Company
Portfolio Data
Back to Company SearchKITWARE INC
UEI: DK6LPWMS5LP5
Number of Employees: 166
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
SBIR/STTR Involvement
Year of first award: 1998
135
Phase I Awards
80
Phase II Awards
59.26%
Conversion Rate
$24,862,598
Phase I Dollars
$88,205,155
Phase II Dollars
$113,067,754
Total Awarded
Awards
TOPIC 426: ATLASCOPE: COMPOSABLE VISUALIZATION TOOLS FOR MULTISCALE INTEGRATIVE BIOLOGY
Amount: $1,499,969 Topic: 426
In Phase I, we were able to prototype several features for Atlascope and demonstrate that they form a plausible system in which to conduct multiscale biological workflows and visualizations. In Phase II, we will aim to scale up our Phase I success in several directions: technologically, we will aim to harden the features and the web application we have developed to host our Phase I experiments; biologically, we will aim to bring in more production data and reproduce more of our research partners’ workflows; commercially, we will market the new capability to interested parties within and without our network of customers and research partners.e
Tagged as:
SBIR
Phase II
2023
HHS
NIH
UVDAT: Urban Visualization and Data Analysis Toolkit
Amount: $250,000 Topic: C55-16c
High concentrations of underserved populations with low social capital in urban areas, along with assets and ecosystems in vulnerable zones, present high risks of cascading failures and expected loss of lives and property. A key gap is the lack of urban models that can help to address and visualize threats, especially under a changing climate and with growing urbanization and “coastalization” as well as aging lifeline systems and protective infrastructures. We propose to build UVDAT: Urban Visualization and Data Analysis Toolkit that will support data-driven research for the growth, progress, and welfare of urban areas, as well as to enable and inform decisions and policy. It will provide access to an extensive data library in a secure computing environment, support for data science and AI methods to enhance and merge data specifically for analyzing the effects of natural hazards on infrastructure, and neighborhood-scale high-resolution visualization that offers visual representations to present the data and uncertainty at different levels of navigation. In Phase I, we will create a software system to understand climatic threats (in this case, floods) and their influence on critical infrastructure in the Boston area. We will analyze extreme tidal water levels in the future, create a flood vulnerability map analyzing the fragile nodes, and observe the impacts of tidal flooding on existing networks. To achieve this, we will build an urban data library, make advances toward a comprehensive intuitive multi-scale visualization system, and implement workflows to highlight the important trends and relationships between layers represented in the model outputs. The proposed system will aid planners, policymakers, and logistics engineers in grappling with data that is growing in size, complexity, and diversity. By placing climate data alongside other infrastructure, sensor, and demographic data in new ways with our proposed analytic and visualization tools, planners will make data-driven decisions that result in better holistic outcomes for urban areas.
Tagged as:
SBIR
Phase I
2023
DOE
Pan3D: Open Source Scalable and Reproducible Scientific Workflows for 3D Data Analytics
Amount: $1,600,000 Topic: C53-01a
C53-01a-271272The problem to be addressed arises from the recent and rapid growth of large three- dimensional (3D) and time-varying datasets produced from sensor/observation systems, numerical simulation, and AI models and the current difficulties incorporating these data into research workflows in many data science communities. To address this problem, Pan3D will fill critical needs for (i) data interoperability between modeling (numerical, AI) and visualization systems, (ii) easy-to-use visualizations for web- and Jupyter-based workflows, and (iii) software platforms integrating these features to provide interactive analytics environments for both cloud and high-performance computing (HPC) workflows. The Phase I effort established technical feasibility by successfully prototyping two software capabilities. The first Phase I prototype produced a working interface between standard software for labeled NumPy arrays (Xarray) and standard software for 3D visualization (VTK). The second prototype demonstrated the visualization of 3D datasets in Jupyter notebooks with both "back end" and "front end" rendering capabilities (the latter being important for JupyterHub and other distributed-computing environments). In Phase II, the data interoperability software will be reorganized and revised for better modularity and more seamless integration with visualization pipelines. The visualization prototype will be streamlined in several ways that include refactoring out unnecessary code associated with pre-existing software components used in Phase I. The resulting software products will be demonstrated with desktop workflows and will also be integrated with cloud and HPC systems to demonstrate their benefits in collaborative environments at scale. The Phase II results will significantly improve scientists' productivity and efficiency by enabling them to focus on data interpretation, analysis, and model development. The open, modular platform to be developed in Phase II will provide significant commercial opportunities to Kitware through customization and other professional services, leveraging the business model Kitware has successfully used since its founding over two decades ago.
