This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

A rapid single-flux-quantum (RSFQ) truncated multiplier based on bit-level processing is proposed. In the multiplier, two operands are transformed to two serialized patterns of bits (pulses), and the multiplication is carried out by processing those bits. The result is obtained by counting bits. By calculating in bit-level, the proposed multiplier can be implemented in small area. The gate level design of the multiplier is shown. The layout of the 4-bit multiplier was also designed.

Compressed sensing is an effective compression algorithm. It is widely used to measure signals in distributed sensor networks (DSNs). Considering the limited resources of DSNs, the measurement matrices used in DSNs must be simple. In this paper, we construct a deterministic measurement matrix based on Gordon-Mills-Welch (GMW) sequence. The column vectors of the proposed measurement matrix are generated by cyclically shifting a GMW sequence. Compared with some state-of-the-art measurement matrices, the proposed measurement matrix has relative lower computational complexity and needs less storage space. It is suitable for resource-constrained DSNs. Moreover, because the proposed measurement matrix can be realized by using simple shift register, it is more practical. The simulation result shows that, in terms of recovery quality, the proposed measurement matrix performs better than some state-of-the-art measurement matrices.

Wireless communication security has been increasingly important nowadays. Directional modulation (DM) is seen as a promising wireless physical layer security technology. Traditional DM is a transmit-side technology that projects digitally modulated information signals in the desired directions (or at the desired locations) while simultaneously distorting the constellation formats of the same signals in other directions (or at all other locations). However, these directly exposed digitally modulated information signals are easily intercepted by eavesdroppers along the desired directions (or around the desired locations). A new DM scheme for secure point-to-multipoint communication based on the spread spectrum assisted orthogonal frequency diverse array (short for SS-OFDA-M-DM) is proposed in this paper. It can achieve point-to-multipoint secure communication for multiple cooperative receivers at different locations. In the proposed SS-OFDA-M-DM scheme, only cooperative users that use specific DM receivers with right spread spectrum parameters can retrieve right symbols. Eavesdroppers without knowledge of spread spectrum parameters cannot intercept useful signals directly at the desired locations. Moreover, they cannot receive normal symbols at other locations either even if the right spread spectrum parameters are known. Numerical simulation results verify the validity of our proposed scheme.

This paper proposes an improved closed-form method for moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate measurements. After linearizing the measurement equations by introducing three additional parameters, a rough estimate is obtained by using the weighted least-square (WLS) estimator. To further refine the estimate, the relationship between additional parameters and source location is utilized. The proposed method gives a final closed-form solution without iteration or the extra mathematics operations used in existing methods by employing the basic idea of WLS processing. Numerical examples show that the proposed method exhibits better robustness and performance compared with several existing methods.

Among the five carrier aggregation (CA) deployment scenarios, the most preferred scenario is Scenario 1, which maximizes CA gain by fully overlapping a primary cell (PCell) and one or more secondary cells (SCells). It is possible since the same frequency band is used between component carriers (CCs) so nearly the same coverage is expected. However, Scenario 1 cannot guarantee high throughput in multi-radio access technology carrier aggregation (multi-RAT CA) which is actively being researched. Different carrier frequency characteristics in multi-RAT CA makes it hard to accurately match different frequency ranges. If the ranges of PCell and SCell differ, high throughput may not be obtained despite the CA operation. We found a coverage mismatch of approximately 37% between the PCell and SCell in the deployed network and realized a reduced CA gain in those areas. In this paper, we propose a novel PCell change approach named “PCell frequency switching (PFS)” to guarantee high throughput against cell coverage mismatch in multi-RAT CA deployment scenario 1. The experiment results show that the throughput increased by 9.7% on average and especially by 80.9% around the cell edge area when PFS is applied instead of the legacy CA handover operation.

