Advances in Smart Vehicular Technology, Transportation, Communication and Applications

There are many kinds of image processing application in the industry, to improve better performance of producing, Automatic Guide vehicles (AGV) solution have been driven in service, for instance, the expensive solution such as light detection and ranging (LIDAR), magnetic tape guided and RFID are used. In the industry which not only search for better delivery performance but also search for economic efficiency. Here we present an economic efficiency solution, Easy i-Move for AGVs guiding system to fulfill customers’ necessity. In this study, we focus on color tape image recognition technology based on HSI color model. The implementation of the system architecture and the technology of image recognition in the proposed system are described and validated in this paper.

As professional drivers typically were driving more time on the road than general drivers, the relationship between age and multi-dimensional driving abilities of bus drivers should be of concern to transportation officials tasked with improving workplace safety. This study examined the multi-dimensional abilities of bus drivers through their responses to a self-assessment questionnaire and on tests of hand-eye coordination, balance ability and hand grip strength. Among sixteen participants recruited from an urban bus company, gender and age significantly correlated with self-rating evaluation, hand-eye coordination tests and grip strength capabilities. Recommended of this study were conducted the self-rating evaluation, hand-eye coordination, balance ability and hand grip strength in the process of licensing exams test to assess driving qualification, renew driving license particularly. Bus drivers should also regularly take these ability tests to assess their driving quality and ability. In this way, an employer can accurately assess drivers’ multi-dimensional abilities and verify whether they meet the demands of their jobs.

At present, the research of road network change is hot in the study of the new road, the road one-way limit line and the construction caused by the reduction of road lanes, etc. Based on the change rule of road network intersection structure and steering rules and the improved map matching algorithm, this paper proposes a trajectory feature analysis method based on vehicle steering angle. At the same time, the mathematical model of geospatial data is constructed. Based on the existing network topology information, the massive traffic trajectory data are mined and analyzed, and the detection and recognition of the change of the intersection Lane rules are realized.

Travel time plays an important role in many ITS application such as traffic control and trip guidance. However the travel time information is acquired after the real driving. This makes travel time prediction an important way to estimate the real-travel time before actual traveling. In this paper, we focus on predicting the travel time of a road segment using deep learning methods. In our work, the historical travel time information collected by Fuzhou taxicab is extracted. Different recurrent networks architectures are applied. Experimental result shows that the deep learning models considering the temporal relation work well on travel time prediction.

The development of floating car technology provides a new method for studying driving behavior motivation. Massive vehicle trajectory data often contains rich potential semantic information such as traffic patterns and driving behavior habits. To overcome the nonstationarity of the spatial distribution of the original trajectory data, this paper designs a semantic mining method of traffic trajectory data to construct complex behavioral modeling of traffic trajectory data based on LDA latent semantic topic model, so that to depict the driving behavior interest patterns and explore the cognitive mechanism of driving behavior. Research results show that the driving motivation model constructed can estimate the probability of the potential driving motivation topic in each spatial grid, and then the driving trajectory will be transformed from random probability events into driving intention with intrinsic certainty.

In order to detect the behavior of illegal vehicle jumping and provide a simple and reliable basis for it, this paper presents an algorithm based on Kalman filter for measuring the distance between two cars. This paper mainly studies the ranging algorithm based on the constant size of the license plate area. Meanwhile, by introducing the Kalman algorithm to predict the position of the license plate in the next frame of the picture and to improve efficiency. In order to verify the reliability and accuracy of the algorithm, this paper has carried out static and dynamic experiments. According to the experimental results, we can find that the algorithm can meet the requirements of ranging and have good accuracy.

Driving recorder has become one the necessary equipment for the current automotive. In Taiwan, there are various brands driving recorder with different capabilities, and almost all of the automotives are installed at least one set of driving recorder. The main function of driving recorder is to record the road video in case of clarify for the responsibility of an accident or unexpected situations. Therefore, the design of driving recorder will focus on the higher recorded video quality as well as higher compression ratio in order to take up less storage space. This paper mainly discusses the different video codec technologies and evaluates their performance on the driving recorder applications. The video codec technologies mentioned in this paper includes the early MPEG-4, currently H.264, and the newest H.265. This paper simulates these three different video codecs, i.e., MPEG-4, H.264, and H.265 to evaluate their coding performances on 6 road videos captured from different vehicle speeds. Then this paper will find the appropriate video codec for the specific vehicle speed as well as road conditions. The experimental results could provide a well reference for the selection of driving record video codec.

