Physical layer security is effective in wireless communications because it makes a transmission secure from the beginning of protocols. We have proposed a chaos multiple-input multiple-output (C-MIMO) transmission scheme that achieves both physical layer security and channel coding gain using chaos signals. C-MIMO is a type of encryption modulation and it obtains the coding gain in conjunction with encryption without a decrease in the transmission efficiency. Thus, the error rate performance is improved in C-MIMO. However, decoding complexity increases exponentially with code length because of the use of maximum likelihood sequence estimation (MLSE), which restricts the code length of C-MIMO and thus the channel coding gain. Therefore, in this paper, we consider outer channel code concatenation instead of code length expansion for C-MIMO, and propose an iterative turbo decoding scheme for performance improvement by introducing a log-likelihood ratio (LLR) into C-MIMO and by utilizing turbo principle. The improved performances of the proposed scheme, compared to the conventional scheme when the outer channel codes are convolutional code and low-density parity check (LDPC) code, are shown by computer simulations.

Range estimation based on time of arrival (TOA) is becoming increasingly important with the emergence of location-based applications and next-generation location-aware wireless sensor networks. For radar and positioning systems, chirp signals have primarily been used due to their inborn signal properties for decomposition. Recently, chirp signal has been selected as the baseline standard of ISO/IEC 24730-5 and IEEE 802.15.4a in 2.4GHz, organized for the development of a real-time accurate positioning system. When estimating the TOA of the received signals in multipath channel, the super-resolution algorithms, known as estimation of signal parameters via rotational invariance techniques (ESPRIT), multiple signal classification method (MUSIC) and matrix pencil (MP), are preferred due to their superiority in decomposing the received paths. For the super-resolution algorithm-based TOA estimation of chirp signals, the received chirp signals must be transformed into a sinusoidal form for the super-resolution algorithm. The conventional transformation, the de-chirping technique, changes the received chirp signals to sinusoids so that the super-resolution algorithms can estimate the TOA of the received chirp signals through a frequency estimation of the transformed sinusoids. In practice, the initial timing synchronizer at receiver tries to find the maximum energy point at which the received paths are overlapped maximally. At this time, the conventional de-chirping yields lossy transformed sinusoids for the first arrival path from the received samples synchronized to the maximum energy point. The first arrival path is not involved in the transformed sinusoids with the conventional transformation, leading to performance degradation. However, the proposed energy efficient time-frequency transformation achieves lossless transformation by using the extended reference chirp signals. The proposed transformation is incorporated with MUSIC-based TOA estimation. The effectiveness of the proposed transformation is analyzed and verified. The root mean squared error (RMSE) of the proposed transformation is compared with Cramer-Rao lower bound and those for the conventional algorithms such as super-resolution, ESPRIT and matrix pencil algorithm in multipath channel.

The present study proposes a method for estimation of subjective image quality, for combinations of display physical factors, based on the Mahalanobis-Taguchi system in the field of quality engineering. The proposed method estimates subjective image quality by the estimated equation based on the Mahalanobis-Taguchi System and subjective evaluation experiments using the method of successive categories for images of which parameters are combinations of gamma, maximum luminance and minimum luminance. The estimated image quality is in good agreement with the experimental subjective image quality.

Using a flash/no-flash image pair, we propose a novel white-balancing technique that can effectively correct the color balance of a complex scene under multiple light sources. In the proposed method, by using multiple images of the same scene taken under different lighting conditions, we estimate the reflectance component of the scene and the multiple shading components of each image. The reflectance component is a specific object color which does not depend on scene illumination and the shading component is a shading effect caused by the illumination lights. Then, we achieve white balancing by appropriately correcting the estimated shading components. The proposed method achieves better performance than conventional methods, especially under colored illumination and mixed lighting conditions.

In recent years, various applications based on propagation characteristics have been developed. They generally utilize the locality of the fading characteristics of multipath environments. On the other hand, if a received signal at a remote location can be estimated beyond the correlation distance of the multipath fading environment, a wide variety of new applications can be possible. In this paper, we attempt to estimate a received signal at a remote location using the MUSIC method and the least squares method. Based on the plane wave assumption for each arriving wave, multipath environment is analyzed through estimation of the directions of arrival by the MUISC method and the complex amplitudes of the received signals by the least squares method, respectively. We present evaluation results on the estimation performance of the method by computer simulations.

In this paper, we propose a jogging support system that plays back background music while synchronizing its tempo with the user's jogging pace. Keeping an even pace is important in jogging but it is not easy due to tiredness. Our system indicates the variation of the runner's pace by changing the playback speed of music according to the user's pace variation. Because this requires the runner to keep an even pace in order to enjoy the music at its normal speed, the runner will be spontaneously influenced to keep an even pace. Experimental results show that our system reduced the variation of jogging pace.

