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By the natural parents are the essentials of educating young generations. In China during Covid-19, especially the lock-down period, parents companied children at home and observed their learning behavior. Parental acceptance of online learning can offer a valuable reference for the further adaptation of online k-12 education. This study used an integrated model of technology acceptance and expected confirmation, demonstrating Parental acceptance by three variables: acceptance, satisfaction, and continuance intention. Moreover, an influential, factor model of Parental acceptance was built, which including teacher support, perceive autonomy and interactivity, technology preference and experience. Measurement and structural data models are verified by using partial least squares regression (PLS), which supports all the hypotheses empirically.

In the beginning of2020, COVID-19 pandemic emerged in many regions of China, and the spring semester of primary and middle schools was postponed. At the call of "Suspension of Classes but not Learning" by MOE, all educational institutes adopted the online learning methods. However, the home-based online learning lacks teacher supervision, peer support, classroom environment constraints. These intensify students’ attention difficulty when compared -with face-to-face learning in the classroom, which makes students’ learning engagement more important to ensure the learning effect. According to online focus group interviews with the education experts and K-12 teachers respectively, the researchers found out some possible influencing factors to K-12 students’ online learning engagement: perceived teacher involvement, perceived parental involvement, students’ self-discipline, and student emotion. Therefore, this study proposes a prediction model from the above four aspects. By using multivariate linear regression analysis and variance analysis, this study finds: (1) Perceived teacher involvement, perceived parent support, student self-discipline and student emotion all have significant positive effects on online learning engagement. (2) There are significant differences in students’ online learning engagement for different learning stages and different network environments at home Students’ online learning engagement has no significant difference between urban and rural areas.

College students are the online learning subjects in the Covid-19 pandemic and their preferences towards online learning have certain reference value for the subsequent improvement of online learning platforms and the optimization of online teaching and learning. The study combined information literacy with Uses and Gratifications Theory, characterized students’ online learning preferences with usage, gratifications, and acceptance, and introduced online interaction to construct an influencing factor model for university students’ preferences towards online learning. The partial least squares method was used to verify the measurement model and structural model. All the research hypotheses are supported by the empirical research.

In recent years, online teaching has become a hot topic in K-12 education reform. Based on the expectation confirmation model of information systems continuance (ECM-IS), four individual characteristic factors of self-efficacy, innovation, perceived risk and information literacy as well as two external environmental factors of subjective norms and facilitating conditions were introduced to build a theoretical model of factors affecting teachers’ continuance intention of online teaching from the perspective of "technology-individual-environment". This study tested model encompassing nine variables through empirical research. Data were collected on a sample of 59156 K-12 teachers who have had an online teaching experience during COVID-19 using an online questionnaire. Data were modelled using the partial least squares-structural equation model (PLS-SEM) to test the hypotheses. Results indicated that perceived usefulness and satisfaction in the ECM-IS model have significant effect on teachers’ continuance intention while self-efficacy, information literacy, innovation and subjective norms were found to significantly affect teachers’ continuance intention. However, perceived risk and facilitating conditions have no effect on continuance intention. According to the results, there are some suggestions for better online teaching effects: improving hardware facilities and software resources, innovating teacher training and research methods, and optimizing online teaching service supply.

Competition for cellular resources often indirectly creates unintended connections between otherwise independent genetic modules, leading to loss of modularity and impairment of intended circuit function. Both global and local negative feedback control strategies have been widely-used as attempts to mitigate the undesired effects of resource competition. However, these controllers demonstrate limited tunability and scalability with increasing circuit complexity. Our previous work attempting to achieve two successive cell fate transitions via cascading bistable switches demonstrates how resource competition can lead to winner-take-all (WTA) dynamics that deviate from the intended behavior. Here, we attempt to remediate these issues by synthetically introducing a shared and tunable system of negatively competitive regulation (NCR), incorporating repressive CRISPR moieties to free up cellular resources from the winner module to a degree proportional to its activity. This system punishes transcriptional modules that take up more than their fair share of resources while having minimal effect on modules operating within normal activity ranges. We compare this novel mode of regulation to global and local negative feedback controllers and demonstrate the significantly increased efficacy of controlling WTA resource competition NCR can achieve. Thus, we provide an alternative strategy for controlling resource competition.