Research background and significance of railway train working diagram plan system is introduced, and system research process including six stages is proposed, and point out that at present, the train working diagram planning system research is in the fifth stage. Then, the train working diagram system 4.0 developed at present is introduced, and effect of the system are proposed. Furthermore, system key technology and main innovation are analyzed, including the unified algorithm applying for both single lines and double lines, the general data model and computers-supported cooperative work method for train working diagram plan system. Then, based on the constantly changing railway network construction and transportation market environment, the challenges of high-speed railway transportation organization and freight transportation organization based on limit delivery time are analyzed. Finally, research prospects of the system is put forward.

The development of “Internet+” railway passenger service mode has important significance under the new situation of economic development. At first, this paper uses the service chain theory to analyze railway passenger service demand, content and elements comprehensively. Then, the existing railway passenger service mode of the content, characteristics and existing problems are discussed. At last, for realizing the goals for providing a full range of passengers services, extension services, advanced services, an new railway passenger service mode based on “Internet+” is proposed, and railway passenger service information platform technology architecture is put forward based on cloud computing. Thus, a reference for the railway passenger service to achieve “Internet+” can be proposed.

In view of the existing high-speed railway train operation plan system cannot accurately, comprehensively and intuitively reflect the transport equipment operating load and ability to adapt to the situation, cannot reflect the train group operation, operation diagram, station work plan between the complex interactive relationship, a train operation simulation module is designed in this paper which through the construction of high-speed railway traffic related electronic map and technical standards database, computer simulation on the train diagram has been made. Then, a high-speed railway operation plan simulation system is developed, which includes train planning, traction calculation and train operation simulation. Finally, simulation results show that high-speed railway operation plan simulation system can accurately simulate the whole process technical operation of the train group operation, operation process, generate statistical analysis results, thus we can get real-time visual inspection of the adaptability of operating equipment ability in the operation scheme, and provide strong support for the work of high-speed train running organization. It is of great significance to improve the level of high-speed railway traffic organization.

With the continuous improvement of the railway freight network, the effective evaluation can ensure that the railway remains competitive in the competition of freight market. Based on the analysis of the influencing factors of railway freight transportation network service quality, this paper constructs the event tree model according to the logical relationship among the factors, and then maps it into Bayesian network model. By calibrating the probability value of the basic events, the Bayesian network probability calculation method is used to obtain the top event probability value, which can determine the grade of the railway freight network service quality. Finally, a case study of Chengdu-Chongqing region is made was carried out to verify the effectiveness of the method.

Train headway is very important for the operation efficiency and safety of high-speed railway. With the demand of high-speed train headway calculation and simulation, based on the principle of traction effort calculation and the control in train operation system, train headway calculation and simulation system for high-speed railway was designed. For the system, some key issues were discussed such as automatic train traction effort calculation, train headway calculation and simulation method. With this system, it will be benefit for signal-block layout and train operation optimization.

Train Scheduling is very important for Heavy haul railway transportation organization. Based on the train scheduling procedure and rules of heavy haul train operation in China, this paper discusses some key problems for heavy haul train scheduling, which included train line plan for single track and double track line combined section, time window plan, train combined and decomposition problem, and train loading/unloading problem. Then the relative direction balanced departure method was put forward for train line plan at combined section, making the time window with the size of the interval capacity loss for time window plan, balanced train line layout for the combined and decomposition problem. For train loading and unloading problem, train plan should be adjusted to meet the working time at the arrived station.

This paper studies the method of applying big data technology to the analysis of train delay in train diagram. The actual data of train operation is obtained based on field collection, which formed data warehouse by data cleaning, data integration and other preprocessing techniques, then we compare it with the basic train diagram. According to different types of train delays, this paper proposes methods to identify the areas self-delay or aftereffect-delay occurred, evaluate redundant capability of station and interval, analyze relationship between train delay and stop time in station or and running time in interval based on big data technology and computer graphic technology.

In this paper, we present a precoding scheme of unitary space-time modulation (USTM) system considering that the fading channel is spatial-temporal correlated, which is a typical wireless communication scenario for high speed railway. The design of the precoder uses transmit correlation matrix of the channel, aiming at minimizing the mean-square error of channel estimation, where the receiver utilizes all of channel correlation information to the non-coherent detection method. Simulation results show that the proposed scheme can significantly outperform the non-coherent receiver without precoding.

