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Network connectivity is a key index for designing of a vehicular ad hoc network(VANET). Road messages are propagated by V2X(includes V2V: vehicles to vehicles and V2I:vehicle to infrastructure) communications. Therefore, the access probability and connectivity probability play important roles for message dissemination. In this paper, we take the minimum safety distance between vehicles into consideration for designing of VANET. More over, we analyze the effects of the different distances between adjacent BSs, coverage radius of a vehicle and the vehicle density, respectively. The performance results get a conclusion that the safety distance has a certain effect in a VANET model.

The identification algorithm for a multiple-input and multiple-output system employing the Spatial Multiplexing (SM) and Alamouti (AL) space-time block codes (STBC) is investigated under the assumption of unknown parameters of the coding scheme, the received signal and noise variance. The order statistic of the cross-correlation spectrum between different received antennas is utilized as a feature to discriminate the coding type, and the tests threshold is selected based on the constant false alarm rate criterion. We proposed two methods to compute the threshold, one is based on the exact cumulative distribution function (CDF) of the maximum of the cross-correlation spectrum, and the other relies on the extreme value distribution. Simulations show that the proposed algorithms are effective and the performances of them are both superior to the convention cyclostationarity-based algorithm.

Based on the comprehensive analysis and comparison in the characteristic of acoustics between the sample speaker and the case sample speaker, we can get the identification of identity. The identification can be used in many cases like providing clues, directions and ranges to investigate and solve a case. In addition, it can also provide independent evidence for litigation. As one of the main features of speaker, fundamental frequency curve plays an important role in the identification of identity. In this passage, we create an improved pitch period extraction algorithm based on the traditional way of detection method of pitch period. This experiment has proved that the method in this passage works more accurately than the traditional way in the noise circumstance and it can provide more convincing evidence for judicial expertise.

α-stable distribution is a sort of non-Gaussian random distribution and is widely applied to radar, sonar, atmosphere and biomedicine process field and so on. The salient feature of these random signals or noises are that the spike pulse is more significant than conventional Gaussian signal or noise. When dealing with non-Gaussian noises, the time delay algorithm using fractional lower order covariance (FLOC) can achieve better results, but it requests usually the previous estimation of characteristic exponent. In this paper, an adaptive time delay estimation algorithm using asinh transform is proposed. Compare with FLOC algorithm, the new aproach doesn't need the previous estimation of characteristic exponent, through changing the signal into a Gaussian with inverse hyperbolic sine transform under α-stable distributed noise condition, the new method can reliably converge and has got a good estimated precision.

In recent years, traffic congestion, traffic accidents, environmental pollution and other issues are particularly prominent with the rapid development of urbanization, motorization. It is extremely important to send the road information to the user promptly so that the user can fully understand the road situation and make safer and smarter decisions on the road. Internet of Vehicle (IOV) as a typical application of the technology of Internet of Things (IOT) in the field of transportation system promotes the transportation system to move closer to the intelligent. This paper first introduces the basic concepts of IOV and the hierarchical architecture based on perception layer, network layer and application layer. Secondly, it points out several key technologies in the development of IOV. Finally, the related applications of the technology of IOV are briefly described.

To address the problem of low precision and pour robustness, this paper will introduce deep learning into visual tracking field and propose an object tracking algorithm based on depth sparse learning. The algorithm first constructs a deep network based on the auto-encoder and then adding the sparsity constraint in the deep network to sparse connection matrix between the hidden layer and the output layer. As a result, the algorithm optimizes the parameters of the deep network and improve its efficiency. It means that more essential features of the target will be extracted with this network. In the prediction of the target, the algorithm introduces the difference between target and background into the particle filter and design a scoring device based on support vector machine, so that particle performance is enhanced and the risk of drift in the process of tracking target is reduced. Experiments on different video sequences have been carried out for many times. According to the results of experiments, we can come to a conclusion that our algorithm has higher accuracy and better robustness, especially under the circumstance of illumination change, similar background and occlusion.

Semantic image segmentation plays a central role in image understanding and could be applied in various applications. With the development of deep learning techniques, great progress has been made in recent years. However, it is still very challenging to produce accurate segmentation results especially near object boundaries. Furthermore, all the methods with top performances are contributed by pixel-level annotations which needs expensive human effort and tremendous labeling time. In this paper, we explore a simple semantic segmentation approach using region-based object detector which only needs bounding box annotations. The main idea is using object detector to classify region proposals and then applying saliency detection method to segment such classified proposals. Experimental results on PASCAL VOC 2012 validation dataset show its comparable performance with fully supervised methods by masks.

In this paper, an algorithm for recognizing BPSK/QPSK signal is proposed. Extreme value theory is introduced, and an exceedance sequence is built from the modified spectrum obtained by the squaring operation of the observed signal, and the problem of recognizing the BPSK/QPSK signal is transformed to the problem of whether the distribution of the exceedance sequence approximately follows its standard generalized Pareto distribution or not. Finally, the Kolmogorov — Smirnov test is applied to perform the goodness-of-fit test of the generalized Pareto distribution of the exceedance sequence to complete the task of BPSK/QPSK signal recognition. Computer simulation results show that the recognition performance is good when the signal-to-noise ratio is moderate and does not require prior information such as noise variance.

This paper introduces the realization method of Beidou anti-jamming antenna hardware and software, theory analysis and MATLAB simulation of antenna array and adaptive algorithm. The space time joint anti-jamming algorithm based on the linear constraint minimum variance criterion is adopted as the core of the anti-jamming processor. It is designed and implemented successfully. the anti-jamming ability of the antenna is tested and verified. The antenna has the capability of resisting single jam 75 dB and resisting three jam 60 dB. Finally, the difference between theoretical simulation and practical implementation is analyzed, and future work is prospected.

A covariance matrix denoising based Direction-of-arrive (DOA) estimation approach for spherical array is developed in this paper. Firstly, based on the spherical phase mode transformation, a spherical harmonic (SH) domain covariance matrix model of the spherical array is presented as multiple measurement vectors, where the atomic norm minimization approach is extended to SH domain and is applied for covariance matrix denoising. Then the spherical ESPRIT method is used to DOA estimation. Unlike the conventional approaches, the proposed method not only requires no prior knowledge of the noise statistic property, but also has the capability of dealing with the coherent signals. Simulations show the out performance of out method compared with the conventional ones.

In order to solve the problem of compressive tracking algorithm which cannot adapt to the scale change in object tracking, a scale adaptive compressed object tracking algorithm is proposed. Firstly, we extract the low-dimensional gray and texture features of the target and its surrounding region by sparse measurement matrix which reduces the complexity of compute. Then the tracking task is formulated as a binary classification via support vector machine (SVM) classifier with online update in the compressed domain. Meanwhile, we utilize the classifier to obtain the target's position in new frame. In addition, we build the hamming distance between hash values of current target and original one to match the template to achieve adaptive template size. Numerous experimental results show the proposed algorithm can keep tracking effectively when the tracked object is under the situation of scale variation and illumination change.

Salient object detection is a significant part of a great deal of vision tasks and a large number of saliency detection measures have been presented of late, and omong them are complementary reciprocally. A majority of the works in computer vision concentrates on the next two particular assignments of saliency: eye fixation prediction and salient object segmentation. In this paper, we divides saliency detection into two sub tasks: salient object localization and segmentation, which can achieve more precise results of significant object detection, and also can obtain more accurate segmentation results directly. The results are validated on publicly available ECSSD and PASCAL datasets, which are superior to current state-of-the-art methods.