Abstract: In a patient monitoring system (10), shorter interval physiological parameters and longer interval clinical data are collected from a monitored patient (12). A composite acuity score generator (70) generates or updates a composite acuity score indicative of wellbeing of the patient (12) based at least on the sensed physiological parameters and the longer interval data. A monitor (22, 56) displays current values of at least one of selected sensed physiological parameters, longer interval data, and the composite acuity score.

Abstract: Described herein is a patient monitoring system that includes a body network (16) with at least one sensor (12) that senses physiological information about a patient and a cognitive device (2) for communicating the physiological information to a remote location. The cognitive device includes a cognitive radio (4), a cognitive monitor (10), and a transmitter (8). The cognitive radio (4) checks detected frequency spectra (6) for unused bandwidth and recommends one or more bands on which to transmit clinically relevant information received from the body network (16) to the remote location; the cognitive monitor (10) receives the information from the body network (16), prioritizes the information based at least in part on a set of rules (30), and selects which information to transmit based on the prioritization and the recommended transmission bands; and the transmitter (8) transmits the selected information as a junction of priority over at least one or the recommended transmission bands.

Abstract: Video encoding computations are optimized by dynamically adjusting slice patterns of video frames based on complexity of each frame and allocating multi-core threading based on the slices. The complexity may be based on predefined parameters such as color, motion, and comparable ones for each slice. Allocation is determined based on capacity and queue of each processing core such that overall computation performance for video encoding is improved.

Abstract: A low complexity block-based, no-reference objective blockiness metric is provided that may be combined with other artifact metrics to measure overall quality of received video stream in a video conferencing application such that measures can be taken at the transmitter or in post-processing to enhance video quality. Prior knowledge of the blockiness boundaries may be used to reduce number of computations in determining the blockiness of a particular video frame.

Abstract: A block-based, no-reference sharpness metric is provided taking advantage of Human Visual System (HVS) characteristics. Texture and smooth region blocks are excluded in computing the metric since sharpness is perceived mostly around edges. Overall sharpness metric is computed by pooling simulated combination of information in human brain employing a logistic function to replicate the behavior of HVS.

Abstract: A patient physiological information monitoring system includes a plurality of patient monitoring devices (6) and a physiological information analyzer (2). The plurality of patient monitoring devices (6) monitor physiological information from a patient and generate corresponding physiological signals. The physiological information analyzer (2) processes the monitored physiological information and determines whether a physiological change is a clinically significant event or an artifact.

Abstract: A block-based, no-reference sharpness metric is provided taking advantage of Human Visual System (HVS) characteristics. Texture and smooth region blocks are excluded in computing the metric since sharpness is perceived mostly around edges. Overall sharpness metric is computed by pooling simulated combination of information in human brain employing a logistic function to replicate the behavior of HVS.

Abstract: A low complexity block-based, no-reference objective blockiness metric is provided that may be combined with other artifact metrics to measure overall quality of received video stream in a video conferencing application such that measures can be taken at the transmitter or in post-processing to enhance video quality. Prior knowledge of the blockiness boundaries may be used to reduce number of computations in determining the blockiness of a particular video frame.

Abstract: Video encoding computations are optimized by dynamically adjusting slice patterns of video frames based on complexity of each frame and allocating multi-core threading based on the slices. The complexity may be based on predefined parameters such as color, motion, and comparable ones for each slice. Allocation is determined based on capacity and queue of each processing core such that overall computation performance for video encoding is improved.

Abstract: Construction and use of forward error correction codes is provided. A systematic MDS FEC code is obtained having a property wherein any set of contiguous or non-contiguous r packets can be lost during a data transmission of k data packets and r encoded packets and the original k packets can be recovered unambiguously. The systematic MDS FEC code is transformed into a (k+r, k) systematic MDS FEC code that guarantees at least one of the encoded packets is a parity packet. The starting systematic MDS FEC code may be Cauchy-based, and the transformation code derived from the starting Cauchy-based MDS FEC code allows for very efficient initialization, encoding and decoding operations.

Abstract: A patient monitoring system that simultaneously analyzes physiological signals from at least one patient monitoring device (4) to detect unstable conditions includes a frequency component extractor (6) that separates each received signal into a plurality of frequency components over different time scales; a generator (8) that provides mappings of physiological signals against one another called morphograms, which show how the physiological signals more together and a processing component (10) that analyzes the morphograms to determine whether an unstable condition exists.

Abstract: In a patient monitoring system (10), shorter interval physiological parameters and longer interval clinical data are collected from a monitored patient (12). A composite acuity score generator (70) generates or updates a composite acuity score indicative of wellbeing of the patient (12) based at least on the sensed physiological parameters and the longer interval data. A monitor (22, 56) displays current values of at least one of selected sensed physiological parameters, longer interval data, and the composite acuity score.

