An Early Warning System for Curved Road Based on OV7670 Image Acquisition and STM32

Nowadays,the number of vehicles in China has increased significantly.The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need.However,the existing early warning devices such as geomagnetic,ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance.In addition,geomagnetic detection will damage the road surface,while ultrasonic and infrared detection will be greatly affected by the environment.Considering the shortcomings of the existing solutions,this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and microcontrollers.This solution combines image acquisition and processing technology,which uses image sensor to perceive traffic condition and image data analysis algorithm to process perceived image,and then utilize LED display screen to issue an early warning.

A Novel Anonymous Authentication Scheme Based on Edge Computing in Internet of Vehicles

The vehicular cloud computing is an emerging technology that changes vehicle communication and underlying trafc management applications.However,cloud computing has disadvantages such as high delay,low privacy and high communication cost,which can not meet the needs of realtime interactive information of Internet of vehicles.Ensuring security and privacy in Internet of Vehicles is also regarded as one of its most important challenges.Therefore,in order to ensure the user information security and improve the real-time of vehicle information interaction,this paper proposes an anonymous authentication scheme based on edge computing.In this scheme,the concept of edge computing is introduced into the Internet of vehicles,which makes full use of the redundant computing power and storage capacity of idle edge equipment.The edge vehicle nodes are determined by simple algorithm of dening distance and resources,and the improved RSA encryption algorithm is used to encrypt the user information.The improved RSA algorithm encrypts the user information by reencrypting the encryption parameters.Compared with the traditional RSA algorithm,it can resist more attacks,so it is used to ensure the security of user information.It can not only protect the privacy of vehicles,but also avoid anonymous abuse.Simulation results show that the proposed scheme has lower computational complexity and communication overhead than the traditional anonymous scheme.

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

Single image super resolution(SISR)is an important research content in the field of computer vision and image processing.With the rapid development of deep neural networks,different image super-resolution models have emerged.Compared to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single image.In addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual performance.In this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics.Then,we review the improved network structures and loss functions of GAN-based perceptual SISR.Subsequently,the advantages and disadvantages of different networks are analyzed by multiple comparative experiments.Finally,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.

Design of Intelligent Mosquito Nets Based on Deep Learning Algorithms

An intelligent mosquito net employing deep learning has been one of the hotspots in the field of Internet of Things as it can reduce significantly the spread of pathogens carried by mosquitoes,and help people live well in mosquito-infested areas.In this study,we propose an intelligent mosquito net that can produce and transmit data through the Internet of Medical Things.In our method,decision-making is controlled by a deep learning model,and the proposed method uses infrared sensors and an array of pressure sensors to collect data.Moreover the ZigBee protocol is used to transmit the pressure map which is formed by pressure sensors with the deep learning perception model,determining automatically the intention of the user to open or close the mosquito net.We used optical flow to extract pressure map features,and they were fed to a 3-dimensional convolutional neural network(3D-CNN)classification model subsequently.We achieved the expected results using a nested cross-validation method to evaluate our model.Deep learning has better adaptability than the traditional methods and also has better anti-interference by the different bodies of users.This research has the potential to be used in intelligent medical protection and large-scale sensor array perception of the environment.

Novel graphene enhancement nanolaser based on hybrid plasmonic waveguides at optical communication wavelength

