In this letter, we explore joint optimization of perceptual gain function and deep neural networks (DNNs) for a single-channel speech enhancement task. A DNN architecture is proposed which incorporates the masking properties of the human auditory system to make the residual noise inaudible. This new DNN architecture directly trains a perceptual gain function which is used to estimate the magnitude spectrum of clean speech from noisy speech features. Experimental results demonstrate that the proposed speech enhancement approach can achieve significant improvements over the baselines when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

In this paper, we present a low and variable computation complexity decoder based on K-Best for uncoded detection in spatially multiplexed MIMO systems. In the variable complexity K-Best (VKB), the detection of each symbol is carried out using only a symbol constellation of variable size. This symbol constellation is obtained by considering the channel properties and a given target SNR. Simulations show that the proposed technique almost matches the performance of the original K-Best decoder. Moreover, it is able to reduce the average computation complexity by at least 75% in terms of the number of visited nodes.

We integrate a pressure sensing capacitor and a low operation voltage OFET to develop a pressure sensor. The OFET was used as a readout device and an external pressure was loaded on the sensing capacitor. The OFET operates at less than 5 V and the change in the drain current in response to the pressure load (100 kPa) is two orders of magnitude.

With the rapid development of information and Web technologies, people are facing ‘information overload’ in their daily lives. The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. In this paper, a PRS model based on the Support Vector Machine (SVM) is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.

Thin films of vinyl derivatives of naphthalene diimide were prepared by electron-assisted vapor deposition. Monomer materials of N, N'-bis(allyl)-naphthalene diimide (Allyl-NDI) and N,N'-bis(p-vinyl-benzyl)-naphthalene diimide (Sty-NDI) were newly synthesized for this purpose. Uniform films were obtained by vapor-depositing these materials, whereas spin-coating yielded nonuniform films. IR analysis suggested that Sty-NDI can be polymerized upon vapor deposition. An insoluble film of Sty-NDI was obtained by the electron-assisted vapor deposition. On the other hand, Allyl-NDI had lower reactivity for polymerization. It was concluded that Sty-NDI is a promising material for preparing thin films of vinyl polymer having naphthalene diimide units.

Controlling synchrony as well as desynchrony in a network of neuronal oscillators has been one of the focus issues in nonlinear science and engineering. It has been well known that spike stimuli injected commonly to multiple neurons can synchronize them if the strength of the common spike stimuli is high enough. Our recent study showed that this common spike-induced synchrony could be suppressed by introducing heterogeneity to inhibitory connections, through which the common spikes are transmitted. The aim of the present study is apply this methodology to electronic neurons as a real physical hardware. Using an Axon-Hillock circuit that represents basic properties of the leaky integrate-and-fire (LIF) neuron, our experiment demonstrated that the method was quite effective for desynchronizing the neuron circuits. The experimental results are also in a good agreement with the linear response theory that describes the input-output relationship of LIF neurons. Our method of suppressing the neuronal synchrony should be of practical use for enhancement of neural information processing as well as for improvement of pathological state of the brain.

In order to prevent the synonym substitution breaking the balance among frequencies of synonyms and improve the statistical undetectability, this paper proposed a novel linguistic steganography based on synonym run-length encoding. Firstly, taking the relative word frequency into account, the synonyms appeared in the text are digitized into binary values and expressed in the form of runs. Then, message are embedded into the parities of runs' lengths by self-adaptively making a positive or negative synonym transformation on boundary elements of two adjacent runs, while preserving the number of relative high and low frequency synonyms to reduce the embedding distortion. Experimental results have shown that the proposed synonym run-length encoding based linguistic steganographic algorithm makes fewer changes on the statistical characteristics of cover texts than other algorithms, and enhances the capability of anti-steganalysis.

Restoration is an important area in improving the visual quality, and lays the foundation for accurate object detection or terrain classification in image analysis. In this paper, we introduce Beta process priors into hierarchical sparse Bayesian learning for recovering underlying degraded hyperspectral images (HSI), including suppressing the various noises and inferring the missing data. The proposed method decomposes the HSI into the weighted summation of the dictionary elements, Gaussian noise term and sparse noise term. With these, the latent information and the noise characteristics of HSI can be well learned and represented. Solved by Gibbs sampler, the underlying dictionary and the noise can be efficiently predicted with no tuning of any parameters. The performance of the proposed method is compared with state-of-the-art ones and validated on two hyperspectral datasets, which are contaminated with the Gaussian noises, impulse noises, stripes and dead pixel lines, or with a large number of data missing uniformly at random. The visual and quantitative results demonstrate the superiority of the proposed method.

An adaptive time-step control method is proposed for the damped pseudo-transient analysis (DPTA) method. The new method is based on the idea of switched evolution/relaxation (SER), which can automatically adapt the step size for different circuit states. Considering the number of iterations needed for the convergence of Newton-Raphson (NR) method and the states in previous steps, the proposed method can automatically optimize the time-step size. Using numerical examples, the new method is proven to improve robustness, simulation efficiency, and the convergence of DPTA for solving nonlinear DC circuit equations.

