Data-Driven Denoising of Stationary Accelerometer Signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table

Characterization of errors and noises in MEMS inertial sensors using Allan variance method

This thesis work has addressed the problems of characterizing and identifying the noises inherent to inertial sensors as gyros and accelerometers, which are embedded in inertial navigation systems, with the purpose of estimating the errors on the obtained position. The analysis of the Allan Variance method (AVAR) to characterize and identify the noises related to these sensors, has been done. The practical implementation of the AVAR method for the noises characterization has been performed over an experimental setup using the IMU 3DM-GX3 -25 data and the Matlab environment. From the AVAR plots it was possible to identify the Angle Random Walk and the Bias Instability in the gyros, and the Velocity Random Walk and Bias Instability in the accelerometers. A denoising process was also performed by using the Discrete Wavelet Transforms and the Median Filter. After the filtering the AVAR plots showed that the ARW was almost removed or attenuated using Wavelets, but not good results were obtained with the Median Filter

脳波信号解析に注目したノイズ除去、特徴抽出、実験観測応用を最適化する数理基盤に関する研究

Electroencephalography (EEG) data inevitably contains a large amount of noise particularly from ocular potentials in tasks with eye-movements and eye-blink, known as electrooculography (EOG) artifact, which has been a crucial issue in the braincomputer- interface (BCI) study. The eye-movements and eye-blinks have different time-frequency properties mixing together in EEGs of interest. This time-frequency characteristic has been substantially dealt with past proposed denoising algorithms relying on the consistent assumption based on the single noise component model. However, the traditional model is not simply applicable for biomedical signals consist of multiple signal components, such as weak EEG signals easily recognized as a noise because of the signal amplitude with respect to the EOG signal. In consideration of the realistic signal contamination, we newly designed the EEG-EOG signal contamination model for quantitative validations of the artifact removal from EEGs, and then proposed the two-stage wavelet shrinkage method with the undecimated wavelet decomposition (UDWT), which is suitable for the signal structure. The features of EEG-EOG signal has been extracted with existing decomposition methods known as Principal Component Analysis (PCA), Independent Component Analysis (ICA) based on a consistent assumption of the orthogonality of signal vectors or statistical independence of signal components. In the viewpoint of the signal morphology such as spiking, waves and signal pattern transitions, A systematic decomposition method is proposed to identify the type of signal components or morphology on the basis of sparsity in time-frequency domain. Morphological Component Analysis (MCA) is extended the traditional concept of signal decomposition including Fourier and wavelet transforms and provided a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases being independent of each other and uniqueness representation, called the concept of “dictionary”. MCA is applied to decompose the real EEG signal and clarified the best combination of dictionaries for the purpose. In this proposed semi-realistic biological signal analysis, target EEG data was prepared as mixture signals of artificial eye movements and blinks and iEEG recorded from electrodes embedded into the brain intracranially and then those signals were successfully decomposed into original types by a linear expansion of waveforms such as redundant transforms: UDWT, DCT,LDCT, DST and DIRAC. The result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST and DIRAC to represent the baseline envelop, multi frequency wave forms and spiking activities individually as representative types of EEG morphologies. MCA proposed method is used in negative-going Bereitschaftspotential (BP). It is associated with the preparation and execution of voluntary movement. Thus far, the BP for simple movements involving either the upper or lower body segment has been studied. However, the BP has not yet been recorded during sit-to-stand movements, which use the upper and lower body segments. Electroencephalograms were recorded during movement. To detect the movement of the upper body segment, a gyro sensor was placed on the back, and to detect the movement of the lower body segment, an electromyogram (EMG) electrode was placed on the surface of the hamstrings and quadriceps. Our study revealed that a negative-going BP was evoked around -3 to -2 seconds before the onset of the upper body movement in the sit-to-stand movement in response to the start cue. The BP had a negative peak before the onset of the movement. The potential was followed by premotor positivity, a motor-related potential, and a reafferent potential. The BP for the sit-to-stand movement had a steeper negative slope (-0.8 to -0.001 seconds) just before the onset of the upper body movement. The slope correlated with the gyro peak and the max amplitude of hamstrings EMG. A BP negative peak value was correlated with the max amplitude of the hamstring EMG. These results suggested that the observed BP is involved in the preparation/execution for a sit-to-stand movement using the upper and lower body. In summary, this thesis is help to pave the practical approach of real time analysis of desired EEG signal of interest toward the implementation of rehabilitation device which may be used for motor disabled people. We also pointed out the EEG-EOG contamination model that helps in removal of the artifacts and explicit dictionaries are representing the EEG morphologies.九州工業大学博士学位論文 学位記番号:生工博甲第290号 学位授与年月日:平成29年3月24日1 Introduction|2 Research Background and Preliminaries|3 Introduction of Morphological Component Analysis|4 Two-Stage Undecimated Wavelet Shrinkage Method|5 Morphologically Decomposition of EEG Signals|6 Bereitschaftspotential for Rise to Stand-Up Behavior九州工業大学平成28年

