D
Those two guys on the right look strangely similar?View attachment 33699
The next four people to begin the next chapter of spaceflight.
I believe they maybe JK200SXThose two guys on the right look strangely similar?
I believe they maybe JK200SX
Took a while for an official acknowledge from BRN so I wonder what stage we are at.
Adapting SRT’s M1 Hardware Portal for Navy Facility Health Monitoring and Prioritization
Award Information
Agency:
Department of Defense
Branch:
Navy
Contract:
N68335-21-C-0013
Agency Tracking Number:
N202-099-1097
Amount:
$239,831.00
Phase:
Phase I
Program:
SBIR
Solicitation Topic Code:
N202-099
Solicitation Number:
20.2
Timeline
Solicitation Year:
2020
Award Year:
2021
Award Start Date (Proposal Award Date):
2020-10-07
Award End Date (Contract End Date):
2021-12-10
Small Business Information
Blue Ridge Envisioneering, Inc.
5180 Parkstone Dr. Suite 200
Chantilly, VA 20151-1111
United States
DUNS:
616396953
HUBZone Owned:
No
Woman Owned:
No
Socially and Economically Disadvantaged:
No
Principal Investigator
Name: Jason Pualoa
Phone: (571) 349-0900
Email: andrew@br-envision.com
Business Contact
Name: Edward Zimmer
Phone: (703) 927-0450
Email: ned@br-envision.com
Research Institution
N/A
Abstract
Deep Neural Networks (DNN) have become a critical component of tactical applications, assisting the warfighter in interpreting and making decisions from vast and disparate sources of data. Whether image, signal or text data, remotely sensed or scraped from the web, cooperatively collected or intercepted, DNNs are the go-to tool for rapid processing of this information to extract relevant features and enable the automated execution of downstream applications. Deployment of DNNs in data centers, ground stations and other locations with extensive power infrastructure has become commonplace but at the edge, where the tactical user operates, is very difficult. Secure, reliable, high bandwidth communications are a constrained resource for tactical applications which limits the ability to routed data collected at the edge back to a centralized processing location. Data must therefore be processed in real-time at the point of ingest which has its own challenges as almost all DNNs are developed to run on power hungry GPUs at wattages exceeding the practical capacity of solar power sources typically available at the edge. So what then is the future of advanced AI for the tactical end user where power and communications are in limited supply? Neuromorphic processors may provide the answer. Blue Ridge Envisioneering, Inc. (BRE) proposes the development of a systematic and methodical approach to deploying Deep Neural Network (DNN) architectures on neuromorphic hardware and evaluating their performance relative to a traditional GPU-based deployment. BRE will develop and document a process for benchmarking a DNN’ s performance on a standard GPU, converting it to run on near-commercially available neuromorphic hardware, training and evaluating model accuracy for a range of available bit quantizations, characterizing the trade between power consumption and the various bit quantizations, and characterizing the trade between throughput/latency and the various bit quantizations. This process will be demonstrated on a Deep Convolutional Neural Network trained to classify objects in SAR imagery from the Air Force Research Laboratory’s MSTAR open source dataset. The BrainChip Akida Event Domain Neural Processor development environment will be utilized for demonstration as it provides a simulated execution environment for running converted models under the discrete, low quantization constraints of neuromorphic hardware.
Might want to dust off the hard hat tomorrow.NASA discovers a massive asteroid heading towards the Earth
The asteroid is as wide as 21 buses parked end to end.7news.com.au
Very interesting that nasa are pushing to develope(finalise) a VOC device and I wonder if they are trying to utilise Akida into it.
Now that would be something else and maybe that’s where a few of our chips have gone.
