Design and Implementation of an Ultrasonic Heat Meter Based on MSP430F437

Flowing with the reform of the hot water heating method in China,heat meter will enter into households in the near future.A portable ultrasonic heat meter is designed in this paper.The meter uses chip microprocessor MSP430F437 as the data process core,and uses ultrasonic flow sensor to measure flow rate of the hot water,and capture input and output temperatures of the hot water using the thermal resistance sensor Pt1000,and then household energy consumption is calculated via temperature difference between input temperature and output temperature of the hot water multiplied by volume of hot water that is calculated though flow rate integration of hot water.In order to test the performance of the proposed heat meter,experiments is carried out.Both the temperature and flow measurement results satisfy the requirements of accuracy and the heat meter is effective in the heat measurement.     

Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.     

使用运动监视传感网络对行走过程中腿部屈伸角的跟踪

An accelerometry-based gait analysis approach via the platform of sensor network is reported in this paper. The hardware units of the sensor network are wearable accelerometers that are attached at the limbs of human body. For the specific task of gait analysis, flexion angles of the thighs during gait cycles are computed. A Kalman filter is designed to estimate the flexion-extension angle, angular velocity of the thigh using the output of the wearable accelerometers. The proposed approach has been applied to four subjects and the performance is compared with videobased approach. Comparative results indicate that with the proposed Kalman filter, the sensor network is able to track the movement of the thighs during gait cycles with good accuracy and simultaneously detect major gait event of foot contact from the waveform of the angular velocity.     

Nested microring resonator sensor using beat frequency detection北大核心CSCD

A nested microring resonator is presented and its application for beat frequency sensing is demonstrated. The theoretical analysis of this structure has been obtained by transfer matrix method. By changing bending radius of the inner ring, the relations between it and resonance wavelength spacing have been theoretically analyzed. The simulation results show that the resonator frequency of inner microring splits to two peaks and the frequency interval of this two peaks is very narrow which show this structure can be used as a sensor detector in beat frequency sensing system. Therefore, by changing the refractive index, the output spectrum response is analyzed in optical and microwave regime. For this beat frequency system, the sensitivity is 1×10-4 refractive index unit (RIU) and the frequency shift is 4 MHz/1×10-4 RIU. ©, 2014, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.     

A Wide Band Differential 50% Duty Cycle Corrector for High Speed Sensors北大核心CSCD

A differential 50% Duty Cycle Corrector for high speed sensor is proposed in this paper. Compared with the conventional analog duty-cycle detectors designed in CMOS process, the proposed circuit has a simple and robust architecture and proofs the possibility of clock duty cycle correction at frequencies as high as 4 GHz. The novel features include a duty cycle detector using continuous-time integrator with a low-pass pre-filter and clock buffers chain designed with Source-Coupled Logic. The Duty Cycle Corrector is designed under Chartered 0.18 μm CMOS process and optimized for high-speed operation. The experimental results show that the circuit can work well at frequencies ranging from 500 MHz to 4.0 GHz and the acceptable input duty cycle range is 30%~70%. The power consumption is 5.37 mW and output jitter is 19.3 ps at 4GHz. The area of the test chip(include the probe pad)is 550 μm×370 μm. © 2017, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.     

Magnetic coupling governed pinning directions in magnetic tunnel junctions under magnetic field annealing with zero magnetic field cooling

<正>With the rapid development of emerging concepts, such as the Internet of things and big data, the use of various magnetoresistance(MR) sensors, especially tunneling MR(TMR) in magnetic tunnel junctions(MTJs), for information perception is considered the critical first step. A typical topic in this field is the construction of a Wheatstone bridge structure to restrain the temperature drift and amplify the magnetic response signal.     

Joint multiple parameters estimation for coherent chirp signals using vector sensor array

In this paper, an algorithm is proposed to estimate starting frequency （SF）, chirp rate （CR）, 2-D direction-of-arrivals （DOA） and polarization of coherent chirp signals with vector sensor arrays. The fractional Fourier transformation （FRFT） is used to estimate SF and CR of chirp signals in this method. And a new correlation matrix is reconstructed to suppress the noise. The property of the vector sensor array is employed to solve the problem of insufficient rank from signal coherence. The L-shaped uniform array of expend aperture is used to improve the precision of es- timation, and the method of solving the ambiguity of angle under the condition of coherent signals is presented. The performance of this algorithm is compared with that of spatial smoothing method to verify the efficacy of this approach.     

Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output disturbance. The imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead zone. Because of the presence of output dead zone and the additive disturbance, the states cannot be expected to driven into an arbitrarily small neighborhood of the origin. To solve the global practical stabilization problem, we proposes a low gain-based linear dynamic output feedback law, under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set, both in finite time. Simulation results illustrate the effectiveness of our proposed control method.     