Cooperative Vehicle Infrastructure System (CVIS) is one of the most valued technologies in intelligent transportation system. By use of roadside sensing, it provides extended coverage and more dimensions traffic information compared with independent perception by vehicle sensors. Sensing fusion of camera and radar can overcome the shortcomings of both sensors so that it is the trend of roadside sensing in CVIS. In this paper, we propose a novel roadside sensing fusion system. It filters out background objects from radar detection to avoid wrong calibration and fusion with camera detection, and makes calibration of camera and radar automatically to reduce time cost of system implementation. Experiment results show that it can be fast implemented to automatically acquire accurate roadside sensing information.

In this paper, we propose an improved matrix information geometric detector with a strategy parameter selection in nonhomogeneous noise. In particular, the correlation or power of sample data is captured by a Hermitian positive-definite (HPD) matrix. Then, the problem of signal detection is treated as distinguishing the matrices of noise and target signal on the matrix manifold. A strategy to choose the initial input matrix and step-size of the iterative matrix equation is presented to accelerate the convergence for the computation of Riemannian mean. Unlike the classical detectors that is designed by resorting to the known (assumed) statistical characteristics of noise, our proposed detector only considers the underlying geometry of Riemannian manifold of HPD matrices. Numerical results show that the proposed detector achieves a detection performance improvement over the conventional detector in nonhomogeneous noise.

This paper proposes a Log-Euclidean metric (LEM)-based signal detector with a manifold filter in the framework of matrix information geometry to deal with the problem of detection of signals in the background of noise or clutter. In particular, the problem of signal detection is reformulated into the problem of discriminating two matrices on the manifold. At the design stage, we assume that the sample data is modelled as a Hermitian positive-definite (HPD) matrix. A geometric median detector is designed by resorting to the LEM on the HPD matrix manifold. The geometric median is very robust to outliers that are caused by the interference or the variation of clutter power. Additionally, we design a manifold-to-manifold projection by employing the manifold filter to derive a more discriminative manifold with the same dimension. Signal detection on this manifold can yield a significant performance improvement. Numerical results show that the proposed detector achieves a detection performance improvement over the conventional detectors and its state-of-the-art counterpart, especially in cases of limited data and interference environment.

The combination of online education and traditional education provides many advantages in higher education. Therefore, many universities offer online elective courses for students to learn online. However, little is known about the learning experience of students after completing the online course. On the basis of the Universal Theory of Acceptance and Use of Technology (UTAUT), this study attempts to examine college students' continuous intention to use online course platforms through empirical research. Results show that performance expectancy, effort expectancy, facilitating conditions, content quality is positively related to college students' attitude towards online course platforms; Perceived cost and use attitude also is negatively related to students' continuous intention. However, social influence did not affect college students' attitude towards online course platforms. These findings provide new ideas for the design and development of online course platforms.

Blended Learning has changed the traditional teaching mode and attracted wide attention to the educational community. In blended learning, students have more autonomy. This paper aims to study the autonomous learning behavior of college students in blended learning. In this study, five hypotheses were answered by using multiple linear regression. Results show that learning motivation and academic self-efficacy can effectively predict college students' autonomous learning behavior. In addition, academic self-efficacy has a positive effect on learning motivation, and learning anxiety is negatively correlated with learning motivation, learning anxiety and self-efficacy indirectly affect autonomous learning behavior through learning motivation. The results of this study provide new ideas and suggestions for the implementation of blended learning.

Student engagement is an important indicator to predict students' academic achievements, as well as an important condition to ensure learning quality and promote deep learning. This study investigated the relationship between classroom environment factor (teacher support) and individual factors (situational interest and self-regulation) in blended learning and college students' engagement of specific types (behavioral engagement, cognitive engagement, and emotional engagement). The results show that teacher support and self-regulation have significant influence on college students' behavioral engagement and cognitive engagement. Situational interest and self-regulation have significant influence on college students' emotional engagement. These findings can provide new ideas on improving students' academic achievements and engagement.

