Patents by Inventor Soheil Kolouri

Soheil Kolouri has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11550914
    Abstract: Described is a system for detecting backdoor attacks in deep convolutional neural networks (CNNs). The system compiles specifications of a pretrained CNN into an executable model, resulting in a compiled model. A set of Universal Litmus Patterns (ULPs) are fed through the compiled model, resulting in a set of model outputs. The set of model outputs are classified and used to determine presence of a backdoor attack in the pretrained CNN. The system performs a response based on the presence of the backdoor attack.
    Type: Grant
    Filed: April 21, 2020
    Date of Patent: January 10, 2023
    Assignee: HRL LABORATORIES, LLC
    Inventors: Soheil Kolouri, Heiko Hoffmann
  • Publication number: 20220375520
    Abstract: A memory circuit configured to perform multiply-accumulate (MAC) operations for performance of an artificial neural network includes a series of synapse cells arranged in a cross-bar array. Each cell includes a memory transistor connected in series with a memristor. The memory circuit also includes input lines connected to the source terminal of the memory transistor in each cell, output lines connected to an output terminal of the memristor in each cell, and programming lines coupled to a gate terminal of the memory transistor in each cell. The memristor of each cell is configured to store a conductance value representative of a synaptic weight of a synapse connected to a neuron in the artificial neural network, and the memory transistor of each cell is configured to store a threshold voltage representative of a synaptic importance value of the synapse connected to the neuron in the artificial neural network.
    Type: Application
    Filed: May 4, 2022
    Publication date: November 24, 2022
    Inventors: Wei Yi, Charles Martin, Soheil Kolouri, Praveen Pilly
  • Patent number: 11494486
    Abstract: Described is a system for continuously predicting and adapting optimal strategies for attacker elicitation. The system includes a global bot controlling processor unit and one or more local bot controlling processor units. The global bot controlling processor unit includes a multi-layer network software unit for extracting attacker features from diverse, out-of-band (OOB) media sources. The global controlling processing unit further includes an adaptive behavioral game theory (GT) software unit for determining a best strategy for eliciting identifying information from an attacker. Each local bot controlling processor unit includes a cognitive model (CM) software unit for estimating a cognitive state of the attacker and predicting attacker behavior. A generative adversarial network (GAN) software unit predicts the attacker's strategies.
    Type: Grant
    Filed: November 14, 2019
    Date of Patent: November 8, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Hyun (Tiffany) J. Kim, Rajan Bhattacharyya, Samuel D. Johnson, Soheil Kolouri, Christian Lebiere, Jiejun Xu
  • Patent number: 11448753
    Abstract: Described is a system for transferring learned knowledge from an electro-optical (EO) domain to a synthetic-aperture-radar (SAR) domain. The system uses a measured similarity between the EO domain and the SAR domain to train a model for classifying SAR images using knowledge previously learned from the electro-optical (EO) domain. Using the trained model, a SAR image is processed to determine regions of interest in the SAR image. A region of interest is classified to determine whether the region of interest corresponds to an object of interest, and classified regions of interest that contain the object of interest are output. The object of interest is displayed on a visualization map, and the visualization map is automatically updated to reflect a change in position of the object of interest.
    Type: Grant
    Filed: January 24, 2020
    Date of Patent: September 20, 2022
    Assignee: HRL LABORATORIES, LLC
    Inventors: Mohammad Rostami, Soheil Kolouri
  • Publication number: 20220229173
    Abstract: Disclosed is a synthetic aperture radar (SAR) system for target recognition with complex range profile. The SAR system comprising a memory, a recurrent neural network (RNN), a multi-layer linear network in signal communication the RNN, and a machine-readable medium on the memory. The machine-readable medium is configured to store instructions that, when executed by the RNN, cause the SAR system to perform various operations. The various operation comprise: receiving raw SAR data associated with observed views of a scene, wherein the raw SAR data comprises information captured via the SAR system; radio frequency (RF) preprocessing the received raw SAR data to produce a processed raw SAR data; converting the processed raw SAR data to a complex SAR range profile data; processing the complex SAR range profile data with the RNN having RNN states; and mapping the RNN states to a target class with the multi-layer linear network.
    Type: Application
    Filed: November 23, 2021
    Publication date: July 21, 2022
    Inventors: Qin Jiang, David Wayne Payton, Soheil Kolouri, Adour Vahe Kabakian, Brian N. Limketkai
  • Publication number: 20220221578
    Abstract: Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.
