Narrow Results

The relationship between modeling error of the system and widening multiple of the working frequency band was analyzed quantitatively and its spectrums were obtained. They offered a criterion to estimate the feasibility and effect in engineering practice of the compensating method to improve the sensor's dynamic characteristics, and proved that the compensating method has good reliability. Conclusions were drawn clearly: For a first-order system, the compensation effect is conspicuous, which only relates to modeling error but is independent of time constant; for a second-order system, the compensation effect is determined by modeling error and damping ratio. Within a wide range, if the damping ratio is larger, the modeling precision required to obtain the same compensation effect can be lower. Through quantitatively analysis, we found that the modeling precision in dynamic compensation with analogy or digital filter should not be so high as one is taken for granted currently.

Genetic neural network model of solving the problem of nonlinearity rectification of sensor systems, is put forward in the light of the shortcomings of least square and other conventional methods. And in theory the model is emphatically expounded. Computer simulations are presented to demonstrate that approximation accuracy of the model is far higher than the conventional least square method and the model possesses stronger robustness through adopting the methods in this paper. The research in the paper indicates that the model can also be used to realize nonlinearity rectification in other similar systems.

Because of the micro/nano manipulation's complexity, the accurate feedback information of the micro interactive force acting on micro devices is quite important and necessary for micro/nano manipulation, especially the 2-D micro interactive force feedback information. At present, there are no reliable and accurate 2-D micro force sensors applied in micro/nano manipulation. To solve the above problem, a novel 2-D micro force sensor that can reliably measure force in the range of submicro Newton (μN) is designed and developed in this paper. Based on the model of 1-D micro force sensor designed by us, the model of this 2-D sensor is set up. To verify the model of the 2-D sensor, micromanipulation experiments are designed and realized. Experiment results show the submicro Newton resolution, and verify the validity of the 2-D sensor's model. The developed 2-D micro force sensor will contribute to promoting the complexity of micro/nano manipulation, and will facilitate to automate the micro/nano manipulation.

To accurately measure the micro-interactive force (for example, adhesion, surface tension, friction, and assembly force) acting on microdevices during micro/nano manipulation, a novel microforce sensor that can reliably measure force in the range of sub-micro-Newton (μN) is designed and developed in this study. During the application of this microforce sensor in micro/nano manipulation, the accuracy of this sensor's model is quite important to the force control of the system. Therefore, the accurate infinite dimension model of the microforce sensor and micromanipulator is built up. Based on the infinite dimension model, the impedance control system is designed. To verify the infinite dimension model and the control system, micromanipulation experiments are designed and realized. Experiment results verify the accuracy of the infinite dimension model of the sensor and show the efficiency of the impedance control system. The developed microforce sensor and the infinite dimension modeling provide a feasible and versatile solution in microforce sensing and feedback force control for micro/nano manipulation, and will promote the technology of automating the micro/nano manipulation.

With a tremendous increase in mobile and wearable devices, the study of sensor-based activity recognition has drawn a lot of attention in the past years. In recent years, the applications of Human Activity Recognition are getting more and more attention, especially in eldercare and healthcare as an assistive technology when combined with the Internet of Things. In this paper, we propose three deep learning approaches to improve the accuracy of activity detection on the WISDM dataset. Particularly, we apply a convolutional neural network to extract the interesting features, then we use softmax function, support vector machine, and random forest for classification tasks. The results show that the hybrid algorithm, convolutional neural network combined with the support vector machine, outperforms all the previous methods in classifying every activity. In addition, not only the support vector machine but also the random forest shows better accuracy in classification task than the neural network classification and the former approaches do.

We propose a new Dynamic Address Assignment Protocol for wireless sensor networks. This new protocol is based on the Dynamic Host Configuration Protocol and is modified into wireless sensor networks. The protocol aims to increase the flexibility and agility of wireless sensor networks with changing the amount of sensor nodes in sensor networks system easily. It is well suit for large-scale wireless sensor networks which update their sensor nodes frequently. The goals are achieved by assigning address dynamically and registering in collectors. We present an implement on wireless sensor network and evaluate our approach by discussing the payload ability of a wireless node.

With the advancements of networking technologies and miniaturization of electronic devices, wireless sensor networks (WSN) have become an emerging area of research in academic, industrial, and defense sectors. Sensors combined with low power processors and wireless radios will see widespread adoption in the new future for a variety of applications including battlefield, hazardous area, and structural health monitoring. However, many issues need to be solved before the full-scale implementations are practical. Among the research issues in WSN, security is one of the most challenging. Securing WSN is challenging because of the limited resources of the sensors participating in the network. Moreover, the reliance on wireless communication technology opens the door for various types of security threats and attacks. Considering the special features of this type of network, in this chapter we address the critical security issues in wireless sensor networks. We discuss cryptography, steganography, and other basics of network security and their applicability to WSN. We explore various types of threats and attacks against wireless sensor networks, possible countermeasures, and notable WSN security research. We also introduce the holistic view of security and future trends for research in wireless sensor network security.

Briefly, in this chapter we will present the following topics:

• Basics of security in wireless sensor networks.

• Feasibility of applying various security approaches in WSN.

• Threats and attacks against wireless sensor networks.

• Key management issues.

• Secure routing in WSN.

• Holistic view of security in WSN.

• Future research issues and challenges.

The AT89C51 microcontroller is regarded as the control center in intelligent alarm system for fire, and the system can receive and take a treatment on the concentration and temperature signal of smoke output by the fire detector with sound-light alarm. It can monitor the temperature and smoke concentration, etc. by sending inspection signal to the site continually, and have a feedback to alarm controller constantly. The controller compares the accepted signal with the normal value in storage to judge and determine whether there is a fire. When the smoke and temperature in site are anomalies, or fire occurs, it can realize sound-light alarm, the set of alarm limit of smoke concentration and temperature, self-diagnosis breakdown, delayed alarm, etc., which has a certain practical value.

In order to solve the environmental pollution caused by motor vehicle exhaust, this article designed and developed a miniaturized infrared exhaust gas sensor, can effectively detect the concentration of CO₂, CO, hydrocarbons, solves the existing sensor of large volume, slow response, etc.

Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO₂ and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.