85 results about "Spatial Dependency" patented technology

In one embodiment of the invention, an apparatus is disclosed including an image sensor, a color filter array, and an image processor. The image sensor has an active area with a matrix of camera pixels. The color filter array is in optical alignment over the matrix of the camera pixels. The color filter array assigns alternating single colors to each camera pixel. The image processor receives the camera pixels and includes a correlation detector to detect spatial correlation of color information between pairs of colors in the pixel data captured by the camera pixels. The correlation detector further controls demosaicing of the camera pixels into full color pixels with improved resolution. The apparatus may further include demosaicing logic to demosaic the camera pixels into the full color pixels with improved resolution in response to the spatial correlation of the color information between pairs of colors.

Disclosed herein is a scheduling method and apparatus based on eigen values robust against spatial correlation in a mobile communication system supporting multiple users by use of multiple transmit / receive antennas. A user terminal estimates channel characteristics from the transmit antennas to the user terminal, decomposes the estimated channel characteristics into receive channel characteristics and transmit channel characteristics, and feeds back a transmit principal eigen vector to the base station (BS). The BS selects a predetermined number of user terminals having the least correlation using the feedback principal eigen vectors from a plurality of user terminals, constructs a user precoding matrix by combining the principal eigen vectors of the selected user terminals, multiplies data streams for the selected user terminals by the user precoding matrix and transmits the multiplied data to the selected user terminals.

A vehicle surroundings monitoring apparatus recognizes a pedestrian from existence of a leg, in the case in which a width in real space of a binarized object is suitable as a pedestrian's leg, the binarized object has longwise shape, the lower end position of the binarized object exists in the lower part (lower half) of a gray scale object region, and a serial luminance transition exists in the binarized object. The vehicle surroundings monitoring apparatus also recognizes a pedestrian from existence of an umbrella held up by the pedestrian, in the case in which an umbrella region with a low temperature exists above the binarized object, the width of the umbrella region is suitable as a pedestrian's umbrella, and, the spatial correlation between the centroid position of the umbrella region and the centroid position of the binarized object is suitable as a spatial relationship between a pedestrian and his / her umbrella.

A system and method that provides an efficient scheme to code images and video by exploiting spatial correlations within an image by employing hybrid directional prediction and lifting wavelet techniques.

A method and a device for encoding / decoding video signals by motion compensated temporal filtering. Blocks of a video frame are encoded / decoded using temporal and spatial correlations according to a scalable Motion Compensated Temporal Filtering (MCTF) scheme. When a video signal is encoded using a scalable MCTF scheme, a reference block of an image block in a frame in a video frame sequence constituting the video signal is searched for in temporally adjacent frames. If a reference block is found, an image difference (pixel-to-pixel difference) of the image block from the reference block is obtained, and the obtained image difference is added to the reference block. If no reference block is found, pixel difference values of the image block are obtained based on at least one pixel adjacent to the image block in the same frame. Thus, the encoding procedure uses the spatial correlation between image blocks, improving the coding efficiency.

A Markov Random Field (MRF)-based technique is described for performing clustering of images characterized by poor or limited data. The proposed method is a statistical classification model that labels the image pixels based on the description of their statistical and contextual information. Apart from evaluating the pixel statistics that originate from the definition of the K-means clustering scheme, the model expands the analysis by the description of the spatial dependence between pixels and their labels (context), hence leading to the reduction of the inhomogeneity of the segmentation output with respect to the result of pure K-means clustering.

A method and apparatus are provided for improving compression efficiency or picture quality by selecting a wavelet transform technique suitable to input video / image scene characteristics in video / image compression. The video encoder includes a temporal transform module that removes temporal redundancy of an input frame and generates a residual frame, a selection module that selects an appropriate wavelet filter among a plurality of wavelet filters having different taps according to a spatial correlation of the residual frame, a wavelet transform module that generates wavelet coefficients by performing wavelet transform on the residual frame using the selected wavelet filter, and a quantization module that quantizes the wavelet coefficients.

