129 results about "Time structure" patented technology

The present invention relates to a methodology for the conversion of unstructured, free text data (contained within medical reports) into standardized, structured data, and also relates to a decision support feature for use in diagnosis and treatment options.

Systems and methods are described to supply positioning reference signal (PRS) in a telecommunication system. A base station supplies a PRS sequence according at least to a time-frequency pattern of modulation symbols, wherein the time-frequency pattern assigns a modulation symbol to each frequency tone in a block of time-frequency resources allocated to transmit PRS. The base station associates a modulation symbol in the time-frequency pattern with a reference symbol in the PRS sequence through a mapping that represents the time-frequency pattern. The PRS sequence is conveyed to user equipment through delivery of a set of modulation symbols established through the mapping. Different time-frequency patterns can be exploited based on time-structure of a radio sub-frame. The user equipment receives the PRS sequence according to at least the time-frequency pattern of modulation symbols and utilizes at least the PRS sequence as part of a process to produce a location estimate.

The invention discloses a video content description method by means of space and time attention models. The global time structure in the video is captured by means of the time attention model, the space structure on each frame of picture is captured by the space attention model, and the method aims to realize that the video description model masters the main event in the video and enhances the identification capability of the local information. The method includes preprocessing the video format; establishing the time and space attention models; and training and testing the video description model. By means of the time attention model, the main time structure in the video can be maintained, and by means of the space attention model, some key areas in each frame of picture are focused in each frame of picture, so that the generated video description can capture some key buy easy neglected detailed information while mastering the main event in the video content.

Described is a behavior recognition system for detecting the behavior of objects in a scene. The system comprises a semantic object stream module for receiving a video stream having at least two frames and detecting objects in the video stream. Also included is a group organization module for utilizing the detected objects from the video stream to detect a behavior of the detected objects. The group organization module further comprises an object group stream module for spatially organizing the detected objects to have relative spatial relationships. The group organization module also comprises a group action stream module for modeling a temporal structure of the detected objects. The temporal structure is an action of the detected objects between the two frames, whereby through detecting, organizing and modeling actions of objects, a user can detect the behavior of the objects.

A circuit card stripline Fast Faraday cup quantitatively measures the picosecond time structure of a charged particle beam. The stripline configuration maintains signal integrity, and stitching of the stripline increases the bandwidth. A calibration procedure ensures the measurement of the absolute charge and time structure of the charged particle beam.

An exemplary methodology, procedure, system, method and computer-accessible medium can be provided for receiving physiological data for the subject, extracting one or more patterns of features from the physiological data, and classifying the at least one state of the subject using a spatial structure and a temporal structure of the one or more patterns of features, wherein at least one of the at least one state is an ictal state.

Systems and methods are disclosed for using a first deduplicating store to update a second deduplicating store with information representing how data objects change over time, said method comprising: at a first and a second deduplicating store, for each data object, maintaining an organized arrangement of temporal structures to represent a corresponding data object over time, wherein each structure is associated with a temporal state of the data object and wherein the logical arrangement of structures is indicative of the changing temporal states of the data object; finding a temporal state that is common to and in temporal proximity to the current state of the first and second deduplicating stores; and compiling and sending a set of hash signatures for the content that has changed from the common state to the current temporal state of the first deduplicating store.

The present invention provides a cochlear stimulation system and method for capturing and translating fine time structure (“FTS”) in incoming sounds and delivering this information spatially to the cochlea. The system comprises a FTS estimator/analyzer and a current navigator. An embodiment of the method comprises analyzing the incoming sounds within a time frequency band, extracting the slowly varying frequency components and estimating the FTS to obtain a more precise dominant FTS component within a frequency band. After adding the fine structure to the carrier to identify a precise dominant FTS component in each analysis frequency band (or stimulation channel), a stimulation current may be “steered” or directed, using the concept of virtual electrodes, to the precise spatial location (place) on the cochlea that corresponds to the dominant FTS component. This process is simultaneously repeated for each stimulation channel and each FTS component.

