37 results about "Filter technique" patented technology

Techniques for determining parking spaces and / or parking trajectories for a vehicle based on multistage filtering are discussed herein. In some examples, a vehicle may navigate to a destination within an environment. The vehicle may receive a set of parking locations and perform filtering operations to determine a subset of the parking locations. For example, the vehicle may use a list of parking characteristics (e.g., conditions) to filter the set of parking spaces. Based on determining the subset of parking spaces, the vehicle may generate a set of candidate trajectories to the subset of parking spaces. The vehicle may determine a subset of the candidate trajectories based on filtering the set of candidate trajectories according to various constraints. Further, the vehicle may determine a cost for the subset of candidate trajectories. Based on the costs, the vehicle may determine a candidate trajectory for the vehicle to follow.

Techniques for detecting radar data inaccuracies using a multistage filtering technique are discussed herein. A vehicle may capture radar data including a set of radar points. The vehicle can input the radar data into a machine-learning model configured to detect objects and / or generate bounding boxes associated with such detected objects. In some instances, the vehicle may cluster the radar points such that a cluster includes the radar points located proximate to and / or within a bounding box. To identify double bounce object detections, the vehicle may determine whether a detected object is a false-positive double bounce object detection based on evaluating the azimuth, range, and relative velocity double bounce criteria. Based on the detected object satisfying the double bounce criteria for azimuth, range, and relative velocity, the vehicle may modify the radar data.

Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Systems and methods are provided for effectuating a filtering technique that can enable available bandwidth (e.g., on a network path) to be estimated in the presence of moderate losses caused by certain queue management techniques. When packet losses exist on a network path due to certain types of packet queue transmission mechanisms, methods, or models, a bump detection algorithm (BDA) can be used to perform bandwidth estimation. When a pattern of packet loss is identified (from its signature) as being one with which BDA can be performed to accurately estimate available bandwidth on a network path, the BDA-based bandwidth estimate may be used to place / route and load balance network traffic, or otherwise used to engage in network traffic engineering, take other network-related action(s), or reported out. Otherwise, the bandwidth estimation is suppressed and not used.

Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

The present invention relates to a deblurring method and apparatus based on unsupervised learning and latent space processing. An unsupervised learning method of a deblurring model according to the present invention includes inputting an input image into a deblurring model to generate a restored image, calculating an error between the input image and the restored image, and training the deblurring model based on the error. A latent space processing-based deblurring method according to the present invention includes inputting a deblurring target image into an encoder, applying an image filtering technique to an output of the encoder, and inputting the output of the encoder, to which the image filtering technique has been applied, into a decoder to generate a deblurred image.

This invention discloses a high-speed communication link transmitter modeling method based on a Transformer decoder. This method parameterizes the input signal and establishes a deep learning model with an encoder-decoder architecture, including a non-sequential encoder and a Transformer sequential decoder, to predict the output signal of the transmitter given the input signal. The encoder processes the input signal parameters, generates a context vector, and then passes it to the decoder. The decoder uses the context vector and the transmitter output signal sequence to generate a class probability distribution for each point in the sequence. The model is trained using a random masking strategy and employs non-autoregressive decoding and filtering techniques for inference, achieving parallel output sequence prediction. The final output is obtained through a single signal filtering step. Compared with traditional simulation techniques, this method significantly improves simulation speed while maintaining a very small error.

To provide techniques for managing scale formation in water filtration systems, and reverse osmosis (RO) and nanofiltration (NF) systems implementing the techniques.SOLUTION: The present disclosure is directed to filtering techniques that combine elements of continuous and batch NF / RO based on constraints of end-user capabilities to, for example, achieve a target balance of recovery and power consumption and reduce long-term operating costs of the plant. Also disclosed herein is a method of extending a batch operation to a second induction period with injection of an antiscalant, wherein the second induction period allows for higher water recovery.SELECTED DRAWING: Figure 4

Techniques are disclosed pertaining to layered filtering. A computer system may store records in a hierarchy of levels. The computer system may receive a request to perform a key range search to locate records that fall within a key range and satisfy selection criteria. The computer system may perform the key range search. As part of processing a particular level, the computer system may receive a first set of records associated with another level and select a second set of records from the particular level that fall within the key range and satisfy the selection criteria. The computer system may merge the first and second sets of records into a third set of records, which may include not inserting, into the third set, any record of the first set of records for which there is a newer version in the particular level that does not satisfy the selection criteria.

