Projects

Conformal Wire Self-Structuring Antenna (CW-SSA)


Virtual EM has recently been awarded a Phase II STTR project funded by Navy (Contract No. N00014-14-C-0076, Topic No. N12A-T015), under which it is developing a wideband, high efficiency conformal wire antenna for use on medium-size UAVs in the HF/VHF band (1-80MHz). The antenna uses RF switches to reconfigure its aperture dynamically and is based on the patented Self-Structuring Antenna (SSA) technology. Antenna provides continues tuning across the 1-80MHz band and does not need an impedance matching network on the medium-to-upper band (15-80MHz). At the low end of the band (1-14MHz), the antenna resistance is too small to be connected directly to the radio and an adaptive impedance tuning network along with loading coils are inserted before the antenna for 50 Ohm matching to the radio. Wire mesh is selected for this antenna for light weight and for ease of printing on UAV's composite body. The functional prototype is slated for mid-2015 and commitment of AAI Textron Systems, the manufacturer of the RQ-7B Shadow UAV, is secured for eventual integration of the antenna into a flight worthy UAV. Flight tests are scheduled for 2016.

Predicting RCS of Missile Test Targets


Under a current MDA Phase II SBIR project (Contract No. HQ0147-13-C-7636, Topic No. MDA11-039), Virtual EM has been developing a simulation tool based on VirAntenn™ software for predicting radar cross-section of reentry vehicles and other targets of interest to MDA. Particular feature of the software package is the ability to model arbitrary 3D objects with metallic and dielectric parts and to be scalable on computational machines employing multi-core CPUs and GPUs for hardware acceleration. The code employs a hybrid FE-BI formulation and a full-developed GUI with 3D schematic capture including standard CAD import/export features.

Leaky Wave Self-Structuring Antenna (LW-SSA)


Virtual EM has designed and manufactured successful antenna prototypes in the past five years. The latest antenna developed is a wraparound wearable antenna based-on leaky-wave principle and is called the Leaky Wave Self-Structuring Antenna (LW-SSA). The antenna is configured using 12 MEMS switches and operates in the 2.42.5 ISM band with 8 selectable beams. Large degrees of freedom afforded by the high number of switches on the antenna aperture provide built-in redundancy against switch failure or structural damage, i.e., it is a self-healing antenna. Antenna control board houses a microcontroller with an optimizing algorithm, which is 802.11s mesh network compatible. The antenna was designed for NASA's deep space missions (for transmitting high definition video, voice, vital signs of astronauts, command and control data) and was developed under a NASA Phase II SBIR Contract (NNX09CB67C). A wearable version of the antenna is currently being prototyped on flexible polymer substrate to be integrated into astronauts' space suits and soldiers' protective vest as commercial applications. The design is also ideal for backhaul network communications in industrial, security and smart grid applications, and Silver Spring Networks (Redwood City, CA) is currently evaluating the 8” diameter version of the antenna for its Smart Grid networks.

Using Machine Learning for Target Detection and Classification


Under a previous MDA SBIR project (Contract No. W9113M-07-C-0106), Virtual EM explored the application of Support Vector Machines (SVMs) as a novel optimization technique in solving real-time missile target identification problems. A feasibility study was completed based on a simplified model of real-world scenario in order to demonstrate the capability, efficiency and robustness of SVMs. Two types of SVM-based classifiers were studied using simulated radar data generated by full-wave electromagnetic modeling of representative missile and decoy geometries using VirAntenn™ software. All missile and decoy parameters including projectile were based on publicly available information. Various Signal-to-Noise Ratios (SNRs) and data sampling rates were considered. SVMs demonstrated robust performance in picking missile targets among decoys. Target and decoy were correctly identified 80% of the time for Signal to Noise Ratio (SNR) values above 0dB, and the accuracy increased monotonically for higher SNR values.

Statistical Modeling of Phase Arrays


Virtual EM Inc.'s software product VirAntenn-Array ™ was developed as part of Phase III contract from REMEC Defense & Space, Inc. (now Cobham plc) for analyzing complex phased array architectures. The software will be used to simulate the complete phased array operation of the 2x2 subarray complete with power divider, attenuator and phase shifter/true-time-delay-units (TDUs). VirAntenn-Array ™ is also capable of predicting the effect of manufacturing tolerances such as element position errors, temperature gradients, RMS delay errors and predicts the resulting degradations such as beam pointing errors, beam squint and higher side lobe levels (SLLs).

