Scanning quantized transform coefficients is an important tool for video coding. For example, the MPEG-4 video coder adopts three different scans to get better coding efficiency. This paper proposes an adaptive zero-coefficient distribution scan in inter block coding. The proposed method attempts to improve H.264/AVC zero coefficient coding by modifying the scan operation. Since the zero-coefficient distribution is changed by the proposed scan method, new VLC tables for syntax elements used in context-adaptive variable length coding (CAVLC) are also provided. The savings in bit-rate range from 2.2% to 5.1% in the high bit-rate cases, depending on different test sequences.

A new method of segmentation for Synthetic Aperture Radar (SAR) images using the skewness wavelet energy has been presented. The skewness is the third order cumulant which measures the local texture along the region-based active contour. Nonlinearity in intensity inhomogeneities often occur in SAR images due to the speckle noise. In this paper we propose a region-based active contour model that is able to use the intensity information in local regions and to cope with the speckle noise and nonlinear intensity inhomogeneity of SAR images. We use a wavelet coefficients energy distribution to analyze the SAR image texture in each sub-band. A fitting energy called skewness wavelet energy is defined in terms of a contour and a functional so that, the regions and their interfaces will be modeled by level set functions. A functional relationship has been calculated on these level sets in terms of the third order cumulant, from which an energy minimization is derived. Minimizing the calculated functions derives the optimal segmentation based on the texture definitions. The results of the implemented algorithm on the test images from the Radarsat SAR images of agricultural and urban regions show a desirable performance of the proposed method.

In wireless sensor networks, adversaries can easily launch application layer attacks, such as false data injection attacks and false vote insertion attacks. False data injection attacks may drain energy resources and waste real world response efforts. False vote insertion attacks would prevent reporting of important information on the field. In order to minimize the damage from such attacks, several prevention based solutions have been proposed by researchers, but may be inefficient in normal condition due to their overhead. Thus, they should be activated upon detection of such attacks. Existing detection based solutions, however, does not address application layer attacks. This paper presents a scheme to adaptively counter false data injection attacks and false vote insertion attacks in sensor networks. The proposed scheme consists of two sub-units: one used to detect the security attacks and the other used to select efficient countermeasures against the attacks. Countermeasures are activated upon detection of the security attacks, with the consideration of the current network status and the attacks. Such adaptive countering approach can conserve energy resources especially in normal condition and provide reliability against false vote insertion attacks.

This paper presents a digital predistorter with a wideband memory effect compensator for a Doherty power amplifier (PA). A simple memory-predistortion model, which consists of a look-up-table (LUT) and an adaptive filter equalizing memory effects, and a new memory effect estimation algorithm using a direct-learning architecture are proposed. The proposed estimation algorithm has an advantage that a transfer function of a feedback circuit does not affect the learning process. The predistorter is implemented in a field programmable gate array (FPGA) and a digital signal processor (DSP). The transmitter has achieved distortion level of -50.8 dBr at signal bandwidth away from the carrier, and PA module efficiency of 24% with output power of 43 dBm at 2595 MHz under a 20 MHz-bandwidth orthogonal frequency division multiplexing (OFDM) signal using laterally diffused metal oxide semiconductor (LDMOS) FETs.

We propose an improved reference speaker weighting (RSW) and speaker cluster weighting (SCW) approach that uses an aspect model. The concept of the approach is that the adapted model is a linear combination of a few latent reference models obtained from a set of reference speakers. The aspect model has specific latent-space characteristics that differ from orthogonal basis vectors of eigenvoice. The aspect model is a "mixture-of-mixture" model. We first calculate a small number of latent reference models as mixtures of distributions of the reference speaker's models, and then the latent reference models are mixed to obtain the adapted distribution. The mixture weights are calculated based on the expectation maximization (EM) algorithm. We use the obtained mixture weights for interpolating mean parameters of the distributions. Both training and adaptation are performed based on likelihood maximization with respect to the training and adaptation data, respectively. We conduct a continuous speech recognition experiment using a Korean database (KAIST-TRADE). The results are compared to those of a conventional MAP, MLLR, RSW, eigenvoice and SCW. Absolute word accuracy improvement of 2.06 point was achieved using the proposed method, even though we use only 0.3 s of adaptation data.

