Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

Temporal behavior is a primary aspect of business process executions. Herein, we propose a temporal outlier detection and analysis method for business processes. Particularly, the method performs correlation analysis between the execution times of traces and activities to determine the type of activities that significantly influences the anomalous temporal behavior of a trace. To this end, we describe the modeling of temporal behaviors considering different control-flow patterns of business processes. Further, an execution time matrix with execution times of activities in all traces is constructed by using the event logs. Based on this matrix, we perform temporal outlier detection and correlation-based analysis.

This paper presents a conifer and broad-leaf tree classification scheme that processes high resolution polarimetric synthetic aperture data above X-band. To validate the proposal, fully polarimetric measurements are conducted in a precisely controlled environment to examine the difference between the scattering mechanisms of conifer and broad-leaf trees at 15GHz. With 3.75cm range resolution, scattering matrices of two tree types were measured by a vector network analyzer. Polarimetric analyses using the 4-component scattering power decomposition and alpha-bar angle of eigenvalue decomposition yielded clear distinction between the two tree types. This scheme was also applied to an X-band Pi-SAR2 data set. The results confirm that it is possible to distinguish between tree types using fully polarimetric and high-resolution data above X-band.

The large computational complexity makes stereo matching a big challenge in real-time application scenario. The problem of stereo matching in a video sequence is slightly different with that in a still image because there exists temporal correlation among video frames. However, no existing method considered temporal consistency of disparity for algorithm acceleration. In this work, we proposed a scheme called the dynamic disparity range (DDR) to optimize matching cost calculation and cost aggregation steps by narrowing disparity searching range, and a scheme called temporal cost aggregation path to optimize the cost aggregation step. Based on the schemes, we proposed the DDR-SGM and the DDR-MCCNN algorithms for the stereo matching in video sequences. Evaluation results showed that the proposed algorithms significantly reduced the computational complexity with only very slight loss of accuracy. We proved that the proposed optimizations for the stereo matching are effective and the temporal consistency in stereo video is highly useful for either improving accuracy or reducing computational complexity.

Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

In order to realize intelligent robot system, it is required to process large amount of data input from complex and different kinds of sensors in a short time. FPGA is expected to improve process performance of robots due to better performance per power consumption than high performance CPU, but it has lower development productivity than software. In this paper, we discuss automatic generation of FPGA components for robots. A design tool, developed for easy integration of FPGA into robots, is proposed. The tool named cReComp can automatically convert circuit written in Verilog HDL into a software component compliant to a robot software framework ROS (Robot Operation System), which is the standard in robot development. To evaluate its productivity, we conducted a subject experiment. As a result, we confirmed that the automatic generation is effective to ease the development of FPGA components for robots.

A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.

In order to solve the problem in Automatic Identification System (AIS) that the signal in the target slot cannot be correctly received due to partial overlap of signals in adjacent time slots, the paper introduces a new criterion: maximum expected signal power (MESP) and proposes a novel beamforming algorithm based on generalized singular value decomposition (GSVD) and orthogonal projection. The algorithm employs GSVD to estimate the signal subspace, and adopts orthogonal projection to project the received signal onto the orthogonal subspace of the non-target signal. Then, beamforming technique is used to maximize the output power of the target signal on the basis of MESP. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.

Notes on two constructions of zero-difference balanced (ZDB) functions are made in this letter. Then ZDB functions over Ze×∏ki=0 Fqi are obtained. And it shows that all the known ZDB functions using cyclotomic cosets over Zn are special cases of a generic construction. Moreover, applications of these ZDB functions are presented.

We propose a method to find assembly models contained in another assembly model given as a query from a set of 3D CAD assembly models. A 3D CAD assembly model consists of multiple components and is constructed using a 3D CAD software. The proposed method distinguishes assembly models which consist of a subset of components constituting the query model and also whose components have the same layout as the subset of the components. We compute difference between the shapes and the layouts of the components from the sinograms which are constructed by the Radon transform of their projections from various angles. We evaluate the proposed method experimentally using the assembly models which we prepare as a benchmark. The proposed method can also be used to find the database models which contains a query model.

This paper introduces a new noise generation algorithm for vocoder-based speech waveform generation. White noise is generally used for generating an aperiodic component. Since short-term white noise includes a zero-frequency component (ZFC) and inaudible components below 20 Hz, they are reduced in advance when synthesizing. We propose a new noise generation algorithm based on that for velvet noise to overcome the problem. The objective evaluation demonstrated that the proposed algorithm can reduce the unwanted components.

Multiuser massive multi-input multi-output (MU massive MIMO) is considered as a promising technology for the fifth generation (5G) of the wireless communication system. In this paper, we propose a low-complexity joint antenna and user selection scheme with block diagonalization (BD) precoding for MU massive MIMO downlink channel in the time division duplex (TDD) system. The base station (BS) is equipped with a large-scale transmit antenna array while each user is using the single receive antenna in the system. To reduce the hardware cost, BS will be implemented by limited number of radio frequency (RF) chains and BS must activate some selected transmit antennas in the BS side for data transmitting and some users' receive antennas in user side for data receiving. To achieve the reduction in the computation complexity in the antenna and user selection while maintaining the same or higher sum-rate in the system, the proposed scheme relies on three complexity reduction key factors. The first key factor is that finding the average channel gains for the transmit antenna in the BS side and the receive antenna in the user side to select the best channel gain antennas and users. The second key factor called the complexity control factor ξ(Xi) for the antenna set and the user set limitation is used to control the complexity of the brute force search. The third one is that using the assumption of the point-to-point deterministic MIMO channel model to avoid the singular value decomposition (SVD) computation in the brute force search. We show that the proposed scheme offers enormous reduction in the computation complexity while ensuring the acceptable performance in terms of total system sum-rate compared with optimal and other conventional schemes.