    Type: Application
    Filed: December 7, 2021
    Publication date: July 14, 2022
    Inventors: Adour Vahe Kabakian, David Wayne Payton, Brian N. Limketkai, Soheil Kolouri, Qin Jiang
  • Patent number: 11333753
    Abstract: Described is a stripmap SAR system on a vehicle comprising an antenna that is fixed and directed outward from the side of the vehicle, a SAR sensor, a storage, and a computing device. The computing device comprises a memory, one or more processing units, and a machine-readable medium on the memory. The machine-readable medium stores instructions that, when executed by the one or more processing units, cause the stripmap SAR system to perform various operations. The operations comprise: receiving stripmap range profile data associated with observed views of a scene; transforming the received stripmap range profile data into partial circular range profile data; comparing the partial circular range profile data to a template range profile data of the scene; and estimating registration parameters associated with the partial circular range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Grant
    Filed: October 14, 2019
    Date of Patent: May 17, 2022
    Assignee: The Boeing Company
    Inventors: Adour V. Kabakian, Soheil Kolouri, Brian N. Limketkai, Shankar R. Rao
  • Patent number: 11288498
    Abstract: Described is a system for learning actions for image-based action recognition in an autonomous vehicle. The system separates a set of labeled action image data from a source domain into components. The components are mapped onto a set of action patterns, thereby creating a dictionary of action patterns. For each action in the set of labeled action data, a mapping is learned from the action pattern representing the action onto a class label for the action. The system then maps a set of new unlabeled target action image data onto a shared embedding feature space in which action patterns can be discriminated. For each target action in the set of new unlabeled target action image data, a class label for the target action is identified. Based on the identified class label, the autonomous vehicle is caused to perform a vehicle maneuver corresponding to the identified class label.
    Type: Grant
    Filed: July 16, 2020
    Date of Patent: March 29, 2022
    Assignee: HRL Laboratories, LLC
    Inventors: Amir M. Rahimi, Hyukseong Kwon, Heiko Hoffmann, Soheil Kolouri
  • Patent number: 11255960
    Abstract: A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Grant
    Filed: January 24, 2020
    Date of Patent: February 22, 2022
    Assignee: The Boeing Company
    Inventors: Soheil Kolouri, Shankar R. Rao
  • Patent number: 11210559
    Abstract: An autonomous navigation system for a vehicle includes a controller configured to control the vehicle, sensors configured to detect objects in a path of the vehicle, nonvolatile memory including an artificial neural network configured to classify the objects detected by the sensors, and a processor. The artificial neural network includes a series of neurons in each of an input layer, at least one hidden layer, and an output layer. The memory includes instructions which, when executed by the processor, cause the processor to train the artificial neural network on a first task, identify, utilizing a contrastive excitation backpropagation algorithm, important neurons for the first task, identify, utilizing a learning algorithm, important synapses between the neurons for the first task based on the important neurons identified, and rigidify the important synapses to achieve selective plasticity of the series of neurons in the artificial neural network.
    Type: Grant
    Filed: August 23, 2019
    Date of Patent: December 28, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Nicholas A. Ketz, Praveen K. Pilly, Charles E. Martin, Michael D. Howard
  • Patent number: 11194330
    Abstract: Described is an audio classification system for classifying audio signals. In operation, the system extracts salient patches from an intensity spectrogram of an audio signal. Thereafter, multi-scale global average pooling (GAP) features are extracted for all salient patches. The GAP features are clustered, with each cluster becoming a key attribute. A test audio signal can then be mapped onto a histogram of key attributes. Based on the histogram, the test audio signal can then be classified as a sound class, allowing for operation of a device based on the classification of the sound class.
    Type: Grant
    Filed: August 30, 2018
    Date of Patent: December 7, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Charles E. Martin, Soheil Kolouri, Heiko Hoffmann
  • Patent number: 11176477
    Abstract: Described is a system for unsupervised domain adaptation in an autonomous learning agent. The system adapts a learned model with a set of unlabeled data from a target domain, resulting in an adapted model. The learned model was previously trained to perform a task using a set of labeled data from a source domain. The set of labeled data has a first input data distribution, and the set of unlabeled target data has a second input data distribution that is distinct from the first input data distribution. The adapted model is implemented in the autonomous learning agent, causing the autonomous learning agent to perform the task in the target domain.
    Type: Grant
    Filed: December 18, 2019
    Date of Patent: November 16, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Alexander J. Gabourie, Mohammad Rostami, Soheil Kolouri, Kyungnam Kim
  • Patent number: 11169258
    Abstract: Systems and methods according to one or more embodiments are provided for registration of synthetic aperture range profile data to aid in SAR-based navigation. In one example, a SAR-based navigation system includes a memory comprising a plurality of executable instructions. The SAR-based navigation system further includes a processor adapted to receive range profile data associated with observed views of a scene, compare the range profile data to a template range profile data of the scene, and estimate registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: November 9, 2021
    Assignee: The Boeing Company
    Inventors: Shankar R. Rao, Kang-Yu Ni, Soheil Kolouri
  • Patent number: 11113597
    Abstract: A method for retraining an artificial neural network trained on data from an old task includes training the artificial neural network on data from a new task different than the old task, calculating a drift, utilizing Sliced Wasserstein Distance, in activation distributions of a series of hidden layer nodes during the training of the artificial neural network with the new task, calculating a number of additional nodes to add to at least one hidden layer based on the drift in the activation distributions, resetting connection weights between input layer nodes, hidden layer nodes, and output layer nodes to values before the training of the artificial neural network on the data from the new task, adding the additional nodes to the at least one hidden layer, and training the artificial neural network on data from the new task.
