Abstract: A vehicle braking unit comprises an operating braking pressure inlet which receives an inlet braking pressure built up by a braking cylinder, and at least one operating brake pressure outlet at which an outlet braking pressure is provided for an operating brake modulator, which is upstream of the operating brake and provides anti-lock functionality. At least one connecting channel hydraulically connects the operating brake pressure inlet to the outlet. Brake fluid is supplied from a storage unit through the brake cylinder, the connecting channel and the operating brake modulator. The braking unit has a brake fluid inlet, through which the brake fluid is received from the storage unit, a brake fluid pump, by which the brake fluid is suctioned through the brake fluid inlet and a supply pressure is built up, and a valve unit that controls the outlet braking pressure and/or a parking brake release pressure by the supply pressure.

Abstract: A braking device for a vehicle, comprises a main brake cylinder, which provides, in response to the actuation thereof, an operating brake pressure in at least one operating brake circuit for the actuation of an operating brake A hydraulic fluid pressure intake connected to a hydraulic circuit, which contains a hydraulic fluid having a built-up hydraulic circuit pressure. Through the hydraulic fluid pressure intake, the hydraulic fluid is provided at the hydraulic circuit pressure. The braking device further comprises a valve device, which actively controls a main brake cylinder actuation pressure and/or a parking brake release pressure by means of the hydraulic fluid that is provided. The main brake cylinder is actuated by means of the main brake cylinder actuation pressure for an active brake engagement. Alternatively or additionally, a parking brake is hydraulically released by means of the parking brake release pressure.

Abstract: A system and a method for a blind direction of arrival estimation is provided for a nonlinear 1-dimensional array of M receivers of J<M unknown signals transmitted through an unknown channel and subject to Gaussian noise. The method overcomes the conditions of that signals be disjoint in the time-frequency domain and/or statistically independent. A likelihood function evaluates a probability for the directions of arrivals. A weighting and regularization scheme selects relevant frequency components. A joint likelihood function of all frequencies is interpreted as a cost or objective function. The time domain is included into the estimation procedure by using a particle filter that operates on the constructed likelihood function and a simple transition kernel for the directions.

Abstract: Systems and methods are provided to create an acoustic map of a space containing multiple acoustic sources. Source localization and separation takes place by sampling an ultra large microphone array containing over 1020 microphones. The space is divided into a plurality of masks, wherein each masks represents a pass region and a complementary rejection region. Each mask is associated with a subset of microphones and beamforming filters that maximize a gain for signals coming from the pass region of the mask and minimizes the gain for signals from the complementary region according to an optimization criterion. The optimization criterion may be a minimization of a performance function for the beamforming filters. The performance function is preferably a convex function. A processor provides a scan applying the plurality of masks to locate a target source. Processor based systems to perform the optimization are also provided.

Abstract: An acoustic signal traversing a hot gas is sampled at a source and a receiver and is represented in overlapping windows that maximize useable signal content. Samples in each window are processed to represented in different sparsified bins in the frequency domain. Determining a signal delay between the source and the receiver from a summation of maximum smoothed coherence transform cross-correlation values of different data windows wherein a sparseness of a mean smoothed coherence transform cross-correlation of windows is maximized. Determining a set of delay times wherein outliers are deleted to estimate a time of flight from which a temperature of the hot gas is calculated.

Abstract: A system and methods to deblend seismic data from a plurality of sources and received by a plurality of sensors as shot gathers are disclosed. The deblending is performed by a Mutual Interdependence Analysis Method to separate contributions of different shots. Deblending is also performed by applying a measure of coherence in parallel data domains such as Common Shot Gather and Common Midpoint. Deblending is also achieved by using the hyperbolic nature of seismic data in the common midpoint domain. Deblended signals are estimated and are applied to create a seismic image. Also, Bergman iteration based migration is applied directly on the blended seismic shot gathers without first deblending as an alternative method. The methods are applied in seismic imaging for exploration of natural resources.

