Search

Narbik A Manukian

from Los Angeles, CA
Age ~69
Get Report
Also known as:
Nabik Manukian, Manukian Narbik
Phone and address:
6433 Wynkoop St, Los Angeles, CA 90045
(310) 721-2942
Related to:
Babajan X HossepianHossepian Edick, 64Karabet TorossianA Parsikh, 97Gaiane Manoukian, 65A Torossian
Connected to:
Ara C Manukian, 53Ishkhanuhi Manukian, 86Varaz Manukian, 86Ram B Manukonda, 52Abram V Manukyan, 32Daria Manukyan, 85Emma Manukyan, 78Hovsep J Manukyan, 32Meri Manukyan, 41Meruzhan Manukyan, 55

Narbik Manukian Phones & Addresses

  • 6433 Wynkoop St, Los Angeles, CA 90045 (310) 721-2942
  • Helendale, CA
  • 1254 Thompson Ave, Glendale, CA 91201
  • 440 Myrtle St, Glendale, CA 91203
  • 1451 E Wilson Ave #7, Glendale, CA 91206
  • 8355 W Manchester Ave #5, Playa del Rey, CA 90293
  • Cadiz, CA
  • San Bernardino, CA

Resumes

Resumes

Narbik Manukian Photo 1

Owner, Automated Detection Systems, Inc

View page
Location:
Los Angeles, CA
Industry:
Computer Software
Work:
Us Department of Homeland Security 2007 - 2008
Program Manager
Automated Detection Systems 2007 - 2008
Owner, Automated Detection Systems, Inc
Narbik Manukian Photo 2

Narbik Manukian

View page
Location:
Los Angeles, CA
Industry:
Research
Skills:
Developing Novel Mathematical and Algorithmic Solutions
Leading and Managing Research and Development Group
Creating New Business Opportunities and Obtaining New Contracts
Strong Communicative and Interaction Skills
Ts Clearance
Integration
Physics
Algorithms
Pattern Recognition
Software Engineering
Signal Processing
Radar
Image Processing
Testing
Security Clearance
Systems Engineering
Engineering
System Architecture

Business Records

Name / Title
Company / Classification
Phones & Addresses
Narbik Manukian
President
AUTOMATED DATA UNDERSTANDING SYSTEMS, INC
6433 Wynkoop St, Los Angeles, CA 90045
Narbik Manukian
President
AUTOMATED DETECTION SYSTEMS, INC
Custom Computer Programing Prepackaged Software Services
6433 Wynkoop St, Los Angeles, CA 90045

Publications

Us Patents

Rapidly Converging Projective Neural Network

View page
US Patent:
52767710, Jan 4, 1994
Filed:
Dec 27, 1991
Appl. No.:
7/814357
Inventors:
Narbik Manukian - Glendale CA
Gregg D. Wilensky - Venice CA
Assignee:
R & D Associates - Los Angeles CA
International Classification:
G06F 1516
US Classification:
395 24
Abstract:
A data processing system and method for solving pattern classification problems and function-fitting problems includes a neural network in which N-dimensional input vectors are augmented with at least one element to form an N+j-dimensional projected input vector, whose magnitude is then preferably normalized to lie on the surface of a hypersphere. Weight vectors of at least a lowest intermediate layer of network nodes are preferably also constrained to lie on the N+j-dimensional surface. To train the network, the system compares network output values with known goal vectors, and an error function (which depends on all weights and threshold values of the intermediate and output nodes) is then minimized. In order to decrease the network's learning time even further, the weight vectors for the intermediate nodes are initially preferably set equal to known prototypes for the various classes of input vectors. Furthermore, the invention also allows separation of the network into sub-networks, which are then trained individually and later recombined.

Quantitative Dental Caries Detection System And Method

View page
US Patent:
57427003, Apr 21, 1998
Filed:
Oct 13, 1995
Appl. No.:
8/542674
Inventors:
Douglas C. Yoon - Beverly Hills CA
Gregg D. Wilensky - Venice CA
Joseph A. Neuhaus - Marina del Ray CA
Narbik Manukian - Glendale CA
David C. Gakenheimer - Redondo Beach CA
Assignee:
Logicon, Inc. - Torrance CA
International Classification:
G06K 900
US Classification:
382132
Abstract:
A caries detection system and method for quantifying a probability of lesions existing in tissues are presented. Digital X-ray images are segmented and further processed to generate feature statistics inputs for a neural network. The feature statistics include colinearity measurements of candidate lesions in different tissue segments. The neural network is trained by back propagation with an extensive data set of radiographs and histologic examinations and processes the statistics to determine the probability of lesions existing in the tissues.

Radar System Using A Machine-Learned Model For Stationary Object Detection

View page
US Patent:
20220335279, Oct 20, 2022
Filed:
Apr 14, 2021
Appl. No.:
17/230877
Inventors:
- St. Michael, BB
Yihang Zhang - Calabasas CA, US
John Kirkwood - Playa del Rey CA, US
Shan Zhang - Thousand Oaks CA, US
Sanling Song - Northport AL, US
Narbik Manukian - Los Angeles CA, US
International Classification:
G06N 3/063
G06N 20/00
G06N 3/08
G01S 13/931
Abstract:
This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

Sensor Fusion For Object-Avoidance Detection

View page
US Patent:
20220319328, Oct 6, 2022
Filed:
Mar 31, 2021
Appl. No.:
17/219760
Inventors:
- St. Michael, BB
Narbik Manukian - Los Angeles CA, US
International Classification:
G08G 1/16
G06T 7/62
G06T 7/70
G06T 7/571
G06K 9/00
G06K 9/62
G01S 13/86
G01S 13/931
G01S 7/41
B60W 60/00
B60W 30/09
Abstract:
This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

Accuracy Of Predictions On Radar Data Using Vehicle-To-Vehicle Technology

View page
US Patent:
20230005362, Jan 5, 2023
Filed:
May 11, 2022
Appl. No.:
17/662998
Inventors:
- St. Michael, BB
Kanishka Tyagi - Agoura Hills CA, US
Narbik Manukian - Los Angeles CA, US
International Classification:
G08G 1/01
H04W 4/46
G01S 13/04
Abstract:
This document describes techniques and systems for improving accuracy of predictions on radar data using vehicle-to-vehicle (V2V) technology. V2V communications data and the matching sensor data related to one or more vehicles in the vicinity of a host vehicle are collected. The V2V data is used as label data and the radar data is used as the input data for training the model. The training may either occur onboard the host vehicle or remotely. Further, multiple host vehicles may contribute data to train the model. Once the model has been updated with the included training, the updated model is deployed to the sensor tracking system of the host vehicle. By using the dataset that includes the V2V communications data and the matching sensor data, the updated model may accurately track other vehicles and enable the host vehicle to utilize advanced driver-assistance systems safely and reliably.
Control profile
Narbik A Manukian from Los Angeles, CA, age ~69 Get Report