Forty years after the first application of Superconducting Quantum Interference Devices (SQUIDs) [1], [2] for geophysical purposes, they have recently become a valued tool for mineral exploration. One of the most common applications is time domain (or transient) electromagnetics (TEM), an active method, where the inductive response from the ground to a changing current (mostly rectangular) in a loop on the surface is measured. After the current in the transmitter coil is switched, eddy currents are excited in the ground, which decay in a manner dependent on the conductivity of the underlying geologic structure. The resulting secondary magnetic field at the surface is measured during the off-time by a receiver coil (induced voltage) or by a magnetometer (e.g. SQUID or fluxgate). The recorded transient signal quality is improved by stacking positive and negative decays. Alternatively, the TEM results can be inverted and give the electric conductivity of the ground over depth. Since SQUIDs measure the magnetic field with high sensitivity and a constant frequency transfer function, they show a superior performance compared to conventional induction coils, especially in the presence of strong conductors. As the primary field, and especially its slew rate, are quite large, SQUID systems need to have a large slew rate and dynamic range. Any flux jump would make the use of standard stacking algorithms impossible. IPHT and Supracon are developing and producing SQUID systems based on low temperature superconductors (LTS, in our case niobium), which are now state-of-the-art. Due to the large demand, we are additionally supplying systems with high temperature superconductors (HTS, in our case YBCO). While the low temperature SQUID systems have a better performance (noise and slew rate), the high temperature SQUID systems are easier to handle in the field. The superior performance of SQUIDs compared to induction coils is the most important factor for the detection of good conductors at large depth or ore bodies underneath conductive overburden.

Recently, a next-generation heterodyne mixer detector---a hot electron bolometer (HEB) mixer employing a superconducting microbridge---has gradually opened up terahertz-band astronomy. The surrounding state-of-the-art technologies including fabrication processes, 4 K cryostats, cryogenic low-noise amplifiers, local oscillator sources, micromachining techniques, and spectrometers, as well as the HEB mixers, have played a valuable role in the development of super-low-noise heterodyne spectroscopy systems for the terahertz band. The current developmental status of terahertz-band HEB mixer receivers and their applications for spectroscopy and astronomy with ground-based, airborne, and satellite telescopes are presented.

For digital forensic investigations, the proposed scheme verifies the integrity of video contents in legacy surveillance camera systems with no built-in integrity protection. The scheme exploits video frames remaining in slack space of storage media, instead of timestamp information vulnerable to tampering. The scheme is applied to integrity verification of video contents formatted with AVI or MP4 files in automobile blackboxes.

The physical optics (PO) approximation is one of the widely-used techniques to calculate scattering fields with a reasonable accuracy in the high frequency region. The computational load of PO radiation integral dramatically increases at higher frequencies since it is proportional to the electrical size of scatterer. In order to suppress this load, a variety of techniques, such as the asymptotic evaluation by the stationary phase method (SP), the equivalent edge currents (EECs), the low-order polynomial expansion method and the fast physical optics (FPO), have been proposed and developed. The adaptive sampling method (ASM) proposed by Burkholder is also one of the techniques where the sampling points in radiation integral should be adaptively determined based upon the phase change of integrand. We proposed a quite different approach named ``Localization of the radiation integrals.'' This localization method suggests that only the small portions of the integration with a slow phase change contribute to the scattering field. In this paper, we newly introduce the ASM in the localization method and applied the proposed method into the radar cross section (RCS) analysis of 2-dimensional strip and cylinder. We have confirmed that the proposed method provides the frequency-independent number of division in the radiation integrals and computational time and accuracy. As the starting point for extension to 3-D case, the application of the proposed method for a reflection from an infinite PEC plane and a part of sphere was also examined.

In this paper, a novel high-performance heuristic algorithm, named relay-race algorithm (RRA), which was proposed to approach a global optimal solution by exploring similar local optimal solutions more efficiently within shorter runtime for NP-hard problem is investigated. RRA includes three basic parts: rough search, focusing search and relay. The rough search is designed to get over small hills on the solution space and to approach a local optimal solution as fast as possible. The focusing search is designed to reach the local optimal solution as close as possible. The relay is to escape from the local optimal solution in only one step and to maintain search continuity simultaneously. As one of typical applications, multi-objective placement problem in physical design optimization is solved by the proposed RRA. In experiments, it is confirmed that the computational performance is considerably improved. RRA achieves overall Pareto improvement of two conflicting objectives: power consumption and maximal delay. RRA has its potential applications to improve the existing search methods for more hard problems.

In this paper, we investigate unitary precoder design for multiple-input multiple-output (MIMO) multicasting, where multiple common data streams are sent to a group of users. Assuming that zero-forcing decision feedback equalizers (ZF-DFE) are adopted at the receiver side, we can convert the multicast channel into multiple parallel subchannels. To improve the receiving quality of all data streams, we focus on maximizing the minimal signal-to-noise ratio (SNR) of all data streams. To effectively handle this non-convex optimization problem, we first consider the special case of two data streams and derive the closed-form solution of the SNR vectors for both subchannels. Based on these results, a gradient-based iterative algorithm is developed for the proposed precoder design. For the general case, a Givens rotation-based iterative algorithm is proposed, where at each iteration the original problem of unitary precoder design is transformed into a dual-stream subproblem. Hence it can be solved efficiently by the gradient-based iterative algorithm. Finally, simulation results are presented to demonstrate the outstanding performance of the proposed design.