A radar system that uses a range walk compensation algorithm to reduce system losses resulting from moving targets. During the coherent integration of pulse radar systems, the conventional Fast Fourier Transform (FFT) for Doppler processing needs to change the input range bin number in accordance to the velocity of the target. The algorithm allows for configuration of Doppler groups to compute several Dopplers with one FFT. Each Doppler group has a transform Doppler that is close to the center of the group which will dictate the change of range bins for the input data to the FFT. As the range bins are changed, there are two input filter techniques: nearest bin select, and 2-tap filter. After range walk compensation processes all Doppler groups by FFT, the resultant output block has much higher coherent integration as compared to just a conventional FFT.

Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.

Systems and techniques provide for low-latency, full-frequency noise filtering of images through the use of an image-scaling-based filtering technique, or “multiscale filtering technique”, that can provide filtering for low, medium, and / or high frequencies for one or more components of an image, such that the different resolution scales at each level of the multiscale filtering technique provides a larger receptive field for a denoising process employed at each level than a conventional denoising framework. This multiscale filtering includes receiving an input image to be filtered and then performing a multiscale filtering process in which an input image is, at different resolution scales, denoised, downscaled, upscaled, and fused with a result of a lower resolution scale, to generate a filtered image. This may include temporarily buffering intermediate image data for some of the resolution scales at a memory using direct memory access (DMA) operations.

This invention discloses an intelligent identification method for creep-type landslide hazards that combines image processing and semantic understanding. First, high-precision geometric modeling and filtering techniques are used to suppress irrelevant phases in InSAR interferograms while preserving deformation information related to landslides. Next, signal features are enhanced through phase gradient, RGB channel mapping, and generative adversarial networks to improve signal representation capabilities. Subsequently, convolutional operations, self-attention mechanisms, and structural reparameterization are used to optimize the deep learning model, improving its learning ability for landslide hazards. In the feature encoding stage, a multimodal caption generation model is used to obtain visual and linguistic features, which are then mapped to the same embedding space for seamless integration. Finally, an autoregressive language generation model and a multilayer perceptron are used to predict landslide hazards. This approach, by integrating visual, linguistic, and deep learning technologies, significantly improves the efficiency and accuracy of landslide hazard monitoring, providing effective support for disaster response and management.

This invention discloses a preset-time formation control method for vehicle systems under narrow tunnel constraints, belonging to the field of unmanned vehicle control and cooperative formation control technology. The method includes: setting vehicle position constraints and performance constraints for formation errors; integrating nonlinear command filtering techniques with parameter estimation mechanisms; designing a preset-time fault-tolerant formation control strategy by combining generalized fuzzy neural networks and Butterworth low-pass filtering techniques; and achieving stable and safe formation structure maintenance for heterogeneous vehicle systems within a preset time. This invention solves the problems of difficult constraints in general curved tunnels, non-preset convergence time, complex controller design, and difficulty in non-affine fault tolerance, achieving safe, efficient, and preset-time convergent formation control.

Various embodiments of the present disclosure relate to bandwidth estimation filtering based on packet loss patterns. Systems and methods are provided for implementing filtering techniques that are capable of estimating available bandwidth (e.g., over a network path) in the presence of modest loss caused by certain queue management techniques. When there is packet loss over a network path due to certain types of packet queue transmission mechanisms, methods, or models, bandwidth estimation can be performed using a bump in the road algorithm (BDA). When the pattern of packet loss (according to its signature) is identified as a pattern that can be used to perform a BDA to accurately estimate available bandwidth over the network path, the BDA-based bandwidth estimation can be used to place / route and load balance network traffic, or to participate in network traffic engineering, take other network-related action(s), or be reported out. Otherwise, the bandwidth estimation is suppressed and not used.

Spectrum sharing among networks is known to lead to spatial domain interference. Especially, for example, between a terrestrial networks (TN) and a non-terrestrial networks (NTN). Aspects of the present application endeavor to suppress such interference to, thereby, facilitate the spectrum sharing. An apparatus in a first network may receive assistance information related to a second network. The apparatus may obtain, using the assistance information, an estimate for a direction of an interfering signal from the second network. The apparatus may apply a spatial domain filtering technique to weaken the direction of the interfering signal, thereby reducing interference with a desired signal arriving from an estimated direction of the desired signal.

During association with the AP, an association and authentication media access control (aaMAC) address is allocated to a station (STA). The STA initially communicates using an aaMAC address, and may then communicate using an over-the-air MAC (otaMAC) address to maintain anonymity. Filtering techniques may be used to associate frames having active aaMAC addresses with temporary otaMAC addresses, requesting and responding to the frames using an active MAC address set to exchange the temporary otaMAC addresses between nodes. The STA transmits a frame to a node (e.g., STA2) using an association associated with the STA and an aaMAC address, and transmits a first active MAC address set request to the node including an otaMAC address for wireless transmissions from the STA. The STA2 performs a reciprocal procedure and may randomly exchange subsequent communications between STAs using otaMAC addresses and / or aaMAC addresses to enhance privacy.

