Abstract: The wireless sensor network can including a plurality of anchor sensors each including a signal receiver, a processing module, and a transmitter. The signal receiver can be configured to detect a received signal. The received signal can include noise and further can include a transmitted signal from the node when the node is transmitting the transmitted signal. The node can be located at an exact location that is unknown to each of the plurality of anchor sensors. The exact location can be in a region that is known to each of the plurality of anchor sensors. The transmitted signal can be wirelessly transmitted from the node when the node is transmitting the transmitted signal. The wireless sensor network also can include a fusion center. The processing module of each anchor sensor of the plurality of anchor sensors can performs acts. The acts can include determining a probability of whether the node is present at each of a plurality of grid points of the region.

Abstract: The disclosure relates to an image recognition algorithm implemented by a hardware control system which operates directly on data from a compressed sensing camera. A computationally expensive image reconstruction step can be avoided, allowing faster operation and reducing the computing requirements of the system. The method may implement an algorithm that can operate at speeds comparable to an equivalent approach operating on a conventional camera's output. In addition, at high compression ratios, the algorithm can outperform approaches in which an image is first reconstructed and then classified.

Abstract: A method includes the steps of calculating a power spectrum from an auditory stimulus, filtering the power spectrum to obtain an effective power spectrum, calculating an intensity pattern from the effective power spectrum, calculating a median intensity pattern from the intensity pattern, determining an initial set of pruned detector locations, examining the initial set of pruned detector locations to determine an enhanced set of pruned detector locations, and calculating an excitation pattern from the effective power spectrum using the enhanced set of pruned detector locations. By determining the enhanced set of pruned detector locations from the initial set of pruned detector locations and computing the excitation pattern therefrom, the computational complexity of the above method can be significantly reduced when compared to conventional approaches while maintaining the accuracy thereof.

Abstract: Methods and systems for recovering corrupted/degraded images using approximations obtained from an ensemble of multiple sparse models are disclosed. Sparse models may represent images parsimoniously using elementary patterns from a “dictionary” matrix. Various embodiments of the present disclosure involve simple and computationally efficient dictionary design approach along with low-complexity reconstruction procedure that may use a parallel-friendly table-lookup process. Multiple dictionaries in an ensemble model may be inferred sequentially using greedy forward-selection approach and can incorporate bagging/boosting strategies, taking into account application-specific degradation. Recovery performance obtained using the proposed approaches with image super resolution and compressive recovery can be comparable to or better than existing sparse modeling based approaches, at reduced computational complexity.

Abstract: Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

Abstract: A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Abstract: A method includes the steps of calculating a power spectrum from an auditory stimulus, filtering the power spectrum to obtain an effective power spectrum, calculating an intensity pattern from the effective power spectrum, calculating a median intensity pattern from the intensity pattern, determining an initial set of pruned detector locations, examining the initial set of pruned detector locations to determine an enhanced set of pruned detector locations, and calculating an excitation pattern from the effective power spectrum using the enhanced set of pruned detector locations. By determining the enhanced set of pruned detector locations from the initial set of pruned detector locations and computing the excitation pattern therefrom, the computational complexity of the above method can be significantly reduced when compared to conventional approaches while maintaining the accuracy thereof.

Abstract: Some embodiments include a wireless sensor network system. Other embodiments of related systems and methods are also disclosed.

Abstract: Some embodiments include a distributed estimation system. Other embodiments of related systems and methods are also disclosed.

Abstract: The wireless sensor network can including a plurality of anchor sensors each including a signal receiver, a processing module, and a transmitter. The signal receiver can be configured to detect a received signal. The received signal can include noise and further can include a transmitted signal from the node when the node is transmitting the transmitted signal. The node can be located at an exact location that is unknown to each of the plurality of anchor sensors. The exact location can be in a region that is known to each of the plurality of anchor sensors. The transmitted signal can be wirelessly transmitted from the node when the node is transmitting the transmitted signal. The wireless sensor network also can include a fusion center. The processing module of each anchor sensor of the plurality of anchor sensors can performs acts. The acts can include determining a probability of whether the node is present at each of a plurality of grid points of the region.

