Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.

Abstract: The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Abstract: Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

Abstract: The present disclosure addresses the technical problem of information loss while representing a physiological signal in the form of symbols and for recognizing patterns inside the signal. Thus making it difficult to retain or extract any relevant information which can be used to detect anomalies in the signal. A system and method for anomaly detection and discovering pattern in a signal using morphology aware symbolic representation has been provided. The system discovers pattern atoms based on the strictly increasing and strictly decreasing characteristics of the time series physiological signal, and generate symbolic representation in terms of these pattern atoms. Additionally the method possess more generalization capability in terms of granularity. This detects discord/abnormal phenomena with consistency.

Abstract: Systems and methods for detecting an anomaly in a cardiovascular signal using hierarchical extremas and repetitions. The traditional systems and methods provide for some anomaly detection in the cardiovascular signal but do not consider the discrete nature and strict rising and falling patterns of the cardiovascular signal and frequency in terms of hierarchical maxima points and minima points. Embodiments of the present disclosure provide for detecting the anomaly in the cardiovascular signal using hierarchical extremas and repetitions by smoothening the cardiovascular signal, deriving sets of hierarchical extremas using window detection, identifying signal patterns based upon the sets of hierarchical extremas, identifying repetitions in the signal patterns based upon occurrences and randomness of occurrences of the signal patterns and classifying the cardiovascular signal as anomalous and non-anomalous for detecting the anomaly in the cardiovascular signal.

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

Abstract: Accurate detection of anomaly in sensor signals is critical and can have an immense impact in the health care domain. Accordingly, identifying outliers or anomalies with reduced error and reduced resource usage is a challenge addressed by the present disclosure. Self-learning of normal signature of an input sensor signal is used to derive primary features based on valley and peak points of the sensor signals. A pattern is recognized by using discrete nature and strictly rising and falling edges of the input sensor signal. One or more defining features are identified from the derived features based on statistical properties and time and frequency domain properties of the input sensor signal. Based on the values of the defining features, clusters of varying density are identified for the input sensor signal and based on the density of the clusters, anomalous and non-anomalous portions of the input sensor signals are classified.

Abstract: This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

Abstract: Provided embodiments include a method, a system, a device, and an article of manufacture. A system for terminating a genetic algorithm (GA), where the GA uses an iterator and generates one best solution per iteration, includes a memory, an iterative processor, and a terminating processor. The memory is provided for storing a plurality of best solutions generated in a plurality of iterations of the GA. One of the best solutions generated in one of the iterations is stored in the memory if the one of the best solutions is better than a previous one of the best solutions generated in a previous one of the iterations. The iterative processor computes a variance of the plurality of the best solutions stored in the memory. The terminating processor terminates the iterator when the variance is less than or equal to a predetermined threshold.

Abstract: Systems and methods for discovering SCTP associations between devices communicating in a network are described. A method comprises monitoring packets communicated among a plurality of source and destination devices, determining a combination of source EP address, source port number, destination IP address, and destination port number that defines an association between a source device and a destination device, and resolving a combination of source and destination verification tags that further defines the association based upon the combination of EP addresses and port numbers. The method further comprises ascertaining whether a subsequently monitored packet belongs to the association based at least in part upon an element of the combination of verification tags after at least one element of the combination of EP addresses and port numbers has changed during the ongoing communication.

Abstract: Systems and methods for discovering SCTP associations between devices communicating in a network are described. A method comprises monitoring packets communicated among a plurality of source and destination devices, determining a combination of source EP address, source port number, destination IP address, and destination port number that defines an association between a source device and a destination device, and resolving a combination of source and destination verification tags that further defines the association based upon the combination of EP addresses and port numbers. The method further comprises ascertaining whether a subsequently monitored packet belongs to the association based at least in part upon an element of the combination of verification tags after at least one element of the combination of EP addresses and port numbers has changed during the ongoing communication.