Respiratory viral infection can sometimes lead to serious, possibly life threatening complications. Highly contagious respiratory diseases cause significant disruptions to social and economic systems if spread is uncontrolled. Therefore, the rapid and precise identification of viral infection before entering crowded or vulnerable areas is essential for suppressing their transmission effectively. Additionally, a device that is reconfigurable to address the next pandemic is highly desired. Screening for infection via exhaled breath analysis could provide a quick and simple method to find infectious carriers. This breath analyzer conceptualizes a rapid scanning device enabling the user to determine the presence of viral infection in an exhaled breath through analyzing volatile organic compounds (VOCs) concentrations.N5 Sensors will technically evaluate the feasibility of volatile organic compounds (VOCs) sensors for realizing Rapid Infection Screening via Exhalation (RISE) in a breathalyzer able to identify respiratory virus infected individuals, suitable for mass-testing scenarios.The proposed survey is expected to provide the guidance how to devise an integrated sensor system for actualizing initial screening at key check points. The evaluation will be accomplished by performing market survey, research level survey, and receiving consulting from breath analyzer pioneering companies for 1) Breath analyzer platform2) VOC gas sensors and 3) Machine learning algorithm. The survey will be progressed within stepwise assessment from initial database search, article screening and selection, to quality assessment and assortment. A comprehensive final report will be provided in which our findings and research strategy for Phase II are presented.
Triton Systems, Inc. will identify, design, and develop three noninvasive diagnostic tools to screen breath for the presence of communicable respiratory viral infections. The proposed screening technologies will be based on low-cost, high-throughput sensing modalities capable of detecting unique signatures of viral pathogens. After conducting a thorough and technical review of available targets, including volatile organic contaminants (VOCs), and suitable sensing platforms, Triton will develop sensor components into an integrated system with minimal form factor for use as personal health monitors or at travel checkpoints in highly trafficked areas. The proposed sensors will be easy to administer and widely deployable to maximize their benefit during seasonal epidemics and global pandemics involving communicable respiratory viral infections. Emphasis will be placed on an inexpensive platform with superlative sensitivity, selectivity, stability, and throughput. Wireless communication capabilities will enable the presentation and recording of the screening results in under five minutes at the site of use. Combined, the sensor components will be a widely deployable platform for detecting viral respiratory agents with pandemic potential, enhancing public health emergency readiness, and improving the transportation security infrastructure in the United States.
The COVID-19 pandemic is an example of human vulnerability to new communicable respiratory viral infections. Currently, most viral respiratory infections in humans are detected by sensing the presence of the pathogen's genetic material or proteins (i.e., antigens) in bodily fluids. Polymerase chain reaction (PCR)-based methods are the most commonly used to detect a pathogen's genetic material. Although samples can be collected outside the lab, it requires specialized laboratories and skilled technicians to collect samples, perform the tests, and analyze results. Furthermore, these tests requires hours to days to process and provide results. Additionally, their sampling methods are generally invasive. Accelerating the development of new, near real time, inexpensive, user-friendly, non-invasive, accurate, and sensitive detection technologies will contribute significantly to the national and worldwide efforts to curb communicable respiratory viral infections, like the COVID-19 pandemic. During Phase I, Lynntech will use its extensive expertise in portable chemical and biochemical sensor development (including sensors for VOC detection) to select portable, fast, reliable sensors/detectors that could be used to detect VOC markers in exhaled breath and that are associated with infectious agents. During Phase II, Lynntech will develop prototypes of the candidate approach and conduct tests to demonstrate the device's capability in the detection of VOC markers of a viral infection.