The compulsory education and education equality are interrelated topic in China. The student in the rural area is in various shortage of teaching and learning resources, especially the field of music education. The form of Synchronized Interactive Mixed Classroom brings the music class to the vacancy of teaching sites. Under the situation, this study selects three music lessons as the research subject that applies to the LICC classroom observation evaluation. In view of teacher, student and culture of classroom to analysis the music class teaching and the actuality of music education in rural area.

Applying the Slime Mould Algorithm (SMA) in optimization and coding it with Python programming language, we found that runtime warnings occurred because of the inherent limits of arctanh functions. Considering the circumstances that the maximum residual errors would be constrained and the maximum allowed iteration number would be bothering, we therefore proposed an improvement to the SMA with exponential functions. Three kinds of benchmark functions were introduced to verify the suitability, accessibility, and capability. Experiment results demonstrated that the improved SMA with exponential functions would be suitable and accessible. Comparing with the standard SMA, the improved algorithm would result in better performance with exception that they perform similar when optimizing the Quintic function, the representative of those kinds of benchmark functions with basins in their three-dimensional profiles.

Based on the universal theory of acceptance and use of technology (UTAUT), this study explores in-class teachers' use intention of the synchronous two-teacher model, which consists of an elite teacher from the well-developed urban school and one or more in-class teachers working together to teaching students at the underdeveloped rural schools. The results show the in-class teachers have a strong welling to participate in the synchronous two-teacher model. Further, the study finds that effort expectancy and facilitating conditions are two factors that have directly impact on in-class teachers' use intention of the synchronous two-teacher model. Performance expectancy and social influence, on the other hand, are two factors that have not directly impact on in-class teachers' use intention of the synchronous two-teacher model.

Non-contact human sensing based on radar has attracted numerous attentions and been applied in various applications, such as localization, vital sign monitoring and activity identification. This paper presents a wireless healthcare system to achieve life detection and vital sign monitoring based on frequency-modulated continuous-wave (FMCW) radar. Most of previous methods have studied to detect moving targets. The proposed life detection method has achieved stationary human target detection by utilizing the inherent characteristics of human breathing motion: spatial correlation and periodicity. Based on the phase sensitivity of FMCW radar, breathing motion is monitored within the range provided by the life detection step. Besides, different algorithms are adopted for different detection range to remove the distance and environment impacts. Experiments are carried out and have demonstrated that the proposed method can detect the stationary human targets accurately and 95% of the breathing rate estimation error is less than 3 BPM(Beat Per Minute).

Recently, public security becomes more and more important in daily life. Terrorists or extremists can attack common people by small objects such as pistol or knife in densely populated place. In previous work, we proposed a micro-Doppler image based method to detect dangerous objects concealed on walking through persons ubiquitously. The accuracy of the proposal is affected by walking velocity. In this paper, we enhance the method in the aspect of feature extraction to overcome such issue. The proposed method can extract and adapt the changeable position of effective feature in the micro-Doppler image. Experimental results show that accuracy of the proposed method is 90% when detect the persons with different walking velocity.

A new method to amplify the sensitivity based on separated Fabry-perot interferometers (FPI) causing Vernier effect is presented and discussed. The spectrum of typical cascaded cavity based on Vernier effect is analyzed and simulated. We also propose a novel structure to realize Vernier effect to amplify the sensitivity, using two separated FPI. The simulation shows that the disturbance term in the spectrum is eliminated, and sensitivity is the same as cascaded cavities’.

Recently, anything and anyone can be the target of terror attack. It is necessary to develop a ubiquitous solution of concealed dangerous object detection that can be deployed flexibly. For this purpose, in this paper we propose a multi-radar system via millimeter wave radar sensors. Furthermore, we present horizontal and vertical radar sensors deployment methods for the purpose of covering full body and enhancing the robustness against detecting various body types. We test the proposal in front body and body side detection scenario, and experimental results confirm the effectiveness of the proposal.

Blended learning in the world has been drawing more attention. And lots of teaching methods in the blended learning environment have also emerged. This study attempts to examine the learners' continuous intention of the teaching method supported by Enterprise WeChat among college students who have taken "The Introduction to Educational Technology" class. In this study, five hypotheses were answered by using the linear regression. Results shows that perceived usefulness and learner satisfaction are shown to be good predators to behavior intention. Also, perceived usefulness and confirmation are significantly associated with learner satisfaction. Confirmation is significantly positive with perceived usefulness. Main findings of this study were discussed, which could provide new ideas for future education.