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

On Improving the Accuracy and Reliability of GPS/INS-Based Direct Sensor Georeferencing

Due to the complementary error characteristics of the Global Positioning System (GPS) and Inertial Navigation System (INS), their integration has become a core positioning component, providing high-accuracy direct sensor georeferencing for multi-sensor mobile mapping systems. Despite significant progress over the last decade, there is still a room for improvements of the georeferencing performance using specialized algorithmic approaches. The techniques considered in this dissertation include: (1) improved single-epoch GPS positioning method supporting network mode, as compared to the traditional real-time kinematic techniques using on-the-fly ambiguity resolution in a single-baseline mode; (2) customized random error modeling of inertial sensors; (3) wavelet-based signal denoising, specially for low-accuracy high-noise Micro-Electro-Mechanical Systems (MEMS) inertial sensors; (4) nonlinear filters, namely the Unscented Kalman Filter (UKF) and the Particle Filter (PF), proposed as alternatives to the commonly used traditional Extended Kalman Filter (EKF). The network-based single-epoch positioning technique offers a better way to calibrate the inertial sensor, and then to achieve a fast, reliable and accurate navigation solution. Such an implementation provides a centimeter-level positioning accuracy independently on the baseline length. The advanced sensor error identification using the Allan Variance and Power Spectral Density (PSD) methods, combined with a wavelet-based signal de-noising technique, assures reliable and better description of the error characteristics, customized for each inertial sensor. These, in turn, lead to a more reliable and consistent position and orientation accuracy, even for the low-cost inertial sensors. With the aid of the wavelet de-noising technique and the customized error model, around 30 percent positioning accuracy improvement can be found, as compared to the solution using raw inertial measurements with the default manufacturer’s error models. The alternative filters, UKF and PF, provide more advanced data fusion techniques and allow the tolerance of larger initial alignment errors. They handle the unknown nonlinear dynamics better, in comparison to EKF, resulting in a more reliable and accurate integrated system. For the high-end inertial sensors, they provide only a slightly better performance in terms of the tolerance to the losses of GPS lock and orientation convergence speed, whereas the performance improvements are more pronounced for the low-cost inertial sensors

Three layer wavelet based modeling for river flow

All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

Under-Sampled Reconstruction Techniques for Accelerated Magnetic Resonance Imaging

Due to physical and biological constraints and requirements on the minimum resolution and SNR, the acquisition time is relatively long in magnetic resonance imaging (MRI). Consequently, a limited number of pulse sequences can be run in a clinical MRI session because of constraints on the total acquisition time due to patient comfort and cost considerations. Therefore, it is strongly desired to reduce the acquisition time without compromising the reconstruction quality. This thesis concerns under-sampled reconstruction techniques for acceleration of MRI acquisitions, i.e., parallel imaging and compressed sensing. While compressed sensing MRI reconstructions are commonly regularized by penalizing the decimated wavelet transform coefficients, it is shown in this thesis that the visual artifacts, associated with the lack of translation-invariance of the wavelet basis in the decimated form, can be avoided by penalizing the undecimated wavelet transform coefficients, i.e., the stationary wavelet transform (SWT). An iterative SWT thresholding algorithm for combined SWT-regularized compressed sensing and parallel imaging reconstruction is presented. Additionally, it is shown that in MRI applications involving multiple sequential acquisitions, e.g., quantitative T1/T2 mapping, the correlation between the successive acquisitions can be incorporated as an additional constraint for joint under-sampled reconstruction, resulting in improved reconstruction performance. While quantitative measures of quality, e.g., reconstruction error with respect to the fully-sampled reference, are commonly used for performance evaluation and comparison of under-sampled reconstructions, this thesis shows that such quantitative measures do not necessarily correlate with the subjective quality of reconstruction as perceived by radiologists and other expert end users. Therefore, unless accompanied by subjective evaluations, quantitative quality measurements/comparisons will be of limited clinical impact. The results of experiments aimed at subjective evaluation/comparison of different under-sampled reconstructions for specific clinical neuroimaging MRI applications are presented in this thesis. One motivation behind the current work was to reduce the acquisition time for relaxation mapping techniques DESPOT1 and DESPOT2. This work also includes a modification to the Driven Equilibrium Single Pulse Observation of T1 with high-speed incorporation of RF field inhomogeneities (DESPOT1-HIFI), resulting in more accurate estimation of T1 values at high strength (3T and higher) magnetic fields

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks