How to manage the video streaming in future networks is becoming a more and more challenging issue. Recent studies on vehicular networks depict a new picture of the next generation Intelligent Transport System (ITS), with high level road safety and more comfortable driving experience. To cope with the heterogeneous network development for the next generation cellular network, centralized medium control is promising to be employed upon Road Side Unit (RSU). To accommodate the QoS constraints posed by video services in vehicular networks, the scalable video coding (SVC) scheme in H.264/AVC standard family offers spatial and temporal scalabilities in the video dissemination. In this paper, we target the resource allocation and layer selection problem for the multi-user video streaming over highway scenario, by employing SVC coding scheme for the video contents. We propose a Resource Allocation and Layer Selection (RALS) algorithm, which explicitly takes account of the utility value of each Group Of Picture (GOP) among all the vehicular users. Simulation results show that our proposed RALS algorithm outperforms the comparison schemes in typical scenarios.

Wireless channel emulators are used for the performance evaluation of wireless systems when actual wireless environment test is infeasible. The main contribution of this paper is the design of a MU-MIMO channel emulator capable of sending channel feedback automatically to the access point from the generated channel coefficients after the programmable time duration. This function is used for MU beamforming features of IEEE 802.11ac. The second contribution is the low complexity design of MIMO channel emulator with a single path implementation for all MIMO channel taps. A single path design allows all elements of the MIMO channel matrix to use only one Gaussian noise generator, Doppler filter, spatial correlation channel and Rician fading emulator to minimize the hardware complexity. In addition, single path implementation allows the addition of the feedback channel output with only a few additional non-sequential elements which would otherwise double in a parallel implementation. To demonstrate the functionality of our MU-MIMO channel emulator, we present actual hardware emulator results of MU-BF receive signal constellation on oscilloscope.

This paper describes a novel harmonic-based robust voice activity detection (H-RVAD) method with harmonic spectral local peak (HSLP) feature. HSLP is extracted by spectral amplitude analysis between the adjacent formants, and such characteristic can be used to identify and verify audio stream containing meaningful human speech accurately in low SNR environment. And, an enhanced low SNR noisy speech recognition system framework with wakeup module, speech recognition module and confirmation module is proposed. Users can determine or reject the system feedback while a recognition result was given in the framework, to prevent any chance that the voiced noise misleads the recognition result. The H-RVAD method is evaluated by the AURORA2 corpus in eight types of noise and three SNR levels and increased overall average performance from 4% to 20%. In home noise, the performance of H-RVAD method can be performed from 4% to 14% sentence recognition rate in average.

Recently, cloud systems have started to be utilized for services which analyze user's data in the field of computer vision. In these services, keypoints are extracted from images or videos, and the data is identified by machine learning with a large database in the cloud. To reduce the number of keypoints which are sent to the cloud, Keypoints of Interest (KOI) extraction has been proposed. However, since its computational complexity is large, hardware implementation is required for real-time processing. Moreover, the hardware resource must be low because it is embedded in devices of users. This paper proposes a hardware-friendly KOI algorithm with low amount of computations and its real-time hardware implementation based on dual threshold keypoint detection by gradient histogram and parallelization of connectivity of adjacent keypoint-utilizing register counters. The algorithm utilizes dual-histogram based detection and keypoint-matching based calculation of motion information and dense-clustering based keypoint smoothing. The hardware architecture is composed of a detection module utilizing descriptor, and grid-region-parallelization based density clustering. Finally, the evaluation results of hardware implementation show that the implemented hardware achieves Full-HD (1920x1080)-60 fps spatio-temporal keypoint extraction. Further, it is 47 times faster than low complexity keypoint extraction on software and 12 times faster than spatio-temporal keypoint extraction on software, and the hardware resources are almost the same as SIFT hardware implementation, maintaining accuracy.

