The present paper proposes two new methods for constructing polyphase asymmetric zero-correlation zone (A-ZCZ) sequence sets. In previous studies, the authors proposed methods for constructing quasi-optimal polyphase A-ZCZ sequence sets using perfect sequences and for constructing optimal polyphase A-ZCZ sequence sets using discrete Fourier transform (DFT) matrices. However, in these methods, the total number of sequences in an A-ZCZ sequence set cannot exceed the period of the perfect sequence or the dimension of the DFT matrix used for constructing the A-ZCZ sequence set. We now propose two extended versions of these methods. The proposed methods can generate a quasi-optimal or optimal polyphase A-ZCZ sequence set where the total number of sequences exceeds the period of the perfect sequence or the dimension of the DFT matrix. In other words, the proposed methods can generate new A-ZCZ sequence sets that cannot be obtained from the known methods.

Due to vehicle movement and lossy wireless channels, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks (VANETs) is a well-known challenging problem. In this paper, we propose BR-NB (broadcast with neighbor information), a fuzzy logic based multi-hop broadcast protocol for VANETs. BR-NB achieves a low overhead by using only a subset of neighbor nodes to relay data packets. For the relay node selection, BR-NB jointly considers multiple metrics of the inter-vehicle distance, vehicle mobility and link quality by employing fuzzy logic. Since the expected coverage and vehicle mobility are inferred from the two-hop neighbor information which can be acquired from the hello message exchange, BR-NB is independent of position information. BR-NB provides a practical and portable solution for broadcast services in VANETs. We use computer simulations and real-world experiments to evaluate the performance of BR-NB.

Video coding plays an important role in human life especially in communications. H.264/AVC is a prominent video coding standard that has been used in a variety of applications due to its high efficiency comes from several new coding techniques. However, the extremely high encoding complexity hinders itself from real-time applications. This paper presents a new encoding algorithm that makes use of particle swarm optimization (PSO) to train discriminant functions for classification based fast mode decision. Experimental results show that the proposed algorithm can successfully reduce encoding time at the expense of negligible quality degradation and bitrate increases.

We propose a natural synchronization scheme for wireless uncoupled devices, without any signal exchange among them. Our proposed scheme only uses natural environmental fluctuations, such as the temperature or humidity of the air, the environmental sounds, and so on, for the synchronization of the uncoupled devices. This proposed synchronization is realized based on the noise-induced synchronization phenomenon, uncoupled nonlinear oscillators synchronize with each other only by adding identical common noises to each of them. Based on the theory of this phenomenon, the oscillators can also be synchronized by noise sequences, which are not perfectly identical signals. Since the environmental natural fluctuations collected at neighboring locations are similar to each other and cross-correlation becomes high, our proposed scheme enabling synchronization only by natural environmental fluctuations can be realized. As an application of this proposed synchronization, we introduce wireless sensor networks, for which synchronization is important for reducing power consumption by intermittent data transmission. We collect environmental fluctuations using the wireless sensor network devices. Our results show that the wireless sensor network devices can be synchronized only by the independently collected natural signals, such as temperature and humidity, at each wireless sensor device.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

This letter presents a novel approach for automatic multimodal affect recognition. The audio and visual channels provide complementary information for human affective states recognition, and we utilize Boltzmann zippers as model-level fusion to learn intrinsic correlations between the different modalities. We extract effective audio and visual feature streams with different time scales and feed them to two component Boltzmann chains respectively. Hidden units of the two chains are interconnected to form a Boltzmann zipper which can effectively avoid local energy minima during training. Second-order methods are applied to Boltzmann zippers to speed up learning and pruning process. Experimental results on audio-visual emotion data recorded by ourselves in Wizard of Oz scenarios and collected from the SEMAINE naturalistic database both demonstrate our approach is robust and outperforms the state-of-the-art methods.

In this paper, we propose a novel content-aware image resizing method based on grid transformation. Our method focuses on not only keeping important regions unchanged but also keeping the aspect ratio of the main object in an image unchanged. The dual conditions can avoid distortion which often occurs when only using the former condition. Our method first calculates image importance. Next, we extract the main objects on an image by using image importance. Finally, we calculate the optimal grid transformation which suppresses changes in size of important regions and in the aspect ratios of the main objects. Our method uses lower and upper thresholds for transformation to suppress distortion due to extreme shrinking and enlargement. To achieve better resizing results, we introduce a boundary discarding process. This process can assign wider regions to important regions, reducing distortions on important regions. Experimental results demonstrate that our proposed method resizes images with less distortion than other resizing methods.

