This paper presents a computationally efficient cyclostationarity detection based spectrum sensing technique in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD), maximum cyclic autocorrelation selection (MCAS), and so on. PD can be affected by noise uncertainty because it requires a noise floor estimation, whereas MCAS does not require the estimation. Furthermore, the computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the estimation unnecessary whereas PD must compute only one statistic. In the presented MCAS based techniques, only one statistic must be computed. The presented technique obtains other necessary statistics from the procedure that computes the statistic. Therefore, the computational complexity of the presented is almost the same as that of PD, and it does not require the noise floor estimation for threshold. Numerical examples are shown to validate the effectiveness of the presented technique.

Understanding which refactoring transformations were performed is in demand in modern software constructions. Traditionally, many researchers have been tackling understanding code changes with history data derived from version control systems. In those studies, problems of the traditional approach are pointed out, such as entanglement of multiple changes. To alleviate the problems, operation histories on IDEs' code editors are available as a new source of software evolution data nowadays. By replaying such histories, we can investigate past code changes in a fine-grained level. However, the prior studies did not provide enough evidence of their effectiveness for detecting refactoring transformations. This paper describes an experiment in which participants detect refactoring transformations performed by other participants after investigating the code changes with an operation-replay tool and diff tools. The results show that both approaches have their respective factors that pose misunderstanding and overlooking of refactoring transformations. Two negative factors on divided operations and generated compound operations were observed in the operation-based approach, whereas all the negative factors resulted from three problems on tangling, shadowing, and out-of-order of code changes in the difference-based approach. This paper also shows seven concrete examples of participants' mistakes in both approaches. These findings give us hints for improving existing tools for understanding code changes and detecting refactoring transformations.

By tracking the changing rate of hard decisions during every two consecutive iterations of the alternating direction method of multipliers (ADMM) penalized decoding, an efficient early termination (ET) criterion is proposed to improve the convergence rate of ADMM penalized decoder for low-density parity-check (LDPC) codes. Compared to the existing ET criterion for ADMM penalized decoding, the proposed method can reduce the average number of iterations significantly at low signal-to-noise ratios with negligible performance degradation.

Microblog data contains rich information of real-world events with great commercial values, so microblog-oriented natural language processing (NLP) tasks have grabbed considerable attention of researchers. However, the performance of microblog-oriented Chinese Word Segmentation (CWS) based on deep neural networks (DNNs) is still not satisfying. One critical reason is that the existing microblog-oriented training corpus is inadequate to train effective weight matrices for DNNs. In this paper, we propose a novel active learning method to extend the scale of the training corpus for DNNs. However, due to a large amount of partially overlapped sentences in the microblogs, it is difficult to select samples with high annotation values from raw microblogs during the active learning procedure. To select samples with higher annotation values, parameter λ is introduced to control the number of repeatedly selected samples. Meanwhile, various strategies are adopted to measure the overall annotation values of a sample during the active learning procedure. Experiments on the benchmark datasets of NLPCC 2015 show that our λ-active learning method outperforms the baseline system and the state-of-the-art method. Besides, the results also demonstrate that the performances of the DNNs trained on the extended corpus are significantly improved.

Many subjective quality assessment methods have been standardized. Experimenters can select a method from these methods in accordance with the aim of the planned subjective assessment experiment. It is often argued that the results of subjective quality assessment are affected by range effects that are caused by the quality distribution of the assessment videos. However, there are no studies on the double stimulus continuous quality-scale (DSCQS) and absolute category rating with hidden reference (ACR-HR) methods that investigate range effects in the high-quality range. Therefore, we conduct experiments using high-quality assessment videos (high-quality experiment) and low-to-high-quality assessment videos (low-to-high-quality experiment) and compare the DSCQS and ACR-HR methods in terms of accuracy, stability, and discrimination ability. Regarding accuracy, we find that the mean opinion scores of the DSCQS and ACR-HR methods were marginally affected by range effects, although almost all common processed video sequences showed no significant difference for the high- and low-to-high-quality experiments. Second, the DSCQS and ACR-HR methods were equally stable in the low-to-high-quality experiment, whereas the DSCQS method was more stable than the ACR-HR method in the high-quality experiment. Finally, the DSCQS method had higher discrimination ability than the ACR-HR method in the low-to-high-quality experiment, whereas both methods had almost the same discrimination ability for the high-quality experiment. We thus determined that the DSCQS method is better at minimizing the range effects than the ACR-HR method in the high-quality range.