Tagged as:
SBIR
Phase II
2023
DOE
Neuromorphic Event-Based Star Tracking (NEST)
Amount: $1,800,000 Topic: AF231-D004
Kitware, in partnership with the University of Dayton, is pleased to propose this SBIR effort to assess the feasibility of using event-based image sensors for star tracking applications. Previous work has demonstrated that event cameras can produce accura
Tagged as:
SBIR
Phase II
2023
DOD
USAF
Bat Detection and Species Determination Around Wind Turbines using AI
Amount: $200,000 Topic: C56-17a
Problem Statement The increased use of wind energy has negatively affected resident and migratory bat species. Innovative and cost-effective technologies, such as passive acoustic monitoring, are needed to refine our understanding of the risks of wind turbine interactions. However, challenges remain in using acoustic data to identify call signatures of bat species, such as the quality of annotated data, the lack of advanced models, and the unavailability of do-it-yourself AI tools. Addressing these challenges is critical to minimize wildlife impacts and supporting sustainable wind energy development in the United States. How This Problem or Situation is Being Addressed To better identify and reduce the effect of wind turbines on resident and migratory bat species, an open source system is proposed to automate bat detection and species determination around wind turbines. The system will allow experts to curate acoustic data, perform clustering for auto-discovery, and use AI models to accurately predict bat species from echolocation bat calls. Interactive visualizations of data and AI model outputs will also be available in a web browser to facilitate data discovery. This system is expected to advance the state of the art in the field and support the environmentally sustainable development of wind energy in the United States. SBIR Phase I Activities The initial phase involves collecting and processing data to develop AI models and user interfaces for bat detection and species identification around wind turbines. The aim is to collect a representative dataset in collaboration with NREL and USGS, which will be used to train deep neural networks to develop an initial prototype classifier. The dataset's high degree of annotation uncertainty will be addressed by clustering and unsupervised methods. A web interface will enable users to run algorithms on the server side and visualize spectrograms and data clusters. Commercial Applications and Other Benefits Our proposed system will create curated data and an ensemble of AI models that can be used and retrained by bat experts – such as those in the North American Bat Monitoring Program – to improve data collection, storage, and sharing accuracy and speed. The open source model will help other communities working on acoustic data by providing a system that integrates data, models, and visualization in an accessible web application. Our approach can also be applied in underwater passive acoustics and related terrestrial wildlife or urban monitoring domains.
Tagged as:
SBIR
Phase I
2023
DOE
Multi-Task Scale -aware Continuous and Localizable Embeddings
Amount: $999,868 Topic: OSD22A-001
In Phase I, our team of Kitware and UC-Berkeley developed Scale-MAE by adding ground sample distance (GSD) to positional encodings, and produced a multiscale representation that achieves state-of-the art results across image classification, semantic segmentation, and object detection tasks. In Phase II, we will create a remote sensing pretraining toolkit to enable fast and easy experimentation with multiple self supervised pertaining techniques that create foundational deep neural network models applicable across NGA. The foundational networks will be tested on the tasks as Scale-MAE in Phase I, and we will also benchmark performance for key point matching. Phase II will extend our Scale-MAE work by integrating additional metadata into the network to increase accuracy by providing it with more information; we will extend the approach to handle inputs including NTM, multi-spectral, and SAR data. Finally, the downstream task networks will be transitioned into NGA SAFFIRE for integration and evaluation. This system will enable NGA to quickly train and deploy new detectors to quickly respond to shifting needs and reduce the time from the analyst’s demand for a new task capability to the execution and availability of said capability.
Tagged as:
STTR
Phase II
2023
DOD
NGA
An Efficient I/O Framework for In Situ Data Extracts
Amount: $111,429 Topic: A22-012
Computational power has far outgrown modern I/O capabilities, leading to a bottleneck in high performance computing when attempting to write generated data to disk for future post-processing. In situ processing techniques mitigate this bottleneck by performing classically post-processing procedures while the simulation is running, without writing significant data to disk. Data extract artifacts can be generated in this way, storing reduced data to disk, still rich enough for further post-processing. However, data extracts are frequently generated with data on small subsets of processes, leading to imbalanced loading, straining I/O and reducing overall performance. We propose the development of an automated data aggregation system targeting wildly load imbalanced in situ data extracts. This system will operate at scale, improving the I/O efficiency of extract-based in situ workflows. The design will be test driven from the beginning, focusing on quantitative performance metrics for future general and machine specific optimizations. Multiple in situ platforms will be targeted, including ParaView Catalyst and VisIt LibSim, ensuring wide availability of the data extract aggregation capabilities.