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Thanks RocketI like this one I have more to post
Wearables at the Edge to Augment Readiness (WEAR)
Agency:
Department of Defense
Branch:
Defense Advanced Research Projects Agency
Program | Phase | Year:
SBIR | BOTH | 2023
Solicitation:
23.4 SBIR Annual BAA
Topic Number:
HR0011SB20234-05
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is:https://www.defensesbirsttr.mil/
Release Date:
November 15, 2022
Open Date:
March 07, 2023
Application Due Date:
January 16, 2024
Close Date:
April 06, 2023
Description:
OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Advanced Materials, Microelectronics OBJECTIVE: The objective of the Wearables at the Edge to Augment Readiness (WEAR) SBIR topic is to develop a secure and lightweight framework for real-time analysis of sensory data from wearables to monitor warfighter health and readiness at the edge. DESCRIPTION: Wearable technology is now fundamental to all areas of the human ecosystem. The term wearable technology refers to small electronic and mobile devices, or computers with wireless communications capability that are incorporated into gadgets, accessories, or clothes, which can be worn on the human body [1]; for the purposes of this SBIR topic, it does not apply to invasive versions such as micro-chips or smart tattoos. Wearable technology can provide invaluable physiological and environmental data that can potentially be used to assess a warfighters’ physical/mental wellness and readiness. Modern edge devices, like smartphones and smart watches, are equipped with an ever-increasing set of sensors, such as accelerometers, magnetometers, gyroscopes, etc., that can continuously record users’ movements and motion [2]. The observed patterns can be an effective tool for seamless Human Activity Recognition which is the process of identifying and labeling human activities by applying Artificial Intelligence (AI)/Machine Learning (ML) to sensor data generated by smart devices both in isolation and in combination [3, 4]. However, smartphone and wearable sensor signals are typically noisy and can lack context/causality due to inaccurate timestamps when the device sleeps, goes into low-power mode, or experiences high resource utilization. Thus, it can be challenging to fuse any of the various raw sensor data to achieve positive or negative assessment in wellness areas such as personal healthcare, injuries, fall detection, as well as monitoring functional/behavioral health. For instance, sensor data can be processed into feature data related to sleep or different physical activities that potentially correlate to effects on an individual’s health [5, 6]. The objective of WEAR is to develop a secure and lightweight framework for real-time analysis of sensory data from wearables to monitor warfighter health and readiness at the edge. Importantly, WEAR will achieve this goal while consuming less than 5% of the wearable battery over 10 days, assuming an initial full battery charge. The battery consumption metric is of particular interest to WEAR as warfighters at the edge (e.g., expeditionary forces deployed to remote locations or Special Operations Forces units) may not be able to recharge wearable batteries due to mission constraints limiting access to power sources for re-charging. Equally important is the need for all processing to occur at the edge because of security concerns [8]. Existing commercial efforts require cloud and off-premises server resources to analyze sensor data. PHASE I: This topic is soliciting Direct to Phase 2 (DP2) proposals only. Phase I feasibility will be demonstrated through evidence of: a completed proof of concept/principal or basic prototype system; definition and characterization of framework properties/technology capabilities desirable for both Department of Defense (DoD)/government and civilian/commercial use; and capability/performance comparisons with existing state-of-the-art technologies/methodologies (competing approaches). Entities interested in submitting a DP2 proposal must provide documentation to substantiate that the scientific/technical merit and feasibility described above has been achieved and also describe the potential commercial applications. DP2 Phase I feasibility documentation should include: • technical reports describing results and conclusions of existing work, particularly regarding the commercial opportunity or DoD insertion opportunity, risks/mitigations, and technology assessments; • presentation materials and/or white papers; • technical papers; • test and measurement data; • prototype designs/models; • performance projections, goals, or results in different use cases; and, • documentation of related topics such as how the proposed WEAR solution can enable accurate and reliable analysis of sensory data at the edge. This collection of material will verify mastery of the required content for DP2 consideration. DP2 proposers must also demonstrate knowledge, skills, and