In order to improve product development, enterprises need to strengthen product lifecycle management (PLM) and collaborative design capability. For the description of product design we have proposed a four-level structural classification scheme, which includes Design Intentions, Design Requirements, Design Parameters and TRIZ Engineering Parameters. This study is based on the classification scheme, and introduces the concept of Design for X (DFX) and then discusses the design principles from four different perspectives including manufacturing aspect, customer aspect, maintenance aspect and environmental aspect. We deliberate the meaning of these aspects and further establish their relevance with the TRIZ 48 engineering parameters. An electric scooter design is presented to illustrate the application.

This paper addresses a new adaptive attitude controller for quadrotor UAV in the presence of parameters uncertainty and bounded external disturbances. First, an attitude dynamic model is built for the quadrotor UAV, considering parameters uncertainty and bounded external disturbances. Moreover, an adaptive nonsmooth attitude controller is developed by integrating nonsmooth control and adaptive control techniques, based on backstepping approach. And the stability of the closed-loop system is analyzed based on the Lyapunov method. Finally, the effectiveness and advantages of the proposed method are illustrated via some simulation results and comparisons.

With aim to save energy that seriously wasted in classroom, this paper propose an intelligent light control system based onARM9 as core processor CPU by dividing the classroom lighting into several square area. The TSL230 programmable optical frequency converters were adopted to detect the current light intensity of the classroom while the pyroelectric electric infrared sensors were adopted to detect the person information in the room. The data was sent to ARM9 to make a reasonable switch lights decisions by comparing the data with the setting threshold of illuminance and that of people. The system was proved with the merits of simple operation, low cost, high reliability and energy saving.

The remainders in attached pipelines of rocket engine are great harm to the reliable operation of engine. Signal noise and misjudgment caused by electromagnetic interference reduce the detection accuracy seriously. This paper analyzes the common-mode voltage of the rectifier bridge side and the inverter side of the detection equipment. According to the cut-off frequency, the fundamental voltage drop and the fundamental current and other parameters, the common-mode voltage filter is designed. The crosstalk coupling process between cable bundles is analyzed in this paper. Based on the finite element method, the distribution parameters of the cable bundle are analyzed and the structure of the cable bundle is optimized. With these methods, the impact of electromagnetic interference on the accuracy of remainders detection is reduced effectively.

The traditional power transformer fault judgment is through the oil chromatography online monitoring data set a threshold to determine whether the transformer is faulty, so there is a problem of the correct rate of fault alarm. This paper adopts the data analysis method, based on the Fuzhou transformer oil chromatography and its repair order data, selects effective characterization of influence the characteristics of transformer running state as evaluation indexes. A double-hidden layer BP neural network models were constructed and applied into transformer fault alarm, and evaluation accuracy is higher than the common fault alarm model.

An artificial indoor farm has been constructed to subvert traditional farming methods, mainly including a main control 32-bit microcontroller module; solar panel; battery; Finel lens; air temperature, humidity, wind, and other kinds of sensors; infrared lamp; water pump; spray; and other components. Room has been allotted for crop growth through the simulation of the natural processes of sunrise, sunset, water supply, and ventilation, to create a desired crop growth environment that includes indoor home plowing. This indoor farm is combined with Bluetooth, Apps, Wi-Fi, Internet, and other platforms, to achieve not only the restoration of arable land resources, but also a combined on-site and remotely controlled indoor farm.

In this research, a design for smart home curtains was proposed. The design is based on an Arduino UNO R3 development board equipped with an ESP8266-01 Wi-Fi module that communicates with an Android client application (app) using HTTP protocols to control the sensor and motor modules.

Graphene as an ideal ultra-thin two-dimensional (2D) form of carbon has not only unique electronic but also wonderful broadband nonlinear optics. Particularly, under strong laser radiation, graphene can modulate laser field in amplitude due to saturable absorption (SA), and in phase due to large Kerr nonlinearity. Herein, we review the nonlinear optics saturable absorption in graphene and its application for fiber laser photonics. The SA in graphene has already led to intensive research advancements on graphene mode-locking/Q-switching ultrafast lasers while the large Kerr nonlinearity in graphene may also result in potential applications for graphene based Kerr photonics. Moreover, to analyze its evolving mechanism, two carrier relaxation times with distinct scales have been measured in graphene using ultrafast optical pump-probe spectroscopy. In addition, graphene-metal hybrid nanomaterials attract tremendous interest and show exceptional tunable and enhanced nonlinearity due to the surface plasmons in metal nanostructures.

