Abstract: A multi-modal bio-sensing apparatus is disclosed including a first sensor module comprising a photoplethysmogram (PPG) sensor configured to produce a first output representative of a blood volume of a human user, wherein the PPG sensor is configured to remove from the first output an error signal due to movement of a user; a second sensor module comprising an electroencephalogram (EEG) sensor configured to produce a third output representative of brain neural activity of the user; a third sensor module comprising an eye-gaze camera configured to capture a gaze direction of one or more eyes of the user; and a wireless communications transceiver coupled to receive sensor data from the first sensor module, the second sensor module, or the third sensor module and configured to wirelessly transmit the received sensor data from the first sensor module, the second sensor module, or the third sensor module out of the multi-modal bio-sensing apparatus.

Abstract: A scanning microscope includes an acousto-optic scanner that produces a scanned beam. A beam separator based on total internal reflection or angle tuning of a dielectric filter separates an unscanned portion of an excitation light flux from a scanned portion. The scanned beam is directed to a specimen, and optical radiation generated in response to the scanned beam is directed to a detector that produces a detected signal that can be used to determine an image. The scanned beam can be directed to the specimen without formation of any intermediate focusing.

Abstract: Projection screens for projection of binocular stereoscopic images include a metallic projection surface that includes a plurality of depressions configured to produce left and right viewable light fluxes in response to received left and right modulated light fluxes. The metallic projection surface can be formed by beadblasting, and multiple panels can be secured together by welding or other process. Projection surfaces can also be formed by molding or otherwise forming a conductive surface on a dielectric or other substrate.

Abstract: Projection screens for projection of binocular stereoscopic images include a metallic projection surface that includes a plurality of depressions configured to produce left and right viewable light fluxes in response to received left and right modulated light fluxes. The metallic projection surface can be formed by beadblasting, and multiple panels can be secured together by welding or other process. Projection surfaces can also be formed by molding or otherwise forming a conductive surface on a dielectric or other substrate.

Abstract: A scanning microscope includes an acousto-optic scanner that produces a scanned beam. A beam separator based on total internal reflection or angle tuning of a dielectric filter separates an unscanned portion of an excitation light flux from a scanned portion. The scanned beam is directed to a specimen, and optical radiation generated in response to the scanned beam is directed to a detector that produces a detected signal that can be used to determine an image. The scanned beam can be directed to the specimen without formation of any intermediate focusing.

Abstract: Objectives and other optical assemblies include a reflective surface that is truncated at or near a focus based on a curvature of the reflective surface. A specimen is situated at or near the focus of the reflective surface, so that the reflective surface captures and collimates optical radiation emitted from the specimen. The reflective surface can be defined on an optical substrate along with a lens surface, so that an illumination flux is focused on the specimen by the lens surface, and a secondary light flux produced in response to the illumination flux is captured and collimated by the reflective surface.

Abstract: A method and apparatus for efficiently encoding images using a set of non-orthogonal basis functions, thereby allowing reduction of file size, shorter transmission time, and improved accuracy. The non-orthogonal basis functions include homogenous color basis functions, luminance-encoding basis functions that have luminance edges and chromatic basis functions that exhibit color opponency. Some of the basis functions are non-orthogonal with respect to each other. Using these basis functions, a source vector is calculated to provide a number of coefficients, each coefficient associated with one basis function. The source vector is compressed by selecting a subset of the calculated coefficients, thereby providing an encoded vector. Because the method is highly efficient, the image data is substantially represented by a small number of coefficients. In some embodiments, the non-orthogonal basis functions include two or more classes. A wavelet approach can also be utilized.

Abstract: A method and apparatus for efficiently encoding images using a set of non-orthogonal basis functions, thereby allowing reduction of file size, shorter transmission time, and improved accuracy. The non-orthogonal basis functions include homogenous color basis functions, luminance-encoding basis functions that have luminance edges and chromatic basis functions that exhibit color opponency. Some of the basis functions are non-orthogonal with respect to each other. Using these basis functions, a source vector is calculated to provide a number of coefficients, each coefficient associated with one basis function. The source vector is compressed by selecting a subset of the calculated coefficients, thereby providing an encoded vector. Because the method is highly efficient, the image data is substantially represented by a small number of coefficients. In some embodiments, the non-orthogonal basis functions include two or more classes. A wavelet approach can also be utilized.

Abstract: A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i.e. characteristics) are initially unknown. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.

Abstract: A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i.e. characteristics) are initially unknown. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.

Abstract: A method and apparatus for efficiently encoding images using a set of non-orthogonal basis functions, thereby allowing reduction of file size, shorter transmission time, and improved accuracy. The non-orthogonal basis functions include homogenous color basis functions, luminance-encoding basis functions that have luminance edges and chromatic basis functions that exhibit color opponency. Some of the basis functions are non-orthogonal with respect to each other. Using these basis functions, a source vector is calculated to provide a number of coefficients, each coefficient associated with one basis function. The source vector is compressed by selecting a subset of the calculated coefficients, thereby providing an encoded vector. Because the method is highly efficient, the image data is substantially represented by a small number of coefficients. In some embodiments, the non-orthogonal basis functions include two or more classes. A wavelet approach can also be utilized.

Abstract: A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i.e. characteristics) are initially unknown. The data set may be generated in a dynamic environment where the sources provide signals that are mixed, and the mixing parameters change without notice and in an unknown manner. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. In some embodiments the class parameters may have been previously learned, and the system is used to classify the data and if desired to separate the sources.