The study proposed a blended synchronous learning environment (BSLE) to address the problems including incomplete subjects, insufficient class, and low-quality teaching in small schools in rural area(SSRA). Two Synchronous interactive classrooms are designed in BSLE, which delivers quality classrooms in urban schools to rural schools via the Internet. The instructor synchronizes classes for students in local and remote classrooms. At the same time, students in SSRAs can join classroom interactions in such an environment. The results of the study show that BSLE can extend some of the functions of face-to-face classroom teaching to remote students, and SSRAs' students like to take classes through two-way video conferencing at remote sites because it is very flexible and convenient. The study further confirmed that BSLE can promote the growth of rural students and the professional development of teachers in SSRA, as well as facilitate the convenient communication between left-behind children and parents.

Microwave circuits and modules are widely used in communication, navigation, detection and other fields. Under the requirement of increasingly miniaturization, integration, high frequency and high reliability of microwave modules, the change of interconnection configuration of circuit modules in microwave modules will have a significant impact on the signal transmission performance. The interconnection configuration of core wire is modeled by parameterized characterization, and the correlation mechanism between interconnection configuration and electrical performance is studied, so as to determine the optimal interconnection configuration parameters for electrical performance transmission, which can significantly improve the interconnection signal transmission performance and guarantee the working quality of highperformance electronic equipment.

One of the key technologies of wireless sensor networks (WSNs) in the development and applications is the determination of the location of events or nodes. The shortcoming of traditional centroid location algorithm is that its position accuracy depends on the density and distribution of anchor nodes. An improved centroid location algorithm based on finite element method for wireless sensor networks is proposed. The initial location is carried out by signal strength information and triangle area of finite element method, and the final location is carried out by centroid location algorithm. The simulation results show that the proposed algorithm effectively improves the localization accuracy of nodes compared to classical trilateration algorithm and triangle centroid location algorithm.

This paper is focused on the localization problems of scattered nodes in the traditional wireless sensor network. A node localization method based on particle swarm optimization aided least square support vector regression is proposed. Firstly, the nearest neighbor clustering algorithm is used to determine the data type, where the nodes closest to the unknown node are employed as the clustering center. Then, a distributed structure based on the meshing of wireless sensor network is presented. Finally, the particle swarm optimization aided least square support vector machine algorithm is proposed for the wireless sensor network localization. Numerical experiments show that the proposed method is accurate and effective in wireless sensor network localization problems.

In this paper, we propose a signal processing method for ubiquitous concealed dangerous object detection via millimeter wave radar sensors. The proposal uses object's compound velocity image instead of optical image to make the detection. The compound velocity image is obtained through analysis of micro-Doppler signature. The proposal is extended to detect multi-persons simultaneously. A 77GHz radar sensor based experiment platform has been built to evaluate its effectiveness. Experiment results show that for 200 tests, its miss alarm rate is 3%, and detection accuracy is 93%.

Wi-Fi Mesh Networks (WMNs) have flexible network structure and support high-volume data traffic. A WMN can be instantly set up to provide networking service for various applications. Relay node placement is a key problem during network design. To design a WMN, it is necessary to consider both network cost and network performance. The Wi-Fi system supports diverse transmission rates in MAC layer, which makes it more challenging to select relay node positions in WMNs while ensuring network performance. In this paper, we propose a Convex Optimization based Relay Node Placement (CO-RNP) method, which formalizes relay node placement as a convex optimization problem. CO-RNP reduces relay node number by minimizing the length of non-routing links and preserves network performance by constraining the length of routing links. We evaluate the performance of networks designed by CO-RNP with network simulations in ns-3. The simulation results suggest that our method achieves effective balance between network cost and network performance.