Stimulus response caused by low-frequency electromagnetic exposure can be used to realize effective medical treatments. However, it is well known that the membrane potential of nerve cells exhibits nonlinearity, particularly in the case of modulated signals, it is important to analyze the nonlinear stimulation effect based on a nerve cell model. This paper adopts the Frankenhaeuser-Huxley (FH) model as the nerve cell model, and evaluates the stimulus response based on the FH model when nerve cells are exposed to low-frequency modulated radio signals such as On-Off Keying (OOK) modulation. This paper investigated the nonlinear effect of the stimulus intensity from the viewpoint of the spike frequency with different modulation parameter values including the induced amplitude and duty cycle. It was confirmed that action potential (AP) rate was higher with OOK-type radio signals than with unmodulated signals.

This paper presents an interference suppression and signal restoration technique that can create the clean signals required by automotive frequency-modulated continuous wave radar systems. When a radar signal from another radar system interferes with own transmitted radar signal, the target detection performance is degraded. This is because the beat frequency corresponding to the target cannot be estimated owing to the increase in the noise floor. In this case, advanced weighted-envelope normalization or wavelet denoising can be used to mitigate the effect of the interference; however, these methods can also lead to the loss of the desired signal containing the range and velocity information of the target. Therefore, we propose a method based on an autoregressive model to restore a signal damaged by mutual interference. The method uses signals that are not influenced by the interference to restore the signal. In experiments conducted using two different automotive radar systems, our proposed method is demonstrated to effectively suppress the interference and restore the desired signal. As a result, the noise floor resulting from the mutual interference was lowered and the beat frequency corresponding to the desired target was accurately estimated.

We have fabricated multiple-stacked Si quantum dots (QDs) with and without Ge core embedded in a SiO2 network on n-Si(100) and studied their field electron emission characteristics under DC bias application. For the case of pure Si-QD stacks with different dot-stack numbers, the average electric field in dot-stacked structures at which electron emission current appeared reached minimum value at a stack number of 11. This can be attributed to optimization of the electron emission due to enhanced electric field concentration in the upper layers of the dot-stacked structures and reduction of the electron injection current from the n-Si substrate, with an increased stack number. We also found that, by introducing Ge core into Si-QDs, the average electric field for the electron emission can be reduced below that from pure Si-QDs-stacked structures. This result implies that the electric field is more concentrated in the upper Si-QDs with Ge core layers due to deep potential well for holes in the Ge core.

We fabricate and characterize a GaAsSb/InGaAs backward diode (BWD) toward a realization of high sensitivity zero bias microwave rectification for RF wave energy harvest. Lattice-matched p-GaAsSb/n-InGaAs BWDs were fabricated and their current-voltage (I-V) characteristics and S-parameters up to 67 GHz were measured with respect to several sorts of mesa diameters in μm order. Our theoretical model and analysis are well fitted to the measured I-Vs on the basis of WKB approximation of the transmittance. It is confirmed that the interband tunneling due to the heterojunction is a dominant transport mechanism to exhibit the nonlinear I-V around zero bias regime unlike recombination or diffusion current components on p-n junction contribute in large current regime. An equivalent circuit model of the BWD is clarified by confirming theoretical fitting for frequency dependent admittance up to 67 GHz. From the circuit model, eliminating the parasitic inductance component, the frequency dependence of voltage sensitivity of the BWD rectifier is derived with respect to several size of mesa diameter. It quantitatively suggests an effectiveness of mesa size reduction to enhance the intrinsic matched voltage sensitivity with increasing junction resistance and keeping the magnitude of I-V curvature coefficient.

A multi-carrier and blind shift-frequency jamming(MCBSFJ) against the pulsed compression radar with order-statistic (OS) constant false alarm rate (CFAR) detector is proposed. Firstly, according to the detection principle of the OS-CFAR detector, the design requirements for jamming signals are proposed. Then, some key parameters of the jamming are derived based on the characteristics of the OS-CFAR detector. As a result, multiple false targets around the real target with the quantity, amplitude and space distribution which can be controlled are produced. The simulation results show that the jamming method can reduce the detection probability of the target effectively.

This paper presents a rigorous analysis of the electromagnetic scattering and transmission of misaligned dual metallic grating screens. The Fourier transform and the mode-matching technique are employed to obtain an analytical solution. Numerical results show that misaligned dual metal grating screens exhibit asymmetric scattering and transmission properties with respect to the scattering and transmission angles. Parametric studies are conducted in terms of the lateral displacement and vertical distance between the dual metallic grating screens. For validation, the proposed method is compared with a numerical simulation and good agreement has been achieved.

