Company
Portfolio Data
Back to Company SearchIntellisense Systems, Inc.
UEI: C4Y5CNN55L37
Number of Employees: 160
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 2018
121
Phase I Awards
40
Phase II Awards
33.1%
Conversion Rate
$16,858,469
Phase I Dollars
$35,411,402
Phase II Dollars
$52,269,871
Total Awarded
Awards
Quantimet Tactical on AWS GovCloud
Amount: $49,995 Topic: AF212-CSO1
To address the United States Air Force need for a secure weather application to provide command and control (C2) capabilities for its growing inventory of tactical weather sensors, Intellisense Systems, Inc. proposes to develop Quantimet Tactical, a secur
Tagged as:
SBIR
Phase I
2022
DOD
USAF
Integrated Weather Observation System with Panoramic Color Imager
Amount: $49,995 Topic: AF212-CSO1
To address the U.S. Air Force need to leverage innovative defense-related dual-purpose technologies for autonomy and meeting general warfighting requirements for ground, sea, and air platform battlespace, Intellisense Systems, Inc. (Intellisense) proposes
Tagged as:
SBIR
Phase I
2022
DOD
USAF
15a. Augmented Intelligence for Microscopy Informatics
Amount: $200,000 Topic: C53-15a
The emergence of big data gathering experiments in microscopy has created new challenges in maintaining standards for scientific integrity and reproducibility. The diversity of data, available microscope modalities, and larger number of collaborators increase the complexity of scientific asset management for evolving experiments. Recently developed scientific asset management software addresses the problem of maintaining scientific integrity compliance with complex experiments, but it still requires significant labor overhead to review data and enforce scientific integrity compliance. A scientific asset management system enhanced with augmented intelligence capabilities is proposed. The system would benefit research institutions across the country by streamlining the administrative tasks required to reach compliance standards for scientific integrity. Deep-learning software architectures such as natural language processing and convolutional neural networks are used to identify correlations among text or images and have been successfully implemented in unsupervised and self-supervised tasks such as clustering, classification, and dynamic document generation. These matured and tested software architectures provide a researcher with augmented intelligence for microscopy informatics since the labor required to organize collected images, produce formal documents, and perform compliance evaluation can be automated by a server-side application. In Phase I the software architectures required to empower a user with augmented intelligence will be designed and implemented into an application that wraps around an existing scientific asset management software application. The proposed software architectures will be trained in a self- supervised manner on data and publications available online. The completed application prototype will then be tested on real microscopy data from collaborators at national labs. This project addresses the shortage of manpower available for parsing, organizing, annotating, and enforcing compliance standards on scientific data. It will benefit research laboratories across the United States by automating the bulk of the data administration tasks, resulting in redirecting more energy and resources towards pursuing scientific inquiries. This automation will allow microscopy research facilities across the nation to focus on training talent for performing experiments rather than on compliance standards that are subject to change and can vary in complexity relative to the experiments performed.
Tagged as:
SBIR
Phase I
2022
DOE
Automatic Visual Inspection and Counterfeit Detection System
Amount: $149,998 Topic: DHS221-003
To address the DHS need for a handheld device for rapid, real-time, non-destructive detection of counterfeit microelectronics devices and systems, Intellisense Systems, Inc. (Intellisense) proposes to develop an Automatic Visual Inspection and Counterfeit Detection (AVID) system by applying dual-band (visible/infrared) nondestructive imaging and novel deep/machine learning-based image analytics. The AVID handheld device will automatically inspect a printed circuit board (PCB) of interest in both visible and infrared (IR) bands. Its cloud processing software will analyze the images, detect exterior physical defects on the PCB and its integrated circuitry (IC) components, and determine at high accuracy and in real time whether the PCBs and ICs are counterfeit. As a result, this system offers real-time, non-destructive detection capability to help the Customs and Border Protectionagents rapidly identify counterfeit PCB and IC components at ports of entry. In Phase I, Intellisense will demonstrate the feasibility of AVID by designing its system architecture, developing a counterfeit image dataset, developing algorithms for defect detection and classification, and integrating key components into a Phase I prototype to demonstrate AVID’s feasibility. In Phase II Intellisense plans to further mature the AVID prototype design for rapid real-time counterfeit electronics detection and develop a Phase II transition plan. The successful completion of this project at the end of Phase III will benefit the nation in both government and commercial sectors as a result of AVID’s highly effective counterfeit IC/PCB detection capability. Commercial applications of this technology include non-destructive inspection, quality control, and defect inspection of microelectronics components.