Sensor fault diagnosis for a class of time delay uncertain nonlinear systems using neural network

In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.     

Brain tissue segmentation based on spatial information fusion by Dempster-Shafer theory

As a result of noise and intensity non-uniformity,automatic segmentation of brain tissue in magnetic resonance imaging (MRI) is a challenging task.In this study a novel brain MRI segmentation approach is presented which employs Dempster-Shafer theory (DST) to perform information fusion.In the proposed method,fuzzy c-mean (FCM) is applied to separate features and then the outputs of FCM are interpreted as basic belief structures.The salient aspect of this paper is the interpretation of each FCM output as a belief structure with particular focal elements.The results of the proposed method are evaluated using Dice similarity and Accuracy indices.Qualitative and quantitative comparisons show that our method performs better and is more robust than the existing method.     

Dynamic output feedback stabilization of deterministic finite automata via the semi‑tensor product of matrices approach

This paper deals with the dynamic output feedback stabilization problem of deterministic finite automata (DFA). The static form of this problem is defined and solved in previous studies via a set of equivalent conditions. In this paper, the dynamic output feedback (DOF) stabilization of DFAs is defined in which the controller is supposed to be another DFA. The DFA controller will be designed to stabilize the equilibrium point of the main DFA through a set of proposed equivalent conditions. It has been proven that the design problem of DOF stabilization is more feasible than the static output feedback (SOF) stabilization. Three simulation examples are provided to illustrate the results of this paper in more details. The first example considers an instance DFA and develops SOF and DOF controllers for it. The example explains the concepts of the DOF controller and how it will be implemented in the closed-loop DFA. In the second example, a special DFA is provided in which the DOF stabilization is feasible, whereas the SOF stabilization is not. The final example compares the feasibility performance of the SOF and DOF stabilizations through applying them to one hundred random-generated DFAs. The results reveal the superiority of the DOF stabilization.     

Sampled-data extended state observer for uncertain nonlinear systems

In this paper, we present a sampled-data nonlinear extended state observer (NLESO) design method for a class of nonlinear systems with uncertainties and discrete time output measurement. To accommodate the inter-sample dynamics, an inter-sample output predictor is employed in the structure of the NLESO to estimate the system output in the sampling intervals, where the prediction is used in the proposed observer instead of the system output. The exponential convergence of the sampled-data NLESO is also discussed and a sufficient condition is given by the Lyapunov method. A numerical example is provided to illustrate the performance of the proposed observer.     

A negative selection algorithm based on hierarchical clustering of self set

Negative selection algorithm(NSA) is an important method of generating artificial immune detectors.However,the traditional NSAs aim at eliminating the self-recognized invalid detectors,by matching candidate detectors with the whole self set.The matching process results in extremely low generation efficiency and significantly limits the application of NSAs.In this paper,an improved NSA called CB-RNSA,which is based on the hierarchical clustering of self set,is proposed.In CB-RNSA,the self data is first preprocessed by hierarchical clustering,and then replaced by the self cluster centers to match with candidate detectors in order to reduce the distance calculation cost.During the detector generation process,the candidate detectors are restricted to the lower coverage space to reduce the detector redundancy.In the paper,probabilistic analysis is performed on non-self coverage of detectors.Accordingly,termination condition of the detector generation procedure in CB-RNSA is given.It is more reasonable than that of traditional NSAs,which are based on predefined detector numbers.The theoretical analysis shows the time complexity of CB-RNSA is irrelevant to the self set size.Therefore,the difficult problem,in which the detector training cost is exponentially related to the size of self set in traditional NSAs,is resolved,and the efficiency of the detector generation under a big self set is also improved.The experimental results show that:under the same data set and expected coverage,the detection rate of CB-RNSA is higher than that of the classic RNSA and V-detector algorithms by 12.3% and 7.4% respectively.Moreover,the false alarm rate is lower by 8.5% and 4.9% respectively,and the time cost of CB-RNSA is lower by 67.6% and 75.7% respectively.     

Recursive algorithm and accurate computation of dyadic Green's functions for stratified uniaxial anisotropic media

A recursive algorithm is adopted for the computation of dyadic Green's functions in three-dimensional stratified uniaxial anisotropic media with arbitrary number of layers. Three linear equation groups for computing the coefficients of the Som- merfeld integrals are obtained according to the continuity condition of electric and magnetic fields across the interface between different layers, which are in corre- spondence with the TM wave produced by a vertical unit electric dipole and the TE or TM wave produced by a horizontal unit electric dipole, respectively. All the linear equation groups can be solved via the recursive algorithm. The dyadic Green's functions with source point and field point being in any layer can be conveniently obtained by merely changing the position of the elements within the source term of the linear equation groups. The problem of singularities occurring in the Sommer- feld integrals is efficiently solved by deforming the integration path in the complex plane. The expression of the dyadic Green's functions provided by this paper is terse in form and is easy to be programmed, and it does not overflow. Theoretical analysis and numerical examples show the accuracy and effectivity of the algo-rithm.     