Abstract: Systems and methods are provided for source localization and separation by sampling a large scale microphone array asynchronously to simulate a smaller size but moving microphone array. Signals that arrive from different angles at the array are shifted differently in their frequency content. The sources are separated by evaluating correlated and even equal frequency content. Compressive sampling enables the utilization of extremely large scale microphone arrays by reducing the computational effort orders of magnitude in comparison to standard synchronous sampling approaches. Processor based systems to perform the source separation methods are also provided.

Abstract: Methods related to Generalized Mutual Interdependence Analysis (GMIA), a low complexity statistical method for projecting data in a subspace that captures invariant properties of the data, are implemented on a processor based system. GMIA methods are applied to the signal processing problem of voice activity detection and classification. Real-world conversational speech data are modeled to fit the GMIA assumptions. Low complexity GMIA computations extract reliable features for classification of sound under noisy conditions and operate with small amounts of data. A speaker is characterized by a slow varying or invariant channel that is learned and is tracked from single channel data by GMIA methods.

Abstract: A patient monitoring and intervention system, comprises an interface for receiving data representing multiple different parameters from multiple different sensors, comprising sensors in a patient bed and attached to a patient including, a heart rate sensor, a respiration sensor and a pressure sensor indicating bed pressure points. A learning processor determines a normal range for a set of the different received patient parameters for the patient by recording the patient parameter values over a time period and analyzing the recorded parameter values to determine their range. A data processor determines if the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient, initiates adjustment of a patient bed and at least one of, (a) changes medication administered to a patient and (b) alerts a worker of the patient parameter change.

Abstract: Methods for one-class learning using support vector machines from a plurality of data batches are provided. A first support vector machine is learned from the plurality of data batches by a processor. A new data batch is received by the processor and is classified by the first support vector machine. If a non-zero loss classification occurs a new support vector machine is trained using the first support vector machine and the new data batch only. Data batches can be discarded if they are represented by the current support vector machine or after being used for training an updated support vector machine. Weighing factors applied to update the first support vector machine depend upon a parameter which is optimized iteratively. Support vectors do not need to be recalculated. A classifier is learned in a number of stages equal to the number of data batches processed on-line.

Abstract: Own voice recognition (OVR) for hearing aids, detects time instances where the person wearing the device is speaking. Classification of the own voice is performed dependent on a fixed or adaptive detection threshold. Automatic tuning in a real-time system depends on general noise statistics in the input signals. The noise is removed from the received signal and is characterized by signal-to-noise ratio and noise color. An optimal detection threshold for own voice recognition is determined based on the noise characteristics. A noise detection model is created by smoothed Voronoi tessellation. Own voice detection is performed by a processor.

Abstract: Systems and methods provide a vehicle hit-and-run prevention and documentation method and system that warn approaching vehicles that pose a collision threat and document the occurrence of a collision. Embodiments use vehicle proximity sensors in conjunction with vehicle video cameras to detect an approaching object, determine the likelihood of collision and if likely, record video data.

Abstract: A system and a method for a blind direction of arrival estimation is provided for a nonlinear 1-dimensional array of M receivers of J<M unknown signals transmitted through an unknown channel and subject to Gaussian noise. The method overcomes the conditions of that signals be disjoint in the time-frequency domain and/or statistically independent. A likelihood function evaluates a probability for the directions of arrivals. A weighting and regularization scheme selects relevant frequency components. A joint likelihood function of all frequencies is interpreted as a cost or objective function. The time domain is included into the estimation procedure by using a particle filter that operates on the constructed likelihood function and a simple transition kernel for the directions.

