In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples.

We propose a retrieval method using scale invariant visual phrases (SIVPs). Our method encodes spatial information into the SIVPs which capture translation, rotation and scale invariance, and employs the SIVPs to determine the spatial correspondences between query image and database image. To compute the spatial correspondences efficiently, the SIVPs are introduced into the inverted index, and SIVP verification is investigated to refine the candidate images returned from inverted index. Experimental results demonstrate that our method improves the retrieval accuracy while increasing the retrieval efficiency.

In 3G CDMA mobile communication systems, high data rate services are essential for many key applications. When an MS approaches the cell border, link performance is degraded and more power should be allocated to maintain the link performance. Since the maximum available signal power is limited, the link adaptation mechanism may diminish the data rate to maintain link performance. This implies that the valid coverage shrinks when the data rate increases. The shrinking of valid coverage under a predetermined data rate will strongly impact on the reliability of high data rate services. In this work, the encoded bit error probabilities of 3G CDMA mobile communication systems, over large-scale and large-small-scale fading channels, were analyzed based on SGA and SIGA methods. Analytic methods were also proposed to investigate the issues of coverage shrinking and service data rate variations. Furthermore, the outage probability, cell coverage percentage and the staying probabilities of available data rates were well examined. The proposed analytic methods can be applied, as a preliminary research, to the design of cellular-system-related techniques, such as QoS control, available data rate prediction, power reservation, and service adaptation.

Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to efectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance.

The use of fluid Generalized Processor Sharing (GPS) algorithm for integrated service networks has received much attention since early 1990's because of its desirable properties in terms of delay bound and service fairness. Many Packet Fair Queuing (PFQ) algorithms have been developed to approximate GPS. However, owing to the implementation complexity, it is difficult to support a large number of sessions with diverse service rates while maintaining the GPS properties. The grouping architecture has been proposed to dramatically reduce the implementation complexity. However, the grouping architecture can only support a fixed number of service rates, thus causing the problems of granularity, bandwidth fairness, utilization, and immunity of flows. In this paper, we propose a new implementation approach called dual-rate grouping, which can significantly alleviate the above problems. Compared with the grouping architecture, the proposed approach possesses better performance in terms of approximating per session-based PFQ algorithms without increasing the implementation complexity.

In this letter, we research the method of using face and gesture image sequences to deal with the video-based bimodal emotion recognition problem, in which both Harris plus cuboids spatio-temporal feature (HST) and sparse canonical correlation analysis (SCCA) fusion method are applied to this end. To efficaciously pick up the spatio-temporal features, we adopt the Harris 3D feature detector proposed by Laptev and Lindeberg to find the points from both face and gesture videos, and then apply the cuboids feature descriptor to extract the facial expression and gesture emotion features [1],[2]. To further extract the common emotion features from both facial expression feature set and gesture feature set, the SCCA method is applied and the extracted emotion features are used for the biomodal emotion classification, where the K-nearest neighbor classifier and the SVM classifier are respectively used for this purpose. We test this method on the biomodal face and body gesture (FABO) database and the experimental results demonstrate the better recognition accuracy compared with other methods.

The increasing complexity of embedded applications and the prevalence of multiprocessor system-on-chip (MPSoC) introduce a great challenge for designers on how to achieve performance and programmability simultaneously in embedded systems. Automatic multithreaded code generation methods taking account of performance optimization techniques can be an effective solution. In this paper, we consider the issue of increasing processor utilization and reducing communication cost during multithreaded code generation from Simulink models to improve system performance. We propose a combination of three-layered multithreaded software with Integer Linear Programming (ILP) based design-time mapping and scheduling policies to get optimal performance. The hierarchical software with a thread layer increases processor usage, while the mapping and scheduling policies formulate a group of integer linear programming formulations to minimize communication cost as well as to maximize performance. Experimental results demonstrate the advantages of the proposed techniques on performance improvements.

