This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions obtained by the canonical gravitational search algorithm (GSA) due to its inherent local exploitation ability. Meanwhile it also has a chance to explore a huge search space by taking advantages of the ergodicity of chaos. To fully utilize the dynamic properties of chaos, we propose three kinds of embedding strategies. The multiple chaotic maps are randomly, parallelly, or memory-selectively incorporated into GSA, respectively. To evaluate the effectiveness and efficiency of the proposed MCGSA, we compare it with GSA and twelve variants of chaotic GSA which use only a certain chaotic map on a set of 48 benchmark optimization functions. Experimental results show that MCGSA performs better than its competitors in terms of convergence speed and solution accuracy. In addition, statistical analysis based on Friedman test indicates that the parallelly embedding strategy is the most effective for improving the performance of GSA.

When haze exists in an image of an outdoor scene, the visibility of objects in the image is deteriorated. In recent years, to improve the visibility of objects in such images, many dehazing methods have been investigated. Most of the methods are based on the atmospheric scattering model. In such methods, the transmittance and global atmospheric light are estimated from an input image and a dehazed image is obtained by substituting them into the model. To estimate the transmittance and global atmospheric light, the dark channel prior is a major and powerful concept that is employed in many dehazing methods. In this paper, we propose a new dehazing method in which the degree of haze removal can be adjusted by changing its parameters. Our method is also based on the atmospheric scattering model and employs the dark channel prior. In our method, the estimated transmittance is adjusted to a more suitable value by a transform function. By choosing appropriate parameter values for each input image, good haze removal results can be obtained by our method. In addition, a quantitative index for evaluating the quality of a dehazed image is proposed in this paper. It can be considered that haze removal is a type of saturation enhancement. On the other hand, an output image obtained using the atmospheric scattering model is generally darker than the input image. Therefore, we evaluate the quality of dehazed images by considering the balance between the brightness and saturation of the input and output images. The validity of the proposed index is examined using our dehazing method. Then a comparison between several dehazing methods is carried out using the index. Through these experiments, the effectiveness of our dehazing method and the quantitative index is confirmed.

Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.

In recent years, covariance descriptors have received considerable attention as a strong representation of a set of points. In this research, we propose a new metric learning algorithm for covariance descriptors based on the Dykstra algorithm, in which the current solution is projected onto a half-space at each iteration, and which runs in O(n3) time. We empirically demonstrate that randomizing the order of half-spaces in the proposed Dykstra-based algorithm significantly accelerates convergence to the optimal solution. Furthermore, we show that the proposed approach yields promising experimental results for pattern recognition tasks.

Personalized PageRank (PPR) is a typical similarity metric between nodes in a graph, and node searches based on PPR are widely used. In many applications, graphs change dynamically, and in such cases, it is desirable to perform ad hoc searches based on PPR. An ad hoc search involves performing searches by varying the search parameters or graphs. However, as the size of a graph increases, the computation cost of performing an ad hoc search can become excessive. In this paper, we propose a method called Castanet that offers fast ad hoc searches of PPR. The proposed method features (1) iterative estimation of the upper and lower bounds of PPR scores, and (2) dynamic pruning of nodes that are not needed to obtain a search result. Experiments confirm that the proposed method does offer faster ad hoc PPR searches than existing methods.

Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.

The present paper introduces the construction of quadriphase sequences having a zero-correlation zone. For a zero-correlation zone sequence set of N sequences, each of length l, the cross-correlation function and the side lobe of the autocorrelation function of the proposed sequence set are zero for the phase shifts τ within the zero-correlation zone z, such that |τ|≤z (τ ≠ 0 for the autocorrelation function). The ratio $rac{N(z+1)}{ell}$ is theoretically limited to one. When l=N(z+1), the sequence set is called an optimal zero-correlation sequence set. The proposed zero-correlation zone sequence set can be generated from an arbitrary Hadamard matrix of order n. The length of the proposed sequence set can be extended by sequence interleaving, where m times interleaving can generate 4n sequences, each of length 2m+3n. The proposed sequence set is optimal for m=0,1 and almost optimal for m>1.