Techniques for method for filtering artifacts from digital video are disclosed. In an example, a method includes accessing a video stream that includes a block with sub-blocks. The method includes, for each candidate filter strength parameter of a set of candidate filter strength parameters associated with the block, filtering each sub-block using a filter having the candidate filter strength parameter, creating a filtered block by combining the filtered sub-blocks, and calculating, for the filtered block, a distortion error measurement between a corresponding source block and the filtered block. The method includes selecting the filter strength parameters associated with a lowest distortion error measurement of the distortion error measurements. The method includes applying the selected filter strength parameters to an in-loop deringing filter to generate a filtered block of video.

The invention discloses a fish tank or aquarium filter assembly system that utilizes a plurality of stacked and compressed disc filters with a plurality of cross-sectional grooves for filtering debris in the fish tank, eliminates the need to use common fabrics such as socks as the filtering technique, and utilizes the pattern of grooves on the disc filters to be arranged in a pattern that filters debris in a more efficient manner than existing filters.

The present disclosure provides a system and method for ranking recommendations in a streaming platform. The system uses temporal information in calculations of Bayesian Personalized Rankings (BPR) to consider a sequence of user interactions. The system incorporates indexing user-items prior to the BPR calculations for faster training. The indices generated through the BPR technique are used as inputs to a long short-term memory (LSTM) optimization model which help in better convergence to an optimization function. Further, the system uses a content-based filtering technique and a collaborative filtering technique for processing user items during various modes of user interactions. Hence, the system provides an improved and meaningful ranking of recommendations to users on streaming platforms.

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and / or the like for generating predicted recommendations by using an input entity representation, a reference entity representation, and collaborative filtering machine learning model.

Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.

The utility model relates to the field of switch cabinets, and discloses a switch cabinet monitoring device with a novel framework, which comprises a switch cabinet body, a detection frame and a display control all-in-one machine, the top end of the detection frame is fixedly connected with an installation rod, the detection frame is installed inside the switch cabinet body, the bottom end inside the detection frame is provided with local detection equipment, and the display control all-in-one machine is connected with the local detection equipment. A fixing plate is fixedly connected to the interior of the detection frame, and a comprehensive data collector and mechanical characteristic detection equipment are installed on the fixing plate. According to the switch cabinet monitoring device capable of supporting multi-parameter comprehensive analysis diagnosis and early warning, the hardware and software dual filtering technology is adopted by the local detection equipment acquisition terminal, the anti-interference performance is improved, a passive temperature measurement sensor and a passive humidity sensor are in a passive working mode, and the device can be directly installed at an electrical connection point part for temperature measurement without cable constraint; the problems of inconvenient flexible installation and comprehensive analysis, diagnosis and detection are solved.

Techniques are described herein for performing generation of vector-based recommendations for entities. One or more embodiments address the challenge of generating recommendations for entities lacking sufficient recommendation generation input data. Unlike traditional collaborative filtering methods, various embodiments leverage content filtering techniques to make tailored and relevant recommendations for entities. By analyzing entity attributes and generating entity attribute vectors in an N-dimensional space, this system identifies the most suitable transactions by determining the closest neighbors in this space, thereby enhancing the accuracy and effectiveness of recommendations for entities associated with insufficient recommendation generation input data within the platform.

Systems and methods for suppressing random noise, acoustic clutter, and / or reverberation clutter in ultrasound images are disclosed. An autoregressive moving average (ARMA) filtering technique that includes generating a predictive error filter and estimating noise in ultrasound channel data is disclosed. Time-space and frequency-space ARMA filtering techniques are disclosed. An ultrasound imaging system capable of implementing the ARMA filtering techniques is disclosed.

A computer implemented method of acquiring and processing autonomous aerial vehicle data comprising obtaining past flight data from an autonomous aerial vehicle; storing the data in a database; conducting netto-variometer calculations to obtain equations to normalize the data; using a filtering technique to normalize the data; storing the equations obtained from the netto-variometer calculations into the database; and using the stored equations and live flight data to generate an optimized sink polar estimate for the autonomous aerial vehicle, wherein the optimized sink polar estimate is to be used in computing netto-variometer during flight.

Techniques for calculating risk scores of entity assignments are discussed herein. The system generates a probability matrix using a collaborative filtering technique such as singular value decomposition. The probability matrix is populated with probability values for each entity representing a probability that, based on the various relationships or associations of that entity with other entities, the entity has been granted an assignment. Risk values are used to provide a weighting value to assignments, separating relatively higher risk assignments from relatively lower risk assignments. The system thereafter calculates a risk score for one or more of the entities using the information in the assignment matrix, the probability matrix, and the risk values. The system can flag or identity one or more entities whose risk scores do not meet various criteria.