Open-loop (OL) transmit diversity is more subject to the influence of channel estimation error than closed-loop (CL) transmit diversity, although it has the merit of providing better performance in fast Doppler frequency environments because it doesn't require a feedback signal. This paper proposes an OL transmit diversity scheme combined with intra-subframe frequency hopping (FH) and iterative decision-feedback channel estimation (DFCE) in a shared channel for discrete Fourier transform (DFT)-precoded orthogonal frequency division multiple access (OFDMA). We apply intra-subframe FH to OL transmit diversity to mitigate the reduction in the diversity gain under high fading correlation conditions among antennas and iterative DFCE to improve the channel estimation accuracy. Computer simulation results show that the required average received signal-to-noise power ratio at the average block error rate (BLER) of 10-2 of the space-time block code (STBC) with intra-subframe FH is reduced to within approximately 0.8dB compared to codebook-based CL transmit diversity when using iterative DFCE at the maximum Doppler frequency of fD =5.55Hz. Moreover, it is shown that STBC with intra-subframe FH and iterative DFCE achieves much better BLER performance compared to CL transmit diversity when fD is higher than approximately 30Hz since the tracking ability of the latter degrades due to the fast fading variation in its feedback loop.

In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

This paper presents the detailed investigations on a simple multi-band method that allows inverted-F antennas (IFAs) to achieve good impedance matching in many different frequency bands. The impressive simplicity of the method arises from its sharing of a shorting strip among multiple branch elements to simultaneously generate independent resonant modes at arbitrary frequencies. Our simulation and measurement results clarify that, by adjusting the number of branch elements and their lengths, it is very easy to control both the total number of resonant modes and the position of each resonant frequency with impedance matching improved concurrently by adjusting properly the distance ds between the feeding and shorting points. The effectiveness of the multi-band method is verified in antenna miniaturization designs, not only in the case of handset antenna, but also in the design upon an infinite ground plane. Antenna performance and operation principles of proposed multi-band models in each case are analyzed and discussed in detail.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

This paper shows that the induced peak voltage on the short monopole antenna by the EM field radiated from a small gap discharge when the gap width was experimentally changed from 10 to 360µm was not directly proportional to the discharge voltage between the gap. It was found that the 10mm short monopole antenna induced peak voltage had a peak value between 40 and 60µm gap width.

String-topology multi-hop network is often selected as an analysis object because it is one of the fundamental network topologies. The purpose of this paper is to establish expression for end-to-end delay for IEEE 802.11 string-topology multi-hop networks. For obtaining the analytical expression, the effects of frame collisions and carrier-sensing effect from other nodes under the non-saturated condition are obtained for each node in the network. For expressing the properties in non-saturated condition, a new parameter, which is frame-existence probability, is defined. The end-to-end delay of a string-topology multi-hop network can be derived as the sum of the transmission delays in the network flow. The analytical predictions agree with simulation results well, which show validity of the obtained analytical expressions.

It is unclear whether Hidden Markov Model (HMM) or Dynamic Time Warping (DTW) mapping is more appropriate for visual speech recognition when only small data samples are available. In this letter, the two approaches are compared in terms of sensitivity to the amount of training samples and computing time with the objective of determining the tipping point. The limited training data problem is addressed by exploiting a straightforward template matching via weighted-DTW. The proposed framework is a refined DTW by adjusting the warping paths with judicially injected weights to ensure a smooth diagonal path for accurate alignment without added computational load. The proposed WDTW is evaluated on three databases (two in the public domain and one developed in-house) for visual recognition performance. Subsequent experiments indicate that the proposed WDTW significantly enhances the recognition rate compared to the DTW and HMM based algorithms, especially under limited data samples.

A cascaded video denoising method based on frame averaging is proposed in this paper. A novel segmentation approach using intensity and structure tensor is used for change compensation, which can effectively suppress noise while preserving the structure of an image. The cascaded framework solves the problem of noise residual caused by single-frame averaging. The classical Wiener filter is used for spatial denoising in changing areas. Our algorithm works in real-time on an FPGA, since it does not involve future frames. Experiments on standard grayscale videos for various noise levels demonstrate that the proposed method is competitive with current state-of-the-art video denoising algorithms on both peak signal-to-noise ratio and structural similarity evaluations, particularly when dealing with large-scale noise.