Abstract: A patient physiological information monitoring system includes a plurality of patient monitoring devices (6) and a physiological information analyzer (2). The plurality of patient monitoring devices (6) monitor physiological information from a patient and generate corresponding physiological signals. The physiological information analyzer (2) processes the monitored physiological information and determines whether a physiological change is a clinically significant event or an artifact.

Abstract: Described herein is a patient monitoring system that includes a body network (16) with at least one sensor (12) that senses physiological information about a patient and a cognitive device (2) for communicating the physiological information to a remote location. The cognitive device includes a cognitive radio (4), a cognitive monitor (10), and a transmitter (8). The cognitive radio (4) checks detected frequency spectra (6) for unused bandwidth and recommends one or more bands on which to transmit clinically relevant information received from the body network (16) to the remote location; the cognitive monitor (10) receives the information from the body network (16), prioritizes the information based at least in part on a set of rules (30), and selects which information to transmit based on the prioritization and the recommended transmission bands; and the transmitter (8) transmits the selected information as a junction of priority over at least one or the recommended transmission bands.

Abstract: A method for combining a random set of video features non-linearly to evaluate perceptual quality of video sequences includes (a) receiving a video sequence for image quality evaluation; (b) providing an objective metric image quality controller comprising a random set of metrics ranging from M1 to Mn without dependency information for each one metric; (c) applying each one metric individually to the video sequence to provide an individual objective scoring value of the video sequence ranging from x1 to xn; (d) determining a plurality of sets of weights (w1 to wn) which correlate to predetermined subjective evaluations of image quality for a predetermined plurality of video sequences (n), each one set of weights being assigned a range having an incremental value equal to the range divided by a number of combinations for each one set of weights; (e) weighting each individual objective scoring value x1 to xn provided by each one metric of the random set of metrics in step (c); (f) combining metrics of the weigh

Abstract: An optimizing video processing method and system selects algorithms for best obtainable video quality for the available computation resources. A video processing module, which processes an input of a video stream, architectural parameters for identifying an order of cascaded video functions and determining a bit precision between data of any consecutive cascaded functions according to an associated complexity level which correlates with a value of available computational resources. An optimizer module optimizes processing of the video stream and includes a plurality of optimization engines each having an associated complexity level. The optimizer module selects an optimization engine according a complexity level which correlates with the value of available computational resources. An Object Image Quality (OIQ) evaluator module evaluates an image quality of an output of the video stream from the video processing module.

Abstract: There is disclosed an improved system and method for providing a scalable dynamic objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. The system determines a scalable dynamic objective metric from a weighted average of the plurality of objective metric figures of merit. The scalable dynamic objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The weight value of individual objective metric figures of merit may be increased or decreased depending upon the type of video image being evaluated. Individual objective metric figures of merit may be added to the system or deleted from the system.

Abstract: A composite image is segmented into regions corresponding to different objects within the image based upon motion vectors for pixel blocks within the image. Each image segment is assigned an importance based on relative size of the region and average scalar value of motion vectors for pixel blocks within the region. Objective image quality values are computed for each region, and the products of importance indicators and objective image quality values for each segment are summed across all segments within the image to obtain an overall image quality.

Abstract: An optimizing video processing method and system selects algorithms for best obtainable video quality for the available computation resources. A video processing module, which processes an input of a video stream, architectural parameters for identifying an order of cascaded video functions and determining a bit precision between data of any consecutive cascaded functions according to an associated complexity level which correlates with a value of available computational resources. An optimizer module optimizes processing of the video stream and includes a plurality of optimization engines each having an associated complexity level. The optimizer module selects an optimization engine according a complexity level which correlates with the value of available computational resources. An Object Image Quality (OIQ) evaluator module evaluates an image quality of an output of the video stream from the video processing module.

Abstract: A method for combining a random set of video features non-linearly to evaluate perceptual quality of video sequences includes (a) receiving a video sequence for image quality evaluation; (b) providing an objective metric image quality controller comprising a random set of metrics ranging from M1 to Mn without dependency information for each one metric; (c) applying each one metric individually to the video sequence to provide an individual objective scoring value of the video sequence ranging from x1 to xn; (d) determining a plurality of sets of weights (w1 to wn) which correlate to predetermined subjective evaluations of image quality for a predetermined plurality of video sequences (n), each one set of weights being assigned a range having an incremental value equal to the range divided by a number of combinations for each one set of weights; (e) weighting each individual objective scoring value x1 to xn provided by each one metric of the random set of metrics in step (c); (f) combining metrics of the weigh