The outage performance of a multiuser two-way amplify-and-forward (AF) relaying network, where N-th best selection scheme with the consideration to the feedback delay, is investigated. Specifically, the new closed-form expressions for cumulative distribution function (CDF) and outage probability (OP) are presented over time varying Rayleigh-fading channels. Furthermore, simple approximate OP is derived assessing the high signal-to-noise-ratio (SNR), which identifies the diversity behavior. Numerical results show excellent agreement with theoretical results.

In this letter, a new sparse locality preserving projection (SLPP) algorithm is developed and applied to facial expression recognition. In comparison with the original locality preserving projection (LPP) algorithm, the presented SLPP algorithm is able to simultaneously find the intrinsic manifold of facial feature vectors and deal with facial feature selection. This is realized by the use of l1-norm regularization in the LPP objective function, which is directly formulated as a least squares regression pattern. We use two real facial expression databases (JAFFE and Ekman's POFA) to testify the proposed SLPP method and certain experiments show that the proposed SLPP approach respectively gains 77.60% and 82.29% on JAFFE and POFA database.

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.

In this paper, we present a novel regression-based robust locality preserving projections (RRLPP) method to effectively deal with the issue of noise and occlusion in facial expression recognition. Similar to robust principal component analysis (RPCA) and robust regression (RR) approach, the basic idea of the presented RRLPP approach is also to lead in the low-rank term and the sparse term of facial expression image sample matrix to simultaneously overcome the shortcoming of the locality preserving projections (LPP) method and enhance the robustness of facial expression recognition. However, RRLPP is a nonlinear robust subspace method which can effectively describe the local structure of facial expression images. The test results on the Multi-PIE facial expression database indicate that the RRLPP method can effectively eliminate the noise and the occlusion problem of facial expression images, and it also can achieve better or comparative facial expression recognition rate compared to the non-robust and robust subspace methods meantime.

All digital phased arrays generate multiple beams concurrently through the digital beam forming technique, which features digital processing with multiple identical receiving/transmitting channels in RF or microwave frequencies. However, the performance of this process strongly depends on accurately matching the amplitude and phase of the channels, as mismatching is likely to degrade radar performance. In this paper, we present a method to calibrate receiving array by using NCO phase increasing algorithm, which simplifies array system by removing the external far-field calibration signals often needed in array systems. Both analysis and simulation results suggest that the proposed method attains better calibration performance than existing approaches, even with a low SNR input signal. Experiments also varify that the proposed calibration method is effective and achieves a desired radiation pattern. We can further boost calibration accuracy and reduce calibration time by programming NCO phase width and NCO phase resolution.

Surface integrity of 3D medical data is crucial for surgery simulation or virtual diagnoses. However, undesirable holes often exist due to external damage on bodies or accessibility limitation on scanners. To bridge the gap, hole-filling for medical imaging is a popular research topic in recent years [1]-[3]. Considering that a medical image, e.g. CT or MRI, has the natural form of a tensor, we recognize the problem of medical hole-filling as the extension of Principal Component Pursuit (PCP) problem from matrix case to tensor case. Since the new problem in the tensor case is much more difficult than the matrix case, an efficient algorithm for the extension is presented by relaxation technique. The most significant feature of our algorithm is that unlike traditional methods which follow a strictly local approach, our method fixes the hole by the global structure in the specific medical data. Another important difference from the previous algorithm [4] is that our algorithm is able to automatically separate the completed data from the hole in an implicit manner. Our experiments demonstrate that the proposed method can lead to satisfactory results.

In this paper, we analyze the performance of a dual-hop multiuser amplify-and-forward (AF) relay network with the effect of the feedback delay, where the source and each of the K destinations are equipped with Nt and Nr antennas respectively, and the relay is equipped with a single antenna. In the relay network, multi-antenna and multiuser diversities are guaranteed via beamforming and opportunistic scheduling, respectively. To examine the impact of delayed feedback, the new exact analytical expressions for the outage probability (OP) and symbol error rate (SER) are derived in closed-form over Rayleigh fading channel, which are useful for a large number of modulation schemes. In addition, we present the asymptotic expressions for OP and SER in the high signal-to-noise ratio (SNR) regime, from which we gain an insight into the system performance with deriving the diversity order and array gain. Moreover, based on the asymptotic expressions, we determine power allocation among the network nodes such that the OP is minimized. The analytical expressions are validated by Monte-Carlo simulations.