In this paper, a novel CMA (constant modulus algorithm) algorithm employing fast convolution in the DFT (discrete Fourier transform) domain is proposed. We propose a non-linear adaptation algorithm that minimizes CMA cost function in the DFT domain. The proposed algorithm is completely new one as compared to the recently introduced similar DFT domain CMA algorithm in that, the original CMA cost function has not been changed to develop DFT domain algorithm, resulting improved convergence properties. Using the proposed approach, we can reduce the number of multiplications to O(Nlog2 N), whereas the conventional CMA has the computation order of O(N2). Simulation results show that the proposed algorithm provides a comparable performance to the conventional CMA.

A novel filter is introduced in this paper to improve the ability of radiometric based method on suppressing impulse noise. Firstly, a new method is introduced to design the impulsive weight by measuring how impulsive a pixel is. Then, the impulsive weight is combined with the radiometric weight to obtain the evaluated values on each pixel in the whole corrupted image. The impulsive weight is mainly designed to suppress the impulse noise, while the radiometric weight is mainly designed to protect the noise-free pixel. Extensive experiments demonstrate that the proposed algorithm can perform much better than other filters in terms of the quantitative and qualitative aspects.

In this article, we propose a new way to estimate fog extinction at night with a camera. We also propose a method for the classification of fog depending on the forward scattering. We show that a characterization of fog based on the atmospheric extinction parameter only is not sufficient, specifically in the perspective of adaptive lighting for road safety. This method has been validated on synthetic images generated with a semi Monte-Carlo ray tracing software dedicated to fog simulation as well as with experiments in a fog chamber, we present the results and discuss the method, its potential applications and its limits.

We propose a moving picture coding by lapped transform and an edge adaptive deblocking filter to reduce the blocking distortion. We apply subband coding (SBC) with lapped transform (LT) and zero pruning set partitioning in hierarchical trees (zpSPIHT) to encode the difference picture. Effective coding using zpSPIHT was achieved by quantizing and pruning the quantized zeros. The blocking distortion caused by block motion compensated prediction is reduced by an edge adaptive deblocking filter. Since the original edges can be detected precisely at the reference picture, an edge adaptive deblocking filter on the predicted picture is very effective. Experimental results show that blocking distortion has been visually reduced at very low bit rate coding and better PSNRs of about 1.0 dB was achieved.

Future high-resolution short-range automotive radar will have a higher false alarm probability than the conventional low-resolution radar has. In a high-resolution radar, the reception signal becomes sensitive to the difference between intended and unintended objects. However, automotive radars must distinguish targets from background objects that are the same order of size; it leads to an increase in the false alarm probability. In this paper, a CFAR circuit for obtaining the target mean power, as well as the background mean power, is proposed to reduce the false alarm probability for high-resolution radars working in automotive environments. The proposed method is analytically evaluated with use of the characteristic function method. Spatial correlation is also considered in the evaluation, because the sizes of the both target and background objects approach the dimension of several range cells. Result showed the proposed CFAR with use of two alongside range cells could reduce the ratio of 6.4 dB for an example of an automotive situation.

In this letter, we develop an analytical model for the drive-thru applications based on the IEEE 802.11p WAVE. The model shows that prioritizing the bitrates via the 802.11e EDCA mechanism leads to significant throughput improvement.

In this paper, we describe a novel model training method for one-to-many eigenvoice conversion (EVC). One-to-many EVC is a technique for converting a specific source speaker's voice into an arbitrary target speaker's voice. An eigenvoice Gaussian mixture model (EV-GMM) is trained in advance using multiple parallel data sets consisting of utterance-pairs of the source speaker and many pre-stored target speakers. The EV-GMM can be adapted to new target speakers using only a few of their arbitrary utterances by estimating a small number of adaptive parameters. In the adaptation process, several parameters of the EV-GMM to be fixed for different target speakers strongly affect the conversion performance of the adapted model. In order to improve the conversion performance in one-to-many EVC, we propose an adaptive training method of the EV-GMM. In the proposed training method, both the fixed parameters and the adaptive parameters are optimized by maximizing a total likelihood function of the EV-GMMs adapted to individual pre-stored target speakers. We conducted objective and subjective evaluations to demonstrate the effectiveness of the proposed training method. The experimental results show that the proposed adaptive training yields significant quality improvements in the converted speech.