This paper presents an automatic gain control (AGC) system suitable for 60GHz direct conversion receivers. By using a two step gain control algorithm with high-pass filter cutoff frequency switching, the proposed AGC system realizes fast settling time and wide dynamic range simultaneously. The paper also discusses wide-bandwidth variable gain amplifier (VGA) design. By introducing digitally-controlled resistors and gain flattening capacitors, the proposed VGA realizes wide gain range while compensating gain variations due to parasitic capacitance of MOS switches. The AGC system is implemented in a transceiver chipset where RFIC and BBIC are fabricated in 90nm CMOS and 40nm CMOS respectively. The measurement shows excellent dynamic range of 47dB with +/-1dB gain accuracy within 1µs settling time, which satisfies the stringent requirements of the IEEE802.11ad standard.

For quantized control, one of the powerful approaches is to use a dynamic quantizer, which has internal memories for signal quantization, with a conventional controller in the feedback control loop. The design of dynamic quantizers has become a major topic, and a number of results have been derived so far. In this paper, we extend the authors' recent result on dynamic quantizers, and applied them to a more general class of nonlinear systems, called the nonaffine nonlinear systems. Based on the performance index representing the degradation caused by the signal quantization, we propose practical dynamic quantizers, which include the authors' former result as a special case. Moreover, we provide theoretical results on the performance and on the stability of the resulting quantized systems.

Near range microwave imaging systems have broad application prospects in the field of concealed weapon detection, biomedical imaging, nondestructive testing, etc. In this paper, the technique of optimized sparse antenna array is applied to near range microwave imaging, which can greatly reduce the complexity of imaging systems. In detail, the paper establishes three-dimensional sparse array imaging geometry and corresponding echo model, where the imaging geometry is formed by arranging optimized sparse antenna array in elevation, scanning in azimuth and transmitting broadband signals in range direction; and by analyzing the characteristics of near range imaging, that is, the maximum interval of transmitting and receiving elements is limited by the range from imaging system to targets, we propose the idea of piecewise sparse line array; secondly, by analyzing the convolution principle, we develop a method of arranging piecewise sparse array which can generate the same distribution of equivalent phase centers as filled antenna array; then, the paper deduces corresponding imaging algorithm; finally, the imaging geometry and corresponding algorithm proposed in this paper are investigated and verified via numerical simulations and near range imaging experiments.

We propose a maximum likelihood estimation approach for the recovery of continuously-defined sparse signals from noisy measurements, in particular periodic sequences of Diracs, derivatives of Diracs and piecewise polynomials. The conventional approach for this problem is based on least-squares (a.k.a. annihilating filter method) and Cadzow denoising. It requires more measurements than the number of unknown parameters and mistakenly splits the derivatives of Diracs into several Diracs at different positions. Moreover, Cadzow denoising does not guarantee any optimality. The proposed approach based on maximum likelihood estimation solves all of these problems. Since the corresponding log-likelihood function is non-convex, we exploit the stochastic method called particle swarm optimization (PSO) to find the global solution. Simulation results confirm the effectiveness of the proposed approach, for a reasonable computational cost.

This paper presents a Complex-Valued Neural Network-based sound localization method. The proposed approach uses two microphones to localize sound sources in the whole horizontal plane. The method uses time delay and amplitude difference to generate a set of features which are then classified by a Complex-Valued Multi-Layer Perceptron. The advantage of using complex values is that the amplitude information can naturally masks the phase information. The proposed method is analyzed experimentally with regard to the spectral characteristics of the target sounds and its tolerance to noise. The obtained results emphasize and confirm the advantages of using Complex-Valued Neural Networks for the sound localization problem in comparison to the traditional Real-Valued Neural Network model.