In this study, under the assumption that a robot (1) has a remotely controllable yawing camera and (2) moves in a uniform linear motion, we propose and investigate how to improve the target recognition rate with the camera, by using wireless feedback loop control. We derive the allowable data rate theoretically, and, from the viewpoint of error and delay control, we propose and evaluate QoS-Hybrid ARQ schemes under data rate constraints. Specifically, the theoretical analyses derive the maximum data rate for sensing and control based on the channel capacity is derived with the Shannon-Hartley theorem and the path-loss channel model inside the human body, i.e. CM2 in IEEE 802.15.6 standard. Then, the adaptive error and delay control schemes, i.e. QoS-HARQ, are proposed considering the two constraints: the maximum data rate and the velocity of the camera's movement. For the performance evaluations, with the 3D robot simulator GAZEBO, we evaluated our proposed schemes in the two scenarios: the static environment and the dynamic environment. The results yield insights into how to improve the recognition rate considerably in each situation.

This paper presents the mode matching (MM)/finite element method (FEM) hybrid analysis for a short-slot 2-plane coupler, and an optimization process for a wideband design based on a genetic algorithm (GA). The method of the analysis combines a fast modal analysis of the MM which reduces the computation time, with the flexibility of an FEM which can be used with an arbitrary cross-section. In the analysis, the model is reduced into the one-eighth model by using the three-dimensional structural symmetry. The computed results agree well with those by the simulation and the computation time is reduced. The bandwidth is improved by the optimization based on the GA from 2.4% to 6.9% for the 2-plane hybrid coupler and from 5.4% to 7.5% for the 2-plane cross coupler. The measured results confirm the wideband design.

Intrusion detection system (IDS) is a device or software to monitor a network system for malicious activity. In terms of detection results, there could be two types of false, namely, the false positive (FP) which incorrectly detects normal traffic as abnormal, and the false negative (FN) which incorrectly judges malicious traffic as normal. To protect the network system, we expect that FN should be minimized as low as possible. However, since there is a trade-off between FP and FN when IDS detects malicious traffic, it is difficult to reduce the both metrics simultaneously. In this paper, we propose a sequential classifiers combination method to reduce the effect of the trade-off. The single classifier suffers a high FN rate in general, therefore additional classifiers are sequentially combined in order to detect more positives (reduce more FN). Since each classifier can reduce FN and does not generate much FP in our approach, we can achieve a reduction of FN at the final output. In evaluations, we use NSL-KDD dataset, which is an updated version of KDD Cup'99 dataset. WEKA is utilized as a classification tool in experiment, and the results show that the proposed approach can reduce FN while improving the sensitivity and accuracy.

The aim of this research is to support real-time drawingin talking by using multimodal user interface technologies. In this situation, if talking and drawing are considered as commands by mistake during presentation, it will disturb users' natural talking and drawing. To prevent this problem, we introduce two modes of a command mode and a free mode, and explore smooth mode switching techniques that does not interfere with users' natural talking and drawing. We evaluate four techniques. Among them, a technique that specifies the command mode after actions using a pen gesture was the most effective. In this technique, users could quickly draw diagrams, and specifying mode switching didn't interfere with users' natural talk.

ECN, as a decisive approach for TCP congestion control, has been proposed for many years. However, its deployment on the Internet is much slower than expected. In this paper, we investigate the state of the deployment of ECN (Explicit Congestion Notification) on the Internet from a different viewpoint. We use the data set of web domains published by Alexa as the hosts to be tested. We negotiate an ECN-Capable and a Not ECN-Capable connections with each host and collect all packets belonging to the connections. By analyzing the header fields of the TCP/IP packets, we dig out the deployment rate, connectivity, variation of round-trip time and time to live between the Not ECN-Capable and ECN-Capable connections as well as the rate of IPv6-Capable web servers. Especially, it is clear that the connectivity is different from the domains (regions on the Internet). We hope that the findings acquired from this study would incentivize ISPs and administrators to enable ECN in their network systems.

Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.