Abstract: Provided embodiments include a method, a system, a device, and an article of manufacture. A system for terminating a genetic algorithm (GA), where the GA uses an iterator and generates one best solution per iteration, includes a memory, an iterative processor, and a terminating processor. The memory is provided for storing a plurality of best solutions generated in a plurality of iterations of the GA. One of the best solutions generated in one of the iterations is stored in the memory if the one of the best solutions is better than a previous one of the best solutions generated in a previous one of the iterations. The iterative processor computes a variance of the plurality of the best solutions stored in the memory. The terminating processor terminates the iterator when the variance is less than or equal to a predetermined threshold.

Abstract: Systems and methods for discovering SCTP associations between devices communicating in a network are described. A method comprises monitoring packets communicated among a plurality of source and destination devices, determining a combination of source EP address, source port number, destination IP address, and destination port number that defines an association between a source device and a destination device, and resolving a combination of source and destination verification tags that further defines the association based upon the combination of EP addresses and port numbers. The method further comprises ascertaining whether a subsequently monitored packet belongs to the association based at least in part upon an element of the combination of verification tags after at least one element of the combination of EP addresses and port numbers has changed during the ongoing communication.

Abstract: Disclosed are methods and custom computing apparatuses for identifying gene-gene interactions from gene expression data, based on which a gene regulatory sub-network can be built. In particular, relationships in which multiple genes co-regulate one target gene can also be identified.

Abstract: A system and related method for calculating parity information for disk array drive failure recovery. More specifically, using eight bit coefficients and calculating parity information using valid eight bit encryption keys to produce finite field encrypted resultant multiplication. Further disclosed is a method of determining whether a potential encryption key of a particular number of bits produces valid results for all possible multiplications in determining parity values.

Abstract: Isolated nucleic acid molecules comprising a gene segment from a rhesus rotavirus (RRV) or from one of three rhesus:human reassortant viruses are disclosed, including isolated nucleic acid molecules having a sequence selected from the group consisting of: SEQ ID NO: 1-14, inclusive, and isolated nucleic acid molecules encoding a protein having a sequence selected from the group consisting of SEQ ID NO: 15-28, inclusive, as well as variants of the isolated nucleic acid molecules.

Abstract: Systems and methods for discovering SCTP associations between devices communicating in a network are described. A method comprises monitoring packets communicated among a plurality of source and destination devices, determining a combination of source EP address, source port number, destination IP address, and destination port number that defines an association between a source device and a destination device, and resolving a combination of source and destination verification tags that further defines the association based upon the combination of EP addresses and port numbers. The method further comprises ascertaining whether a subsequently monitored packet belongs to the association based at least in part upon an element of the combination of verification tags after at least one element of the combination of EP addresses and port numbers has changed during the ongoing communication.

Abstract: Isolated nucleic acid molecules comprising a gene segment from a rhesus rotavirus (RRV) or from one of three rhesus: human reassortant viruses are disclosed, including isolated nucleic acid molecules having a sequence selected from the group consisting of: SEQ ID NO: 1-14, inclusive, and isolated nucleic acid molecules encoding a protein having a sequence selected from the group consisting of SEQ ID NO: 15-28, inclusive, as well as variants of the isolated nucleic acid molecules.

Abstract: Disclosed is a method, apparatus, and system to provide for robust digital image watermarking utilizing a Walsh transform algorithm. A first image processor performs operations including partitioning a cover image, generating a key, and inserting a watermark symbol into the cover image utilizing a Walsh transform and the key. A second image processor performs operations including extracting the watermark symbol from the cover image utilizing a Walsh transform and the key.

Abstract: Disclosed is a method and apparatus to provide for fingerprint minutiae matching using scoring techniques. A plurality of minutiae in a fingerprint image is defined. A score associated with each minutia corresponding to the validity of each minutia is estimated. The fingerprint image is then matched against one or more sample fingerprint images utilizing a partial point set pattern matching (PSPM) algorithm.