Tagged as:
SBIR
Phase I
2022
DHS
Micro Radio-Navigation Aid
Amount: $99,995 Topic: AF212-0001
The Air Force is seeking to develop a two-man-packable, man-portable, air-deliverable, covert tactical navigational aid system that is interoperable with all models of current tactical navigation systems (TACAN). To address this Air Force need, Intellisense Systems, Inc. (Intellisense) proposes to develop a new Micro Radio-Navigation Aid (MIRANA) that is based on novel implementation of a light-weight, electrically scanned TACAN antenna packaged as a rugged, highly portable system. The proposed MIRANA will be capable of high power radio frequency (RF) transmission to users within 20 nautical miles of the device at altitudes up to 30,000 feet. This capability will be enabled by innovative signal processing electronics bundled in a densely packed, battery-powered, single kit weighing less than 35 lb. This design takes advantages of Intellisense’s strong track record of developing and fielding low size, weight, and power (SWaP) tactical equipment for the Air Force. Specifically, the innovations in antenna design and tightly integrated, high-speed digital electronics for RF signal generation with novel, rugged packaging design will provide equivalent or better transmission range to existing mobile VORTAC stations in a significantly reduced form factor. The MIRANA will also be capable of rapid setup and tear-down in support of remote, first-in operations and other temporary deployments. Users interface with the MIRANA via a local display tablet that provides on-site configurability, system status, and tracking capabilities. As a result, this system will offer the desired bearing and range functionality found in VOR, DME, TACAN, or combined station VORTACs, in a man-portable package, which directly addresses the Air Force requirements. Further, the MIRANA is designed to flexibly augment TACAN standard signaling with additional covert operational modes upon user reconfiguration with options for encryption, use of frequency hopping, and RF directional configuration. In Phase I, Intellisense will establish the feasibility of the MIRANA by modeling and simulation, as well as proof-of-concept experimentation on test hardware, establishing a preliminary design leading up to Phase II. In Phase II, Intellisense plans to develop the MIRANA as an initial functional prototype, followed by a fully integrated solution which will be procured, assembled, and demonstrated in a fielded environment. This prototype will be used to perform experiments, analyze performance results, and fully establish the adequacy of the solution and minimize transition risk. Throughout the Phase II effort, Intellisense will contact potential customers as well as transition partners to support Phase III activities as well as refining the path forward for FAA certification as a part of commercialization. Regular communication will be provided to the government sponsor throughout Phase II to ensure understanding of the required solution and to discuss risk mitigation strategies.
Tagged as:
SBIR
Phase I
2022
DOD
USAF
Plenoptic Rain Drop Measurement System
Amount: $749,994 Topic: AF191-039
To address the Air Force’s need for a raindrop measurement system, Intellisense Systems, Inc. (Intellisense) proposes, in Phase II, to advance the development of the Plenoptic Rain Drop Measurement (PARES) system proven feasible in Phase I. The PARES syst
Tagged as:
SBIR
Phase II
2021
DOD
USAF
Sea-Skimming Missile Tracking Radar Array
Amount: $750,000 Topic: AF191-052
To address the U.S. Air Force’s need for technology to detect and track low radar cross section, high-speed, low-altitude weapons over a large area of water, Intellisense Systems, Inc. (Intellisense) proposes, in Phase II, to advance development of the ne
Tagged as:
SBIR
Phase II
2021
DOD
USAF
6a. Nuclear Forensics Scanning
Amount: $199,997 Topic: 06a
Nuclear forensics analysis involves the scanning of potentially contaminated material samples with high resolution microscopy to search for evidence of nuclear activity. An electron microscope can resolve details with resolutions on the order of nanometers, but it can take several days to image a one-centimeter squared region of interest at nanometer resolution. A need exists to reduce the required imaging time for effective material sample analysis. What is needed to help nuclear forensics analysis is a software architecture for reducing the imaging time in nuclear forensics by automating material sample scans with electron microscopes. The proposed software would perform a lower-resolution surface scan over the area of interest, identify anomalies and select a distribution of relevant sample subregions for high resolution imaging. The high-resolution images would then be passed to downstream statistical analysis packages to gather statistical data on the various subregion representations found in the material sample. In Phase I, relevant deep learning techniques in anomaly detection, classification, and segmentation will be unified to develop a software architecture for automation of critical processes in material sample scanning. The system’s architecture will be studied and refined in consultation with experts in materials science and electron microscopy. This will involve the acquisition of material samples and development of simulated sample data for the training, validation, and testing of the final deep learning architectures. The proposed software tool will then be tested on a previously analyzed sample to demonstrate its efficacy over human-based detection processes. Nuclear forensics would benefit because the cost of scanning samples would be reduced. Biological and materials sciences can also benefit from automation techniques in microscopy. The ability to efficiently scan material samples in hours rather than days or months, if not years, to acquire important statistics would help revolutionize basic research in systems biology as well as materials science. The ability to efficiently correlate microstructures to a larger sample would aid researchers in targeting their collective efforts to areas of significance and help drive basic research in areas such as pathology and oncology.