Abstract: Methods for one-class learning using support vector machines from a plurality of data batches are provided. A first support vector machine is learned from the plurality of data batches by a processor. A new data batch is received by the processor and is classified by the first support vector machine. If a non-zero loss classification occurs a new support vector machine is trained using the first support vector machine and the new data batch only. Data batches can be discarded if they are represented by the current support vector machine or after being used for training an updated support vector machine. Weighing factors applied to update the first support vector machine depend upon a parameter which is optimized iteratively. Support vectors do not need to be recalculated. A classifier is learned in a number of stages equal to the number of data batches processed on-line.

Abstract: Own voice recognition (OVR) for hearing aids, detects time instances where the person wearing the device is speaking. Classification of the own voice is performed dependent on a fixed or adaptive detection threshold. Automatic tuning in a real-time system depends on general noise statistics in the input signals. The noise is removed from the received signal and is characterized by signal-to-noise ratio and noise color. An optimal detection threshold for own voice recognition is determined based on the noise characteristics. A noise detection model is created by smoothed Voronoi tessellation. Own voice detection is performed by a processor.

Abstract: A method for determining a signature vector of a high dimensional dataset includes initializing a mutual interdependence vector wGMIA from a a set X of N input vectors of dimension D, where N?D, randomly selecting a subset S of n vectors from set X, where n is such that n>>1 and n<N, calculating an updated mutual interdependence vector wGMIA from wGMIA—new=wGMIA+S·(ST·S+?I)?1·( 1?MT·wGMIA), where ? is a regularization parameter, M ij = S ij ? k ? S kj ? 2 , I is an identity matrix, and 1 is a vector of ones, and repeating the steps of randomly selecting a subset S from set X, and calculating an updated mutual interdependence vector until convergence, where the mutual interdependence vector is approximately equally correlated with all input vectors X.

Abstract: A spring-actuated parking brake includes a spring brake actuator communicating with a fluid source via a supply line. In brake released condition, the actuator is pressurized via a supply valve; in engaged condition, the actuator communicates with a pressure sink via the supply valve and a pressure-relief line. During brake engagement, the supply valve and a shutoff valve between the supply valve and actuator are switched to flow-enabled position for pressure relief of the actuator placing it in communication with the pressure sink. For brake release, the supply valve is switched to the fluid source and the shutoff valve is switched to fluid-passing position communicating with the fluid source to pressurize the brake cylinder. After brake engagement, the shutoff valve is switched to shut-off position decoupling the actuator from the supply line; the supply valve is switched to flow-enabled position placing the supply line in communication with the pressure sink.

Abstract: The invention relates to a switching device for a parking brake, especially a commercial-vehicle parking brake, with a handle that can be moved between a pulled position for engaging the parking brake and a pushed position for releasing the parking brake, and to such a parking brake. The handle is subjected to a preload force toward a central position when it is in the pulled or pushed position. Depending on the position of the handle, an evaluation device outputs different control signals for an actuating device of the parking brake.

Abstract: A spring-actuated parking brake includes a spring brake actuator communicating with a fluid source via a supply line. In brake released condition, the actuator is pressurized via a supply valve; in engaged condition, the actuator communicates with a pressure sink via the supply valve and a pressure-relief line. During brake engagement, the supply valve and a shutoff valve between the supply valve and actuator are switched to flow-enabled position for pressure relief of the actuator placing it in communication with the pressure sink. For brake release, the supply valve is switched to the fluid source and the shutoff valve is switched to fluid-passing position communicating with the fluid source to pressurize the brake cylinder. After brake engagement, the shutoff valve is switched to shut-off position decoupling the actuator from the supply line; the supply valve is switched to flow-enabled position placing the supply line in communication with the pressure sink.

Abstract: The invention relates to a switching device for a parking brake, especially a commercial-vehicle parking brake, with a handle that can be moved between a pulled position for engaging the parking brake and a pushed position for releasing the parking brake, and to such a parking brake. The handle is subjected to a preload force toward a central position when it is in the pulled or pushed position. Depending on the position of the handle, an evaluation device outputs different control signals for an actuating device of the parking brake.