    Type: Grant
    Filed: September 5, 2019
    Date of Patent: September 7, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Charles E. Martin, Nicholas A. Ketz, Praveen K. Pilly, Soheil Kolouri, Michael D. Howard, Nigel D. Stepp
  • Patent number: 11086299
    Abstract: Described is a system for controlling autonomous platform. Based on an input image, the system generates a motor control command decision for the autonomous platform. A probability of the input image belonging to a set of training images is determined, and a reliability measure for the motor control command decision is generated using the determined probability. An exploratory action is performed when the reliability measure is above a predetermined threshold. Otherwise, an exploitation action corresponding with the motor control command decision is performed when the reliability measure is below a predetermined threshold.
    Type: Grant
    Filed: January 30, 2019
    Date of Patent: August 10, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Soheil Kolouri, Heiko Hoffmann
  • Publication number: 20210231795
    Abstract: A synthetic aperture radar (SAR) system is disclosed. The SAR comprises a memory, a convolutional neural network (CNN), a machine-readable medium on the memory, and a machine-readable medium on the memory. The machine-readable medium storing instructions that, when executed by the CNN, cause the SAR system to perform operations. The operation comprises: receiving range profile data associated with observed views of a scene; concatenating the range profile data with a template range profile data of the scene; and estimating registration parameters associated with the range profile data relative to the template range profile data to determine a deviation from the template range profile data.
    Type: Application
    Filed: January 24, 2020
    Publication date: July 29, 2021
    Inventors: Soheil Kolouri, Shankar R. Rao
  • Patent number: 11069069
    Abstract: Described is a system for implicitly predicting movement of an object. In an aspect, the system includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of providing an image of a first trajectory to a predictive autoencoder, and using the predictive autoencoder, generating a predicted tactical response that comprises a second trajectory based on images of previous tactical responses that were used to train the predictive autoencoder, and controlling a device based on the predicted tactical response.
    Type: Grant
    Filed: April 9, 2018
    Date of Patent: July 20, 2021
    Assignee: HRL Laboratories, LLC
    Inventors: Amir M. Rahimi, Soheil Kolouri, Rajan Bhattacharyya
  • Publication number: 20210192363
    Abstract: Described is a system for continual adaptation of a machine learning model implemented in an autonomous platform. The system adapts knowledge previously learned by the machine learning model for performance in a new domain. The system receives a consecutive sequence of new domains comprising new task data. The new task data and past learned tasks are forced to share a data distribution in an embedding space, resulting in a shared generative data distribution. The shared generative data distribution is used to generate a set of pseudo-data points for the past learned tasks. Each new domain is learned using both the set of pseudo-data points and the new task data. The machine learning model is updated using both the set of pseudo-data points and the new task data.
    Type: Application
    Filed: October 8, 2020
    Publication date: June 24, 2021
    Inventors: Soheil Kolouri, Mohammad Rostami, Praveen K. Pilly
  • Publication number: 20210182618
    Abstract: Described is a system for learning object labels for control of an autonomous platform. Pseudo-task optimization is performed to identify an optimal pseudo-task for each source model of one or more source models. An initial target network is trained using the optimal pseudo-task. Source image components are extracted from source models, and an attribute dictionary of attributes is generated from the source image components. Using zero-shot attribution distillation, the unlabeled target data is aligned with the source models similar to the unlabeled target data. The unlabeled target data are mapped onto attributes in the attribute dictionary. A new target network is generated from the mapping, and the new target network is used to assign an object label to an object in the unlabeled target data. The autonomous platform is controlled based on the object label.
    Type: Application
    Filed: October 26, 2020
    Publication date: June 17, 2021
    Inventors: Heiko Hoffmann, Soheil Kolouri
  • Patent number: 11037030
    Abstract: A method for computing classifications of raw tomographic data includes: supplying the raw tomographic data to a sinogram-convolutional neural network including blocks, at least one of the blocks being configured to perform a convolution of the raw tomographic data in Radon space with a convolutional kernel by: slicing the raw tomographic data into a plurality of one-dimensional tomographic data slices along an angle dimension of the raw tomographic data; slicing the convolutional kernel into a plurality of one-dimensional kernel slices along the angle dimension of the convolutional kernel; for each angle, computing a one-dimensional convolution between: a corresponding one of the one-dimensional tomographic data slices at the angle; and a corresponding one of the one-dimensional kernel slices at the angle; and collecting the one-dimensional convolutions at the angles; computing a plurality of features from the convolution; and computing the classifications of the raw tomographic data based on the features.
    Type: Grant
    Filed: October 4, 2019
    Date of Patent: June 15, 2021
    Assignee: HRL Laboratories, LLC
    Inventor: Soheil Kolouri