In present, adjustable speed drive systems are widely used in industrial production and ship propelling. One of the type of inverters that are commonly applied to high-voltage great-power adjustable speed drive system is Cascaded H-bridge multilevel inverters. In practical applications, many factors may result in power supply losses. Therefore, the capability of riding through power supply losses is required to improve the reliability. Unfortunately, few researcher pay attention to the riding through method for cascaded H-bridge multilevel inverters. This paper proposes a riding through method and designs the controller. The proposed method is verified by simulation.

This paper uses a variety of machine learning methods to predict the hourly power consumption of a central air-conditioning system in a public building. It is found that the parameters of the central air-conditioning system are different at different times, so is the corresponding power consumption. The paper applies the time series to predict the power consumption on account of the time, which predicts the hourly power consumption based on historical time series data. Comparing the prediction accuracy of multiple machine learning methods, we find that the Gradient Boosting Regression Tree (GBRT), one of the ensemble learning methods, has the highest prediction accuracy.

Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of ontology entities to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers, so-called ontology meta-matching, is one of the main challenges in ontology matching domain. In recent years, Evolutionary Algorithm (EA) based ontology meta-matching technique has become the state-of-the-art methodology to solve the ontology meta-matching problem, but it suffers from some defects like the slow convergence, premature convergence and the huge memory consumption. To overcome these drawbacks, in this paper, a Compact EA (CEA) based ontology meta-matching technique is proposed, which makes use of a probabilistic representation of the population to perform the optimization process. In particular, we construct an optimal model for the ontology meta-matching problem, propose a problem-specific CEA to optimize the aggregating weights of various matchers, and utilize a Cross Sum Quality Measure (CSQM) to adaptively extract the final alignment. The experimental results show that our approach outperforms other EA based ontology matching techniques and Ontology Alignment Evaluation Initiative (OAEI 2016)’s participants.

Population size plays an important role in the optimization performance of Differential Evolution. Researches in earlier literature usually employed constant population size, and these recommended settings of different population sizes usually varied from one DE variant to another. As we know, smaller population size settings perform better on some objective functions while bigger settings perform better on the other within the same number of function evaluations. Therefore, adaptive schemes for population size became much more popular recently and performed very well on a large number of benchmark functions. These schemes dynamically changed the population size either in increasing or decreasing approaches during the evolution. Moreover, most of these adaptive schemes mainly focused on decreasing population size. Nevertheless, this paper reveals an approach to diversify the individuals (increase the population size) by employing an external population without increasing number of function calls. This approach employs transfer knowledge learned from the target population in the evolution of an external population for Differential Evolution. CEC2013 test suite for real-parameter single objective optimization is employed in the verification of our approach and experiment results show that the proposed approach is very useful in maintaining a better diversity of individuals without increasing function calls.

Reasonable ways and suitable decision of ships collision avoidance are the guarantee of the safety navigation. Based on analysis of the existing collision avoidance strategy, this paper studies on the effectiveness of chaotic particle swarm optimization (CPSO) algorithm for ships collision avoidance, by introducing the improved Tent mapping to solve the premature convergence problem of PSO. The mathematical model and objective function of collision avoidance are put forward and the simulation is carried out to compare the CPSO with PSO in various encounter situations of ships. The results shows that the CPSO is feasible and practical in conducting an optimal steering collision-avoidance scheme.

In order to optimize the design of gear reducer, gear reducer optimal design to improve reliability and security, slow convergence and local optimum for FOA algorithm is proposed based on the improved type FOA gear reducer optimization design model. To avoid falling into local optimum Drosophila optimization algorithms, improved FOA algorithm by introducing a correction factor. Superior to the gear reducer seven variables optimization design model for the study, to ensure the safety and reliability of the premise, the FOA has the advantage of improved convergence speed and avoid local optimization problems, in order to verify the proposed method and reliability.

Intelligent computing systems can automatically sense environmental changes in the sensor network, make judgments and prediction on the environmental status in time, and provide response strategies in different environments, applying some technologies of pattern recognition, time series prediction and big data analytics. Nowadays, intelligent computing is successfully used in a lot of areas such as transportation, healthcare, home, performance and environmental monitoring. In this paper, the concept of intelligent computing is introduced. Then, the applications in different areas are discussed detailedly. Finally, the future of intelligent computing is analyzed and the conclusion is briefly given.