This paper proposes a sequentially iterative equalizer based on Kalman filtering and smoothing (SIEKFS) for multiple-input multiple-output (MIMO) systems under frequency selective fading channels. In the proposed SIEKFS, an iteration consists of sequentially executed subiterations, and each subiteration performs equalization and detection procedures of the symbols transmitted from a specific transmit antenna. During this subiteration, all available observations for the transmission block are utilized in the equalization procedures. Furthermore, the entire soft estimate of the desired symbols to be detected does not participate in the equalization procedures of the desired symbols, i.e., the proposed SIEKFS performs input-by-input equalization procedures for a priori information nulling. Therefore, compared with the original iterative equalizer based on Kalman filtering and smoothing, which performs symbol-by-symbol equalization procedures, the proposed SIEKFS can also perform iterative equalization based on the Kalman framework and turbo principle, with a significant reduction in computation complexity. Simulation results verify that the proposed SIEKFS achieves suboptimum error performance as the size of the antenna configuration and the number of iterations increase.

Malicious software (malware) poses various significant challenges. One is the need to retrieve plain-text messages transmitted between malware and herders through an encrypted network channel. Those messages (e.g., commands for malware) can be a useful hint to reveal their malicious activities. However, the retrieving is challenging even if the malware is executed on an analysis computer. To assist analysts in retrieving the plain-text from the memory, this paper presents FCReducer(Function Candidate Reducer), which provides a small candidate set of cryptographic functions called by malware. Given this set, an analyst checks candidates to locate cryptographic functions. If the decryption function is found, she then obtains its output as the plain-text. Although existing systems such as CipherXRay have been proposed to locate cryptographic functions, they heavily rely on fine-grained dynamic taint analysis (DTA). This makes them weak against under-tainting, which means failure of tracking data propagation. To overcome under-tainting, FCReducer conducts coarse-grained DTA and generates a typical data dependency graph of functions in which the root function accesses an encrypted message. This does not require fine-grained DTA. FCReducer then applies a community detection method such as InfoMap to the graph for detecting a community of functions that plays a role in decryption or encryption. The functions in this community are provided as candidates. With experiments using 12 samples including four malware specimens, we confirmed that FCReducer reduced, for example, 4830 functions called by Zeus malware to 0.87% as candidates. We also propose a heuristic to reduce candidates more greatly.

Network Functions Virtualization (NFV) is expected to provide network systems that offer significantly lower cost and greatly flexibility to network service providers and their users. Unfortunately, it is extremely difficult to implement Virtualized Network Functions (VNFs) that can equal the performance of Physical Network Functions. To realize NFV systems that have adequate performance, it is critical to accurately grasp VNF workload. In this paper, we focus on the virtual firewall as a representative VNF. The workload of the virtual firewall is mostly determined by firewall rule processing and the Access Control List (ACL) configurations. Therefore, we first reveal the major factors influencing the workload of the virtual firewall and some issues of monitoring CPU load as a traditional way of understanding the workload of virtual firewalls through preliminary experiments. Additionally, we propose a new workload metric for the virtual firewall that is derived by mathematical models of the firewall workload in consideration of the packet processing in each rule and the ACL configurations. Furthermore, we show the effectiveness of the proposed workload metric through various experiments.

Advanced driver assistance system (ADAS) can recognize traffic signals, vehicles, pedestrians, and so on all over the vehicle. However, because the ADAS is based on images taken in an outdoor environment, it is susceptible to ambient weather such as fog. So, preprocessing such as de-fog and de-hazing techniques is required to prevent degradation of object recognition performance due to decreased visibility. But, if such a fog removal technique is applied in an environment where there is little or no fog, the visual quality may be deteriorated due to excessive contrast improvement. And in foggy road environments, typical fog removal algorithms suffer from color distortion. In this paper, we propose a temporal filter-based fog detection algorithm to selectively apply de-fogging method only in the presence of fog. We also propose a method to avoid color distortion by detecting the sky region and applying different methods to the sky region and the non-sky region. Experimental results show that in the actual images, the proposed algorithm shows an average of more than 97% fog detection accuracy, and improves subjective image quality of existing de-fogging algorithms. In addition, the proposed algorithm shows very fast computation time of less than 0.1ms per frame.

