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Traffic signal optimization is a significant means for smoothing urban traffic flow. However, the operation of traffic signals is currently seriously constrained by the data available from traditional point detectors. In recent years, an emerging technology, connected vehicle (CV), which can percept the overall traffic environment in real time, has drawn researchers’ attention. With the new data source, traffic controllers should be able to make smarter decisions. A lot of work has been done to develop a new traffic signal control pattern under connected-vehicle environment. This paper provides a comprehensive review of these studies, aiming at sketching out the state of the arts in this research field. Several basic control problems, communication, control input, and objectives, are briefly introduced. The commonly used optimization models for this problem are summarized into three types: rule-based models, mathematical programming-based models, and artificial intelligence-based models. Then some major technical issues are discussed in detail. Finally, we raise the limitation of the existing studies and give our perspectives of the future research directions.

Ultra wide band (UWB) has emerged as a promising fuction in the field of indoor positioning. The geometric position relationship between UWB base station and the measured target affects positioning accuracy directly. Based on the basic principles of UWB positioning system, this paper takes the horizontal dilution of precision (HDOP) as the evaluation index of positioning accuracy, and proposes the layout optimization method of UWB base station under both ideal condition and actual conditions through simulation and practical experiment verification. The three-stage design method for layout optimization of UWB base station in underground parking lots is developed, which provides a scientific workflow for the practical application.

The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. The mixing matrix estimation is a major step in the UBSS, this paper focuses on boosting the accuracy level of the estimated mixing matrix in the underdetermined case. Since the traditional clustering algorithms may not capture the signal characteristics well and secure a satisfactory estimation of the mixing matrix, an effective two-stage clustering algorithm is proposed to estimate the mixing matrix through a combination of hierarchical clustering and K-means. More specifically, first, the sum of frequency points energy in the time-frequency (TF) domain is calculated to estimate the number of source signals before clustering, and the initial clustering centers are obtained with a hierarchical clustering algorithm. Second, after eliminating outliers deviating from the initial clustering centers with the cosine distance, the new clustering centers are obtained by recalculating the mean value of each sub-cluster. Finally, the new clustering centers are set as the initial clustering centers of the K-means algorithm to estimate the mixing matrix. Extensive simulations and experiments show that the proposed method can effectively separate the source signals and ensure an estimate of the mixing matrix that is substantially more accurate than the K-means algorithm alone.

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

As one of the most widely used algorithms in recommendation field, collaborative filtering (CF) predicts the unknown rating of items based on similar neighbors. Although many CF-based recommendation methods have been proposed, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms to find similar neighbors. Secondly, there are many redundant similar items in the recommendation list generated by traditional CF algorithms, which cannot meet the user wide interest. Therefore, we propose a diversified recommendation method combining topic model and random walk. A weighted random walk model is presented to find all direct and indirect similar neighbors on the sparse data, improving the accuracy of rating prediction. By taking both users’ behavior data and items’ lags into account, we give a diversity measurement method based on the topic distribution of items discovered by Linked-LDA model. Furthermore, a diversified ranking algorithm is developed to balance the accuracy and diversity of recommendation results. We compare our method with six other recommendation methods on a real-world dataset. Experimental results show that our method outperforms the other methods and achieves the best personalized recommendation effect.

Quantum key distribution technology refers to a method to share keys between communication parties by transmitting quantum states in public channels. Although unconditional security is the main advantage of QKD, its application prospect has been controversial in practical implementation due to the potential security risks caused by device imperfections. Fortunately, the measurement device independent quantum key distribution protocol removes the vulnerability of all measurement devices and greatly improves the practical security of the quantum key distribution system. However, the security key rate of this protocol is still lower than that of other quantum key distribution protocols. At present, using high-dimensional coding to improve the performance of the quantum key distribution protocol has been proved in theory and experiment, and recently, it has been proposed to use high-dimensional coding to improve the performance of measurement device independent quantum key distribution protocol, but because these protocols have higher requirements for the laboratory equipment performance, that the high-dimensional encoding is applied to the aforementioned protocol still has many difficulties in practical application. In this paper, we propose a hybrid coding based on the polarization and two-degree phase of freedom measurement device independent quantum key distribution protocol. In the first place in an ideal case, we introduce in detail the protocol decoding rules, then introduce 4intensity decoy-state method to solve the problem of actual light source multiphoton, in addition we also consider the statistical fluctuation effect under the condition of limited code length, channel loss, actual dark count of single photon detector and detection efficiency problem. Finally, the optimal security code rate and its corresponding optimal parameters are obtained by full parameter optimization method, And the numerical simulation results show that the security key rate of this protocol is increased by 50% by considering the practical implementation. We point out that compared with other measurement device independent quantum key distribution protocol, the proposed agreement requires local users only to have a phase encoding device and a polarization coding device, and 4 single photon detectors for detecting side. The proposed device can use the existing experimental condition, beyond that, compared with the time encoding based high dimensional measurement device independent quantum key distribution protocol, the proposed protocol possesses the advantage that the rate of system security key can be increased without increasing the repetition frequency of users. It is proved that this protocol has great application value in the future field of quantum communication, especially, in the field of quantum network communication.

In order to deal with the QoS(Quality of Services)value fluctuations of Web services in dynamic environment, a service selection method based on dynamic QoS is proposed. Firstly, the method establishes an interval QoS model to represent the dynamic changes of QoS values. Then it uses the interval similarities to measure the proximity of the QoS values provided by candidate services to the QoS requirement values provided by consumers. Based on the concept of similarity, the objective weight of QoS attribute for each basic service is calculated using TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)method for MADM(Multiple Attribute Decision Making)problems. After calculating the comprehensive weight by combining with the consumers' subjective preference, recommendations are used to sort the candidate services. The experimental results show that this service selection method not only considers the consumers' subjective preference, but also overcomes the impact of fluctuations in QoS values of Web services, and enhances the correctness of service selection.

In cloud computing, the resources of service are widely distributed, complex and fickle. The trust relationship between service entities is hard to establish and maintain with high uncertainty. The characteristics of randomness and fuzziness of services could not be responded by traditional trust evaluation approach comprehensively, a service trust evaluation method based on weighted multi-attribute cloud was proposed. Time decay factor was introduced to reflect the timeliness of trust,and multi-attribute trust cloud was used to refine the evaluation granularity. In order to prevent conspiracy to defraud and malicious attacks, the reliability and weight of recommender were confirmed by similarity of evaluation. The trust rating could be confirmed by cloud similarity calculation, which could provide security decisions for the user's services selection. Simulation results show that this method can improve the success rate of services interaction obviously, and be applied to service trust evaluation under cloud computing environment.