Tagged as:
SBIR
Phase I
2021
DOE
WiFi and Mobile Converged Network
Amount: $150,000 Topic: DHS211-002
To address the DHS's need for 5G and WiFi 6/6E coexistence for secure federal networks, Intellisense Systems, Inc. (Intellisense) proposes to develop a new WiFi and Mobile Converged Network (WAMCON) solution. The proposed solution is based on a new radio access network (RAN)-agnostic 5G core network, WiFi 6/6E radio access network for indoor fixed or nomadic use cases and 5G new radio unlicensed (NR-U) stand-alone radio access network for outdoor applications where mobility is required. The innovative WAMCON solution will offer a converged private wireless network supporting indoor and outdoor use cases. Optimal allocation of the private network resources, including spectral agility and handover decisions between WiFi and 5G NR-U radio access networks, will be based on quality of service (QoS) using reinforcement learning and artificial intelligence algorithms. Security features like wireless intrusion detection and prevention using deep learning algorithms will be explored. The 5G core network will provide unified WiFi and NR-U stand-alone network management and policy enforcement functions. In Phase I, Intellisense will demonstrate the feasibility of WAMCON by simulation and analysis. In Phase II, Intellisense plans to prototype WAMCON through virtual network components, commercial-of-the-shelf WiFi 6/6E radios, NR-U stand-alone radios and a small number of mobile devices like laptops, tablets, and mobile phones. The successful completion of this project at the end of Phase III will benefit the nation in both government and commercial sectors by improving WiFi 6/6E and 5G coexistence. Commercial applications for this technology include WiFi and mobile wireless converged private wireless networks.
Tagged as:
SBIR
Phase I
2021
DHS
Neuromorphic Enhanced Cognitive Radio
Amount: $124,993 Topic: H6
NASA is seeking innovative neuromorphic processing methods and tools to enable autonomous space operations on platforms constrained by size, weight, and power (SWaP). To address this need, Intellisense Systems, Inc. (Intellisense) proposes to develop a new Neuromorphic Enhanced Cognitive Radio (NECR) device based on neuromorphic processing and its efficient implementation on neuromorphic computing hardware. NECR is a low-SWaP cognitive radio that integrates the open source software radio framework with a new neuromorphic processing module to automatically process the incoming radio signal, identify the modulation types and parameters of the signal, and send the identification results to the controller module to properly decode the incoming signal. Due to its efficient implementation on neuromorphic computing hardware, NECR can be easily integrated into SWaP-constrained platforms in spacecraft and robotics to support NASA missions in unknown and uncharacterized space environments, including the Moon and Mars. In Phase I, we will develop the concept of operations (CONOPS) and key algorithms, integrate a Phase I prototype software in a simulated environment to demonstrate its feasibility, and develop a Phase II plan with a path forward. In Phase II, the NECR algorithms will be further matured, implemented on commercial off-the-shelf nbsp;neuromorphic computing hardware, and then integrated with radio frequency (RF) modules and radiation-hardened packaging into a Phase II working prototype device. The Phase II prototype will be tested to demonstrate its fault and mission tolerances and delivered with documentation and tools to NASA for applications to CubeSat, SmallSat, and rover flight demonstrations.
Tagged as:
SBIR
Phase I
2021
NASA