In this paper, a capacitive type MEMS accelerometer is designed based on surface micromachining technology. The synthesis is limited to the mechanical part not the electronic conditioning circuits. A lateral comb type transducer is selected. The design layout is based on manufacturing limitations of MUMPs; a surface micromachining technology for polysilicon. The sensor parameters were obtained for an automotive application in the airbag systems. To reach the desired bandwidth and good stability, a (force) feedback system was used applying negative stiffness. The comb spring stiffness was derived analytically. A finite element analysis was also conducted to verify the results. Other performance characteristics of the sensor such as shock resistance and transverse sensitivity (which were complicated to drive analytically) were calculated by finite element simulation within an optimization algorithm to meet the required sensor specifications.

This study has introduced a three-axis capacitive accelerometer, in which the part of the capacitor that calculates acceleration is installed in the z direction in the spring to improve the sensitivity in the said direction. In addition to having the advantages of previous accelerometers, the suggested accelerometer has compensated for previous shortcomings by increasing both sensitivity and pull-in voltage. Moreover, this accelerometer is able to decrease spring torsion and spring nonlinear behavior and provide a more straightforward rigidity computation. Therefore, without increasing the total occupancy level of the sensor, this accelerometer can increase the capacitive planes’ surface area to measure acceleration in z direction, resulting in an increase in sensitivity, while all the advantages of previous accelerometers are kept. In designing this accelerometer, factors such as rise time, overshoot, settling time, and peak time were considered. The proposed properties of the accelerometer were also derived from the perspective of a second-order system. Our designed accelerometer showed an operating frequency up to 20 kHz and a dynamic range up to 1000 g. The sensitivity of the accelerometer was 4fF/g in the z axis direction. Moreover, the sensitivity of the accelerometer in x and y directions was 9fF/g.

In this paper, design and performance analysis of a resonance nanosensor for earthquake low frequency geoacoustic waves detection is proposed. The model comprises of a proof mass suspended to the substrate, and a nanobeam attached to the intersection of the proof mass to the substrate. The nanobeam could be cosidered as a clamped-clamped nanoresonator actuated electrostartically. The induced accelaration to the proof mass could lead to an axial tensile or compression force in the nanoresonator. The axial induced force could change the system stored potential energy and result in the shift of the resonator natural frequncy. Measuring the frequncy shift of the resonator, could lead to the estimation of the applied accelaration to the proof mass. Furthermore, the nanobeam is laminated between two piezoelectric layers wich applying voltage to them could improve the perfomance of the nanosensor. Governing equations are obtained using Hamilonian’ s principle that considers the main sources of nonlinearity including electrostatic fringing field effect, piezoelectric and casimir force, and stretching effect. The equations are solved using numerical and analytical methods. The simulation results are being used to investigate the nanosensor performance charactersitics including the device dynamic response, resolution, sensitivity, bandwidth, dynamic range and the structural resitance. The results show that the proposed nano accelerometer could have a better performance compared the existing micro and macro earthquake detection devices measuring geoacoustic infrasonic and low frequency waves.

Due to the population growth in metropolitan regions such as Tehran and the existence of the underground constructions, the importance of seismic investigation is evident to reduce damages caused by probable earthquakes. Accordingly, the precise detection of micro to medium earthquakes is effective tool for tracking fault dynamics in seismic cycles, as well as for earthquake prediction and seismic hazard assessment. In this study, the recorded ambient noise at Tehran Disaster Mitigation and Management Organization (TDMMO) as well as Road, Housing and Urban Development Research Center (BHRC) networks as an accelerometer network installed in Tehran city, have been used on the point of characterizing the noise spectrum for each station as a function of time for obtaining the detection threshold of these networks. Therefore, an indirect approach based on the signal-to-noise ratio (SNR) in the time domain, with parameterization in the frequency domain is applied. Based on SNR method, the source signature is simulated by a simple source model called a circular fault model. Thus, the signal is estimated via the Brune function as most common models for circular faults. While, to determine the noise, the real data of 13 accelerometer stations of the TDMMO and seven joint stations with the BHRC are used. In this respect, the Power Spectral Density (PSD) of noise is calculated using PQLX software in the frequency domain and then is transferred to the time domain by the Parsville theorem. Eventually, the SNR value is acquired for each station by dividing these two quantities. As a result, the minimum detectable magnitude in at least five stations with an SNR larger than 5 is 3. 0 for S-waves and 3. 3 for P-waves, which frequently occurs in the center of the network. Another finding of these studies is to analyze the effect of spatial variations of the noise on the detection ability. For this, a constant noise is allotted to all stations, lowest observed noise level, as a result of which, the smallest magnitude detectable is 1. 7 for S-waves and 2. 2 for P-waves. At last, the sensitivity of the detection capability to three fundamental parameters, including stress drop, focal depth and reduced time, which are assumed as constant values within the network, are investigated. In fact, these parameters are strongly affected by uncertainty and are not absolute values. Consequently, the impact of their changes was studied. In our case, it is implied that the variation in the stress drop has no effect on the detection threshold, but the focal depth and the reduced time are effectual. A 15 km variation in the focal depth, the detectable magnitude changes by 0. 3 units, and by changing the reduced time from 0. 015 s to 0. 035 s, the detectable magnitude varies by 0. 4 units in Mw.

The numerous capabilities of smartphones have made them suitable alternative to expensive tools and methods in intelligent transportation systems. This study surveys the literature on the role of the accelerometer of smartphones in intelligent transportation applications. At first, the opportunities and challenges of using the accelerometer are stated. Then, the architecture of using this sensor including preprocessing, feature extraction, mode detection, reorientation and applications are explained. Finally, different applications that have used the accelerometer of mobile phones in the intelligent transportation systems have been investigated.