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Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

The design of a MEMS ultrasonic sensor has been presented that exploits the Single Bubble Sonoluminescence (SBSL) phenomenon to realize an energy transduction mechanism from acoustical to electrical domain. In the developed scheme, highly stable laser like short duration light pulses are emitted when ultrasound waves strike a thermally generated microbubble stabilized in a confined volume of 1% xenon-enriched water. The emitted light pulses are detected by an integrated profiled silicon type photodetector to generate ultrastable 100 picoseconds duration current pulses per acoustical cycle. The sensor exhibits energy amplification during the transduction process itself that is not achievable by conventional types of MEMS or non-MEMS acoustical sensors. The cylindrical sensor geometry is 650 μm in diameter and 300 μm in height and is designed to have a sensitivity of 5.56 mA/atm/cycle. The sensor can be used in applications where detection of high pressure ultrasound waves is necessary or as an ultrastable very short duration current pulse generator for use in tissue and nerve repair or microsurgery.

A microcontroller-based control system is a direct outgrowth of the extensive advances in the Integrated Circuit design and microelectronic device processing technology. This has led to the development of new forms of technologies. This paper presents a technique of microcontroller based control system for controlling the lights of a room. Using the technique, according to the intensity of the sunlight in a room, the states of light of that room will change. Therefore we need to collect data or information from the environment using light sensors to control lights. The microcontroller collects the information from the atmosphere and changes the state of different lights. The analog data collected by the sensors are converted to digital form by an Analog to Digital Converter (ADC) and then fed to the microcontroller. The output data stream of the microcontroller is in digital form by which analog device lights will be controlled.

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.

Agriculture catalyzes the economy in developing nations. Malaysian agriculture constitutes 4.06 million hectares, with 80% encompassing industrial crops and agro-food production, boosting the economy through implementing precision agriculture (PA). Precision agriculture gives minimal environmental implications by using an unmanned aerial vehicle (UAV), improving sustainability, productivity, and crop production 30-fold instead of conventional methods. This study aims to review the UAV application based on technical requirements with insights into the potentiality of precision agriculture in UAV agriculture technologies, limitations, and solutions.

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.

A cantilever beam and fiber Bragg grating is used to measure acceleration. The cantilever induces strain on the grating resulting in a Bragg wavelength modification that is subsequently detected. The output signal is insensitive to temperature variations and for a temperature change from −20°C to 40 °C, the output signal fluctuated less than 5% without any temperature compensation schemes. Because the sensor does not utilize expensive and complex demodulation techniques it is potentially inexpensive. For the experimental system a linear output range of 8 g could be detected.

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.

This paper develops a cable-less pipeline robot which can adapt to the change of pipe diameter. It introduces the composition and function of robot mechanism as well as control system in detail. In this study, the robot employs the crawler traveling mechanism with symmetrical distribution in three groups. Through the rotating of ball screw and the change of the quadrilateral structure both driven by the stepper motor, the radial dimension of robot will be changed. The control system of pipeline robot adopts the modularized design method, takes stm32f103 as its embedded processor, provides a variety of hardware functions as well as the expansion of the circuit interfaces, has a high versatility and applicability. Through verification, the designed pipeline robot can work in various environments of pipeline, realizing in-pipe detection, data acquisition as well as other functions.

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.

The paper determines the optimal position of thermistors in sensitive components of gas pendulum omnibearing level posture sensor is d=900 μm (d is distance of two relatively detection thermistors). By using the finite element method, through the establishment of three-dimensional modeling of the thermistors of sensitive element in different positions, different tilting states, the distribution of temperature field and flow field inside the sensitive element are calculated. The results show that: (1) When the d is changed, the temperature field and flow field changes. (2) The relation curve, which is about the difference Δv of airflow velocity around the two relatively detection thermistors and tilt angle, changes with the d. This opens the effective approaches for the optimization design of the sensor.

The Internet of Things is a new type of technical system combining various types of information technology, the application of Internet of Things in agriculture meets the requirement of modern agriculture development and represents the orientation of future agriculture. In this article the Internet of Things system in agriculture is analyzed according to its systematic structure including its perception level, transmission level and application level and perception level, the sensor is the core of this level, multidimensional agricultural information can be obtained by advanced technology such as sensing and remote sensing about crops, soil and environment etc. The transmission level consists of wireless transmission network combing wireless transmission methods such as Zigbee, GPRS, WIFI and Bluetooth and network technology. The application level involves in intelligent management of agriculture including multi-dimensional information integration, intelligent decision and automatic control. The orientation of research and development is shown for the Internet of Things system in agriculture in this article. Its development will bring a brand-new reform for modern agriculture and realize agricultural automation and intellectualization.

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.