In this paper, robust stability of nonlinear feedback systems with unknown disturbance is considered by using the operator-based right coprime factorization method. For dealing with the unknown disturbance, a new design scheme and a nonlinear controller are given. That is, robust stability of the nonlinear systems with unknown disturbance is guaranteed by combining right coprime factorization with the proposed controller. Simultaneously, adverse effects resulting from the disturbance are removed by using the proposed nonlinear operator controller. Finally, a simulation example is given to show the effectiveness of the proposed design scheme of this paper.

When performing measurements in an outdoor field environment, various interference factors occur. So, many studies have been performed to increase the accuracy of the localization. This paper presents a novel probability-based approach to estimating position based on Apollonius circles. The proposed algorithm is a modified method of existing trilateration techniques. This method does not need to know the exact transmission power of the source and does not require a calibration procedure. The proposed algorithm is verified in several typical environments, and simulation results show that the proposed method outperforms existing algorithms.

There are increasing demands for improved analysis of multimodal data that consist of multiple representations, such as multilingual documents and text-annotated images. One promising approach for analyzing such multimodal data is latent topic models. In this paper, we propose conditionally independent generalized relational topic models (CI-gRTM) for predicting unknown relations across different multiple representations of multimodal data. We developed CI-gRTM as a multimodal extension of discriminative relational topic models called generalized relational topic models (gRTM). We demonstrated through experiments with multilingual documents that CI-gRTM can more effectively predict both multilingual representations and relations between two different language representations compared with several state-of-the-art baseline models that enable to predict either multilingual representations or unimodal relations.

This paper presents a design optimization method for a Gm-C active filter via geometric programming (GP). We first describe a GP-compatible model of a cascaded Gm-C filter that forms a biquadratic output transfer function. The bias, gain, bandwidth, and signal-to-noise ratio (SNR) of the Gm-C filter are described in a GP-compatible way. To further enhance the accuracy of the model, two modeling techniques are introduced. The first, a two-step selection method, chooses whether a saturation or subthreshold model should be used for each transistor in the filter to enhance the modeling accuracy. The second, a bisection method, is applied to include non-posynomial inequalities in the filter modeling. The presented filter model is optimized via a GP solver along with proposed modeling techniques. The numerical experiments over wide ranges of design specifications show good agreement between model and simulation results, with the average error for gain, bandwidth, and SNR being less than 9.9%, 4.4%, and 14.6%, respectively.

We propose a pre-filtering system for blind equalization in order to separate orthogonal frequency division multiplexing (OFDM) symbols in a multiple-input multiple-output (MIMO) - OFDM system. In a conventional blind MIMO-OFDM equalization without the pre-filtering system, there is a possibility that originally transmitted streams are permutated, resulting in the receiver being unable to retrieve desired signals. We also note that signal permutation is different for each subcarrier. In order to solve this problem, each transmitted stream of the proposed MIMO-OFDM system is pre-filtered by a unique allpass filter. In this paper, the pre-filter is referred to as transmit tagging filter (TT-Filter). At a receiver, an inverse filter of the TT-filter is used to blindly equalize a MIMO channel without permutation problem. Further, in order to overcome the issue of phase ambiguity, this paper introduces blind phase compensation.

In this paper, a non-isolated bidirectional DC-DC converter with zero voltage switching and constant switching frequency is proposed. Unlike the active clamp bidirectional converters, to create soft switching condition in both direction, only one auxiliary switch is used, which reduces conduction losses and the complexity of the circuit. The proposed converter is controlled by pulse width modulation and the switches are gated complementary, thus the implementation of the control circuit is simple. Low switching losses, high efficiency, high power density, are the advantages of this converter. The simulation and experimental results of the converter verify theoretical analysis. Based on an implemented prototype of the proposed converter at 80 watts, the measured efficiency is 96.5%.

Zero correlation zone (ZCZ) aperiodic complementary sequence (ZACS) sets have potential applications in multi-carriers (MC) CDMA communication systems, which can support more users than traditional complementary sequence sets. In this letter, methods for constructing ZACS sets based on orthogonal matrices are proposed. The new constructions may propose ZACS sets with optimal parameters. The new ZACS sets can be applied in approximately synchronized MC-CDMA to remove interferences.