abilities in AI/ML, data analytics, edge technologies, software development/engineering, and mobile security/privacy. For detailed information on DP2 requirements and eligibility, please refer to the DoD Broad Agency Announcement and the DARPA Instructions for this topic. PHASE II: The Personal Health Determinations (WEAR) SBIR topic seeks to develop a secure and lightweight framework that can perform real-time analysis of sensory data from wearables to monitor warfighter operational health and readiness at the edge, while consuming less than 5% of the wearable battery over 10 days, assuming an initial full battery charge (i.e., WEAR component overhead can be no more than 5% of the wearable battery over 10 days). One potential direction to achieve this goal is to leverage advances in low-power sensing at the chipset level present in modern mobile and wearable devices, including but not limited to “always-on sensing.” The primary interest is in commercial-off-the-shelf hardware paired with novel sensor drivers and algorithms developed to operate at low power. A secondary objective is to offer modular application programming interfaces (APIs) to access sensor data and edge ML models/algorithms that can fit into the resource-constrained environments of commercial wearables. The end goal is the capability to monitor and accurately assess warfighter operational health and readiness by using the sensory information on the edge devices without transporting information outside of the wearable or smartphone devices. Any custom hardware or sensors are out of scope for this solicitation. DP2 proposals should: • describe a proposed framework design/architecture to achieve the above stated goals; • present a plan for maturation of the framework to a demonstrable prototype system; and • detail a test plan, complete with proposed metrics and scope, for verification and validation of the prototype system performance. Phase II will culminate in a prototype system demonstration using one or more compelling use cases consistent with commercial opportunities and/or insertion into a DARPA program (e.g., Warfighter Analytics using Smartphones for Health (WASH [7]), which seeks to use data collected from cellphone sensors to enable novel algorithms that conduct passive, continuous, real-time assessment of the warfighter). The Phase II Option period will further mature the technology for insertion into a DoD/Intelligence Community (IC) Acquisition Program, another Federal agency; or commercialization into the private sector. The below schedule of milestones and deliverables is provided to establish expectations and desired results/end products for the Phase II and Phase II Option period efforts. Schedule/Milestones/Deliverables: Proposers will execute the research and development (R&D) plan as described in the proposal, including the below: • Month 1: Phase I Kickoff briefing (with annotated slides) to the DARPA Program Manager (PM) including: any updates to the proposed plan and technical approach, risks/mitigations, schedule (inclusive of dependencies) with planned capability milestones and deliverables, proposed metrics, and plan for prototype demonstration/validation. • Months 4, 7, 10: Quarterly technical progress reports detailing technical progress to date, tasks accomplished, risks/mitigations, a technical plan for the remainder of Phase II (while this would normally report progress against the plan detailed in the proposal or presented at the Kickoff briefing, it is understood that scientific discoveries, competition, and regulatory changes may all have impacts on the planned work and DARPA must be made aware of any revisions that result), planned activities, trip summaries, and any potential issues or problem areas that require the attention of the DARPA PM. • Month 12: Interim technical progress briefing (with annotated slides) to the DARPA PM detailing progress made (including quantitative assessment of capabilities developed to date), tasks accomplished, risks/mitigations, planned activities, technical plan for the second half of Phase II, the demonstration/verification plan for the end of Phase II, trip summaries, and any potential issues or problem areas that require the attention of the DARPA PM. • Month 15, 18, 21: Quarterly technical progress reports detailing technical progress made, tasks accomplished, risks/mitigations, a technical plan for the remainder of Phase II (with necessary updates as in the parenthetical remark for Months 4, 7, and 10), planned activities, trip summaries, and any potential issues or problem areas that require the attention of the DARPA PM. • Month 24: Final technical progress briefing (with annotated slides) to the DARPA PM. Final architecture with documented details; a demonstration of the ability to perform real-time analysis of sensory data at the edge while consuming less than 5% of the wearable battery over 10 days; documented APIs; and any other necessary documentation (including, at