The coding efficiency of an audio codec using a controllable—switchable or even adjustable—harmonic filter tool is improved by performing the harmonicity-dependent controlling of this tool using a temporal structure measure in addition to a measure of harmonicity in order to control the harmonic filter tool. In particular, the temporal structure of the audio signal is evaluated in a manner which depends on the pitch. This enables to achieve a situation-adapted control of the harmonic filter tool so that in situations where a control made solely based on the measure of harmonicity would decide against or reduce the usage of this tool, although using the harmonic filter tool would, in that situation, increase the coding efficiency, the harmonic filter tool is applied, while in other situations where the harmonic filter tool may be inefficient or even destructive, the control reduces the appliance of the harmonic filter tool appropriately.

The invention discloses a human motion detection and positioning method based on space-time combination, and the method comprises the steps: taking an unpruned video as an input, dividing the video into a plurality of short units with the same length through data preprocessing, carrying out the sparse sampling randomly, and extracting the space-time characteristics through a double-flow convolutional neural network; Secondly, entering a space-time joint network to judge the occurrence interval of actions to obtain a group of action scoring oscillograms, inputting the action oscillograms into aGTAG network, and setting different thresholds to meet different positioning precision requirements and obtain action proposal sections with different granularities; All the action proposal sectionsdetect the types of actions through an action classifier, the time boundary of action occurrence is finely corrected through an integrity filter, and human body action detection and positioning in a complex scene are achieved. The method provided by the invention can be applied to actual scenes with serious human body shielding, changeable postures and more interference objects, and can be used for well processing activity categories with different time structures.

A system and method of signal processing for a hearing implant. The hearing implant includes at least one electrode, each electrode associated with a channel specific sampling sequence. An acoustic audio signal is processed to generate for each electrode a band pass signal representing an associated band of audio frequency. For each electrode, a sequence signal is determined as a function of the electrode's associated band pass signal and channel specific sampling sequence. An envelope of each band pass signal is determined. The envelope of each band pass signal is filtered to reduce modulations resulting from unresolved harmonics, creating for each electrode an associated filtered envelope signal. Each electrode's sequence signal is weighted based, at least in part, on the electrode's associated filtered envelope signal.

Methods and systems process a semiconductor substrate having a plurality of structures to be selectively irradiated with multiple laser beams. The structures are arranged in a plurality of substantially parallel rows extending in a generally lengthwise direction. The method generates a first laser beam that propagates along a first laser beam axis that intersects a first target location on or within the semiconductor substrate. The method also generates a second laser beam that propagates along a second laser beam axis that intersects a second target location on or within the semiconductor substrate. The second target location is offset from the first target location in a direction perpendicular to the lengthwise direction of the rows by some amount such that, when the first target location is a structure on a first row of structures, the second target location is a structure or between two adjacent structures on a second row distinct from the first row. The method moves the semiconductor substrate relative to the first and second laser axes in a direction approximately parallel to the rows of structures, so as to pass the first target location along the first row to irradiate for a first time selected structures in the first row, and so as to simultaneously pass the second target location along the second row to irradiate for a second time structures previously irradiated by the first laser beam during a previous pass of the first target location along the second row.

The invention relates to a target forecasting and tracking method based on a recurrent neural network. The method includes the steps: firstly, extracting characteristic information according to a current video frame, acquiring 1000-dimensional characteristics by a single network of an SFE-Net+ model and inputting the characteristics to a motion model SM-Net; secondly, forecasting the center position of a target in a current frame according to the characteristic information and the center position of a previous frame target by the SM-Net of a time structure; thirdly, selecting a certain numberof candidate boxes from the periphery of the center position in a random sampling mode; finally, inputting the candidate boxes to a matching model SMT-Net, judging similarities of the candidate boxesand target boxes of a first frame one by one, and selecting the candidate boxes with the highest similarity. An area occupied by the candidate boxes with the highest similarity in an original image isa final area of the target in the current frame. Objects can be tracked in complicated situations.

What is disclosed is a system and method for real-time enhancement of an identified time-series signal of interest in a video that has a similar spatial and temporal structure to a given reference signal, as determined by a measure of closeness. A closeness measure is computed for pixels of each image frame of each channel of a multi-channel video to identify a time-series signal of interest. The intensity of pixels associated with that time-series signal is modified based on a product of the closeness measure and the reference signal scaled by an amplification factor. The modified pixel intensity values are provided back into the source video to generate a reconstructed video such that, upon playback of the reconstructed video, viewers thereof can visually examine the amplified time-series signal, see how it is distributed and how it propagates. The methods disclosed find their uses in remote sensing applications such as telemedicine.