In this paper, we study a novel method to avoid a local minimum stagnation in the design problem of IIR (Infinite Impulse Response) filters using PSO (Particle Swarm Optimization). Although PSO is appropriate to solve nonlinear optimization problems, it is reported that a local minimum stagnation occurs due to a strong intensification of particles during the search. Then, multi-swarm PSO based on the particle reallocation strategy is proposed to avoid the local minimum stagnation. In this method, a reallocation space is determined by using some global bests. In this paper, the relationship between the number of swarms and the best value of design error is shown and the effectiveness of the proposed method is shown through several design examples.

A number of successful tone mapping operators (TMOs) for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range devices. This paper proposes a novel inverse tone mapping (TM) operation and a new remapping framework with the operation. Existing inverse TM operations require either the store of some parameters calculated in forward TM, or data-depended operations. The proposed inverse TM operation enables to estimate HDR images from LDR ones mapped by the Reinhard's global operator, not only without keeping any parameters but also without any data-depended calculation. The proposed remapping framework with the inverse operation consists of two TM operations. The first TM operation is carried out by the Reinhard's global operator, and then the generated LDR one is stored. When we want different quality LDR ones, the proposed inverse TM operation is applied to the stored LDR one to generate an HDR one, and the second TM operation is applied to the HDR one to generate an LDR one with desirable quality, by using an arbitrary TMO. This framework allows not only to visualize an HDR image on low dynamic range devices at low computing cost, but also to efficiently store an HDR one as an LDR one. In simulations, it is shown that the proposed inverse TM operation has low computational cost, compared to the conventional ones. Furthermore, it is confirmed that the proposed framework allows to remap the stored LDR one to another LDR one whose quality is the same as that of the LDR one remapped by the conventional inverse TMO with parameters.

A secure identification scheme for JPEG images is proposed in this paper. The aim is to robustly identify JPEG images which are generated from the same original image under various compression levels in security. A property of the positive and negative signs of DCT coefficients is employed to achieve a robust scheme. The proposed scheme is robust against a difference in compression levels, and does not produce false negative matches in any compression level. Conventional schemes that have this property are not secure. To construct a secure identification system, we combine a new error correction technique with 1-bit parity with a fuzzy commitment scheme, which is a well-known biometric cryptosystem. In addition, a way for speeding up the identification is also proposed. The experimental results show the proposed scheme is effective for not only still images, but also video sequences in terms of the querying such as false positive, false negative and true positive matches, while keeping a high level of the security.

In this paper, an insect classification method using scanning electron microphotographs is presented. Images taken by a scanning electron microscope (SEM) have a unique problem for classification in that visual features differ from each other by magnifications. Therefore, direct use of conventional methods results in inaccurate classification results. In order to successfully classify these images, the proposed method generates an optimal training dataset for constructing a classifier for each magnification. Then our method classifies images using the classifiers constructed by the optimal training dataset. In addition, several images are generally taken by an SEM with different magnifications from the same insect. Therefore, more accurate classification can be expected by integrating the results from the same insect based on Dempster-Shafer evidence theory. In this way, accurate insect classification can be realized by our method. At the end of this paper, we show experimental results to confirm the effectiveness of the proposed method.

A method of color scheme is proposed considering contrast of luminance between adjacent regions and design property. This method aims at setting the contrast of luminance high, in order to make the image understandable to visually handicapped people. This method also realizes preferable color design for visually normal people by assigning color components from color combination samples. Interactive evolutionary computing is adopted to design the luminance and the color, so that the luminance and color components are assigned to each region appropriately on the basis of human subjective criteria. Here, the luminance is designed first, and then color components are assigned, keeping the luminance unchanged. Since samples of fine color combinations are applied, the obtained color design is also fine and harmonic. Computer simulations verify the high performance of this system.

