The Air Force and the Department of Defense have need for deployable, reconfigurable, multifunctional antennas. They must be versatile, mechanically sound, and have predictable and reproducible properties. Physical reconfigurability is an especially effective means to enable such antennas. A goal is for these antennas to achieve in each configuration properties and performance over time equivalent to those of static, single-function antennas.
Current approaches and capabilities do not allow for multiple-conformation, physically reconfigurable antennas to be realized fully. This research topic seeks novel approaches for physically reconfigurable hardware to complement software approaches to manipulating and adapting on-the-fly Radio Frequency (RF) properties through means of folding, deforming, and electromagnetic tuning. The end products of this approach are to be antennas and possibly other front-end RF components that provide significantly enhanced and adaptable electromagnetic capabilities compared to current devices. Mechanisms of physical reconfigurability can include, but are not limited to, approaches utilizing origami and kirigami designs.
The HPEM Modeling & Effects research and development effort consists of the following areas: understanding the connectivity of infrastructure and mobile targets; understanding the phenomenology of front door effects; developing a predictive effects model; developing a theoretical and empirical basis for HPEM effects ranging from the basic circuit level to the system level effects; empirical effects testing on operational targets as a function of frequency, pulsed duration, and power of the incident HPEM pulse; battle damage assessment; rapid modeling of HPEM system sources and components; improving particle-in-cell code (PIC) capabilities; first principles materials modeling; improving models for electron emission, gas desorption to enhance the predictive capability of virtual prototyping; and developing electromagnetic (EM) algorithms to propagate radio frequency (RF) from platform-specific high power RF systems to targets to assess the performance measures of effective HPEM sources. Sound software engineering and development principles must be employed for all developed software and documentation. Robust software testing, validation, and verification are critical to software development efforts. As appropriate, software must scale to large simulation sizes and be portable to massively parallel computer architecture.
This program seeks to accelerate fundamental, broad-based research on wireless-specific machine learning (ML) techniques, towards a new wireless system and architecture design, which can dynamically access shared spectrum, efficiently operate with limited radio and network resources, and scale to address the diverse and stringent quality-of-service requirements of future wireless applications. In parallel, this program also targets research on reliable distributed ML by addressing the challenge of computation over wireless edge networks to enable ML for wireless and future applications. Model-based approaches for designing the wireless network stack have proven quite efficient in delivering the networks in wide use today; research enabled by this program is expected to identify realistic problems that can be best solved by ML and to address fundamental questions about expected improvements from using ML over model-based methods.
The National Science Foundation (NSF) hereby solicits proposals to manage and operate the Arecibo Observatory (AO). The AO is a multidisciplinary research and education facility. AO's cornerstone research instrument is a 305-meter diameter fixed spherical reflector, located on approximately 120 acres of U.S. Federal Government-owned land near Arecibo, Puerto Rico. AO conducts research in passive radio astronomy, solar system radar astronomy, and space and atmospheric sciences.
The National Science Foundation's Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE) are coordinating efforts to identify bold new concepts to significantly improve the efficiency of radio spectrum utilization while addressing new challenges in energy efficiency and security, thus enabling spectrum access for all users and devices, and allowing traditionally underserved Americans to benefit from wireless-enabled goods and services. The SpecEES program solicitation (pronounced "SpecEase") seeks to fund innovative collaborative research that transcends the traditional boundaries of existing programs.
The National Science Foundation's Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE) are coordinating efforts to identify bold new concepts to significantly improve the efficiency of radio spectrum utilization while addressing new challenges in energy efficiency and security, thus enabling spectrum access for all users and devices, and allowing traditionally underserved Americans to benefit from wireless-enabled goods and services. The SpecEES program solicitation (pronounced "SpecEase") seeks to fund innovative collaborative research that transcends the traditional boundaries of existing programs.
The objective of the ALAS program is to conduct research and development (R&D) of innovative technological solutions to enhance radio frequency (RF), electro-optical (EO), and multi-spectral (MS) system technologies and sensors along with advancing test measurement techniques and methods necessary to meet cutting edge and emerging warfighter needs. Enhanced RF sensing technologies and systems include; improved radar systems, exploration of RF waveforms, signal processing and algorithm exploration, sensor resource management (SRM), and improved EO-RF systems. Advancing test measurement techniques and methods includes; clutter characterization and mitigation, bistatic and multi-static measurement, distributed/multispectral sensing measurement, and improved antenna and radar cross section (RCS) measurement.
The Defense Advanced Research Projects Agency (DARPA) is soliciting innovative proposals in the following technical areas: radio architectures and technology and network architectures and technology that maintain network reliability through periods of data channel degradation.
The Affordable RF Multifunction Sensor (ARMS) program will focus on developing new manufacturing processes to enable an increase in reliability and a decrease in cycle time and cost. The program will be broken into a two phase approach with the Phase I being the identification, baseline, and optimization phase; the Phase II being the enhancement and validation phase. There will be a decision milestone towards the end of Phase I to determine whether Phase II will be funded or not. Phase I will identify the manufacturing challenges, establish key performance parameters, and demonstrate progress towards meeting the threshold and objective goals of the program. Phase II will continue addressing the manufacturing challenges from Phase I with the goal of meeting all of the objective key performance parameters and achieving a manufacturing readiness level of 6.
The goal of the RF Machine Learning Systems (RFMLS) program is to develop the foundations for applying modern data-driven Machine Learning to the RF Spectrum domain as well as to develop practical applications in emerging spectrum problems, which demand vastly improved discrimination performance over today's hand-engineered RF systems. Ultimately, these innovations will result in a new generation of RF systems that are goal-driven and can learn from data rather than being hand-engineered by experts.
