Sharif Digital Repository

Underwater navigation is generally accomplished through the data fusion of INS (Inertial Navigation System) and auxiliary sensors such as DVL (Doppler Velocity Logger) sensor. However, because of the possibility of DVL outage, alternative low-cost solutions are attractive. Among these, one is using vehicle kinetic model information extracted by the Newton-Euler equation to improve INS performance, which is called model-aided navigation. In this paper, only the vehicle translational motion dynamics are used to replace DVL in underwater navigation in DVL outage. The vehicle 3D translational dynamics has been obtained by using general Newton-Euler equations. Integrating these dynamics leads to... 

Energy use in buildings contributes a large part in global energy demand. To reduce energy use in this group of consumers, specially in cold seasons, an automatic control technique is proposed. In this paper, a model predictive controller (MPC) is employed to minimize the boiler activation time. The method uses the building model and incorporates the weather forecast data to act on the actuator in an optimal fashion while treating the user comfort constraints. This technique, as a part, can be embedded into the building energy management system. The building model parameters are obtained via an online identification process using unscented kalman filter (UKF). This identification is... 

In this study, a new heuristic multiple model filter, called Multiple Model Extended Continuous Ant Colony Filter, is proposed to solve a nonlinear multiple model state estimation problem. In this filter, a bank of extended continuous ant colony filters are run in parallel to solve the multiple model estimation problem. The probability of each model is continually updated and consequently both the true model and the states of the nonlinear system are updated based on the weighted sum of the filters. The new multiple model filter is tested on an engineering problem. The problem is to estimate simultaneously the states of a fixed-wing unmanned aerial vehicle as well as the wind model, applied... 

Brain connectivity estimation is a useful method to study brain functions and diagnose neuroscience disorders. Effective connectivity is a subdivision of brain connectivity which discusses the causal relationship between different parts of the brain. In this study, a dual Kalman-based method is used for effective connectivity estimation. Because of connectivity changes in autism, the method is applied to autistic signals for effective connectivity estimation. For method validation, the dual Kalman based method is compared with other connectivity estimation methods by estimation error and the dual Kalman-based method gives acceptable results with less estimation errors. Then, connectivities... 

Inspired by algorithms utilized in inertial navigation, an inertial motion capturing algorithm capable of position and heading estimation is introduced. The fusion algorithm is capable of real-time link geometry estimation, which allows for the imposition of biomechanical constraints without a priori knowledge regarding sensor placements. Furthermore, the algorithm estimates gyroscope and accelerometer bias, scaling, and non-orthogonality parameters in real-time. The stationary phases of the links, during which pseudo-measurements such as zero velocity or heading stabilization updates are applied, are detected using optically trained neural networks with buffered accelerometer and gyroscope... 

This paper presents an efficient algorithm based on a nonlinear dynamical model for the precise extraction of the characteristic points of electrocardiogram (ECG), which facilitates the HRV analysis. Determining the precise position of the waveforms of an ECG signal is complicated due to the varying amplitudes of its waveforms, the ambiguous and changing form of the complex and morphological variations with unknown sources of drift. A model-based approach handles these complications; therefore a method based on the usage of this concept in an extended Kalman filter structure has been developed. The fiducial points are detected using both the parameters of Gaussian-functions of the model, and... 

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and... 

Unlike a single-body approach, modeling based on a two-body approach has been employed to prepare the required system dynamic model as a time update equation in the applied filtering technology and measurement data for the estimation process. This more precise mathematical model enabled better understanding about the dynamics of the change in the aircraft mass properties during the airdropping operation. The problem is defined as estimation of the optimal mass properties parameters for the best possible fit of the model output to the real data. The parameter estimation problem is investigated by a nonlinear filtering methodology in two sequential steps. In the first step, the single extended... 

Motion capture is a process that movements of living organisms like human or objects are captured and the results are processed for the desired applications. These applications are in rehabilitation, sports, film industry and etc. There are many techniques and instruments for motion capture that optical camera systems are the most accurate ones. But these cameras are high cost and limited to labs. Some sensors like Inertial Measurement Units (IMU) and recently, Kinect cameras have been considered by many researchers because these are low cost and easy to use. But problems like bias, accumulated error and occlusion make them look for improvements. Fusion algorithms are one of the best methods... 

Motion capture is a process that movements of living organisms like human or objects are captured and the results are processed for the desired applications. These applications are in rehabilitation, sports, film industry and etc. There are many techniques and instruments for motion capture that optical camera systems are the most accurate ones. But these cameras are high cost and limited to labs. Some sensors like Inertial Measurement Units (IMU) and recently, Kinect cameras have been considered by many researchers because these are low cost and easy to use. But problems like bias, accumulated error and occlusion make them look for improvements. Fusion algorithms are one of the best methods... 

Online selection of the appropriate model and identifying its parameters based on measured vibrational data are among the challenging issues in dynamic system identification. After a severe earthquake, quick monitoring and assessment of structural health status play a crucial role in effective critical risk management for the building owners and decision-makers. The Bayesian multiple modeling approach is a suitable tool for optimal model class selection, which is used in this article mainly for improving data fitting precision, decreasing dimensions of structural unknown vector through removing unnecessary parameters, detecting the occurrence and type of predominant phenomenon related to... 

The navigation algorithms which use inertial measurement units (IMUs), such as inertial navigation systems (INSs), always suffer from intrinsic accumulated errors. Bias in gyros induces a significant drift in navigation output especially when micro-electro-mechanical sensor (MEMS) type is used. This error has high-and low-frequency components. De-noising of the long-term error (LTE) (the low-frequency component) is more challenging due to undeterministic behavior and overlapping with carrier motion in the low-frequency band. In this article, a method for de-noising of long-term MEMS-based gyro is presented. In this approach, an auto-regressive (AR) model for the LTE is developed which is...