Due to the interference issue in unlicensed band, sensor nodes frequently encounter degraded performance or lack of connection. This paper provides a real-time external interference source classification method for an 802.15.4-based wireless sensor network using deep learning. It uses RSSI sampling for collecting training data as well as online test data in an office environment. The output interference source type includes Wi-Fi beacon, different classes of WLAN traffic, BLE iBeacon, and microwave oven. Wireless sniffers are used to help labeling the ground truth of the sample data. We have trained a deep neural network with two hidden convolutional layers using raw RSSI samples as inputs. A micro-level model and a macro-level model are provided to predict the interference source based on the deep learning result. With implementation on both IEEE 802.15.4 SoC and Linux-based system, our experimental results show that the proposed framework can classify the major interference types with high accuracy.

According to the characteristics of too many types of plane parameter, increasing parameters needing to be collection, complex system and diverse requirements of aircraft flight data acquisition, a configurable aircraft data acquisition system based on FPGA + CPU was designed. This system architecture can give full play to the parallel working ability of FPGA in which to collecting multi-type and multichannel flight data simultaneously. At the same time, the sequential function of CPU is used to realize system configuration loading conveniently and complete flight data acquisition tasks without complex operation of professional technicians on the plane. This system has the advantages of simple circuit structure, high integration and high universal, which can be used for engine sensor parameter, air frame sensor parameter, flight attitude and other flight data.

In this paper, we propose a method which can detect dangerous objects such as pistol and knife concealed on pedestrian via a 77GHz radar. The proposed method uses range-FFT and Doppler-FFT to obtain micro-Doppler signature of the concealed object. We extract features from micro-Doppler signature with motion pattern of the pedestrian. A classifier based on Support Vector Machine (SVM) is implemented to distinguish dangerous objects from all detected objects by micro-Doppler features. We set up an experiment platform which uses a PVC dummy to simulate pedestrian to verify our proposed method. Results show that our proposed method can get accuracy of 89.8% and miss alarm rate of 3% among 200 times of detection.

Automated guided vehicle (AGV) is playing an major role in industrial applications to move materials around a manufacturing facility or warehouse. As task complexity increases, however, the traditional design of AGV can't meet user's demands any longer. Therefore, a new AGV control system is introduced in this paper, which is designed by the two-layer controller system structure. This structure includes the lower computer and the upper computer, which is the main controller with autonomous navigation functions. To exchange control messages, there is also a special communication protocol between the two controllers. In addition, different algorithms and circuits are designed according to the differences of each task and the characters of two controllers. The implementation approaches are presented in both hardware and algorithms as well. Finally, a series of experiments has been done, and the results show that the AGV from this new two-layer controller system design has perfect performance in motion, localization, path planning and other aspects.

Visual inertial odometry (VIO) is a technique which estimates the motion of a mobile platform using the measurements from visual and inertial sensors. Recently, these sensors are made available with higher accuracy, smaller size, but lower cost thanks to the developing manufacturing of chips and micro-electromechanical systems (MEMS) devices. However, it is very challenging for real-time visual inertial odometry to obtain robust and accurate localization and mapping with the limit of computational resources. In this paper, we present a stereo visual inertial odometry which pre-integrates IMU measurements to reduce the variables to be optimized and avoid repeated IMU integration during optimization. In addition, incremental smoothing is employed to obtain Maximum A Posteriori (MAP) estimates. The proposed method is shown to achieve high accuracy and efficiency in simulation results.

This paper presents a RGB-D SLAM system, which can simultaneous local every pose of a camera motion and then construct an visualizable pointcloud map for the mobile robots. The system includes four main modules:visual odometry, optimization, loop closing and mapping. With the help of this system, a mobile robot in an unknown environment can acquire a global map and local its own position. In order to satisfy the requirement of high-accuracy location, we have used nonlinear optimization method with Bundle Adjustment based on the local map in optimization. Moreover, a new filter method has been used to remove redundant information points, it will speed up the mapping procedure. Finally we acquire a dense pointcloud map that could help robots finish its own job. The result of our experiment demonstrated the performance of our system in the speed and accuracy under these methods.

In recent ten years, China has been promoting supply-side structural reform positively and speeding up the development of the national blue economy to accelerate the transformation of the mode of Marine economic development. However, there are many limitations in the traditional mathematical statistics methods which are widely used in the analysis of economic trend. This paper applies the grey system theory method to analyze the sequence of output value of the marine industries, three costal economic zones and the major marine industries of China. The results indicate that our national marine economy has basically formed the pattern of "three, two, one" industrial structure. The southern ocean economic zone has a good momentum of development and is ahead of the growth rate in the three major marine economics zones. The emerging industries of the sea have grown in scale. The proportion of Marine services has increased steadily. In addition, we forecast some values about marine economy by the GM(1,1) model and do the error analysis. The predictions suggest that the national gross ocean product will exceed 8 trillion RMB by 2018.

