehicle-mounted OS. With the increasing functionalities in vehicles, more and more complex applications are be developed based on the OSEK/VDX OS. However, how to ensure the reliability of developed applications is becoming a challenge for developers. To ensure the reliability of developed applications, model checking as an exhaustive technique can be applied to discover subtle errors in the development process. Many model checkers have been successfully applied to verify sequential software and general multi-threaded software. However, it is hard to directly use existing model checkers to precisely verify OSEK/VDX applications, since the execution characteristics of OSEK/VDX applications are different from the sequential software and general multi-threaded software. In this paper, we describe and develop an approach to translate OSEK/VDX applications into sequential programs in order to employ existing model checkers to precisely verify OSEK/VDX applications. The value of our approach is that it can be considered as a front-end translator for enabling existing model checkers to verify OSEK/VDX applications.

This paper considers how to evaluate the resiliency for virtualized system with software rejuvenation. The software rejuvenation is a proactive technique to prevent the failure caused by aging phenomenon such as resource exhaustion. In particular, according to Gohsh et al. (2010), we compute a quantitative criterion to evaluate resiliency of system by using continuous-time Markov chains (CTMC). In addition, in order to convert general state-based models to CTMCs, we employ PH (phase-type) expansion technique. In numerical examples, we investigate the resiliency of virtualized system with software rejuvenation under two different rejuvenation policies.

Two-track squeeze and adjacent track interference (ATI) are major barriers to increasing track density in hard disk drives (HDD). These depend on skew angles made by a magnetic head and circumferential direction on a magnetic disk. This paper describes relationships between the skew angle and the magnetic core width (MCW) which affects two-track squeeze and ATI performance. We propose a design concept of a track pitch profile at different skew angles considering MCW. Equivalent robustness of ATI performance on different skew angle conditions is obtained with the optimized track pitch.

Radio channel modeling is fundamental for designing wireless communication systems. In millimeter or sub-millimeter wave short range communication, shadowing effect by electrically-large objects is one of the most important factors determining the field strength and thus the coverage. Unfortunately, numerical methods like MoM, FDTD, FEM are unable to compute the field scattered by large objects due to their excessive time and memory requirements. Ray theory like geometrical theory of diffraction (GTD) by Keller is an effective and popular solution but suffers various kinds of singularities at geometrical boundaries such as incidence shadow boundary (ISB) or reflection shadow boundary (RSB). Modified edge representation (MER) equivalent edge current (EEC) is an accurate and a fast high frequency diffraction technique which expresses the fields in terms of line integration. It adopts classical Keller-type knife-edge diffraction coefficients and still provides uniform and highly accurate fields everywhere including geometrical boundaries. MER is used here to compute the millimeter-wave field distribution in compact range communication systems where shadowing effects rather than multi-path ones dominate the radio environments. For further simplicity, trigonometric functions in Keller's diffraction coefficients are replaced by the path lengths of source to the observer via the edge point of integration of the scatterers in the form of Fresnel zone number (FZN). Complexity, Computation time and the memory were reduced drastically without degrading the accuracy. The dipole wave scattering from flat rectangular plates is discussed with numerical examples.

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.

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.

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.

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.

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.

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.

This paper proposes a novel approach to generate stereo video in which the zoom magnification is not constant. Although this has been achieved mechanically in a conventional way, it is necessary for this approach to develop a mechanically complex system for each stereo camera system. Instead of a mechanical solution, we employ an approach from the software side: by using a pair of zoomed and non-zoomed video, a part of the non-zoomed video image is cut out and super-resolved for generating stereo video without a special hardware. To achieve this, (1) the zoom magnification parameter is automatically determined by using distributions of intensities, and (2) the cutout image is super-resolved by using optically zoomed images as exemplars. The effectiveness of the proposed method is quantitatively and qualitatively validated through experiments.

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.