The disclosure relates to a method for performing context based binary arithmetic coding with a stochastic bit-reshuffling scheme in order to improve MPEG-4 fine granularity scalability (FGS) based bit-plane coding. The method comprises steps of: replacing 8×8 DCT with 4×4 integer transform coefficient in MPEG-4 AVC (Advance Video-Coding); partitioning each transform coefficient into significant bit and refinement bit; setting up significant bit context based on energy distribution within a transform block and spatial correlation in adjacent blocks; using an estimated Laplacian distribution to derive coding probability for the refinement bit; and using the context across bit-planes to partition each significant bit-plane for saving side information bit.

An antenna selection technique (transmitter / receiver antenna selection) that reduces the cost of the MIMO system while maintaining high performance. A combined selection algorithm for MIMO-OFDM is provided which offers the best tradeoff between spatial correlation and instantaneous SNR. In one case, antenna selection is based on instant channel information. In another case antenna is based on statistical channel state information. In another case, antenna selection is based on a hybrid of instant channel state information and statistical channel state information.

A motion estimator and an estimation method for a video encoder to reduce power consumption by reducing the computational complexity of the motion estimator. In an upper step, a full search for a ±4 pixel search region for a 4×4 pixel block is performed at ¼ video resolution, to detect two motion vector candidates. In a medium step, a partial search for two vector candidates selected in the upper step and one vector candidate using a spatial correlation is performed for a 8×8 block within a ±1 or ±2 search region, to decide one motion vector candidate. In a lower step, a partial search for the ±1 or ±2 search region on 16×16 block is performed at full resolution, and a half pixel search for a motion vector candidate obtained in the lower step is performed to estimate a final motion vector. A ±4 pixel search region is operatively divided into four search regions, and the estimator sequentially searches the four ±2 pixel search regions to sequentially output SAD values.

A signal transmission method for a multiple input multiple output (MIMO) communication system. A transmitter having a plurality of transmission antennas receives antenna selection information from a receiver having at least one reception antenna. The transmitter selects at least two transmission antennas based on the antenna selection information, beamforms space-time block coding (STBC)-coded input signals with a weighting matrix, and transmits the beamformed signals via the selected transmission antennas. The antenna selection information is generated based on exact channel characteristics and statistical channel characteristics for the transmission antennas and a spatial correlation between the transmission antennas.

An adaptive antenna unit includes feeding antenna elements arranged so as to reduce spatial correlations thereof, parasitic antenna elements, provided with respect to each of the feeding antenna elements and arranged so as to increase mutual coupling between a corresponding one of the feeding antenna elements, variable reactance elements each terminating a corresponding one of the parasitic antenna elements, and a control section. The control section controls reactances of the reactance elements and controls weighting of the reception signals received by the feeding antenna elements, in response to the reception signals.

The invention provides a base station traffic prediction method and device. The method comprises the following steps: inputting an intra-node traffic characteristic and an inter-node traffic characteristic corresponding to a base station to be predicted to a traffic prediction model that is established based on a spatial dependence relationship of the base station in advance, and obtaining an output traffic prediction value of the base station to be predicted, wherein the intra-node traffic characteristic is the intra-base station traffic of the base station to be predicted and the base station adjacent to the base station to be predicted, and the inter-node traffic characteristic is the inter-node traffic characteristic between the base station to be predicted and the base station adjacent to the base station to be predicted. By decomposing the traffic of the base station into the intra-node traffic characteristic and the inter-node traffic characteristic according to the mobility characteristics of the user, and performing traffic prediction by using the traffic prediction model that is established based on a spatial dependence relationship of the base station, the influence of the movement of the user to the base station traffic is fully considered, so that the accurate traffic prediction is achieved.

A data source may provide a plurality of time-series measurements that represent normal operation of a cyber-physical system (e.g., in substantially real-time during online operation of the cyber-physical system). A stateful, nonlinear embedding computer may receive the plurality of time-series measurements and execute stateful, nonlinear embedding to project the plurality of time-series measurements to a lower-dimensional latent variable space. In this way, redundant and irrelevant information may be reduced, and temporal and spatial dependence among the measurements may be captured. The output of the stateful, nonlinear embedding may be utilized to automatically identify underlying system characteristics of the cyber-physical system. In some embodiments, a stateful generative adversarial network may be used to achieve stateful embedding.