Microwave imaging techniques, particularly for synthetic aperture radar (SAR), produce high-resolution terrain surface images regardless of the weather conditions. Focusing on a feature of complex SAR images, coherent change detection (CCD) approaches have been developed in recent decades that can detect invisible changes in the same regions by applying phase interferometry to pairs of complex SAR images. On the other hand, various techniques of polarimetric SAR (PolSAR) image analysis have been developed, since fully polarimetric data often include valuable information that cannot be obtained from single polarimetric observations. According to this background, various coherent change detection methods based on fully polarimetric data have been proposed. However, the detection accuracies of these methods often degrade in low signal-to-noise ratio (SNR) situations due to the lower signal levels of cross-polarized components compared with those of co-polarized ones. To overcome the problem mentioned above, this paper proposes a novel CCD method by introducing the Pauli decomposition and the weighting of component with their respective SNR. The experimental data obtained in anechoic chamber show that the proposed method significantly enhances the performance of the receiver operation characteristic (ROC) compared with that obtained by a conventional approach.

Novel multi-band mixers that can receive multiple band signals concurrently are proposed and evaluated. The mixers achieve independent gain control through novel relative power control method of the multiple local oscillator (LO) signals. Linear control is also achieved through multiple LO signal input with total LO power control. Theoretical analysis shows that odd-order nonlinearity components of the multiple LO signals support linear conversion gain control. Dual- and triple-band tests are conducted using typical three MOSFET mixers fabricated by a 0.25 µm SiGe BiCMOS process. Measurements confirm over 40 dB independent control of conversion gain, linear control achieved through LO input power control. The proposed mixers have high input linearity with a 5 dBm output third intercept point. A method is also proposed to reduce interference caused by mixing between multiple LO signals.

This paper provides a method based on electromagnetic (EM) analysis to predict conducted currents in the bulk current injection (BCI) test system for automotive components. The BCI test system is comprised of an injection probe, equipment under test (EUT), line impedance stabilization networks (LISNs), wires and an electric load. All components are modeled in full-wave EM analysis. The EM model of the injection probe enables us to handle multi wires. By using the transmission line theory, the BCI setup model is divided into several parts in order to reduce the calculation time. The proposed method is applied to an actual BCI setup of an automotive component and the simulated common mode currents at the input terminals of EUT have a good accuracy in the frequency range of 1-400MHz. The model separation reduces the calculation time to only several hours.

It is not easy to provide energy supply based on renewable energy enough to satisfy energy demand anytime and anywhere because the amount of renewable energy depends on geographical conditions and the time of day. In order to maximize the satisfaction of energy demand by renewable energy, surplus energy generated with renewable energy should be stored in batteries, and transmitted to electric loads with high demand somewhere in the electricity system. This paper proposes a novel autonomous decentralized mechanism of energy interchanges between distributed batteries on the basis of the diffusion equation and MCMC (Markov Chain Monte Carlo) for realizing energy supply appropriately for energy demand. Experimental results show that the proposed mechanism effectively works under several situations. Moreover, we discuss a method to easily estimate the behavior of the entire system by each node with the proposed mechanism, and the application potentiality of this estimating method to an efficient method working with non-renewable generators while minimizing the dependence of non-renewable energy, and an incentive mechanism to prevent monopolizing energy in systems.

Let r be an odd prime, such that r≥5 and r≠p, m be the order of r modulo p. Then, there exists a 2pth root of unity in the extension field Frm. Let G(x) be the generating polynomial of the considered quaternary sequences over Fq[x] with q=rm. By explicitly computing the number of zeros of the generating polynomial G(x) over Frm, we can determine the degree of the minimal polynomial, of the quaternary sequences which in turn represents the linear complexity. In this paper, we show that the minimal value of the linear complexity is equal to $ rac{1}{2}(3p-1) $ which is more than p, the half of the period 2p. According to Berlekamp-Massey algorithm, these sequences viewed as enough good for the use in cryptography.

