Design, modelling and simulation of vibratory micromachined gyroscopes

Among various MEMS sensors, a rate gyroscope is one of the most complex sensors from the design point of view. The gyro normally consists of a proof mass suspended by an elaborate assembly of beams that allow the system to vibrate in two transverse modes. The structure is normally analysed and designed using commercial FEM packages such as ANSYS or MEMS specific commercial tools such as Coventor or Intellisuite. In either case, the complexity in analysis rises manyfolds when one considers the etch hole topography and the associated fluid flow calculation for damping. In most cases, the FEM analysis becomes prohibitive and one resorts to equivalent electrical circuit simulations using tools like SABER in Coventor. Here, we present a simplified lumped parameter model of the tuning fork gyro and show how easily it can be implemented using a generic tool like SIMULINK. The results obtained are compared with those obtained from more elaborate and intense simulations in Coventor. The comparison shows that lumped parameter SIMULINK model gives equally good results with fractional effort in modelling and computation. Next, the performance of a symmetric and decoupled vibratory gyroscope structure is also evaluated using this approach and a few modifications are made in this design to enhance the sensitivity of the device.

An introduction to inertial and visual sensing

In this paper we present a tutorial introduction to two important senses for biological and robotic systems — inertial and visual perception. We discuss the fundamentals of these two sensing modalities from a biological and an engineering perspective. Digital camera chips and micro-machined accelerometers and gyroscopes are now commodities, and when combined with today's available computing can provide robust estimates of self-motion as well 3D scene structure, without external infrastructure. We discuss the complementarity of these sensors, describe some fundamental approaches to fusing their outputs and survey the field.

Displacement Amplifying Compliant Mechanisms for Two-axis High-resolution Monolithic Inertial Sensors

Novel designs for two-axis, high-resolution, monolithic inertial sensors are presented in this paper. Monolithic, i.e., joint-less single-piece compliant designs are already common in micromachined inertial sensors such as accelerometers and gyroscopes. Here, compliant mechanisms are used not only to achieve de-coupling between motions along two orthogonal axes but also to amplify the displacements of the proof-mass. Sensitivity and resolution capabilities are enhanced because the amplified motion is used for sensing the measurand. A particular symmetric arrangement of displacement-amplifying compliant mechanisms (DaCMs) leads to de-coupled and amplified motion. An existing DaCM and a new topology-optimized DaCM are presented as a building block in the new arrangement. A spring-mass-lever model is presented as a lumped abstraction of the new arrangement. This model is useful for arriving at the optimal parameters of the DaCM and for performing system-level simulation. The new designs improved the performance by a factor of two or more.

Electrothermally tunable MEMS filters

MEMS resonators have potential application in the area of frequency selective devices (e.g., gyroscopes, mass sensors, etc.). In this paper, design of electro thermally tunable resonators is presented. SOIMUMPs process is used to fabricate resonators with springs (beams) and a central mass. When voltage is applied, due to joule heating, temperature of the conducting beams goes up. This results in increase of electrical resistance due to mobility degradation. Due to increase in the temperature, springs start softening and therefore the fundamental frequency decreases. So for a given structure, one can modify the original fundamental frequency by changing the applied voltage. Coupled thermal effects result in non-uniform heating. It is observed from measurements and simulations that some parts of the beam become very hot and therefore soften more. Consequently, at higher voltages, the structure (equivalent to a single resonator) behaves like coupled resonators and exhibits peak splitting. In this mode, the given resonator can be used as a band rejection filter. This process is reversible and repeatable. For the designed structure, it is experimentally shown that by varying the voltage from 1 to 16V, the resonant frequency could be changed by 28%.

Hybrid Elements for Modelling Squeeze Film Effects Coupled with Structural Interactions in Vibratory MEMS Devices

We present a hybrid finite element based methodology to solve the coupled fluid structure problem of squeeze film effects in vibratory MEMS devices, such as gyroscopes, RF switches, and 2D resonators. The aforementioned devices often have a thin plate like structure vibrating normally to a fixed substrate, and are generally not perfectly vacuum packed. This results in a thin air film being trapped between the vibrating plate and the fixed substrate which behaves like a squeeze film offering both stiffness and damping. For accurate modelling of such devices the squeeze film effects must be incorporated. Extensive literature is available on squeeze film modelling, however only a few studies address the coupled fluid elasticity problem. The majority of the studies that account for the plate elasticity coupled with the fluid equation, either use approximate mode shapes for the plate or use iterative solution strategies. In an earlier work we presented a single step coupled methodology using only one type of displacement based element to solve the coupled problem. The displacement based finite element models suffer from locking issues when it comes to modelling very thin structures with the lateral dimensions much larger than the plate thickness as is typical in MEMS devices with squeeze film effects. In this work we present another coupled formulation where we have used hybrid elements to model the structural domain. The numerical results show a huge improvement in convergence and accuracy with coarse hybrid mesh as compared to displacement based formulations. We further compare our numerical results with experimental data from literature and find them to be in good accordance.

基于多传感器融合的组合导航方法研究

本文提出一种基于多传感器融合的组合导航方法,能够在小型旋翼无人机上实现低成本、高精度导航定位.该方法通过建立导航系统的机械编排模型,设计了一个17状态的扩展卡尔曼滤波器(EKF).对加速计的零偏和陀螺仪的漂移进行在线估计,实时的补偿传感器的测量误差.从而对旋翼无人机的速度、位置、角速度和姿态等参数进行精确的估计.通过对实际飞行数据仿真实验,并对比参考的导航系统,证明该方法在飞机的全包线飞行下均能够解算出可靠的导航信息。

Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player’s forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.