Advances in intelligent vehicle systems have led to modern automobiles being able to aid drivers with tasks such as lane following and automatic braking. Such automated driving tasks increasingly require reliable ego-localization. Although there is a large number of sensors that can be employed for this purpose, the use of a single camera still remains one of the most appealing, but also one of the most challenging. GPS localization in urban environments may not be reliable enough for automated driving systems, and various combinations of range sensors and inertial navigation systems are often too complex and expensive for a consumer setup. Therefore accurate localization with a single camera is a desirable goal. In this paper we propose a method for vehicle localization using images captured from a single vehicle-mounted camera and a pre-constructed database. Image feature points are extracted, but the calculation of camera poses is not required — instead we make use of the feature points' scale. For image feature-based localization methods, matching of many features against candidate database images is time consuming, and database sizes can become large. Therefore, here we propose a method that constructs a database with pre-matched features of known good scale stability. This limits the number of unused and incorrectly matched features, and allows recording of the database scales into “tracklets”. These “Feature scale tracklets” are used for fast image match voting based on scale comparison with corresponding query image features. This process reduces the number of image-to-image matching iterations that need to be performed while improving the localization stability. We also present an analysis of the system performance using a dataset with high accuracy ground truth. We demonstrate robust vehicle positioning even in challenging lane change and real traffic situations.

Periodic interference frequently affects the measurement of small signals and causes problems in clinical diagnostics. Adaptive filters can be used as potential tools for cancelling such interference. However, when the interference has a frequency fluctuation, the ideal adaptive-filter coefficients for cancelling the interference also fluctuate. When the adaptation property of the algorithm is slow compared with the frequency fluctuation, the interference-cancelling performance is degraded. However, if the adaptation is too quick, the performance is degraded owing to the target signal. To overcome this problem, we propose an adaptive filter that suppresses the fluctuation of the ideal coefficients by utilizing a $rac{pi}{2}$ phase-delay device. This method assumes a frequency response that characterizes the transmission path from the interference source to the main input signal to be sufficiently smooth. In the numerical examples, the proposed method exhibits good performance in the presence of a frequency fluctuation when the forgetting factor is large. Moreover, we show that the proposed method reduces the calculation cost.

This letter presents a method for solving several linear equations in max-plus algebra. The essential part of these equations is reduced to constraint satisfaction problems compatible with mixed integer programming. This method is flexible, compared with optimization methods, and suitable for scheduling of certain discrete event systems.

In this paper, we present an FPGA hardware implementation for a phylogenetic tree reconstruction with a maximum parsimony algorithm. We base our approach on a particular stochastic local search algorithm that uses the Progressive Neighborhood and the Indirect Calculation of Tree Lengths method. This method is widely used for the acceleration of the phylogenetic tree reconstruction algorithm in software. In our implementation, we define a tree structure and accelerate the search by parallel and pipeline processing. We show results for eight real-world biological datasets. We compare execution times against our previous hardware approach, and TNT, the fastest available parsimony program, which is also accelerated by the Indirect Calculation of Tree Lengths method. Acceleration rates between 34 to 45 per rearrangement, and 2 to 6 for the whole search, are obtained against our previous hardware approach. Acceleration rates between 2 to 36 per rearrangement, and 18 to 112 for the whole search, are obtained against TNT.

Anonymous password-based authentication protocols are designed to provide not only password-based authentication but also client anonymity. In [22], Qian et al. proposed a simple anonymous password-based authentication protocol (SAPAKE). In this paper, we reconsider the SAPAKE protocol [22] by first showing that an (third party) active attacker can impersonate the server and compute a session key with probability 1. After giving a formal model that captures such attacks, we propose a simple and secure anonymous password-based authentication (for short, S2APA) protocol that provides security against modification attacks on protocol-specific values and is more efficient than YZWB09/10 [32], [33] and SAPAKE [22]. Also, we prove that the S2APA protocol is AKE-secure against active attacks as well as modification attacks under the computational Diffie-Hellman problem in the random oracle model, and provides unconditional client anonymity against a semi-honest server, who honestly follows the protocol.

In this paper, we study the degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) multiway relay channel (mRC) with two relays, two clusters and K (K≥3) users per cluster. We consider a clustered full data exchange model, i.e., each user in a cluster sends a multicast (common) message to all other users in the same cluster and desires to acquire all messages from them. The DoF results of the mRC with the single relay have been reported. However, the DoF achievability of the mRC with multiple relays is still an open problem. Furthermore, we consider a more practical scenario where no channel state information at the transmitter (CSIT) is available to each user. We first give a DoF cut-set upper bound of the considered mRC. Then, we propose a distributed interference neutralization and retransmission scheme (DINR) to approach the DoF cut-set upper bound. In the absence of user cooperation, this method focuses on the beamforming matrix design at each relay. By investigating channel state information (CSI) acquisition, we show that the DINR scheme can be performed by distributed processing. Theoretical analyses and numerical simulations show that the DoF cut-set upper bound can be attained by the DINR scheme. It is shown that the DINR scheme can provide significant DoF gain over the conventional time division multiple access (TDMA) scheme. In addition, we show that the DINR scheme is superior to the existing single relay schemes for the considered mRC.