Texture feature descriptors such as local binary patterns (LBP) have proven effective for ground-based cloud classification. Traditionally, these texture feature descriptors are predefined in a handcrafted way. In this paper, we propose a novel method which automatically learns discriminative features from labeled samples for ground-based cloud classification. Our key idea is to learn these features through mutual information maximization which learns a transformation matrix for local difference vectors of LBP. The experimental results show that our learned features greatly improves the performance of ground-based cloud classification when compared to the other state-of-the-art methods.

Barrel distortion is a critical problem that can hinder the successful application of wide-angle cameras. This letter presents an implementation method for fast correction of the barrel distortion. In the proposed method, the required scaling factor is obtained by interpolating a mapping polynomial with a non-uniform spline instead of calculating it directly, which reduces the number of computations required for the distortion correction. This reduction in the number of computations leads to faster correction while maintaining quality: when compared to the conventional method, the reduction ratio of the correction time is about 89%, and the correction quality is 35.3 dB in terms of the average peak signal-to-noise ratio.

We present a new framework for embedding holographic halftone watermarking data into images by fusion of scale-related wavelet coefficients. The halftone watermarking image is obtained by using error-diffusion method and converted into Fresnel hologram, which is considered to be the initial password. After encryption, a scrambled watermarking image through Arnold transform is embedded into the host image during the halftoning process. We characterize the multi-scale representation of the original image using the discrete wavelet transform. The boundary information of the target image is fused by correlation of wavelet coefficients across wavelet transform layers to increase the pixel resolution scale. We apply the inter-scale fusion method to gain fusion coefficient of the fine-scale, which takes into account both the detail of the image and approximate information. Using the proposed method, the watermarking information can be embedded into the host image with recovery against the halftoning operation. The experimental results show that the proposed approach provides security and robustness against JPEG compression and different attacks compared to previous alternatives.

This paper proposes reflection and transmission control panels using artificially designed materials. As the artificially designed material, finite- and infinite-length metal wire array sheets are used here. Laminated structures consisting of the metal wire array sheets and dielectric material are proposed. Reflection and transmission characteristics of these structures can be controlled by changing the metal wire parameters such as wire length, spacing gaps between the wires, and the dielectric material's thickness and relative permittivity. The reflection and transmission characteristics of the laminated structures are evaluated by measurements in free space and by transmission line theory.

Electromagnetic analysis (EMA) against public-key cryptographic software on an embedded OS is presented in this paper. First, we propose a method for finding an observation point for EMA, where the EM radiation caused by cryptographic operations can be observed with low noise. The basic idea is to find specific EM radiation patterns produced by cryptographic operations given specific input pattern. During the operations, we scan the surface of the target device(s) with a micro magnetic probe. The scan is optimized in advanced using another compatible device that has the same central processing unit (CPU) and OS as the target device. We demonstrate the validity of the proposed EMAs through some EMA experiments with two types of RSA software on an embedded OS platform. The two types of RSA software have different implementations for modular multiplication algorithms: one is a typical and ready-made implementation using BigInteger class on Java standard library, and another is a custom-made implementation based on the Montgomery multiplication algorithm. We conduct experiments of chosen-message EMA using our scanning method, and show such EMAs successfully reveal the secret key of RSA software even under the noisy condition of the embedded OS platform. We also discuss some countermeasures against the above EMAs.

In this paper, a new parameter estimator for coherently distributed (CD) noncircular (NC) signals is proposed, and can estimate both the central direction-of-arrivals (DOAs) and the angular spreads. It can also be considered as an extended version of the generalized Capon method by using both covariance matrix and an elliptic covariance matrix. The central DOAs and angular spreads are obtained by two-dimensional spectrum-peak searching. Numerical examples illustrate that the proposed method can estimate the central DOAs and the angular spreads when the number of signals is greater than the number of sensors. The proposed method also offers better performance than the methods against which it is compared.