A method is proposed for improving the accuracy of the characteristic basis function method (CBFM) using the multilevel approach. With this technique, CBFs taking into account multiple scattering calculated for each block (IP-CBFs; improved primary CBFs) are applied to CBFM using a multilevel approach. By using IP-CBFs, the interaction between blocks is taken into account, and thus it is possible to reduce the number of CBFs while maintaining accuracy, even if the multilevel approach is used. The radar cross section (RCS) of a cube, a cavity, and a dielectric sphere were analyzed using the proposed CBFs, and as a result it was found that accuracy is improved over the conventional method, despite no major change in the number of CBFs.

This letter presents numerical results of lossy source coding for non-uniformly distributed binary source with trellis codes. The results show how the performance of trellis codes approaches the rate-distortion function in terms of the number of states.

This paper shows a describing method of an image processing software in C for high-level synthesis (HLS) technology considering function chaining to realize an efficient hardware. A sophisticated image processing would be built on the sequence of several primitives represented as sub-functions like the gray scaling, filtering, binarization, thinning, and so on. Conventionally, generic describing methods for each sub-function so that HLS technology can generate an efficient hardware module have been shown. However, few studies have focused on a systematic describing method of the single top function consisting of the sub-functions chained. According to the proposed method, any number of sub-functions can be chained, maintaining the pipeline structure. Thus, the image processing can achieve the near ideal performance of 1 pixel per clock even when the processing chain is long. In addition, implicitly, the deadlock due to the mismatch of the number of pushes and pops on the FIFO connecting the functions is eliminated and the interpolation of the border pixels is done. The case study on a canny edge detection including the chain of some sub-functions demonstrates that our proposal can easily realize the expected hardware mentioned above. The experimental results on ZYNQ FPGA show that our proposal can be converted to the pipelined hardware with moderate size and achieve the performance gain of more than 70 times compared to the software execution. Moreover, the reconstructed C software program following our proposed method shows the small performance degradation of 8% compared with the pure C software through a comparative evaluation preformed on the Cortex A9 embedded processor in ZYNQ FPGA. This fact indicates that a unified image processing library using HLS software which can be executed on CPU or hardware module for HW/SW co-design can be established by using our proposed describing method.

In this paper, we describe the direct learning of an end-to-end mapping between under-/over-exposed images and well-exposed images. The mapping is represented as a deep convolutional neural network (CNN) that takes multiple-exposure images as input and outputs a high-quality image. Our CNN has a lightweight structure, yet gives state-of-the-art fusion quality. Furthermore, we know that for a given pixel, the influence of the surrounding pixels gradually increases as the distance decreases. If the only pixels considered are those in the convolution kernel neighborhood, the final result will be affected. To overcome this problem, the size of the convolution kernel is often increased. However, this also increases the complexity of the network (too many parameters) and the training time. In this paper, we present a method in which a number of sub-images of the source image are obtained using the same CNN model, providing more neighborhood information for the convolution operation. Experimental results demonstrate that the proposed method achieves better performance in terms of both objective evaluation and visual quality.

Seed detection or sometimes known as nuclei detection is a prerequisite step of nuclei segmentation which plays a critical role in quantitative cell analysis. The detection result is considered as accurate if each detected seed lies only in one nucleus and is close to the nucleus center. In previous works, voting methods are employed to detect nucleus center by extracting the nucleus saliency features. However, these methods still encounter the risk of false seeding, especially for the heterogeneous intensity images. To overcome the drawbacks of previous works, a novel detection method is proposed, which is called secant normal voting. Secant normal voting achieves good performance with the proposed skipping range. Skipping range avoids over-segmentation by preventing false seeding on the occlusion regions. Nucleus centers are obtained by mean-shift clustering from clouds of voting points. In the experiments, we show that our proposed method outperforms the comparison methods by achieving high detection accuracy without sacrificing the computational efficiency.