a minimum, user manuals and a detailed system design document; and the commercialization plan). • Month 30 (Phase II Option period): Interim report of matured prototype performance against existing state-of-the-art technologies, documenting key technical gaps towards productization. • Month 36 (Phase II Option period): Final Phase II Option period technical progress briefing (with annotated slides) to the DARPA PM including prototype performance against existing state-of-the-art technologies, including quantitative metrics for battery consumption and assessment of monitoring/assessment capabilities to support determinations of warfighter health status. PHASE III DUAL USE APPLICATIONS: Phase III Dual use applications (Commercial DoD/Military): WEAR has potential applicability across DoD and commercial entities. For DoD, WEAR is extremely well-suited for continuous, low-cost, opportunistic monitoring of warfighter health in the field, where specialized equipment and medical experts are not necessarily available. WEAR has the same applicability for the commercial sector and has the potential to provide doctors and physicians with invaluable historical patient health data that can be correlated to their activities, environment, and physiological responses. Phase III refers to work that derives from, extends, or completes an effort made under prior SBIR funding agreements, but is funded by sources other than the SBIR Program. The Phase III work will be oriented towards transition and commercialization of the developed WEAR technologies. The proposer is required to obtain funding from either the private sector, a non-SBIR Government source, or both, to develop the prototype into a viable product or non-R&D service for sale in military or private sector markets. Primary WEAR support will be to national efforts to explore the ability to collect and fuse sensor data and apply ML algorithms at the edge to that data in a manner that does not drain device battery. Results of WEAR are intended to improve healthcare monitoring and assessment at the edge, across government and industry. REFERENCES: [1] Aleksandr Ometov, Viktoriia Shubina, Lucie Klus, Justyna Skibinska, Salwa Saafi, Pavel Pascacio, Laura Flueratoru, Darwin Quezada Gaibor, Nadezhda Chukhno, Olga Chukhno, Asad Ali, Asma Channa, Ekaterina Svertoka, Waleed Bin Qaim, Raúl Casanova-Marqués, Sylvia Holcer, Joaquín Torres-Sospedra, Sven Casteleyn, Giuseppe Ruggeri, Giuseppe Araniti, Radim Burget, Jiri Hosek, Elena Simona Lohan, A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges, Computer Networks, Volume 193, 2021, 108074, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2021.108074. (Available at https://www.sciencedirect.com/science/article/pii/S1389128621001651) [2] Paula Delgado-Santos, Giuseppe Stragapede, Ruben Tolosana, Richard Guest, Farzin Deravi, and Ruben Vera-Rodriguez. 2022. A Survey of Privacy Vulnerabilities of Mobile Device Sensors. ACM Comput. Surv. 54, 11s, Article 224 (January 2022), 30 pages. https://doi.org/10.1145/3510579. (Available at https://dl.acm.org/doi/pdf/10.1145/3510579) [3] Straczkiewicz, M., James, P. & Onnela, JP. A systematic review of smartphone-based human activity recognition methods for health research. npj Digit. Med. 4, 148 (2021). https://doi.org/10.1038/s41746-021-00514-4. (Available at https://www.nature.com/articles/s41746-021-00514-4) [4] E. Ramanujam, T. Perumal and S. Padmavathi, "Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review," in IEEE Sensors Journal, vol. 21, no. 12, pp. 13029-13040, 15 June15, 2021, doi: 10.1109/JSEN.2021.3069927. (Available at https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9389739) [5] Jun-Ki Min, Afsaneh Doryab, Jason Wiese, Shahriyar Amini, John Zimmerman, and Jason I. Hong. 2014. Toss 'n' turn: smartphone as sleep and sleep quality detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). Association for Computing Machinery, New York, NY, USA, 477–486. https://doi.org/10.1145/2556288.2557220 (Available at https://dl.acm.org/doi/pdf/10.1145/2556288.2557220) [6] Sardar, A. W., Ullah, F., Bacha, J., Khan, J., Ali, F., & Lee, S. (2022). Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization. Computers in biology and medicine, 146, 105662. https://doi.org/10.1016/j.compbiomed.2022.105662. (Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137241/pdf/main.pdf) [7] Defense Advanced Research Projects Agency. (2017, May 8). Warfighter Analytics using Smartphones for Health (WASH) Broad Agency Announcement HR001117S0032. (Available at https://cpb-us-w2.wpmucdn.com/wp.wpi.edu/dist/d/274/files/2017/05/DODBAA-SMARTPHONES.pdf) [8] Fitness tracking app Strava gives away location of secret US army bases. https://www.theguardian.com/world/2...p-gives-away-location-of-secret-us-army-bases KEYWORDS: Wearable Technology, Health Monitoring, Health Assessment, Data Analytics, Edge Technology, Activity Recognition, Machine Learning
Good to have him back… Go The HammersThanks Rocket