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This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

With the rapid growth of cloud computing services, there is an increasing need to expand applications across data centers. In these paradigms, common information exchange between two data centers can be assisted via one or more substrate nodes, which correspond to bidirectional relaying communication. In this paper, we investigate the performance of time division broadcast TDBC protocol in bidirectional cloud networks in the presence of channel estimation errors CEEs. The tight asymptotic expressions for individual and system outage probabilities are first presented in closed-form. It is shown that CEE causes outage probability to remain fixed even if the channel is noiseless. Then, the symbol error rate performance for TDBC protocol with M-ary Phase Shift Keying and M-ary quadrature amplitude modulation signals is analyzed. Simulation results validate the accuracy of our analytical results. Furthermore, comparison of signal-to-noise ratio gap ratio shows that TDBC protocol is less sensitive to the effect of CEE than analog network coding protocol. Copyright © 2014 John Wiley & Sons, Ltd.

This paper proposes a novel technique to detect the main moving trajectory of indoor pedestrians. Based on Long Short- Term Memory(LSTM) Network, this deep learning network is capable of learning the trajectory of human beings using indoor Wi-Fi positioning data. The data is collected by Wi-Fi detectors densely installed in a public building in the urban area, which can ensure the detection of any portable devices as long as the Wi-Fi module is turned on. Then the model works in the form of sequence modeling to learn the trajectory of the main stream extracted from massive pedestrian positioning data. In compare with methods like Recurrent Neural Network (RNN) and Gated Recurrent Unit(GRU), there is an obvious performance improvement of this method.

This paper presents a novel method for 3D face recognition under expression and occlusion variations. It is based on the combination of the matching results from matching multiple partial regions on the 3D face. Spin image features are used for comparison between faces. Meanwhile, an efficient approach is proposed to detect nose tip and the nose tip determines the location of each candidate region. To fuse the contribution of each region, score-based and rank-based fusion scheme are performed and compared. The proposed method is evaluated on the Bosphorus database and achieves good results. It obtains 96.68% recognition rate in the presence of expression and occlusion variations.

One of the major challenges in orthogonal frequency division multiplexing system is its high peak-to-average-power ratio (PAPR). Among the existing PAPR reduction technologies, clipping is the most often used one due to its simplicity of implementation. But it induces signal distortion. In this paper, we propose a new PAPR reduction method which introduces compressive sensing theory to help the clipping and signal recovery processes. Our method has superior symbol-error-rate (SER) performance compared with traditional clipping, and at the same time has better PAPR reduction performance compared with traditional tone reservation based algorithms. What is more, different from the existing high-complexity compressive sensing based scheme which tries to solve an optimization problem, the proposed algorithm uses orthogonal matching pursuit scheme to recover the distorted signals, thereby it significantly reduces the computational complexity with the same PAPR reduction efficiency. Simulation results show that our proposed scheme can achieve dramatic PAPR reduction with only about $$10\,\%$$ 10 % of the existing method, while still keeps good SER performance.

In this study, the authors address the problem of tracking a single target in a calibrated multi-camera surveillance system with information on its location in the first frame of each view. Recently, tracking with online multiple instance learning (OMIL) has been shown to give promising tracking results. However, it may fail in a real surveillance system because of problems arising from target orientation, scale or illumination changes. In this study, the authors show that fusing target information from multiple views can avoid these problems and lead to a more robust tracker. At each camera node, an efficient OMIL algorithm is used to model target appearance. To update the OMIL-based classifier in one view, a co-training strategy is applied to generate a representative set of training bags from all views. Bags extracted from each view hold a unique weight depending on similarity of target appearance between the current view and the view which contains the classifier that needs to be updated. In addition, target motion on a camera's image plane is modelled by a modified particle filter guided by the corresponding object two-dimensional (2D) location and fused 3D location. Experimental results demonstrate that the proposed algorithm is robust for human tracking in challenging scenes.

This paper proposes an explicit adaptive algorithm to solve the problem of heterogeneous dynamic synchronization of multi-agent uncertain systems. Different from the existing works, the proposed algorithm depends on less control parameters and achieves zero synchronization error between the agents of heterogeneous uncertain dynamic networks and virtual target. By introducing appropriate assumptions, a class of explicit adaptive synchronization protocols is designed based on a combination of the virtual target's states, distributed feedback gain, and the limited neighboring agents' information. By using Lyapunov function approach, it is proved that the explicit adaptive synchronization problem of closed-loop multi-agent uncertain systems with heterogeneous dynamic networks can be solved, if the control parameters of the explicit adaptive synchronization algorithm are suitably selected. Finally, an example is given to demonstrate the proposed theory.

