Abstract: An architecture for linear-time extraction of maximally stable extremal regions (MSERs) having an image memory, heap memory, a pointer array and processing hardware is disclosed. The processing hardware is configured to in real-time analyze image pixels in the image memory using a linear-time algorithm to identify a plurality of components of the image. The processing hardware is also configured to place the image pixels in the heap memory for each of the plurality of components of the image, generate a pointer that points to a location in the heap memory that is associated with a start of flooding for another component and store the pointer in the array of pointers. The processing hardware is also configured to access the plurality of components using the array of pointers and determine MSER ellipses based on the components and MSER criteria.

Abstract: Hardware architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for image regions within the intensity image that match a given intensity threshold. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER area. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: A thermoelectric energy harvesting system for charging an energy store from ambient thermal energy includes a thermoelectric energy generator (TEG), an automatic polarity monitor, and switching matrix. The polarity monitor detects when the polarity of an input voltage in the system is reversed relative to a standard voltage polarity, and causes the switching matrix to switch the inputs from the thermoelectric energy harvester.

Abstract: A medical device having automated electrocardiogram (ECG) feature extraction is disclosed. The medical device includes input circuitry configured to receive an ECG signal. Processing circuitry coupled to the input circuitry is configured to identify at least one fiducial point of heartbeat signature of the ECG signal. The processing circuitry is further configured to perform substantially simultaneously both a discrete wavelet transform (DWT) and a curve length transform (CLT) to identify the at least one fiducial point.

Abstract: A medical device and method for detecting a ventricular arrhythmia event is disclosed. The medical device includes input circuitry configured to receive an electrocardiogram (ECG) signal, processing circuitry coupled to the input circuitry and configured to identify at least one fiducial point of a first heartbeat signature and at least fiducial point of a second heartbeat signature of the ECG signal, and feature extraction circuitry coupled to the processing circuitry. The feature extraction circuitry is configured to determine at least one difference between the at least one fiducial point of the first heartbeat signal and the at least one fiducial point of the second heartbeat signal. Machine learning circuitry is coupled to the feature extraction circuitry and is configured to select a ventricular arrhythmia class based on the at least one difference.

Abstract: Methods and systems for detecting and/or tracking one or more objects utilize depth data. An example method of detecting one or more objects in image data includes receiving depth image data corresponding to a depth image view point relative to the one or more objects. A series of binary threshold depth images are formed from the depth image data. Each of the binary threshold depth images is based on a respective depth. One or more depth extremal regions in which image pixels have the same value are identified for each of the binary depth threshold images. One or more depth maximally stable extremal regions are selected from the identified depth extremal regions based on change in area of the one or more respective depth extremal regions for different depths.

Abstract: Methods and systems process an MRI image to detect cancer. A method includes forming a series of binary threshold intensity images from an MRI image of a patient. Each of the binary threshold intensity images is based on a respective intensity. The binary threshold intensity images are processed to identify one or more bright extremal regions in which image pixels have the same value, and for which corresponding image pixels in the MRI image have a higher intensity than surrounding image pixels in the MRI image. One or more bright maximally stable extremal regions are selected from the identified bright extremal regions based on change in area of one or more respective bright extremal regions for different binary threshold images in the series. At least one of the selected one or more bright maximally stable extremal regions may be identified as potentially cancerous.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for light image regions and dark image regions within the intensity image that match a given intensity threshold during a single processing pass. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER areas. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for light image regions and dark image regions within the intensity image that match a given intensity threshold during a single processing pass. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER areas. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: Architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes communication interface and processing circuitry that is adapted in hardware to receive a data streams of an intensity image and a depth image in real-time and provide intensity labels for image regions within the intensity image that match a given intensity threshold and provide depth labels for image regions within the depth image that match a given depth threshold. The processing circuitry is also adapted in hardware to find intensity extremal regions within the intensity image based upon the intensity labels and to find depth extremal regions within the depth image based upon the depth labels. The processing circuitry determines strong extremal regions based upon significant overlap between the intensity extremal regions and depth extremal regions. The processing circuitry then determines X-MSER ellipses parameters based upon the strong extremal regions and X-MSER criteria.

Abstract: Hardware architecture for real-time extraction of maximally stable extremal regions (MSERs) is disclosed. The architecture includes a communication interface and processing circuitry that are configured in hardware to receive a data stream of an intensity image in real-time and provide labels for image regions within the intensity image that match a given intensity threshold. The communication interface and processing circuitry are also configured in hardware to find extremal regions within the intensity image based upon the labels and to determine MSER ellipses parameters based upon the extremal regions and MSER criteria. In at least one embodiment, the MSER criteria include minimum and maximum MSER areas, and an acceptable growth rate value for MSER area. In another embodiment, the MSER criteria include a nested MSER tolerance value.

Abstract: During a pop phase of hierarchical repartitioning of an IC design, all cells within a current hierarchy may be identified, the list of cells may be ungrouped to dissolve the current hierarchy, one or more specified cells may be removed from the list of cells, where the specified one or more cells are to be moved to a different hierarchy, and the new list of cells without the specified one or more cells may be re-grouped, to re-form the previously dissolved hierarchy. During a push phase of the hierarchical repartitioning, all cells within the next lower-level hierarchy may be identified, the identified list of cells may be ungrouped to dissolve that hierarchy, the specified one or more cells may be added to the identified list of cells, and the new list of cells that includes the specified one or more cells may be grouped to reform the previously dissolved hierarchy.

Abstract: During a pop phase of hierarchical repartitioning of an IC design, all cells within a current hierarchy may be identified, the list of cells may be ungrouped to dissolve the current hierarchy, one or more specified cells may be removed from the list of cells, where the specified one or more cells are to be moved to a different hierarchy, and the new list of cells without the specified one or more cells may be re-grouped, to re-form the previously dissolved hierarchy. During a push phase of the hierarchical repartitioning, all cells within the next lower-level hierarchy may be identified, the identified list of cells may be ungrouped to dissolve that hierarchy, the specified one or more cells may be added to the identified list of cells, and the new list of cells that includes the specified one or more cells may be grouped to reform the previously dissolved hierarchy.