Abstract: Embodiments of the present invention are directed to a noise-model based sensor converter configured to map a sensor measurement output to discrete, nonlinear steps of constant uncertainty. In a non-limiting embodiment of the invention, the sensor converter receives an output signal from a sensor. The output signal can include a measurement. The sensor converter can also receive a noise model. The output signal is mapped to a discrete set of steps based on the noise model. The discrete set of steps are nonlinearly spaced to provide constant uncertainty between adjacent steps. The sensor converter generates an output based on the discrete set of steps.

Abstract: A system for allocating tasks within a moving multi-hop mesh network includes a processor operatively coupled to memory.

Abstract: Embodiments of the present invention are directed to a noise-model based sensor converter configured to map a sensor measurement output to discrete, nonlinear steps of constant uncertainty. In a non-limiting embodiment of the invention, the sensor converter receives an output signal from a sensor. The output signal can include a measurement. The sensor converter can also receive a noise model. The output signal is mapped to a discrete set of steps based on the noise model. The discrete set of steps are nonlinearly spaced to provide constant uncertainty between adjacent steps. The sensor converter generates an output based on the discrete set of steps.

Abstract: A mechanism is provided in a data processing system for minimizing uncertainty envelopes in trajectories of evolving ensemble members. The mechanism generates a trajectory forecast of each member object of an ensemble based on an initial state-space and a model for predicting trajectories of the member objects to generate a plurality of trajectory forecasts. Each of the plurality of trajectory forecasts has an individual uncertainty envelope. The mechanism applies a classification algorithm on the plurality of trajectory forecasts to identify at least one group of member objects having similar trajectory forecasts, generates a reduced ensemble of member objects including the identified group of member objects, and reconfigures the state-space and the model for predicting trajectories. The mechanism generates an updated trajectory forecast of each member object of the reduced ensemble based on the reconfigured state-space and the reconfigured model for predicting trajectories of the member objects.

Abstract: A system, method and program product for managing a moving peer-to-peer network. A system is provided that a node tracking system that collects tracking information of nodes in a moving P2P network including position, connection status and computation capability; a network representation system that stores the tracking information in a dynamic graph that specifies nodes and connections between the nodes; a task manager for receiving a task submitted by a requesting node for a service implemented by a provider node; and a network allocation system that determines and allocates an optimal path back and forth between the requesting node and the provider node, wherein the optimal path is selected to minimize an overall wall-time, and wherein the network allocation system estimates future positions of moving nodes for determining the optimal path.

Abstract: A system, method and program product for managing a moving peer-to-peer network. A system is provided that a node tracking system that collects tracking information of nodes in a moving P2P network including position, connection status and computation capability; a network representation system that stores the tracking information in a dynamic graph that specifies nodes and connections between the nodes; a task manager for receiving a task submitted by a requesting node for a service implemented by a provider node; and a network allocation system that determines and allocates an optimal path back and forth between the requesting node and the provider node, wherein the optimal path is selected to minimize an overall wall-time, and wherein the network allocation system estimates future positions of moving nodes for determining the optimal path.

Abstract: A system for allocating tasks within a moving multi-hop mesh network includes a processor operatively coupled to memory.

Abstract: Techniques for power sharing in DC networks using virtual impedance frequency droop control are provided. In one aspect, a method for power sharing in a DC network having multiple electrical energy generation sources connected to at least one load includes the steps of, at each of the electrical energy generation sources: generating a controllable DC voltage; superimposing a controllable AC signal on top of the DC voltage; regulating the AC signal using virtual impedance frequency droop control; and determining a desired DC voltage output using the regulated AC signal. The DC voltage can then be regulated to match the desired DC voltage output. A system for power sharing in a DC network is also provided.

Abstract: A mechanism is provided in a data processing system for minimizing uncertainty envelopes in trajectories of evolving ensemble members. The mechanism generates a trajectory forecast of each member object of an ensemble based on an initial state-space and a model for predicting trajectories of the member objects to generate a plurality of trajectory forecasts. Each of the plurality of trajectory forecasts has an individual uncertainty envelope. The mechanism applies a classification algorithm on the plurality of trajectory forecasts to identify at least one group of member objects having similar trajectory forecasts, generates a reduced ensemble of member objects including the identified group of member objects, and reconfigures the state-space and the model for predicting trajectories. The mechanism generates an updated trajectory forecast of each member object of the reduced ensemble based on the reconfigured state-space and the reconfigured model for predicting trajectories of the member objects.