This paper addresses reliable and accurate step length estimation using inertial sensors. Step length is an important parameter for the accurate position required in the location and navigation system. To tackle the challenges of drifting in accelerometer, sensitivity to user physical characteristics and walking profiles, as well as variability in environment, we have developed a calibration algorithm for reliable detection of gait parameters including step number, step frequency, maximum and minimum of acceleration magnitude. We''ve built a Radial-basis Function (RBF) neural network to train the model of step length that can adapt to different users. The established mathematical model can achieve the simple and efficient estimation of step length in realtime system. Extensive experiments have been conducted on 5 subjects with 3263 steps testing in total. Evaluation results showed our improved step length estimation method can achieve the recognition rate for step detection of 96% and a mean error of 0.04m for the step length estimation. Index Terms-location and navigation; gait parameters calibration; RBF network; step length model

Computational and power efficiency is one of the crucial enabling factors to wearable device based human activity recognition (HAR) system. However, limited research efforts in literature have been available toward reducing theses costs without loss of accuracy. In this paper, we propose an improved random forest (RF) based HAR system for elderly-care. The system extracts three kinds of pairwise correlation features in hybrid sliding windows, and uses location information to enhance the recognition performance. A mutual information based feature selection is adopted to optimize the recognition of confused local set of activities. A new random feature selection strategy for each node in RF enables the proposed system to reduce the number of trees while maintaining the recognition accuracy. Numerical experiments show that the proposed method can predict 10 types of activities with 93.01% accuracy and 74.9% reduction of energy consumption. Furthermore, the fall detection accuracy in this proposed system can reach up to 99%.

In recent years, public security becomes more and more important. In this paper, we design a hardware platform to use a 24GHz band radar module to detect multiple objects. We propose a signal processing method that can segment multiple objects in a signal sequence, and a data processing method that can identify different materials, including metal, water, and petrol. Experiment results show that our proposed method can divide multiple objects with 100% accuracy and identify each object's material with 91% accuracy. The platform can be easily deployed widely to make a ubiquitous security surveillance system benefits from radar's cheap cost and small size.

In this paper, we propose a human activity recognition (HAR) method based on inertial sensors and barometer. The proposed method recognizes eight human activities following a multi-layer strategy. Activities are classified into two categories: dynamic and static activities; then explicit activity recognition is taken individually in the two categories. Three classifiers are adopted for different classification, including random forest (RF) and support vector machine (SVM). Different feature sets have been selected for different classifiers which are more targeted and effective. In addition, the classifier result is further verified by additional parameters and previous recognition results to decide the final recognition result. Experiments have shown the effectiveness and good performance of the proposed HAR method.

Multi-hop wireless networks have advantages over the single-hop ones in terms of reliability and coverage range. Moreover, the capacity of multi-hop wireless network can be substantially increased via multiple radios tuned to non-overlapping channels. However, the channel allocation, network interface cards assignment and routing selecting remain challenging due to the interference of the neighboring transmissions. These three problems have proved to be a NP-hard problem. Previous studies separating the routing selecting from the channel allocation, instead of considering the three problems as a whole, cannot get the overall optimal solution. In this work, we employ an improved Multi-Objective Genetic Algorithm to optimize the channel allocation, interface assignment and the routing selection, so as to minimize the overall network interference. The proposed algorithm includes two parts: 1) dynamic genetic mutation based on diversity measure; and 2) elite preservation based on ideal points. In order to eliminate the impact of illegal solutions, a new individual encoding method is proposed. In addition, an interference model taking into account the effects of channel separation and the traffic of neighbor links is applied to evaluate the quality of the interference of the network. Finally, a fitness function is defined to obtain the best search results. Simulation results show that our improved Multi-Objective Genetic Algorithm can reduce the interference and cost of total network compared to the standard Genetic Algorithm.