In cognitive radio (CR) networks, unlicensed secondary users need to conduct spectrum sensing to gain access to a licensed spectrum band. And cooperation among CR users will solve the problems caused by multipath fading and shadowing. In this paper, we propose a multi-threshold method at local nodes to cope with noises of great uncertainty. Functions of distance between bodies of evidence are used at fusion centre to make synthetic judgment. To guarantee security which is an essential component for basic network functions, we will take selfish nodes into account which try to occupy channels exclusively. The proposed technique has shown better performance than conventional algorithms without increase the system overhead.

In this paper, the energy efficiency (EE) of distributed antenna system (DAS) with multiple receive antennas is investigated over composite fading channel that takes the path loss, shadow fading and Rayleigh fading into account. Our aim is to maximize EE which is defined as the ratio of the transmission rate to the total consumed power under the constraints of maximum transmit power of each remote antenna. According to the definition of EE and using the upper bound of average EE, the optimized objective function is provided. Based on this, utilizing Karush-Kuhn-Tucker (KKT) conditions and mathematical derivation, a suboptimal energy efficient power allocation (PA) scheme is developed, and closed-form expression of PA coefficients is achieved. The developed scheme can obtain the EE close to the existing optimal scheme due to better approximation, especially for larger receive antenna number. Moreover, it has lower calculation complexity than the existing optimal scheme because only the statistic channel information is needed. Simulation results verify the effectiveness of the developed scheme.

Mobile localization is a significant issue for wireless sensor networks (WSNs). However, it is a problem for the indoor localization using received signal strength (RSS) measurements that the signal is contaminated by the anisotropy fading and interference due to walls and furniture. Standard schemes such as Kalman filter are inadequate as the random transition of line-of-sight (LOS)/non-line-of-sight (NLOS) conditions occurs frequently. This paper proposes an indoor mobile localization scheme with RSS measurements in a mixed LOS and NLOS environment. First, a new efficient composite measurement model is induced and validated, which approximates the complex effects of LOS and NLOS channels. Second, a greedy anchor sensor selection strategy is adopted to break through the constraints of hardware consistency and the multipath interference. Third, for the Markov transition between LOS and NLOS conditions, an effective unscented Kalman filter (UKF) based interactive multiple model (IMM) is proposed to estimate not only the posterior model probabilities but also a weighted-sum position estimation with the aid of likelihood function. To evaluate the proposed algorithm, a complete hardware and software platform for mobile localization has been constructed. The numerical study, relying on the actual experiments, illustrates that the proposed UKF based IMM achieves a substantial gain in precision and robustness, compared with other works.

One of the critical middleware services for sensor networks is the time synchronization, which provides supports to different applications. Synchronization protocols used for Internet and LANs are not appropriate in the sensor networks for the high-density and limited energy resource. This paper describes two-hop time synchronization (TTS) that aims at reducing the synchronization overhead and providing more accurate network-wide synchronization. The synchronization message exchanges are minimized by making full use of sensors’ broadcast domain and enlarging the common node synchronization range in multi-hop scenarios. By halving synchronization hops, the TTS achieves high multi-hop synchronization precision. The proposed protocol contains single-hop synchronization model, multi-hop synchronization algorithm, and a power control scheme. We prove that the extension of single-hop TTS to network-wide synchronization is NP-complete. The complexity and convergence time of multi-hop TTS are analyzed in detail. We simulate TTS on MATLAB and show that it requires minimal overhead and convergence time compared with other protocols. We also implement TTS on common sensors and its multi-hop synchronization error is less than that of receiver-receiver synchronization (R-RS).

This paper discusses a node selection problem for bearings-only tracking in wireless sensor networks (WSNs). Saving energy and prolonging the lifetime of the network are the research focuses due to the severely constrained resource of WSNs. An energy-efficient network management strategy is necessary to achieve good tracking performance at low cost. In this paper, an energy-efficient node selection algorithm for bearings-only sensors in decentralized sensor networks is proposed. The residual energy of a node is incorporated into the objective function of node selection. A new criterion of node selection is also made to coordinate with the objective function. Compared with the other common methods, the proposed method can reduce the cost of the entire network, balance nodes energy expenditure, and extend the lifetime of network. Simulation results prove the effectiveness of the proposed method and show good performance in tracking accuracy and energy consumption.

The blasting spot monitoring is very important to reduce the occurrence of emergencies and to protect the workers' safety in practical applications. In this paper, we design an early warning system based on WSN (Wireless Sensor Network) which can be used in explosion operations. This system consists of two parts: the hardware part mainly uses several sensors to capture data we need and transmits them to the monitor center through wireless module; the software part mainly receives and processes the captured data, then analyzes the parameters to judge whether the environment parameters are abnormal, if yes, it will alarm in advance to protect the workers. Experiment results show that this system can alarm correctly when the environment parameters are out of limit, which improves the system's reliability and gets some social economic benefits in the meantime. This system does have a grand application prospect.