The present invention provides a video motion identification method based on a time domain segmentation network. The main content of the method comprises: time domain segmentation network (TSN) based on segmented sampling, clustering function and analysis, time domain segmentation network input and training strategy and motion identification in non-cutting video. The process of the method comprises: dividing video into equal continuous time, randomly extracting one segment from corresponding segments, each segment in a sequence generating motion-level segment-stage prediction, designing a common function, aggregating the segment-level prediction into a video-level score, and in the training process, defining an optimal target in the video-level prediction, and performing optimization through an iteration updating model parameters. The sampling and aggregation modules based on segmentation are employed to establish a long-term time structure to use the whole motion video to effectively learn a motion model, store long-term video and improve the sensitivity and accuracy for the detection and identification of the motion.

The invention discloses a method for deeply learning and predicting medical track based on medical records. The method comprises the following steps: S1, encoding diagnostic information and intervention information on admission through an encoding scheme and converting code into vector to acquire diagnostic information conversion vector (the formula is shown in the description) and intervention information conversion vector (the formula is shown in the description) separately, and converting the diagnostic information and intervention information on admission for one time into one 2M-dimensional vector [xt, pt]; S2, input the vector [xt, pt] into an LSTM model, and evaluating the current output value ht to obtain the current disease state; S3, predicting a diagnostic code dt+1 according tothe disease state ht and predicting the disease progression through the diagnostic code dt+1; S4, calculating an intervention code st of the time t, increasing a time structure in the LSTM model, collecting the historical disease states in multiple time ranges, collecting the state of each section of a horizontal time shaft, collecting all the diseases states, stacking into a vector (the formulais shown in the description), and feeding back the vector (the formula is shown in the description) into a nerve network to predict the future risk result Y.

The invention discloses a video behavior timeline detection method. The method comprises the following steps: performing modeling based on a deep learning and time structure, detecting a video behavior timeline in combination with coarse granularity detection and fine granularity detection, and extracting temporal and spatial characteristics of a video by using a dual-flow model on the basis of anexisting model SSN; modeling a time structure of a behavior, and dividing a single behavior into three stages; then providing a new characteristic pyramid capable of effectively extracting time boundary information of a video behavior; and finally, combining the coarse granularity detection and the fine granularity detection to make a detection result more precise. The video behavior timeline detection method is high in detection precision, and the detection precision is higher than the detection precision of all of the current existing disclosed methods; the video behavior timeline detectionmethod is wide in application range, is applicable to detection of video clips in which people are interested in an intelligent monitoring system or a human-machine supervision system, is favorable for subsequent analysis and processing, and has an important application value.

Methods for using a detector to monitor and detect channel occupancy are disclosed. The detector resides on a station within a network using a framed format having a periodic time structure. When non-cooperative transmissions are detected by the network, the detector assesses the availability of a backup channel enabling migration of the network. The backup channel serves to allow the network to migrate transparently when the current channel becomes unavailable. The backup channel, however, could be occupied by another network that results in the migrating network interfering with the network already using the backup channel. Thus, the detector detects active transmission sources on the backup channel to determine whether the backup channel is occupied. Methods for using the detector include scheduling detection intervals asynchronously. The asynchronous detection uses offsets from a reference point within a frame.

A system for ion acceleration for medical purposes includes a conventional or superconducting cyclotron, a radiofrequency linear accelerator (Linac), a Medium Energy Beam Transport line (MEBT) connected, at the low energy side, to the exit of the cyclotron, and at the other side, to the entrance of the linear radiofrequency accelerator, as well as a High Energy Beam Transport line (HEBT) connected at high energy side to the radiofrequency linear accelerator exit and at the other end, to a system for the dose distribution to the patient. The high operation frequency of the Linac allows for reduced consumption and a remarkable compactness facilitating its installation in hospital structures. The use of a modular LINAC allows varying in active way the energy and the current of the therapeutic beam, having a small emittance and a time structure adapted to the dose distribution based on the technique known as the “spot scanning”.

Systems and methods for detecting and/or identifying signals that employ streaming processing to generate time-frequency surfaces by sampling a datastream according to a temporal structure that may be chosen as needed. The sampled datastream may be correlated with a set of templates that span the band of interest in a continuous manner, and used to generate time-frequency surfaces from irregularly sampled data with arbitrary structure that has been sampled with non-constant and non-Nyquist sampling rates where such non-constant rates are needed or desired.