We analyze the outage probability of the multiuser two-way relay network (TWRN) where the N-th best mobile user (MU) out of M MUs and the base station (BS) exchange messages with the aid of an amplify-and-forward relay. In the analysis, we focus on the practical unbalanced Nakagami-m fading between the MUs-relay link and the relay-BS link. We also consider both perfect and outdated channel state information (CSI) between the MUs and the relay. We first derive tight closed-form lower bounds on the outage probability. We then derive compact expressions for the asymptotic outage probability to explicitly characterize the network performance in the high signal-to-noise ratio regime. Based on our asymptotic results, we demonstrate that the diversity order is determined by both Nakagami-m fading parameters, M, and N when perfect CSI is available. When outdated CSI is available, the diversity order is determined by Nakagami-m fading parameters only. In addition, we quantify the contributions of M, N, and the outdated CSI to the outage probability via the array gain.

The contradictions created by the differences in mass P2P data and transfer capability of wireless networks, and mismatch of overlay network topology and physical network topology are the main barriers hindering the implementation of P2P resource sharing in wireless multi-hop networks. This paper investigates the problem of enabling P2P resource sharing in WMNs with two-tier architecture. SpiralChord, the DHT approach implemented through routers in the upper tier, is proposed to address the major problems of wireless resource sharing – how to efficiently find resources currently available and reduce redundant messages as much as possible. SpiralChord uses an ID assignment technique to integrate location awareness with cross-layering. Location awareness aims at alleviating mismatch in physical network topology and overlay network topology, and it is designed to map neighboring routers to close-by IDs in the logical ring. Cross-layering aims at speeding up resource lookup operations in the application layer by exploiting the information that is available at the MAC layer, and it tends to be more effective when physically neighboring routers have faraway IDs in the logical ring. An ID assignment strategy based on spiral curve is proposed to meet the contradictory requirements of location awareness and cross-layering, mapping a peer's neighbors in the overlay network to peers which are its physical neighbors and distributing the remaining physical neighbors as widely as possible in the overlay network. In addition, a mobility management mechanism is proposed to address the adverse effect of the movements of clients in lower tier on resource sharing. A client is assigned a managing router to take the responsibility for the location of the client. Simulations show SpiralChord is more effective in reducing message overhead and increasing lookup performance than Chord, and mobility management for mobile clients performs well at reducing message overhead caused by mobile clients in SpiralChord.

The use of locating arrays is motivated by the use of generating software test suites to locate interaction faults in component-based systems. In this paper, we introduce a new combinatorial configuration, with which a general combinatorial description of $(ar{1},t)$-locating arrays is presented. Based on this characterization, a number of locating arrays by means of SSOA and difference covering arrays with prescribed properties are constructed effectively. As a consequence, upper bounds on the size of locating arrays with small number of factors are then obtained.

In this letter, we propose a supervised bimodal emotion recognition approach based on two important human emotion modalities including facial expression and body gesture. A effectively supervised feature fusion algorithms named supervised multiset canonical correlation analysis (SMCCA) is presented to established the linear connection between three sets of matrices, which contain the feature matrix of two modalities and their concurrent category matrix. The test results in the bimodal emotion recognition of the FABO database show that the SMCCA algorithm can get better or considerable efficiency than unsupervised feature fusion algorithm covering canonical correlation analysis (CCA), sparse canonical correlation analysis (SCCA), multiset canonical correlation analysis (MCCA) and so on.

A novel method for single image super resolution without any training samples is presented in the paper. By sparse representation, the method attempts to recover at each pixel its best possible resolution increase based on the self similarity of the image patches across different scale and rotation transforms. The experiments indicate that the proposed method can produce robust and competitive results.