This paper presents a CMOS signal detection circuit for 2.5 Gb/s serial data communication system over FR-4 backplane. This overcomes characteristics deviation of full-wave rectifier-based simple power detection circuits due to data pattern and temperature by using an edge detector and a sample-hold circuit.

Adaptable workflow participant assignment (WPA) is crucial to the efficiency and quality of workflow execution. This paper proposes an ontology-based approach to adaptable WPA (OWPA). OWPA introduces domain ontology to organize the enterprise data and uses a well-defined OWPA rule to express an authorization constraint. OWPA can represent more complex authorization constraints by flexibly using the enterprise data, the workflow data, the user-input data, and the built-in functions. By a high-usability interactive interface, OWPA allows users to define and modify the OWPA rules easily without any programming work. Moreover, OWPA is bound to the workflow modeling tool and the workflow monitor respectively to adapt to dynamic workflow modification in workflow definitions and workflow instances. OWPA has been applied in three enterprises in China.

This paper proposes a novel adaptive image interpolation method using an edge-directed smoothness filter. Adaptive image interpolation methods tend to create higher visual quality images than traditional interpolation methods such as bicubic interpolation. These methods, however, often suffer from high computational costs and production of inadequate interpolated pixels. We propose a novel method to overcome these problems. Our approach is to estimate the enlarged image from the original image based on an observation model. Obtaining an image with edge-directed smoothness, we constrain the estimated image to have many edge-directed smooth pixels which are measured by using the edge-directed smoothness filter introduced in this paper. Additionally, we also propose a simplification of our algorithm to run with lower computational complexity and smaller memory. Simulation results show that the proposal method produces images with high visual quality and performs well on PSNR and computational times.

A new adaptive algorithm is proposed by introducing some modifications to the recursive least squares (RLS) algorithm. Except for the noise variance, the proposed algorithm does not require any statistics or knowledge of the desired signal, thus, it is suitable for adaptive filtering for channel estimation in code division multiple access (CDMA) systems in cases where the standard RLS approach cannot be used. A theoretical analysis demonstrates the convergence of the proposed algorithm, and simulation results for CDMA channel estimation show that the proposed algorithm outperforms existing channel estimation schemes.

To handle coherent signals with unknown arrival angles, an adaptive beamforming method is proposed which can be applied to an arbitrary array. The proposed method efficiently solves a generalized eigenvalue problem to estimate the arrival angles of the desired coherent signal group, by exploiting the Brent method in conjunction with alternating maximization. We discuss the condition for the correct direction estimation without erroneously taking interference direction estimates for the desired ones. Simulation results show that the performance of the proposed beamformer is very similar to that of the beamformer with the exact composite steering vector (CSV).

The proportionate normalized least mean square algorithm (PNLMS) greatly improves the convergence of the sparse impulse response. It exploits the shape of the impulse response to decide the proportionate step gain for each coefficient. This is not always suitable. Actually, the proportionate step gain should be determined according to the difference between the current estimate of the coefficient and its optimal value. Based on this idea, an approach is proposed to determine the proportionate step gain. The proposed approach can improve the convergence of proportionate adaptive algorithms after a fast initial period. It even behaves well for the non-sparse impulse response. Simulations verify the effectiveness of the proposed approach.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.

File systems make use of the buffer cache to enhance their performance. Traditionally, part of DRAM, which is volatile memory, is used as the buffer cache. In this paper, we consider the use of of Non-Volatile RAM (NVRAM) as a write cache for metadata of the file system in embedded systems. NVRAM is a state-of-the-art memory that provides characteristics of both non-volatility and random byte addressability. By employing NVRAM as a write cache for dirty metadata, we retain the same integrity of a file system that always synchronously writes its metadata to storage, while at the same time improving file system performance to the level of a file system that always writes asynchronously. To show quantitative results, we developed an embedded board with NVRAM and modify the VFAT file system provided in Linux 2.6.11 to accommodate the NVRAM write cache. We performed a wide range of experiments on this platform for various synthetic and realistic workloads. The results show that substantial reductions in execution time are possible from an application viewpoint. Another consequence of the write cache is its benefits at the FTL layer, leading to improved wear leveling of Flash memory and increased energy savings, which are important measures in embedded systems. From the real numbers obtained through our experiments, we show that wear leveling is improved considerably and also quantify the improvements in terms of energy.