Hardware Trojan or any other kind of unwanted hardware modifications has been thought as a major challenge in many commercial and secure applications. Currently, detection and prevention of hardware Trojans appeared as an important requirement in such systems. In this paper, a new concept, Trojan Vulnerability Map, is introduced to model the immunity of various regions of hardware against hardware attacks. Then, placement and routing algorithms are proposed to improve the immunity of hardware using the Trojan Vulnerability Map. Experimental results show that the proposed placement and routing algorithm reduces the hardware vulnerability by 25.65% and 4.08%, respectively. These benefits are earned in cost of negligible total wire length and delay overhead.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

In this letter, we combine minimum-shift keying (MSK) with physical-layer network coding (PNC) to form a new scheme, i.e., MSK-PNC, for two-way relay channels (TWRCs). The signal detection of the MSK-PNC scheme is investigated, and two detection methods are proposed. The first one is orthogonal demodulation and mapping (ODM), and the second one is two-state differential detection (TSDD). The error performance of the proposed MSK-PNC scheme is evaluated through simulations.

Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.

In this paper, a novel hybrid technique for analyzing complex antennas around the coated object is proposed, which is termed as “iterative vector fields with Physical Optics (PO)”. A closed box is used to enclose the antennas and the complex field vectors on the box' surfaces can then be obtained using Huygens principle. The equivalent electromagnetic currents on Huygens surfaces are computed by Higher-order Method of Moments (HOB-MoM) and the fields scattered from the coated object are calculated by PO method. In addition, the parallel technique based on Message Passing Interface (MPI) and Scalable Linear Algebra Package (ScaLAPACK) is employed so as to accelerate the computation. Numerical examples are presented to validate and to show the effectiveness of the proposed method on solving the practical engineering problem.

In LTE (Long Term Evolution)-Advanced system, a transparent MU-MIMO (Multi-User Multiple-Input Multiple Output) scheduling is basically considered, so the performance degradation in channel estimation may occur due to the unpredictable interference from co-scheduled layers. In order to detect and mitigate the interference, traditional binary hypothesis testing based interference detection method and iterative channel estimation method can be applied. However, there are two major problems. First, the binary hypothesis testing based interference detection is not suitable solution for LTE-Advanced system which has four dynamically changing interference hypotheses. Second, the conventional iterative operation does not guarantee sufficient performance gain with limited iteration time due to the estimation error in initial estimation stage. To overcome these problems, we introduce an enhanced iterative channel estimation method which considers simple matrix operation-based partial interference estimation. Based on the outcomes of the partial interference estimation, we can not only detect interference layers individually, but also partially eliminate the interference in initial channel estimation stage. Consequently, the proposed method can effectively mitigate the interference adaptively to the dynamically changing interference condition.

Artificial blurring is a typical operation in image forging. Most existing image forgery detection methods consider only one single feature of artificial blurring operation. In this manuscript, we propose to adopt feature fusion, with multifeatures for artificial blurring operation in image tampering, to improve the accuracy of forgery detection. First, three feature vectors that address the singular values of the gray image matrix, correlation coefficients for double blurring operation, and image quality metrics (IQM) are extracted and fused using principal component analysis (PCA), and then a support vector machine (SVM) classifier is trained using the fused feature extracted from training images or image patches containing artificial blurring operations. Finally, the same procedures of feature extraction and feature fusion are carried out on the suspected image or suspected image patch which is then classified, using the trained SVM, into forged or non-forged classes. Experimental results show the feasibility of the proposed method for image tampering feature fusion and forgery detection.

The label estimation technique provides a new way to design semi-supervised learning algorithms. If the labels of the unlabeled data can be estimated correctly, the semi-supervised methods can be replaced by the corresponding supervised versions. In this paper, we propose a novel semi-supervised learning algorithm, called Geodesic Weighted Sparse Representation (GWSR), to estimate the labels of the unlabeled data. First, the geodesic distance and geodesic weight are calculated. The geodesic weight is utilized to reconstruct the labeled samples. The Euclidean distance between the reconstructed labeled sample and the unlabeled sample equals the geodesic distance between the original labeled sample and the unlabeled sample. Then, the unlabeled samples are sparsely reconstructed and the sparse reconstruction weight is obtained by minimizing the L1-norm. Finally, the sparse reconstruction weight is utilized to estimate the labels of the unlabeled samples. Experiments on synthetic data and USPS hand-written digit database demonstrate the effectiveness of our method.