Abstract: A mechanism is provided in a data processing system for minimizing uncertainty envelopes in trajectories of evolving ensemble members. The mechanism generates a trajectory forecast of each member object of an ensemble based on an initial state-space and a model for predicting trajectories of the member objects to generate a plurality of trajectory forecasts. Each of the plurality of trajectory forecasts has an individual uncertainty envelope. The mechanism applies a classification algorithm on the plurality of trajectory forecasts to identify at least one group of member objects having similar trajectory forecasts, generates a reduced ensemble of member objects including the identified group of member objects, and reconfigures the state-space and the model for predicting trajectories. The mechanism generates an updated trajectory forecast of each member object of the reduced ensemble based on the reconfigured state-space and the reconfigured model for predicting trajectories of the member objects.

Abstract: Techniques for power sharing in DC networks using virtual impedance frequency droop control are provided. In one aspect, a method for power sharing in a DC network having multiple electrical energy generation sources connected to at least one load includes the steps of, at each of the electrical energy generation sources: generating a controllable DC voltage; superimposing a controllable AC signal on top of the DC voltage; regulating the AC signal using virtual impedance frequency droop control; and determining a desired DC voltage output using the regulated AC signal. The DC voltage can then be regulated to match the desired DC voltage output. A system for power sharing in a DC network is also provided.

Abstract: A computer processor may determine a predicted object attribute for a target object by analyzing a prediction model. The camera may capture a first image and then, after some time, a second image that both contain the target object. By comparing the first and second images, the computer processor may determine a measured object attribute for the target object. The processor may then determine whether the prediction model is accurate by comparing the measured object attribute to the predicted object attribute.

Abstract: A system for measuring velocity of a substance using data pattern characteristics. A computer receives a data set from a first location. A computer receives a second data set from second location. A computer determines that the second set of data is substantially similar to the first set of data, and calculates the velocity using both sets.

Abstract: A mechanism is provided in a data processing system for minimizing uncertainty envelopes in trajectories of evolving ensemble members. The mechanism generates a trajectory forecast of each member object of an ensemble based on an initial state-space and a model for predicting trajectories of the member objects to generate a plurality of trajectory forecasts. Each of the plurality of trajectory forecasts has an individual uncertainty envelope. The mechanism applies a classification algorithm on the plurality of trajectory forecasts to identify at least one group of member objects having similar trajectory forecasts, generates a reduced ensemble of member objects including the identified group of member objects, and reconfigures the state-space and the model for predicting trajectories. The mechanism generates an updated trajectory forecast of each member object of the reduced ensemble based on the reconfigured state-space and the reconfigured model for predicting trajectories of the member objects.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: Semiconductor devices, chromatography devices and integrated circuits for detecting one or more molecules and methods for forming a semiconductor device for detecting one or more molecules are presented. For example, a semiconductor device for detecting one or more molecules includes a channel formed within a semiconductor structure, and at least one detector formed within the semiconductor structure. The at least one detector detects the one or more molecules in the channel. The semiconductor device may optionally comprise one or more additional channels formed within the semiconductor structure. The semiconductor device may, for example, be operative to detect a single molecule.

Abstract: A method that identifies common peaks among unidentified peaks in the data from different LC-MS or LC-MS/MS runs is provided. The method employs an algorithm, herein referred to as “Precision Peak Matching (PPM).” The different runs can be from different laboratories, instruments, and biological samples that result in a significant variability in the data. PPM allows estimation and control of precision, defined as the fraction of truly identical peptide pairs among all pairs retrieved, in the matching process. PPM finds the maximal number of peptide pairs at a prescribed precision, thereby allowing quantitative control over the trade off between the number of true pairs missed, and false pairs found. PPM finds common peptides from a database of LC-MS runs of heterogeneous origins, and at the specified precision. PPM fills a much-needed role in proteomics by extracting useful information from disparate LC-MS databases in a statistically rigorous and interpretable manner.

Abstract: The problem of aligning multiple liquid chromatography mass spectrometry (LC-MS) runs to a common reference time frame is solved to facilitate comparison among LC-MS runs. The alignment of multiple LCMS can be achieved by solving a sparse system of linear equations to optimally stretch or compress local retention times for maximal similarity among the multiple runs. The multiple LCMS runs can be aligned simultaneously, thereby providing the advantage of efficient use of data by employing a sparse solver. A method of quality control in retention time alignment is also provided.