Electrophoretic deposition (EPD) usingpolydimethylsiloxane(PDMS)-based organic-inorganic hybrid materials as binders can be used to prepare alumina-binder composites on metal substrates. Herein, we investigated the deposition mechanism of PDMS-based polymers. The composition and porosity of EPD composites can be controlled by adjusting the EPD condition, and shape of alumina particles.

Cloud computing has attracted a growing number of enterprises to move their business to the cloud because of the associated operational and cost benefits. Improving availability is one of the major concerns of cloud application owners because modern applications generally comprise a large number of components and failures are common at scale. Fault tolerance enables an application to continue operating properly when failure occurs, but fault tolerance strategy is typically employed for the most important components because of financial concerns. Therefore, identifying important components has become a critical research issue. To address this problem, we propose a failure-sensitive structure-based component ranking approach (FSCRank), which integrates component failure impact and application structure information into component importance evaluation. An iterative ranking algorithm is developed according to the structural characteristics of cloud applications. The experimental results show that FSCRank outperforms the other two structure-based ranking algorithms for cloud applications. In addition, factors that affect application availability optimization are analyzed and summarized. The experimental results suggest that the availability of cloud applications can be greatly improved by implementing fault tolerance strategy for the important components identified by FSCRank.

Two points x, y inside a simple polygon P are said to be mutually link-2 visible if there exists the third point z ∈ P such that z is visible from both x and y. The polygon P is link-2 LR-visible if there are two points s, t on the boundary of P such that every point on the clockwise boundary of P from s to t is link-2 visible from some point of the other boundary of P from t to s and vice versa. We give a characterization of link-2 LR-visibility polygons by generalizing the known result on LR-visibility polygons. A new idea is to extend the concepts of ray-shootings and components to those under notion of link-2 visibility. Then, we develop an O(n log n) time algorithm to determine whether a given polygon is link-2 LR-visible. Using the characterization of link-2 LR-visibility polygons, we further present an O(n log n) time algorithm for determining whether a polygonal region is searchable by a k-searcher, k ≥ 2. This improves upon the previous O(n2) time bound [9]. A polygonal region P is said to be searchable by a searcher if the searcher can detect (or see) an unpredictable intruder inside the region, no matter how fast the intruder moves. A k-searcher holds k flashlights and can see only along the rays of the flashlights emanating from his position.

Two kinds of composite right/left-handed coaxial lines (CRLH CLs) are designed for an antenna element. The dispersion relations of the infinite periodic CRLH CLs are designed to occur -1st resonance at around 700 MHz, respectively. The designed CRLH CLs comprise a monopole and a choke structure for antenna elements. To verify the resonant modes and frequencies, the monopole structure, the choke structure, and the antenna element which is combined the monopole and the choke structures are simulated by eigenmode analysis. The resonant frequencies correspond to the dispersion relations. The monopole and the choke structures are applied to the coaxially fed antenna. The proposed antenna matches at 710 MHz and radiates. At the resonant frequency, the total length of the proposed antenna which is the length of the monopole structure plus the choke structure is 0.12 wavelength. The characteristics of the proposed antenna has been compared with that of the conventional coaxially fed monopole antenna without the choke structure and the sleeve antenna with the quarter-wavelength choke structure. The radiation pattern of the proposed antenna is omnidirectional, the total antenna efficiency is 0.73 at resonant frequencies, and leakage current is suppressed lesser than -10 dB at resonant frequency. The propose antenna is fabricated and measured. The measured |S11| characteristics, radiation patterns, and the total antenna efficiency are in good agreement with the simulated results.

The potential for using millimeter-wave (mmWave) frequencies in future 5G wireless cellular communication systems has motivated the study of large-scale antenna arrays to achieve highly directional beamforming. However, the conventional fully digital beamforming (DBF) methods which require one radio frequency (RF) chain per antenna element are not viable for large-scale antenna arrays due to the high cost and large power consumption of high frequency RF chain components. Hybrid precoding can significantly reduce the number of required RF chains and relieve the huge power consumption in mmWave massive multiple-input multiple-output (MIMO) systems, thus attracting much interests from academic and industry. In this paper, we consider the downlink communication of a massive multiuser MIMO (MU-MIMO) system in the mmWave channel, and propose a low complexity hybrid block diagonal geometric mean decomposition (BD-GMD) scheme. More specially, a joint transmit-receive (Tx-Rx) analog beamforming with large-scale arrays is proposed to improve channel gain, and then a low-dimensional BD-GMD approach is implemented at the equivalent baseband channel to mitigate the inter-user interference and equalize different data streams of each user. With the help of successive interference cancellation (SIC) at the receiver, we can decompose each user's MIMO channel into parallel sub-channels with identical higher SNRs/SINRs, thus equal-rate coding can be applied across the sub-channels of each user. Finally, simulation results verify that the proposed hybrid BD-GMD precoding scheme outperforms existing conventional fully digital and hybrid precoding schemes and is able to achieve much better BER performance.

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.

A significant portion of computational resources of embedded systems for visual detection is dedicated to feature extraction, and this severely affects the detection accuracy and processing performance of the system. To solve this problem, we propose a feature descriptor based on histograms of oriented gradients (HOG) consisting of simple linear algebra that can extract equivalent information to the conventional HOG feature descriptor at a low computational cost. In an evaluation, a leading-edge detection algorithm with this decomposed vector HOG (DV-HOG) achieved equivalent or better detection accuracy compared with conventional HOG feature descriptors. A hardware implementation of DV-HOG occupies approximately 14.2 times smaller cell area than that of a conventional HOG implementation.