The quality assessment of screen content images (SCIs) has been attractive recently. Different from natural images, SCI is usually a mixture of picture and text. Traditional quality metrics are mainly designed for natural images, which do not fit well into the SCIs. Motivated by this, this letter presents a simple and effective method to naturalize SCIs, so that the traditional quality models can be applied for SCI quality prediction. Specifically, bicubic interpolation-based up-sampling is proposed to achieve this goal. Extensive experiments and comparisons demonstrate the effectiveness of the proposed method.

A wideband noise-cancelling receiver front-end is proposed in this brief. As a basic architecture, a low-noise transconductance amplifier, a passive mixer, and a transimpedance amplifier are employed to compose the wideband receiver. To achieve wideband input matching for the transconductor, a global feedback method is adopted. Since the wideband receiver has to minimize linearity degradation if a large blocker signal exists out-of-band, a linearization technique is applied for the transconductor circuit. The linearization cancels third-order intermodulation distortion components and increases linearity; however, the additional circuits used in linearization generate excessive noise. A noise-cancelling architecture that employs an auxiliary path cancels noise signals generated in the main path. The designed receiver front-end is fabricated using a 65-nm CMOS process. The receiver operates in the frequency range of 25 MHz-2 GHz with a gain of 49.7 dB. The in-band input-referred third-order intercept point is improved by 12.3 dB when the linearization is activated, demonstrating the effectiveness of the linearization technique.

We propose a cache-aware method to accelerate texture-based volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 10243-voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of in-place rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

Image deconvolution is the task to recover the image information that was lost by taking photos with blur. Especially, to perform image deconvolution without prior information about blur kernel, is called blind image deconvolution. This framework is seriously ill-posed and an additional operation is required such as extracting image features. Many blind deconvolution frameworks separate the problem into kernel estimation problem and deconvolution problem. In order to solve the kernel estimation problem, previous frameworks extract the image's salient features by preprocessing, such as edge extraction. The disadvantage of these frameworks is that the quality of the estimated kernel is influenced by the region with no salient edges. Moreover, the optimization in the previous frameworks requires iterative calculation of convolution, which takes a heavy computational cost. In this paper, we present a blind image deconvolution framework using a specified high-pass filter (HPF) for feature extraction to estimate a blur kernel. The HPF-based feature extraction properly weights the image's regions for the optimization problem. Therefore, our kernel estimation problem can estimate the kernel in the region with no salient edges. In addition, our approach accelerates both kernel estimation and deconvolution processes by utilizing a conjugate gradient method in a frequency domain. This method eliminates costly convolution operations from these processes and reduces the execution time. Evaluation for 20 test images shows our framework not only improves the quality of recovered images but also performs faster than conventional frameworks.

The impact of intersymbol interference (ISI) on single carrier frequency domain equalization with multiple input multiple output (MIMO-SCFDE) systems is severe. Most existing channel equalization methods fail to solve it completely. In this paper, given the disadvantages of the error propagation and the gap from matched filter bound (MFB), we creatively introduce a decision feedback equalizer with frequency-domain bidirectional noise prediction (DFE-FDBiNP) to tackle intersymbol interference (ISI) in MIMO-SCFDE systems. The equalizer has two-part equalizer, that is the normal mode and the time-reversal mode decision feedback equalization with noise prediction (DFE-NP). Equal-gain combining is used to realize a greatly simplified and low complexity diversity combining. Analysis and simulation results validate the improved performance of the proposed method in quasi-static frequency-selective fading MIMO channel for a typical urban environment.

This paper proposes novel nonlinear precoding for XOR-physical layer network coding (XOR-PNC) to improve the performance of bi-directional MIMO relay systems. The proposed precoder comprises a pre-equalizer and a nonlinear filter, which we also propose in the paper. We theoretically analyze the performance of the XOR-PNC with the proposed nonlinear precoding. As a result, it is shown that the proposed pre-equalizer improves the distribution of the received signals at relays, while the nonlinear precoder not only improves the transmission power efficiency but also simplifies the receiver at the relays. The performance is confirmed by computer simulation. The XOR-PNC with the proposed precoding achieves almost the lower bound in BER performance, which is much better than the amplify-and-forward physical layer network coding (AF-PNC).