@article{11861,
  abstract     = {{In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.}},
  author       = {{Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}},
  issn         = {{2329-9290}},
  journal      = {{IEEE/ACM Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{computational complexity, reverberation, speech recognition, automatic speech recognition, background noise, clean speech, computational complexity, energy compensation, logarithmic mel power spectral domain, mel frequency cepstral coefficients, microphone input signals, model-based feature compensation schemes, noisy reverberant speech automatic recognition, noisy reverberant speech features, reverberation, Atmospheric modeling, Computational modeling, Noise, Noise measurement, Reverberation, Speech, Vectors, Model-based feature compensation, observation model for reverberant and noisy speech, recursive observation model, robust automatic speech recognition}},
  number       = {{1}},
  pages        = {{95--109}},
  title        = {{{A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech}}},
  doi          = {{10.1109/TASLP.2013.2285480}},
  volume       = {{22}},
  year         = {{2014}},
}

@inproceedings{11740,
  abstract     = {{In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.}},
  author       = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian noise, maximum likelihood estimation, parameter estimation, GMM parameter, Gaussian mixture model, MAP estimation, Map-based estimation, maximum a-posteriori estimation, maximum likelihood technique, noisy observation, sequential estimation framework, white Gaussian noise, Additive noise, Gaussian mixture model, Maximum likelihood estimation, Noise measurement, Gaussian mixture model, Maximum a posteriori estimation, Maximum likelihood estimation}},
  pages        = {{3352--3356}},
  title        = {{{MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}}},
  doi          = {{10.1109/ICASSP.2013.6638279}},
  year         = {{2013}},
}

@inproceedings{11816,
  abstract     = {{In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}

@inproceedings{11726,
  abstract     = {{In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car.}},
  author       = {{Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{7th Workshop on Positioning Navigation and Communication (WPNC 2010)}},
  keywords     = {{altitude measurement unit, barometers, barometric altimeter, barometric error Kalman filter, barometric height estimation, direct cosine matrix, global positioning system, Global Positioning System, GPS device, height information, height measurement, inertial measurement unit, Kalman filters, loosely-coupled error state space Kalman filter, loosely-coupled Kalman-filter, map matching, robust information, robust location estimation, sensor fusion, topographic map information, vertical user position}},
  pages        = {{128--134}},
  title        = {{{Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter}}},
  doi          = {{10.1109/WPNC.2010.5650745}},
  year         = {{2010}},
}

@article{21774,
  author       = {{Fricke, Harald and Elsner, Andreas and Weise, Reiner and Bolte, Matthias and van den Hoff, Jörg and Burchert, Wolfgang and Domik, Gitta and Fricke, Eva}},
  issn         = {{0939-3889}},
  journal      = {{Zeitschrift für Medizinische Physik}},
  keywords     = {{Coronary artery disease, CT angiography, cardiac PET, quantitative perfusion measurement, Koronare Herzerkrankung, CT-Angiographie, PET, quantitative Perfusionsmessung}},
  number       = {{3}},
  pages        = {{182--188}},
  title        = {{{Quantitative myocardial perfusion PET combined with coronary anatomy derived from CT angiography: Validation of a new fusion and visualisation software}}},
  doi          = {{10.1016/j.zemedi.2009.05.005}},
  volume       = {{19}},
  year         = {{2009}},
}

@inproceedings{11723,
  abstract     = {{In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{covariance matrices, expectation-maximisation algorithm, expectation-maximization algorithm, global positioning system, Global Positioning System, GPS, inertial measurement unit, interacting multiple model approach, Kalman filters, multilevel sensor fusion, narrow street canyons, narrow tunnels, online parameter estimation, parameter estimation, road vehicles, robust vehicle localization, sensor fusion, state noise covariances, time-variant multilevel Kalman filter, vehicle tracking algorithm}},
  pages        = {{235--242}},
  title        = {{{Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}}},
  doi          = {{10.1109/WPNC.2009.4907833}},
  year         = {{2009}},
}

@inproceedings{11724,
  abstract     = {{In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}},
  keywords     = {{computational complexity, expectation-maximisation algorithm, Global Positioning System, inertial measurement unit, inertial navigation, interacting multiple model, iterative block expectation-maximization algorithm, Kalman filters, multi-stage Kalman filter, parameter estimation, road vehicles, vehicle positioning, vehicle tracking}},
  pages        = {{1--5}},
  title        = {{{Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}}},
  doi          = {{10.1109/VETECS.2009.5073634}},
  year         = {{2009}},
}