Aptamers for Detecting Biological Warfare Agents (BWAs)


Under an Air Force Phase I SBIR project (Contract No. FA9550-07-C-0154), in collaboration with the Michigan Nanotechnology Institute for Medicine and Biological Sciences (MNIMBS) of the University of Michigan, Virtual EM has developed an aptamer-based chemical sensor for detecting BWAs, and in particular anthrax. Figure 1 shows a schematic diagram of the chemical sensor (or switch) and its operation. The substrate material is Silicon (Si) while the electrodes are made of Gold (Au) and the spacing between them varies with the desired sensitivity. A monolayer of DNA capture elements will be bound to the Au through a thiol linkage. In order to increase the switch sensitivity, a discontinuous Au film will be placed between the electrodes. This film will also have aptamers (DNA capture elements) adsorbed on it.

The second part of the system is Au nanoparticles conjugated to aptamers (DNA capture elements). These particles will be introduced into the aqueous fluid to be tested. In the absence of a BWA, it is important that they do not interact with thesurface. When a BWA is introduced into the system, it will act as a binder to pull the nanoparticles toward the sensor surface. The measured electrical conductivity between the electrodes is confirmed to be a very sensitive detector for the addition of Au nanoparticles. Consequently, upon the addition of BWA into the testing solution, the gold particles will flood the channels, lowering the conductivity, and hence triggering the detection instance. The work on the project currently stopped awaiting further funding.

Olfactory Cells for Detecting Improvised Explosive Devices (IEDs)


Under an NSF Phase I SBIR project (Contract No. IIP-0839598), in collaboration with the University of Maryland and Johns Hopkins University, Virtual EM has developed a stand off explosive detection system for detecting and identifying Improvised Explosive Devices (IEDs). Sensing unit is a bio-nose (which, in its commercial version, will be housed in a hand-held device) and utilizes live olfactory sensory neuron (OSN) cells harvested from laboratory mice's noses. The cells are stabilized on a CMOS circuit with the aid of a microfluidics system and vibrate when they come in contact with the explosive molecules (Figure 1). This disturbance is picked up and processed by intelligent algorithms, which rule on the presence or absence of a particular strain of explosive molecule. There are both hand-held (1 meter range) as well as ZigBee-based wireless mesh network (100 meter range) versions. Work is currently being self-funded by all three team members and a custom-designed board is being manufactured to detect signals with a sufficiently high signal to noise ratio to enable spike detection.

Sensor Instrumentation and Wireless Communication


Virtual EM has developed embedded hardware and firmware as a subcontractor to UES, Inc. (Dayton, OH) as part of a Phase II SBIR contract from the U.S. Army (Topic No. A08-169).

Low-Cost Light RFID Sensors for communication navigation in Space Missions


Under a NASA SBIR Phase I contract (Topic No. 2009-O1.05), in Collaboration with Georgia Institute of Technology, Virtual EM developed body-wearable RFID-based health monitoring system for NASA's human missions to Mars and beyond.

Prediction of Wireless Link quality in Urban Areas


Under a DARPA Phase I contract (Topic No. SB022-028), Virtual EM developed a GUI-driven tool (named ViLab-W™) for predicting signal received at multiple received locations due to a transmitting antenna anywhere in an urban environment. Software can import the terrain and building models and user specifies and transmitter and receiver locations. Computational engine is based on the work of Prof. Henry Bertoni of Brooklyn Polytechnic and has been validated against measured data collected in the city of Rosslyn, Virginia.

Modeling of Complex Antenna arrays on aircrafts, ships and ground vehicles


Virtual EM Inc.'s software product VirAntenn™ has been developed under a Navy Phase II SBIR project (Contract No. N68335-03-C-0204) and is currently being tested at a number of prime contractors (Raytheon, L-3 Communications, Ball Aerospace, and ATK to name a few), government labs and small businesses. A perfect example of the capability of the software is the tapered slot array antenna shown in the figure. The array is built starting from the individual elements and all the way to a frequency selective surface (FSS) layer on top of the array. Both the array and the FSS layer are large periodic structures and VirAntenn™ software is more efficient than any other method or software reported in the literature in modeling such structures. VirAntenn™ software utilizes the latest advances in computational engine technology including the fast multiple method (FMM), Domain Decomposition, and preconditioning (for faster convergence). VirAntenn™ is also capable of predicting the effect of the platform on the antenna pattern by a hybrid arrangement whereby the output of the full-wave engine is fed into a high-frequency code (utilizing multi bounce PO), which models the rest of the platform.