In the area of multivariate process quality control, it is sometimes important to monitor the ratio of two normal random variables denoted by RZ over time. The concept of control charts has often been harnessed in this field, leading to the application of various types of statistical models, including Shewhart, Exponentially Weighted Moving Average (EWMA), and so forth. However, there is little attention to implementation of machine learning-based control charts. To bridge this gap, a novel machine learning based model incorporating the attention mechanism approach, as an implemented Artificial Intelligence (AI) model, is proposed to monitor the RZ in Phase II applications. The proposed RZ method not only provides quicker Out-of-Control (OC) shift detection than conventional RZ control charts but also does not require the quality controller to have any prior information about the upward or downward shift patterns, which is a major assumption in most of the previous RZ models. We provide extensive performance comparison results to discuss the statistical performance of our proposed method through Monte Carlo simulations. Moreover, a comprehensive real example about surveillance of the cryptocurrency market is provided to illustrate the practical application of our proposed method. Through simulation and back-testing results, it is shown how the proposed method can lead to an automated trading strategy.

Salient object detection is usually used as a preprocessing step to facilitate a variety of subsequent applications which should take little time cost. With the quick development of deep learning recently, profound progresses have been made to achieve a new state-of-the-art performance. However, the learned features of the existing deep learning-based methods are not accurate enough thus leading to unsatisfactory detection in complex scenes, such as low contrast or very similar between salient object and background region and multiple (small) salient objects with diverse characteristics. In addition, some post-processing techniques are usually needed for refinement, which is time consuming. To address these issues, this paper presents an efficient fully convolutional salient object detection network. Specifically, we first introduce a visual attention mechanism to guide feature learning in side output layers. In detail, attention weight is employed in a top-down manner which can bridge high level semantic information to help shallow layers better locate salient objects and also filter out noisy response in the background region. Second, we propose a residual refinement network to fuse the learned multilevel features gradually. Not to simply add or concatenate them step by step as previous works, we introduce a second-order term into element-wise addition to learn stage-wise residual features for refinement. Such a second-order term not only benefits efficient gradient propagation but also increases network nonlinearity. Extensive experiments on seven standard benchmarks demonstrate that the proposed approach achieves consistently superior performance and performs well on small salient object detection in comparison with the very recent state-of-the-arts, especially in the metric of structure-measure.

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel reverse attention block to guide side-output residual learning in a top-down manner. Specifically, the current predicted salient regions are erased from each side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in high resolution and accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art methods, and with advantages in terms of simplicity, efficiency and model size.

Currently, traffic congestions are the most serious issues that most cites are facing. In order to improving the urban traffic orders, as well as to alleviating traffic pressures, this paper presents an urban traffic control system, which is designed based on the real time traffic flow information. The proposed design has combined with traffic control theory, application of single chip computer and ultrasonic technology, design and research of the traffic control system based on traffic. Article control core of the system is the MCS - 51 single chip microcomputer, which achieves real-time monitoring by using ultrasonic sensors for road vehicle. Compared with the traditional control system, the system has the following characteristics: the duration time of traffic signal can be smartly set according to the number of road vehicles; a priority of lane can be assigned according to the actual demand when a vehicle is rarely at night, etc. Therefore, the traffic signal's duration time can be smartly and intelligently adjusted according to the real time road traffic flow information.

In order to achieve more efficient communication performances in vehicular ad hoc network (VANET) for road safety related message propagation, we propose a special nonbinary network coding scheme to improve the system reliability by reducing the outage probability of the communication system. Suppose all users in the network are provided with multiple antennas, which are responsible for message transmitting, relaying and receiving. Moreover, a cooperative phase is applied into the network. We analyze the exact expression of outage probability and give an approximation expression for calculating the outage probability over the Rayleigh fading channel. The simulation results indicate that the approximation expression has good performances on describing the exact expression.

Recently, due to the development of automobile industry, the number of vehicles is increasing. The traditional parking lots are unable to meet the growing demands from so many vehicles. Therefore, the current parking lots present many problems. Such as, can't find parking spaces quickly, difficult to manage too many vehicles and manual toll collection costs much time. To solve those problems above, this paper designs a new parking lot system which is based on ZigBee technology and Support Vector Machine (SVM). By the designed parking system, the parking issues can be solved more efficiently and intelligently.

In this paper, we study the physical-layer security of amplify-and-forward relaying networks under a spectrum-sharing mechanism over independent non-identically distributed Rayleigh fading channels. Relay selection is presented to select the best relay, which can guarantee the security performance by minimizing the received signal-to-noise ratio at the eavesdropper. In order to guarantee the quality-of-service of primary networks, both the maximum tolerable peak interference power at the primary users and maximum allowable transmit power at secondary users are considered. Closed-form lower and upper bounds as well as asymptotic expressions for the intercept outage probability (OP) are derived. From the asymptotic expressions, it can be observed that the diversity order of intercept OP equals to two. Our analysis results are validated by Monte-Carlo simulation.