Many fatal accidents involving safety-critical reactive systems have occurred in unexpected situations, which were not considered during the design and test phases of system development. To prevent such accidents, reactive systems should be designed to respond appropriately to any request from an environment at any time. Verifying this property during the specification phase reduces the development costs of safety-critical reactive systems. This property of a specification is commonly known as realizability. The complexity of the realizability problem is 2EXPTIME-complete. We have introduced the concept of strong satisfiability, which is a necessary condition for realizability. Many practical unrealizable specifications are also strongly unsatisfiable. In this paper, we show that the complexity of the strong satisfiability problem is EXPSPACE-complete. This means that strong satisfiability offers the advantage of lower complexity for analysis, compared to realizability. Moreover, we show that the strong satisfiability problem remains EXPSPACE-complete even when only formulae with a temporal depth of at most 2 are allowed.

Reverberation chambers that easily create multipath-rich environments are suggested as test environments for the performance evaluation of multiple-input multiple-output (MIMO) terminals. However, the propagation environment characteristic is difficult to control in conventional reverberation chambers. In this paper, we propose an improved double-layered reverberation chamber to control the arrival wave distribution in addition to the cross-polarization power ratio (XPR). We show the design method of the double-layered reverberation chamber and the experimental results of the propagation environment control using our constructed measurement system.

This paper presents a voice conversion (VC) technique for noisy environments, where parallel exemplars are introduced to encode the source speech signal and synthesize the target speech signal. The parallel exemplars (dictionary) consist of the source exemplars and target exemplars, having the same texts uttered by the source and target speakers. The input source signal is decomposed into the source exemplars, noise exemplars and their weights (activities). Then, by using the weights of the source exemplars, the converted signal is constructed from the target exemplars. We carried out speaker conversion tasks using clean speech data and noise-added speech data. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method.

Genetic algorithm (GA) is now an important tool in the field of wireless communications. For multiple-input/multiple-output (MIMO) wireless communications system employing spatial multiplexing transmission, we evaluate the effects of GA parameters value on channel parameters in fading channels. We assume transmit-correlated Rayleigh and Rician fading with realistic Laplacian power azimuth spectrum. Azimuth spread (AS) and Rician K-factor are selected according to the measurement-based WINNER II channel model for several scenarios. Herein we have shown the effects of GA parameters and channel parameters in different WINNER II scenarios (i.e., AS and K values) and rank of the deterministic components. We employ meta GA that suitably selects the population (P), generation (G) and mutation probability (pm) for the inner GA. Then we show the cumulative distribution function (CDF) obtain experimentally for the condition number C of the channel matrix H. It is found that, GA parameters depend on the channel parameters, i.e., GA parameters are the functions of the channel parameters. It is also found that for the poorer channel conditions smaller GA parameter values are required for MIMO detection. This approach will help to achieve maximum performance in practical condition for the lower numerical complexity.

This paper presents design and radiation properties of a radial line microstrip antenna array (RL-MSAA) for linear polarization. A stacked circular microstrip antenna (C-MSA) is used as a radiation element for the RL-MSAA. Radiation phase of the stacked C-MSA is controlled by tuning radii of the lower and upper patches, therefore, the desired phase distribution of the RL-MSAA can be designed. In this paper, a linearly polarized RL-MSAA with three concentric rows of the stacked C-MSAs at a spacing of 0.65 wavelengths for uniform aperture distribution is designed and tested in 12GHz. The experimental results reveal that validity of the linearly polarized RL-MSAA with the stacked C-MSAs for radiation phase control is demonstrated.

A wideband on-body antenna for a wireless body area network for an Industrial, Scientific, and Medical band is proposed. A wideband characteristic is achieved by combining two zeroth-order resonance (ZOR) modes at adjacent frequencies by controlling the value of the shunt capacitance. The size of the proposed antenna is 0.072λ0 × 0.33λ0, and the measured 10-dB return loss bandwidth is 340MHz (14.3%). In addition, the resonance frequencies operating in the ZOR mode are insensitive to the effects of the human body by virtue of the ZOR characteristic.

Three dimensional (3D) terahertz (THz) imaging or THz tomography has recently proven to be useful for non-destructive testing of industrial materials and structures. In place of previous imaging techniques such as THz pulse or continuous wave (CW) radar, we propose a THz optical coherence tomography (OCT) using frequency-swept THz sources, and demonstrate 3D imaging. In addition, we further apply this technique to the millimeter-wave region in order to extend applicable targets.

In this letter, we consider the global exponential stabilization problem by output feedback for a class of nonlinear systems. Along with a newly proposed matrix inequality condition, the proposed control method has improved flexibility in dealing with nonlinearity, over the existing methods. Analysis and examples are given to illustrate the improved features of our control method.