In general, in-focus images are used in visual object detection because image blur is considered as a factor reducing detection accuracy. However, in-focus images make it difficult to separate target objects from background images, because of that, visual object detection becomes a hard task. Background subtraction and inter-frame difference are famous schemes for separating target objects from background but they have a critical disadvantage that they cannot be used if illumination changes or the point of view moves. Considering these problems, the authors aim to improve detection accuracy by using images with out-of-focus blur obtained from a camera with a shallow depth of field. In these images, it is expected that target objects become in-focus and other regions are blurred. To enable visual object detection based on such image blur, this paper proposes a novel scheme using DFT-based feature extraction. The experimental results using synthetic images including, circle, star, and square objects as targets showed that a classifier constructed by the proposed scheme showed 2.40% miss rate at 0.1 FPPI and perfect detection has been achieved for detection of star and square objects. In addition, the proposed scheme achieved perfect detection of humans in natural images when the upper half of the human body was trained. The accuracy of the proposed scheme is better than the Filtered Channel Features, one of the state-of-the-art schemes for visual object detection. Analyzing the result, it is convincing that the proposed scheme is very feasible for visual object detection based on image blur.

In this paper, we propose an improved method of embedding and detecting data in a printed image using a camera of a mobile device. The proposed method is based on the data diffusion method. We discuss several problems in the conventional lens distortion correction method. In addition, the possibility of using multiple captured images by employing a motion-image-capturing technique is also examined. A method of selecting captured images that are expected to obtain a high detection rate is also proposed. From the experimental results, it is shown that the proposed method is effective for improving data detection.

In this letter, we propose a method for obtaining a clear and natural output image by tuning the illumination component in an input image. The proposed method is based on the retinex process and it is suitable for the image quality improvement of images of which illumination is insufficient.

Protanopes and deuteranopes are difficult to distinguish some color pairs. In this letter, a new lightness modification method which considers the Craik-O'Brien effect is proposed. The lightness modification is performed at parts which are difficult to distinguish in the protanopia or deuteranopia. Experiments show the validity of the proposed method.

In this paper, we propose a digital image enlargement method based on a fuzzy technique that improves half-pixel generation, especially for convex and concave signals. The proposed method is a modified version of the image enlargement scheme previously proposed by the authors, which achieves accurate half-pixel interpolation and enlarges the original image by convolution with the Lanczos function. However, the method causes impulse-like artifacts in the enlarged image. In this paper, therefore, we introduce a fuzzy set and fuzzy rule for generating half-pixels to improve the interpolation of convex and concave signals. Experimental results demonstrate that, in terms of image quality, the proposed method shows superior performance compared to bicubic interpolation and our previous method.

This paper proposes an efficient lossless coding scheme for color video in RGB 4:4:4 format. For the R signal that is encoded before the other signals at each frame, we employ a block-adaptive prediction technique originally developed for monochrome video. The prediction technique used for the remaining G and B signals is extended to exploit inter-color correlations as well as inter- and intra-frame ones. In both cases, multiple predictors are adaptively selected on a block-by-block basis. For the purpose of designing a set of predictors well suited to the local properties of video signals, we also explore an appropriate setting for the spatiotemporal partitioning of a video volume.

Although many approaches about ideal channels have been proposed in previous researches, few authors considered the situation of nonideal communication links. In this paper, we study the problem of distributed decision fusion over nonideal channels by using the scan statistics. In order to obtain the fusion rule under nonideal channels, we set up the nonideal channels model with the modulation error, noise and signal attenuation. Under this model, we update the fusion rule by using the scan statstics. We firstly consider the fusion rule when sensors are distributed in grid, then derive the expressions of the detection probability and false alarm probability when sensors follow an uniform distribution. Extensive simulations are conducted in order to investigate the performance of our fusion rule and the influence of signal-noise ratio (SNR) on the detection and false alarm probability. These simulations show that the theoretical values of the global detection probability and the global false alarm probability are close to the experimental results, and the fusion rule also has high performance at the high SNR region. But there are some further researches need to do for solving the large computational complexity.