In-band full-duplex (FD) Multiple-Input and Multiple-Output (MIMO) communication performs uplink and downlink transmission at the same time using the same frequency. In this system, the spectral efficiency is theoretically double that of conventional duplex schemes, such as Time Division Duplex (TDD) and Frequency Division Duplex (FDD). However, this system suffers interference because the uplink and downlink streams coexist within the same channel. Especially at the terminal side, it is quite difficult for the terminal to eliminate the interference signals from other terminals since it has no knowledge about the contents of the interference signals. This paper presents an inter-terminal interference suppression method between the uplink and downlink signals assuming the multi-user environment. This method uses eigen-beamforming at the transmitting terminal to direct the null to the other terminal. Since this beamforming technique reduces the degrees of freedom available, the interference suppression performance and transmitting data-rate have a trade-off relation. This study investigates the system capacity characteristics in multi-user full-duplex MIMO communication using the propagation channel information measured in an actual outdoor experiment and shows that the proposed communication scheme offers higher system capacity than the conventional scheme.

This paper proposes a novel method of reducing channel state information (CSI) feedback by using transmit antenna selection for downlink multiuser multiple input multiple output (DL-MU-MIMO) transmission in dense distributed antenna systems. It is widely known that DL-MU-MIMO transmission achieves higher total bit-rate by mitigating inter-user interference based on pre-coding techniques. The pre-coding techniques require CSI between access point (AP) and multiple users. However, overhead for CSI acquisition degrades the transmission efficiency of DL-MU-MIMO transmission. In the proposed CSI feedback reduction method, AP first selects the antenna set that maximizes the received power at each user, second it skips the sequence of CSI feedback for users whose signal to interference power ratio is larger than a threshold, and finally it performs DL-MU-MIMO transmission to multiple users by using the selected antenna set. To clarify the proposed method, we evaluate it by computer simulations in an indoor scenario. The results show that the proposed method can offer higher transmission efficiency than the conventional DL-MU-MIMO transmission with the usual CSI feedback method.

Three-dimensional (3-D) scattering center models use a finite number of point scatterers to efficiently represent complex radar target signature. Using the CLEAN algorithm, 3-D scattering center model is extracted from the inverse synthetic aperture radar (ISAR) image, which is generated based on the shooting and bouncing ray (SBR) technique. The conventional CLEAN extracts the strongest peak iteratively based on the assumption that the scattering centers are isolated. In a realistic target, however, both interference from the closely spaced points and additive noise distort the extraction process. This paper proposes a matched filter-based CLEAN algorithm to improve accuracy efficiently. Using the matched filtering of which impulse response is the known point spread function (PSF), a point most correlated with the PSF is extracted. Thus, the proposed method optimally enhances the accuracy in the presence of massive distortions. Numerical simulations using canonical and realistic targets demonstrate that the extraction accuracy is improved without loss of time-efficiency compared with the existing CLEAN algorithms.

In this study, we first developed a simultaneous measurement system for accommodation and convergence eye movement and evaluated its precision. Then, using a stuffed animal as the target, whose depth should be relatively easy to perceive, we measured convergence eye movement and accommodation at the same time while a tablet displaying a 3D movie was moved in the depth direction. By adding the real 3D display depth movement to the movement of the 3D image, subjects showed convergence eye movement that corresponds appropriately to the dual change of parallax in the 3D movie and real display, even when a subject's convergence changed very little. Accommodation also changed appropriately according to the change in depth.

The main idea of filter methods in feature selection is constructing a feature-assessing criterion and searching for feature subset that optimizes the criterion. The primary principle of designing such criterion is to capture the relevance between feature subset and the class as precisely as possible. It would be difficult to compute the relevance directly due to the computation complexity when the size of feature subset grows. As a result, researchers adopt approximate strategies to measure relevance. Though these strategies worked well in some applications, they suffer from three problems: parameter determination problem, the neglect of feature interaction information and overestimation of some features. We propose a new feature selection algorithm that could compute mutual information between feature subset and the class directly without deteriorating computation complexity based on the computation of partitions. In light of the specific properties of mutual information and partitions, we propose a pruning rule and a stopping criterion to accelerate the searching speed. To evaluate the effectiveness of the proposed algorithm, we compare our algorithm to the other five algorithms in terms of the number of selected features and the classification accuracies on three classifiers. The results on the six synthetic datasets show that our algorithm performs well in capturing interaction information. The results on the thirteen real world datasets show that our algorithm selects less yet better feature subset.

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.