Tagged as:
SBIR
Phase I
2023
DOD
ARMY
PrintRite3D? AI/ML for In-Situ Additive Manufacturing Defect Detection
Amount: $239,995 Topic: N222-117
Additive manufacturing (AM) increases the speed and flexibility of production and enables traditional part concatenation for advanced manufacturing capabilities. The ability for U.S. manufacturers to 3D-print advanced components in-house reduces reliance on traditional subtractive supply chains and bolsters national security readiness. While AM affords unique flexibility in design for manufacturability, its variance in lack of repeatability and reproducibility introduced various defects, including lack of fusion, gas entrapment, powder agglomeration, balling, internal cracks, and thermal stress, that degrades mechanical properties of final parts. The cost associated with performing post process inspection is an economic limiter and its efficacy is limited by material and material geometry, that is solved by in process nondestructive inspection methodologies. Kitware, in collaboration with Sigma Additive Solutions, proposes to bring the latest advances in deep neural network artificial intelligence and signal fusion to optimize and extend PrintRite3D® for the Navy’s unique needs. PrintRite3D is a platform-independent, interactive, in-process quality assurance system that combines inspection, feedback, data collection and critical analysis. Optimizing PrintRite3D defect detection accuracy will improve confidence in and reduce part-rejection false-alarm rates. Our proposed method builds on an existing proof of concept for in-situ defect detection and extends our capabilities to cover a wider range of builds, printers, locations, and sensors.
Tagged as:
SBIR
Phase I
2023
DOD
NAVY
TelEOSARus Returns: Diving Deeper with EO and SAR in TeleSculptor
Amount: $749,999 Topic: AF212-D007
Current 3D reconstruction techniques from both SAR and EO sensors rely on observing a region of interest from a full 360 degree orbit, with an additional requirement for SAR reconstructions that the data collected come from multiple passes at different altitudes. These requirements are not feasible in a military operational scenario. There exists a need for methods to generate 3D models of both scenes and individual targets where the data is collected from a limited viewpoint. Kitware and Ohio State University propose to develop novel algorithms that will fuse data from both EO and SAR sensors from limited viewpoints by applying deep learning to train algorithms that will leverage the advantages of both sensor modalities and use prior knowledge to reconstruct portions of the scene that were not observed due to the limited viewpoint. These new algorithms will be incorporated into the open source TeleSculptor photogrammetry application, providing a quick transition of these new capabilities to operational use.
Tagged as:
SBIR
Phase II
2022
DOD
USAF
NEAMS Data Analysis
Amount: $200,000 Topic: C54-36b
The DOE Office of Nuclear Energy’s (NE) Nuclear Energy Advanced Modeling and Simulation (NEAMS) Program, in cooperation with the U.S. Nuclear Regulatory Commission (NRC) and in- dustry, develops, demonstrates, and deploys usable advanced modeling and simulation capabilities to accelerate the deployment of advanced nuclear energy technologies, including light-water reactors (LWRs), non-light-water reactors (non-LWRs), and advanced fuels. The availability of the NEAMS codes has enabled transformative scientific discovery and insights otherwise not attainable or af- fordable. These capabilities present enormous opportunities for researchers in design, operation, regulation, safety margin characterization, and performance optimization. However, analyzing the resulting large-scale, complex data into actionable insights is a real challenge for the researcher. The ability of computational experiments to generate large datasets has far exceeded analysis ca- pabilities. Thus, only a small amount of the data is actually employed to create insights. The rich information encapsulated in the simulation data is often neglected for the lack of human resources required to perform tedious and systematic data exploration. We seek to expose user-friendly advanced analytics and visualizations to allow end-users to achieve the best return on investment (ROI) from their NEAMS-enabled activities. We propose to provide an easy-to-use platform to create bespoke visual analytics of NEAMS code capabilities and contribute them to the Virtual Test Bed (VTB) of the National Reactor Innovation Center (NRIC) for the broader nuclear energy community consumption. In our proposed Phase I project, we will build bespoke visual analytics prototypes deployable on a desktop, an HPC supercomputer, and the cloud. We will enhance interactive exploration, pushing the boundaries of possible solutions with large-scale datasets leveraging state-of-the-art technologies for scientific and information analysis and visualization. We propose extending our key innovation of dynamic switching between local and remote analysis and visualization with new NEAMS code-specific components to deliver production-ready bespoke visual analytics through various computational environments. The work proposed here addresses the deficiencies in current platforms to grow a substan- tial share of this market. In particular, we are targeting both the nuclear energy industry and manufacturing and engineering firms that can benefit from the underlying technology to improve productivity.
Tagged as:
SBIR
Phase I
2022
DOE