A Multi-Detector Security Architecture with Local Feature-Level Fusion for Multimodal Biometrics

The hierarchical identification model with multiple detectors is an innovative approach for biometric systems design which improves the identification accuracy while ensuring the computational complexity reduction. This complexity reduction provides additional advantages in terms of execution time and recognition accuracy. The model is different from the actual solutions for biometric data classification because it essentially uses a special kind of classifiers （detectors） and the identification decision is issued in a hierarchical way according to the users importance; this makes it suitable for various security requirements applications （users with different authorization levels）. The model includes a local feature-level fusion for each of the integrated biometrics. The paper defines and explains the multi-detector security architecture with its basic functions. The achieved experimental results are discussed to reveal the proposed method advantages and further potential enhancements for particular use cases.     

Recursive algorithm and accurate computation of dyadic Green’s functions for stratified uniaxial anisotropic media

A recursive algorithm is adopted for the computation of dyadic Green＇s functions in three-dimensional stratified uniaxial anisotropic media with arbitrary number of layers. Three linear equation groups for computing the coefficients of the Sommerfeld integrals are obtained according to the continuity condition of electric and magnetic fields across the interface between different layers, which are in correspondence with the TM wave produced by a vertical unit electric dipole and the TE or TM wave produced by a horizontal unit electric dipole, respectively. All the linear equation groups can be solved via the recursive algorithm. The dyadic Green＇s functions with source point and field point being in any layer can be conveniently obtained by merely changing the position of the elements within the source term of the linear equation groups. The problem of singularities occurring in the Sommerfeld integrals is efficiently solved by deforming the integration path in the complex plane. The expression of the dyadic Green＇s functions provided by this paper is terse in form and is easy to be programmed, and it does not overflow. Theoretical analysis and numerical examples show the accuracy and effectivity of the algorithm.     

Enhanced adaptive nonlinear extended state observer for pure feedback systems withmatched andmismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.     

On localization with robust power control for safety critical wireless sensor networks

A hybrid methodology is proposed for use in low power, safety critical wireless sensor network applications, where quality-of-service orientated transceiver output power control is required to operate in parallel with radio frequency-based localization. The practical implementation is framed in an experimental procedure designed to track a moving agent in a realistic indoor environment. An adaptive time synchronized approach is employed to ensure the positioning technique can operate effectively in the presence of dataloss and where the transmitter output power of the mobile agent is varying due to power control. A deterministic multilateration-based positioning approach is adopted and accuracy is improved by filtering signal strength measurements overtime to account for multipath fading. The location estimate is arrived at by employing least-squares estimation. Power control is implemented at two separate levels in the network topology. First, power control is applied to the uplink between the tracking reference nodes and the centralized access point. A number of algorithms are implemented highlighting the advantage associated with using additional feedback bandwidth, where available, and also the need for effective time delay compensation. The second layer of power control is implemented on the uplink between the mobile agent and the access point and here quantifiable improvements in quality of service and energy efficiency are observed. The hybrid paradigm is extensively tested experimentally on a fully compliant 802.15.4 testbed, where mobility is considered in the problem formulation using a team of fully autonomous robots.     

Effect of properties of zirconia powder on performance of planar oxygen sensor chip北大核心CSCD

Three kinds of powders were used in the experiment. Chemical composition, phase composition, particle morphology, surface area, and sintering properties, Suitability for cast taping, performance of prepared planar oxygen sensor chip were studied comprehensively. The results showed that POWDER A was suitable for tape casting, but the sintering temperature (1500 ℃) is highest among those three powder, and there exist a lot of small-sized pores in sintered ceramics. The reason for this phenomenon is the presence of two-phase composition in POWDER A. But this powder can be prepared to planar oxygen sensor which has good response for oxygen. The substrate prepared by tape casting using POWDER C is very soft, which resulting in the follow-up process is difficult, and there is no measured signal for the preparaed planar oxygen sensor chip samples. Substrate prepared by POWDER B is brittle, and the impurity content also is high, sample signal is not detected for the prepared sensor chip. The requirement of planar oxygen sensor by tape-casting for YSZ powder is: Si and Fe content should be as low as possible; particles have a small particle diameter, monodisperse size distribution; specific surface area should be controlled at 12 m2/g~15 m2/g. ©, 2014, The Editorial Office of Chinese Journal of Sensors and Actuators. All right reserved.