Building detection from high resolution remote sensing images is challenging due to the high intraclass variability and the difficulty in describing buildings. To address the above difficulties, a novel approach is proposed based on the combination of shape-specific feature extraction and discriminative feature classification. Shape-specific feature can capture complex shapes and structures of buildings. Discriminative feature classification is effective in reflecting similarities among buildings and differences between buildings and backgrounds. Experiments demonstrate the effectiveness of the proposed approach.

This paper presents the set of procedures to blend GNSS and V2V communication to improve the performance of the stand-alone on-board GNSS receiver and to assure mutual positioning with a bounded error. Particle filter algorithm is applied to enhance mutual positioning of vehicles, and it fuses the information provided by the GNSS receiver, wireless measurements in vehicular environments, odometer, and digital road map data including reachability and zone probabilities. Measurement-based statistical model of relative distance as a function of Time-of-Arrival is experimentally obtained. The number of collaborative vehicles to the mutual positioning procedure is investigated in terms of positioning accuracy and network performance through realistic simulation studies, and the proposed mutual positioning procedure is experimentally evaluated by a fleet of five IEEE 802.11p radio modem equipped vehicles. Collaboration in a VANET improves availability of position measurement and its accuracy up to 40% in comparison with respect to the stand-alone GNSS receiver.

Labeling a salient region accurately in video with cluttered background and complex motion condition is still a challenging work. Most existing video salient region detection models mainly extract the stimulus-driven saliency features to detect the salient region in video. They are easily influenced by the cluttered background and complex motion conditions. It may lead to incomplete or wrong detection results. In this paper, we propose a video salient region detection framework by fusing the stimulus-driven saliency features and spatiotemporal consistency cue to improve the performance of detection under these complex conditions. On one hand, stimulus-driven spatial saliency features and temporal saliency features are extracted effectively to derive the initial spatial and temporal salient region map. On the other hand, in order to make use of the spatiotemporal consistency cue, an effective spatiotemporal consistency optimization model is presented. We use this model optimize the initial spatial and temporal salient region map. Then the superpixel-level spatiotemporal salient region map is derived by optimizing the initial spatiotemporal salient region map. Finally, the pixel-level spatiotemporal salient region map is derived by solving a self-defined energy model. Experimental results on the challenging video datasets demonstrate that the proposed video salient region detection framework outperforms state-of-the-art methods.

The Helmholtz-Kohlrausch (H-K) effect is a phenomenon in which the perceived brightness levels induced by two stimuli are different even when two color stimuli have the same luminance and different chroma in a particular hue. This phenomenon appears on display devices, and the wider the gamut these devices have, the more the perceived brightness is affected by the H-K effect. The quantification of this effect can be expected to be useful for the development and evaluation of a wide range of display devices. However, quantification of the H-K effect would require considerable subjective evaluation experimentation, which would be a major burden. Therefore, the authors have derived perceived brightness maps for natural images using an estimation equation for the H-K effect without experimentation. The results of comparing and analyzing the calculated maps and ground truth maps obtained through subjective evaluation experiments confirm strong correlation coefficients between such maps overall. However, a tendency for the estimation of the calculation map to be poor on high chroma strongly influenced by the H-K effect was also confirmed. In this study, we propose an accuracy improvement method for the estimation of the H-K effect by correcting the calculation maps using a correction coefficient obtained by focusing on this tendency, and we confirm the effectiveness of our method.

Powerful jammers are able to disable consumer-grade global navigation satellite system (GNSS) receivers under normal operating conditions. Conventional anti-jamming techniques based on the time-domain are unable to effectively suppress wide-band interference, such as chirp-like jammer. This paper proposes a novel anti-jamming architecture, combining wavelet packet signal analysis with adaptive filtering theory to mitigate chirp interference. Exploiting the excellent time-frequency resolution of wavelet technologies makes it possible to generate a reference chirp signal, which is basically a “de-noised” jamming signal. The reference jamming signal then is fed into an adaptive predictor to function as a refined jamming signal such that it predicts a replica of the jammer from the received signal. The refined chirp signal is then subtracted from the received signal to realize the aim of anti-jamming. Simulation results demonstrate the effectiveness of the proposed method in combating chirp interference in Galileo receivers. We achieved jamming-to-signal power ratio (JSR) of 50dB with an acquisition probability exceeding 90%, which is superior to many anti-jamming techniques based on the time-domain, such as conventional adaptive notch filters. The proposed method was also implemented in an software-defined GPS receiver for further validation.