Surface plasmon polariton （SPP） nanolaser, which can achieve an all-optical circuit, is a major research topic in the field of micro light source. In this study, we examine a novel SPP graphene nanolaser in an optoelectronic integration field. The proposed nanolaser consists of metallic silver, two-dimensional （2D） graphene and high refractive index semiconductor of indium gallium arsenide phosphorus. Compared with other metals, Ag can reduce the threshold and propagation loss. The SPP field, excited by coupling Ag and InGaAsE can be enhanced by the 2D material of graphene. In the proposed nanolaser, the maximum value of propagation loss is approximately 0.055 dB/~tm, and the normalized mode area is con- stantly less than 0.05, and the best threshold can achieve 3380 cm l simultaneously. Meanwhile, the proposed nanolaser can be fabricated by conventional materials and work in optical communication （1550 nm）, which can be easily achieved with current nanotechnology. It is also an important method that will be used to overcome the challenges of high speed, miniaturization, and integration in optoelectronic integrated technology.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods,the movement characteristics of trajectory data cannot be well expressed,which in turn affects the accuracy of the prediction results.First,a new trajectory data expression method by associating the movement behavior information is given.The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region.The movement behavior features based on pre-association may not always be the best for the prediction model.Therefore,through association analysis and importance analysis,the final association feature is selected from the pre-association features.The trajectory data is input into the LSTM networks after associated features and genetic algorithm(GA)is used to optimize the combination of the length of time window and the number of hidden layer nodes.The experimental results show that compared with the original trajectory data,the trajectory data associated with the movement behavior information helps to improve the accuracy of location prediction.

Sensitive Information Protection Model Based on Bayesian Game

A game measurement model considering the attacker’s knowledge background is proposed based on the Bayesian game theory aiming at striking a balance between the protection of sensitive information and the quality of service.We quantified the sensitive level of information according to the user’s personalized sensitive information protection needs.Based on the probability distribution of sensitive level and attacker’s knowledge background type,the strategy combination of service provider and attacker was analyzed,and a game-based sensitive information protection model was constructed.Through the combination of strategies under Bayesian equilibrium,the information entropy was used to measure the leakage of sensitive information.Furthermore,in the paper the influence of the sensitive level of information and the attacker’s knowledge background on the strategy of both sides of the game was considered comprehensively.Further on,the leakage of the user’s sensitive information was measured.Finally,the feasibility of the model was described by experiments.

Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network(VANET)is a self-organizing wireless sensor network model,which is extensively used in the existing traffic.Due to the openness of wireless channel and the sensitivity of traffic information,data transmission process in VANET is vulnerable to leakage and attack.Authentication of vehicle identitywhile protecting vehicle privacy information is an advantageous way to improve the security of VANET.We propose a scheme based on fair blind signature and secret sharing algorithm.In this paper,we prove that the scheme is feasible through security analysis.

An Overview of the Miniaturization and Endurance for Wearable Devices

The miniaturization and endurance of wearable devices have been the research direction for a long time.With the development of nanotechnology and the emergence of microelectronics products,people have explored many new strategies that may be applied to wearable devices.In this overview,we will summarize the recent research of wearable devices in these two directions,and summarize some available related technologies.

扇子酒店 上海浦东文华东方酒店

文华东方酒店的标志是一把扇子（The Fan）,一把展开的折扇.它标志着文华东方酒店集团在东方的出身,以及致力提供奢华、雅致和舒适酒店服务的承诺.因此,文华东方酒店也被称为＂扇子酒店＂.这是属于文华东方酒店集团品牌文化的一部分,也因此,这把扇子扮演的角色极为重要.

与时偕行

偶尔闲时翻书,读到《周易·乾·文言》里有句话,终日乾乾,与时偕行。古往今来,时势变迁,在时代的洪流中,如何逆流而上,与时偕行?身处不断发展的设计行业,作为一家民营设计企业的领头人,如何带领企业与时俱进,保持向前向上的发展态势,也是我这些年一直在不断关注并思考的问题。

混合供电发射机的能量调度和自适应功率算法

针对混合供电的点到点无线通信链路,讨论了能量收集过程、数据到达过程以及衰落信道统计分布均未知情况下发射机的能量调度和自适应发送功率问题,目的是在保证通信系统一定性能的要求下最小化传统电网的能耗,即有效利用可再生能源的能量.基于Lyapunov优化提出一种低复杂度动态算法,理论证明了该算法可使优化目标无限趋于最优,同时保证最大数据时延不超过用户要求.仿真结果表明,提出的算法在性能和数据时延上都优于其他2种贪婪算法.