Methods and systems for parallel rate-constrained motion estimation in a video encoder are provided. Embodiments of the present invention provide a solution for the problem of the reliance upon spatial dependencies when processing parallel RCME on a frame. In order to solve this problem, embodiments of the present invention determine a list of at least two Motion Vector Predictor Candidates (MVPC), determine, in parallel for at least two of said at least two MVPCs, corresponding Motion Vector Candidates (MVC) using Rate-Constrained Motion Estimation (RCME) and determine the optimal Motion Vector (MV) among the determined MVCs based on rate distortion optimization.

A holographic image processing method and a holographic image display method are provided. The holographic image processing method performed by a holographic image processing apparatus may include acquiring a holographic image, encoding the holographic image by quantizing the holographic image to improve at least one of a temporal correlation, a spatial correlation, and a channel correlation of the acquired holographic image, and generating the encoded holographic image.

A method of identifying patterns in a digitized acoustic signal is disclosed. The method comprises: (i) converting the digitized acoustic signal into a spatial representation being defined by a plurality of regions on a vibrating membrane, each the regions having a different vibration resonance, each the vibration resonance corresponding to a different frequency of the acoustic signal; (ii) iteratively calculating a weight function, the weight function having a spatial dependence being representative of acoustic patterns of each region of the plurality of regions; and (iii) using the weight function for converting the spatial representation into a reconstructed acoustic signal; thereby identifying the patterns in the acoustic signal.

The present invention provides a cross-city traffic flow joint prediction method based on deep migration learning, the method comprises three parts: firstly, dividing a city into regions, using a heatmap to represent city traffic, and abstracting traffic data at different time points into "image frames"; then using a ConvLSTM method to learn spatial dependence and time dependence of the data; andfinally, using condition maximum average difference to measure distribution distance of different spatio-temporal data. The method achieves the purpose of knowledge migration by introducing the condition maximum average difference to reduce difference between a source domain and a target domain. The method proposes to use the ConvLSTM and the conditional maximum average difference for the first time, uses an idea of migration learning to apply to urban traffic flow prediction, solves the problem of prediction difficulty caused by insufficient data in similar or related cities, improves accuracy of prediction and is of great significance to construction of smart transportation system, transportation planning and risk prediction for future cities.

A method for irrigating terrain comprising the following steps: generating field surface relative elevation data set using Monte-Carlo simulation; adjusting the simulation data set to ensure it falls in a reasonable numerical range; adjusting the data set to establish certain spatial dependence using Kriging interpolation method; and making further statistical characteristics parameter modification to the data set, so that the final corrected field surface relative elevation data shares the same or similar statistics characteristics parameters as the initial simulation. The method factors in both the randomness and spatial dependence of the field relative elevation distribution, which brings the simulation result closer to the actual micro-topography.

The invention relates to a method for the early detection of fires based on the detection of volatile thermolytic products which are characteristic of the object to be monitored for fires. According to the method, ambient air of an area to be monitored for fires is taken in and ionized, the ionized gas stream being guided through an electromagnetic field, the resulting field strength of which in terms of its temporal or spatial dependency changes the trajectories of the ions in a parameter set in such a manner that positive or / and negative ions of the ionized gas are forced into predefined trajectories and detected. The invention further relates to a device for the early detection of fires using the detection of characteristic volatile thermolytic products which are specific to the objects to be monitored for fires. The device consists of an intake unit (1), an ion generation and ionic current measuring chamber (10) in which the gas stream (5) of the taken-in ambient air is ionized, electrodes (16, 17), having a connection (19) for generating and controlling a DC voltage (21), a ground connection and a connection (18) for generating and controlling an alternating field (20), two electrometer plates (22, 23) which detect characteristic ions, and a microcontroller system (8) which evaluates and stores the temporal dependency of the ionic currents and utilizes a significant change of the measured current at at least one DC voltage value to generate a fire alarm signal.