In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions.

In this paper, we propose a multiple CFOs(Carrier Frequency Offset) estimation and mitigation algorithm for the destination node in a cooperative OFDM(Orthogonal Frequency Division Multiplex) system. In a cooperative OFDM system, every relay node has a separate local oscillator, so that each relay-to-destination link has individual CFO parameter, so that the traditional estimation and mitigation algorithms are no longer applicable. In the proposed algorithm, based on the specially designed training sequences, the destination node can decouple the signal from different relay nodes by correlation and simplify the MD(Multi-Dimensional) nonlinear problem to an ID linear problem. Then, an mitigation matrix can be constructed, and the ICI(Inter-Carrier-Interference) caused by multiple CFOs can be perfectly compensated based on the over sampled OFDM symbol vectors. Theoretical analysis and simulation results prove that the proposed algorithm is effective in estimating the multiple CFOs and mitigating the performance degradation.

Orthogonal frequency division multiplexing- (OFDM-) based amplify-and-forward (AF) cooperative communication is an effective way for single-antenna systems to exploit the spatial diversity gains in frequency-selective fading channels, but the receiver usually requires the knowledge of the channel state information to recover the transmitted signals. In this paper, a training-sequences-aided linear minimum mean square error (LMMSE) channel estimation method is proposed for OFDM-based cooperative diversity systems with multiple AF relays over frequency-selective fading channels. The mean square error (MSE) bound on the proposed method is derived and the optimal training scheme with respect to this bound is also given. By exploiting the optimal training scheme, an optimal low-rank LMMSE channel estimator is introduced to reduce the computational complexity of the proposed method via singular value decomposition. Furthermore, the Chu sequence is employed as the training sequence to implement the optimal training scheme with easy realization at the source terminal and reduced computational complexity at the relay terminals. The performance of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme are verified through simulation results.

This paper presents a simple single-carrier (SC) distributed space-time block coded transmission scheme for asynchronous cooperative communications over frequency-selective channels. The cyclic prefix (CP) at the source node and relay nodes is used to combat inter-symbol interference from multi-path fading and the timing errors from the relay nodes. In this scheme, the relay nodes only implement very simple operations on their received signals to construct distributed space-time block code, and decoding, DFT and IDFT operations are only needed at the destination node. The low complexity SC zero-forcing frequency domain equalization (ZF-FDE) is applied at the destination node to recover the transmitted signals. The analysis and simulation results demonstrate that this simple SC scheme can achieve order-two spatial diversity gain for amplify-and-forward based cooperative communication over frequency-selective channels in the presence of asynchronization between relay nodes.

In this paper, we will investigate the energy efficient power loading problem in multiple-input multiple-output singular value decomposition MIMO-SVD architecture. Most existing power loading schemes are developed on the assumption that a scheduler possesses perfect channel state information CSI. However, in practice, perfect CSI is not always available. In this paper, we propose an energy-efficient power loading scheme for MIMO system under imperfect CSI, taking into account the effects of channel estimation error CEE. Specifically, we propose two algorithms to maximise the system energy efficiency. One is a global method which can achieve the global optimal solution with high computational complexity; the other is a suboptimal method which can obtain the approximate optimal solution with lower computational complexity. Simulation results validate the effectiveness and robustness of our proposed power loading scheme.

In this Letter, classical molecular dynamics is used to deal with optimization problems occurring in intensity modulated radiation therapy. By introducing the concepts of virtual atom and virtual cluster, the optimization process in this kind of therapy can be considered as an analogy to finding the equilibrium configuration of a cluster. This viewpoint gives great insight into the optimization problems. To show how the idea works, a dose-based objective function is adopted to obtain the optimized intensity profiles. The results and high computational efficiency show that molecular dynamics is applicable clinically for this therapy. The idea presented here also could be inspiring to other fields where optimization problems exist.

In this paper, we propose a novel cooperative transmission strategy with broadcasting and cellular communication in a hybrid network system. With the help of cellular base station (CBS), two cooperative schemes are analysed to improve hybrid network performance, which does not affect the original broadcasting signal. We investigate two cooperative schemes based on the different channel quality information (CQI) in this paper. For users whose CQI is less than the defined low threshold, e.g., the users near the edge of the broadcast cell, we deploy cooperative diversity transmission. Meanwhile, cooperative multiplexing transmission is exploited for users whose CQI is greater than the defined high threshold, such as those near the broadcasting base station (BBS). Furthermore, we propose a new quasi-orthogonal coding scheme with nested structure applying to the cooperative diversity scheme, which can be easily employed in two or three CBS cooperative scenarios. Our simulation results validate the effectivenes...