Abstract: Methods and systems for recovering corrupted/degraded images using approximations obtained from an ensemble of multiple sparse models are disclosed. Sparse models may represent images parsimoniously using elementary patterns from a “dictionary” matrix. Various embodiments of the present disclosure involve simple and computationally efficient dictionary design approach along with low-complexity reconstruction procedure that may use a parallel-friendly table-lookup process. Multiple dictionaries in an ensemble model may be inferred sequentially using greedy forward-selection approach and can incorporate bagging/boosting strategies, taking into account application-specific degradation. Recovery performance obtained using the proposed approaches with image super resolution and compressive recovery can be comparable to or better than existing sparse modeling based approaches, at reduced computational complexity.

Abstract: Measuring the number of glomeruli in the entire, intact kidney using non-destructive techniques is of immense importance in studying several renal and systemic diseases. In particular, a recent Magnetic Resonance Imaging (MRI) technique, based on injection of a contrast agent, cationic ferritin, has been effective in identifying glomerular regions in the kidney. In various embodiments, a low-complexity, high accuracy method for obtaining the glomerular count from such kidney MRI images is described. This method employs a patch-based approach for identifying a low-dimensional embedding that enables the separation of glomeruli regions from the rest. By using only a few images marked by the expert for learning the model, the method provides an accurate estimate of the glomerular number for any kidney image obtained with the contrast agent. In addition, the implementation of our method shows that this method is near real-time, and can process about 5 images per second.

Abstract: A robust method to automatically segment and identify tumor regions in medical images is extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an efficient algorithm uses sparse models in feature spaces to identify pixels belonging to tumorous regions. By fusing both intensity and spatial location information of the pixels, this technique can automatically localize tumor regions without user intervention. Using a few expert-segmented training images, a sparse coding-based classifier is learned. For a new test image, the sparse code obtained from every pixel is tested with the classifier to determine if it belongs to a tumor region. Particular embodiments also provide a highly accurate, low-complexity procedure for cases when the user can provide an initial estimate of the tumor in a test image.

Abstract: A method and apparatus for determining an auditory pattern associated with an audio segment. An average intensity at each of a first plurality of detector locations on an auditory scale based at least in part on a first plurality of frequency components that describe a signal is determined. A plurality of tonal bands in the audio segment, wherein each tonal band comprises a particular range of detector locations of the first plurality of detector locations is determined. Corresponding strongest frequency components in the tonal bands are determined. A plurality of non-tonal bands is determined, and each non-tonal band is subdivided into multiple sub-bands. Corresponding combined frequency components that are representative of a combined sum of intensities of the first plurality of frequency components that is in a corresponding sub-band are determined. An auditory based on the corresponding strongest frequency components and the corresponding combined frequency components is determined.

Abstract: Some embodiments include a distributed estimation system. Other embodiments of related systems and methods are also disclosed.

Abstract: Some embodiments include a wireless sensor network system. Other embodiments of related systems and methods are also disclosed.

Abstract: A frame is received that has the wideband audio signal. The low band audio signal is encoded to generate an encoded low band signal. The high band signal is analyzed to determine whether the high band signal is perceptually relevant to the low band signal. If the high band signal is not perceptually relevant to the low band signal, the low band signal is encoded and provided in a frame to the decoder without including parameters corresponding to characteristics of the high band signal. If the high band signal is perceptually relevant, the high band signal is encoded to generate an encoded high band signal. The resultant frame that is sent to the decoder will include a combination of the encoded low band signal and the encoded high band signal.

Abstract: A method and apparatus for determining an auditory pattern associated with an audio segment. An average intensity at each of a first plurality of detector locations on an auditory scale based at least in part on a first plurality of frequency components that describe a signal is determined. A plurality of tonal bands in the audio segment, wherein each tonal band comprises a particular range of detector locations of the first plurality of detector locations is determined. Corresponding strongest frequency components in the tonal bands are determined. A plurality of non-tonal bands is determined, and each non-tonal band is subdivided into multiple sub-bands. Corresponding combined frequency components that are representative of a combined sum of intensities of the first plurality of frequency components that is in a corresponding sub-band are determined. An auditory based on the corresponding strongest frequency components and the corresponding combined frequency components is determined.