Shuffled frog leaping algorithm is one of the popular used optimization algorithms. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. In this paper, we propose a directional shuffled frog leaping algorithm (DSFLA) by introducing the directional updating and real-time interacting concepts. A direction flag is set for a frog before moving, if the frog goes better in a certain direction, it will get better in a big probability by moving a little further along that direction. The movement counter is set for preventing the frog move forward infinite. Real-time interacting works by sharing the currently optimal positions from the other groups. There should have some similarities among the best ones, and the worst individual could be improved by using those similarities. The experimental results show that the proposed approach is a very effective method for solving test functions.

Collaborative filtering is a promising recommendation technique for predicting the preferences of users in recommender systems. The date coming from recommender system is not only big but also sparse. It motivates the need for a more intelligent approach to obtain the tastes of users. In this paper we present a novel bidirectional collaborative filtering method based on EM algorithm. We combine user rating with item rating to build a new rating matrix and calculate a steady result by iterating this strategy. The empirical evaluation demonstrates that our technique have encouraging result contrast to the traditional approaches.

The recent trend of research is to hybridize two or several numbers of variants to find out the better quality of solution in practical optimization applications. In this paper, a new approach hybrid Grey Wolf Optimizer (GWO)-Flower Pollination Algorithm (FPA) is proposed based on the combination of exploitation phase in GWO and exploration stage in FPA. The hybrid proposed GWOFPA improves movement directions and speed of the grey wolves in updating positions of FPA. The simulation uses six benchmark tests for evaluating the performance of the proposed method. Compared other metaheuristics such as Particle Swarm Optimization (PSO), FPA, and GWO, the simulation results demonstrate that the proposed approach offers the better performance in solving optimization problems with or without unknown search areas.

This paper proposes a seam carving algorithm based on saliency. This algorithm makes saliency detection to the source image. Images are classified according to the gray-scale of saliency detection. Adding different energy protection methods, it can protect the foreground area of a subject and detailed areas of the edge. Then the cumulative energy map is calculated. According to the principle of deleting the minimum energy pixel, single pixel-wide carves chosen to be deleted or copied. The experimental result shows that the algorithm avoids image restoration caused by extracting too many pixels in saliency area and non equal-ratio scaling. It reduces the deformed structures caused by displacement of edge pixel and also improves the integrity of algorithm.

This paper is concerned with the analysis of the time domain acoustic scattering from locally perturbed flat substrates. For this three dimensional scattering problem with unbounded scatterer, boundary integral equation is directly established on the boundary of the unbounded scatterer without application of the symmetric continuation. The well-posedness of the time domain boundary integral equation is proved.

We study the adversarial multi-armed bandit problem, in which a player must iteratively make online decisions with linear loss vectors and hopes to achieve a small total loss. We consider a natural measure on the loss vectors, called deviation, which is the sum of the distances between every two consecutive loss functions. We propose an online algorithm and the experimental results show that the proposed algorithm can achieve a small total loss when the loss functions have a small deviation.

Black and white films were the main form of human culture records before. Colorization of those films is creative. At present, Colorization of black and white films is still handmade which is expensive and time consuming. In this paper, a framework based on CNN and particle filter tracking algorithm is proposed, which can color black and white video and try to solve the problem of dynamic frame based on context correlation. At the same time, the objective function of CNN structure and particle filter tracking are optimized. The result of colorization on videos is satisfactory.

Due to the increasing amount of opinion data on the internet, opinion mining has become a hot topic, in which extracting opinion targets is a key step. The state-of-the-art approaches only use direct dependency relation patterns to extract opinion targets and the indirect dependency relation patterns have not been used. In this paper, the dependency relations between opinion target and opinion word are defined, and direct and indirect dependency relation patterns are designed. Then, a bootstrapping approach is used to extract and evaluate both candidate patterns and opinion targets. The experimental results show that in formal text, the approach improves the performance compared with the state-of-the-art approaches for opinion target extraction.

Chaotic sequence after digitization is often affected by the limited word length effect. The pseudo-random sequences produced by chaotic sequence generators have short periodicity and less random characteristics. In order to solve the influence of short periodicity, m sequence theory is introduced into chaotic system, and a new scheme of chaotic sequence generator with m sequence perturbation is proposed. The hardware implementation of the new scheme effectively improves the stochastic characteristics of chaotic sequence generator without adding additional logic resources.

Binary sequence of chaos has been widely applied in research and studies on secret communication. Binary chaotic pseudo-random sequence is obtained through assessing and quantifying chaotic sequence and the complexity of binary sequence is got hardly. In this paper, the chaotic sequence linear complexity is discussed, and a new criterion is proposed on binary sequence, based on the approximate entropy. Simulations indicate that the method is effective, and the corresponding theories are proved right.