@inproceedings{11881,
  abstract     = {{A combination of GPS (global positioning system) and INS (inertial navigation system) is known to provide high precision and highly robust vehicle localization. Notably during times when the GPS signal has a poor quality, e.g. due to the lack of a sufficiently large number of visible satellites, the INS, which may consist of a gyroscope and an odometer, will lead to improved positioning accuracy. In this paper we show how velocity information obtained from GSM (global system for mobile communications) signalling, rather than from a tachometer, can be used together with a gyroscope sensor to support localization in the presence of temporarily unavailable GPS data. We propose a sensor fusion system architecture and present simulation results that show the effectiveness of this approach.}},
  author       = {{Peschke, Sven and Bevermeier, Maik and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{cellular radio, distance measurement, global positioning system, Global Positioning System, global system for mobile communications, GPS positioning approach, GSM velocity, gyroscopes, gyroscope sensor, inertial navigation, inertial navigation system, odometer, sensor fusion system architecture, sensors}},
  pages        = {{195--202}},
  title        = {{{A GPS positioning approach exploiting GSM velocity estimates}}},
  doi          = {{10.1109/WPNC.2009.4907827}},
  year         = {{2009}},
}

@inproceedings{9568,
  abstract     = {{A simple pre-stress estimate method of Langevin transducers is studied. The measurement setup consists of a capacitor, an impedance converter and a voltmeter. Based on the piezoelectric equation and the basic circuit theory, the mathematical expression between the pre-stress and the voltage across the capacitor is derived. The pre-stress level can then be calculated out of the measurement of the capacitor voltage. In order to evaluate the precision of this method, a force washer is used to measure the pre-stress of the Langevin transducer. The result shows the pre-stress level obtained from this method is 30-40\% higher than the pre-stress level measured by the force washer. This method is simple and can be used to estimate the pre-stress of various Langevin transducers. The precision of this method can be raised if d33 is identified under different pre-stress levels.}},
  author       = {{Bo, Fu and Ting, Li and Hemsel, Tobias}},
  booktitle    = {{Piezoelectricity, Acoustic Waves, and Device Applications, 2008. SPAWDA 2008. Symposium on}},
  keywords     = {{capacitors, impedance convertors, piezoelectric transducers, stress analysis, Langevin transducers, basic circuit theory, capacitor, impedance converter, piezoelectric equation, pre-stress estimating method, voltmeter, Capacitors, Educational institutions, Equations, Force measurement, Impedance measurement, Manufacturing, Mechatronics, Piezoelectric transducers, Voltage, Voltmeters, Langevin transducer, capacitor, piezoelectric element, pre-stress}},
  pages        = {{324--327}},
  title        = {{{A simple pre-stress estimating method of langevin transducers}}},
  doi          = {{10.1109/SPAWDA.2008.4775801}},
  year         = {{2008}},
}

@inproceedings{9576,
  abstract     = {{In neurosurgery, delineation of tumor boundaries during resection of brain tumors is of substantial relevance. During operation distinction between tumor and healthy tissue rely on the abilities of the surgeon based on visual and tactile differentiation. In this paper a high sensitivity actuator-sensor system using a piezoelectric bimorph is presented. Frequency shift and transfer function of the bimorphpsilas voltages are detected and evaluated. Sensorpsilas sensitivity is evaluated using two frequency controls strategies: A phase-locked loop (PLL) and a self-oscillating circuit. Results of measurements conducted on gel-phantoms are presented and discussed.}},
  author       = {{Uribe, David Oliva and Stroop, Ralf and Hemsel, Tobias and Wallaschek, Jörg}},
  booktitle    = {{Frequency Control Symposium, 2008 IEEE International}},
  issn         = {{1075-6787}},
  keywords     = {{biomedical measurement, brain, cancer, neurophysiology, phantoms, phase locked loops, piezoelectric actuators, surgery, tactile sensors, transfer functions, tumours, PLL, biomedical tissue differentiation system, brain tumor resection, frequency control, frequency shift, gel-phantom, high sensitivity actuator-sensor system, neurosurgery, phase-locked loop, piezoelectric actuators, piezoelectric bimorph, self-oscillating circuit, sensor sensitivity, tactile differentiation, tactile sensor system, transfer function, tumor boundary, visual differentiation, Biomedical measurements, Circuits, Frequency control, Neoplasms, Neurosurgery, Phase locked loops, Piezoelectric actuators, Surges, Transfer functions, Voltage}},
  pages        = {{91--94}},
  title        = {{{Development of a biomedical tissue differentiation system using piezoelectric actuators}}},
  doi          = {{10.1109/FREQ.2008.4622963}},
  year         = {{2008}},
}