For the nonlinear acoustic echo cancellation, we present an algorithm to estimate the threshold of the clipping effect and the room impulse response vector by suppressing their time-varying cost function. A common way to suppress the time-varying cost function of a pair of parameters is to alternatingly minimize the function with respect to each parameter while keeping the other fixed, which we refer to as adaptive alternating minimization. However, since the cost function for the threshold is nonconvex, the conventional methods approximate the exact minimizations by gradient descent updates, which causes serious degradation of the estimation accuracy in some occasions. In this paper, by exploring the fact that the cost function for the threshold becomes piecewise quadratic, we propose to exactly minimize the cost function for the threshold in a closed form while suppressing the cost function for the impulse response vector in an online manner, which we call exact-online adaptive alternating minimization. The proposed method is expected to approximate more efficiently the adaptive alternating minimization strategy than the conventional methods. Numerical experiments demonstrate the efficacy of the proposed method.

This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.

This paper considers the behavior of a master-slave system of two coupled piecewise constant spiking oscillators (PWCSOs). The master of this system exhibits chaos and outputs a chaotic sequence of spikes, which are used as input to the slave. The slave exhibits a periodic-like trajectory (PLT) that is chaotic but that appears to be periodic in the phase plane. We theoretically investigate the generating region of the PLT in the parameter space. Using a test circuit, we confirm the typical phenomena of this coupled system.

We propose subquadratic space complexity multipliers for any finite field $mathbb{F}_{q^n}$ over the base field $mathbb{F}_q$ using the Dickson basis, where q is a prime power. It is shown that a field multiplication in $mathbb{F}_{q^n}$ based on the Dickson basis results in computations of Toeplitz matrix vector products (TMVPs). Therefore, an efficient computation of a TMVP yields an efficient multiplier. In order to derive efficient $mathbb{F}_{q^n}$ multipliers, we develop computational schemes for a TMVP over $mathbb{F}_{q}$. As a result, the $mathbb{F}_{2^n}$ multipliers, as special cases of the proposed $mathbb{F}_{q^n}$ multipliers, have lower time complexities as well as space complexities compared with existing results. For example, in the case that n is a power of 3, the proposed $mathbb{F}_{2^n}$ multiplier for an irreducible Dickson trinomial has about 14% reduced space complexity and lower time complexity compared with the best known results.

We propose an exponent-based partitioning broadcast protocol (EPBP) to promise the prompt dissemination of emergency message (EM) in vehicular networks. EPBP divides the communication range into segments with different widths iteratively. The width varies corresponding to the exponential curve. The design makes the farther no-empty segment thinner, as a result of which the collision rate of candidates' contention for the relay node decreases and the one-hop message progress increases efficiently. In addition, we adjust the interval of back-off timers to avoid the spurious forwarding problem, and develop more accurate analytical models for the performance. Our simulation verifies these models and show a significant increase of EPBP compared with the state-of-the-art protocols. EM dissemination speed can be improved as 55.94% faster in dense vehicle networks, and packet delivery ratio has risen to higher than 99.99%.

To resist algebraic and fast algebraic attacks, Boolean functions used in stream ciphers should have optimal algebraic immunity and good fast algebraic immunity. One challenge of cryptographic Boolean functions is to determine their ability to resist fast algebraic attacks, which can be measured by their fast algebraic immunities. In this letter, we determine the exact values of fast algebraic immunity of the majority function of 2^m and 2^m+1 variables. This is the first time that the exact values of the fast algebraic immunity of an infinite class of symmetric Boolean functions with optimal algebraic immunity are determined.

This letter introduces a non-local means (NLM) denoising algorithm that uses a weight function based on a switching norm. The noise level and local activity are incorporated into the NLM denoising algorithm which enhances performance. This is done by selecting a norm among l₁, l₂, and l₄ norms to determine a weighting function. The experimental results show the capability of the proposed algorithm. In addition, the proposed algorithm is verified as effective for enhancing the performance of other NLM algorithms.

In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.