Aiming at a large number of Sensor Nodes is restricted by hardware resources about the application of Internet of Things so that the AES and CMAC algorithm of LoRaWAN protocol cannot run normally. A novel lightweight block cipher algorithm is proposed with the purpose of overcoming the disadvantage, which combines Logistic chaotic mapping with Arnold transformation. The cipher algorithm considers that two important factors, namely hardware resources consumption and security. It is compatible with LoRaWAN protocol and suitable for limited hardware resources of sensor nodes.

Remote sensing technology is widely used in land surveying and earth science research, such as the study of the earth’s oceans, glaciers, hydrology, ecology, geology and so on, is still in the military, intelligence, commercial, economic and other aspects can also be used to select the appropriate training samples, supervised classification with five kinds of single classifier, the classification accuracy comparison of different methods. It was found that the maximum classification accuracy of the two plots was the highest with the maximum likelihood method, but the classification accuracy was not the highest for 1 of the cultivated areas. Therefore, two decision fusion based multiple classifiers combination algorithms are proposed, and the results of single classifier are processed by using ENVI and IDL software. Compared with the classification results of a single classifier, the overall classification accuracy of multiple classifiers is improved by 2.5%, and the accuracy of 1 of the cultivated land in the study area is increased by 15.5%. Methods the combination of multiple classifiers can use different characteristics of single classifier, so as to compensate for the lack of a single classifier.

This paper presents a design of relay function for multi-UAV communication and evaluates its performance in an implemented simulation environment. The proposed relay function design is based on the popular and commonly used LTE technology. Accordingly, the implementation can be low-cost and performance-efficient. Through the use of relay function, UAVs can forward data messages among UAVs when direct communication is available between a ground console and a remote UAV. Consequently, UAVs can be dispatched to areas that cannot reach before to monitor environment and collect information, then transmit data backward to ground consoles by the proposed relay function in real time.

The fifth generation of mobile communication (5G) will emerge on the horizon estimated before 2020. It will affect our daily activities to become more convenient and diversified. The development of Internet of Things (IoT) is also accessing to our circumstances as a network of objects and/or things. Then, huge data are generated, uploaded onto the cloud to be analyzed, organized, synthesized and outputting valuable messages as well as information for specified applications. Nevertheless, including Internet of Vehicles (IOV), Connected Vehicles (CV), particularly rely on short range communication technologies for data transmission, such as WiFi direct, Bluetooth (iBeacon), RFID, WAVE (IEEE802.11p), LTE V, NB IoT and LoRa. The fundamental demand of public is expected to connect directly among Vehicles, Infrastructure and Persons (cVIP), without bypassing any ISP. This in fact leads to complete the need to a seamless circumstance. This paper discusses which technology for connecting VIPs in 5G era will win the market. Meanwhile, the change will obviously and strictly affect traffic and transportation engineering. Advanced ITS (Intelligent Transportation Systems) to enter 5th generation of transportation era (Tx 5.0) will be presented.

Solidly mounted resonators (SMRs) have a stable structure, high frequency range, and good process yield, and thus are increasingly being used in various applications, including radiofrequency (RF) communication devices. In this study, we simulated SMR structures and thin films of the piezoelectric materials AlN and ZnO using the commonly used RF design program Advanced Design System.

The joint I/Q mismatch calibration, compensation and channel equalization approach are proposed for space-time-block-code (STBC) 2 $$\times $$× 2 multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transceivers. The I/Q mismatches of transmitter and receiver are extracted by using the look-back calibration during the system power-on period. The results of look-back calibration is used to construct a reparation block and to further compensate the I/Q mismatch. In addition, based on MMSE criterion, the joint approach is employed to simultaneously combat the residual remote/end-user transmitter I/Q mismatch and the channel impairment at end-user/remote side.

Learning is the major human activity to acquire new, or reinforce existing knowledge and skills which can make people to accommodate the environment or the society better. The Internet Of Things (IOT) which integrate the sensing, networking and computing, will change the future living of humans totally, and provides more comfort and convenience. It has been an important issue to train enough qualified IOT programmers efficiently. This paper proposes a work which trains the undergraduate students to develop the mobile IOT via a constructive problem-based approach. It is a long term course project cooperated by NingXia Polytechnic and Tajen University, the second semester course is still running now. The study of the first semester basically shows the effectiveness of the proposed approach and the expectation of the students.