A method of determining correlated flows in a network may include obtaining times of arrival and corresponding flows for data units in the network (1020) and assigning weights to a set of data units based on respective times between the set of data units and one data unit (1030). A probability matrix representing interflow connections in the network may be updated based on the assigned weights (1040).

An apparatus of estimating a position of a moving vehicle such as a robot includes a feature point matching unit which generates vectors connecting feature points of a previous image frame and feature points of a current image frame, corresponding to the feature points of the previous image frame, and determines spatial correlations between the feature points of the current image frame, a clustering unit which configures at least one motion cluster by grouping at least one vector among the vectors based on the spatial correlations in a feature space, and a noise removal unit removing noise from each motion cluster, wherein the position of the moving vehicle is estimated based on the at least one motion cluster.

An image recognition method is used to recognize objects in an image in real-time and without requiring storage of the image in an image buffer. Each object is formed from image segments. The method includes: setting a grayscale threshold value of the image; acquiring pixel values of each row sequentially in the image; determining a start point of a newly detected image segment located in a currently inspected row of the image; collecting information of the newly detected image segment point-by-point starting from the start point; determining an end point of the newly detected image segment; identifying the object to which the newly detected image segment belongs according to a spatial correlation between the newly detected image segment and a previously detected image segment in an adjacent previously inspected row of the image; and associating the collected information of the newly detected image segment with the identified object to which the newly detected image segment belongs.

The invention discloses a cloud model based sensor fault identifying system and method and belongs to a field of agricultural IoT. According to the invention, fault identification of abnormal points is realized through space similarity of sensor node environment information and node information of the current moment is predicated according to time relevance of abnormal point history data. The node information is predicated by utilizing the space relevance among homogeneous sensor nodes and heterogeneous sensor nodes. An estimation value of the sensor node of the current moment is generated through combination of the predication information by utilizing a cloud model based data fusion method, so that fault diagnosis and data restoration of the sensor node are realized. The invention can realize abnormal point identification for sensor nodes in facility agriculture and can realize diagnosis and data restoration by utilizing the fault diagnosis method; data collection accuracy of the sensor nodes can be improved effectively and easiness and quickness are achieved. The invention can be applied to fault identification and diagnosis of abnormal data of the sensor nodes and realizes estimation of sensor abnormal data.

A device, system and method for applying a whitening transformation to a plurality of samples of a sampled symbol to reduce both temporal correlation between the sampled symbol and a pre-determined number of previous symbols, and a spatial correlation between a plurality of samples.

The invention provides a space-time attention mechanism method for traffic prediction, and belongs to the field of traffic prediction. The invention relates to a traffic prediction framework of an end-to-end solution, which can be used for modeling spatial, short-term and long-term periodic dependencies. The APTN first models spatial dependencies and periodic dependencies using an attention mechanism of an encoder. Our model can more easily capture these dependencies because each node is to process all other nodes in the network. Time attention is then applied to select a relevant encoder concealment state across all time steps. We use a traffic data set of the real world to evaluate the proposed model and observe consistency improvements on the most advanced baseline.

The invention discloses a lightweight missing spatio-temporal data reconstruction method. The overall steps are as follows: 1, performing spatio-temporal data representation: Abstracting the point data and the mesh data of the static reference into a unified space-time state matrix for representation; 2, performing time dimension interpolation: Introducing an average correlation coefficient to automatically select a time window so as to improve the modeling time dependence capability of the SES algorithm; 3, performing spatial dimension interpolation: respectively adopting constant equal distance and a correlation distance based on a Gaussian function to assign a weight to each spatial neighbor so as to improve the capacity of modeling spatial dependence of the IDW algorithm; 4, performingspace-time integration: introducing an extreme learning machine as a learning algorithm of the neural network model, and integrating an estimation result of a space-time dimension to obtain a final predicted value of missing data. According to the method, a plurality of improved lightweight models are integrated, so that the reconstruction precision of mass missing spatio-temporal data is furtherimproved on the premise that the calculation efficiency of the reconstruction algorithm is ensured.