At Michigan Aerospace Corporation, we know how to extract practical information from large volumes of data to improve decision making. We employ advanced machine learning, pattern recognition, and optimization techniques to detect patterns, classify features, and predict the future. We also specialize in techniques for solving inverse problems that allow us to see through noise and distortion in order to identify and measure the hidden dynamics of complex systems.
See through the noise and get control of your useful information.
Usually scientists, engineers and analysts have models that predict the behavior of a complex system when the state of the system is known. This is called a forward problem. Forward problems are useful for designing systems and simulating their performance before they are built. However, many people find themselves in the position of having data or information about a system – but what they really need is to recover its state, understand its root drivers or control a complex process. Often this data is noisy, and any knowledge concerning the dynamics is heuristic or maybe entirely unknown. This scenario calls for the application of Inverse Problems techniques. Michigan Aerospace specializes in solving these kinds of problems. It has developed techniques through its research in remote sensing for atmospheric measurements and image processing and refined them with projects in identifying radar signals and detection of threats. Michigan Aerospace has begun extending these techniques into the biomedical device arena and business analysis through its Springmatter and Tinkering initiatives.
Michigan Aerospace and Springmatter can help you see through the noise and get control of your useful information.
Some examples of Michigan Aerospace’s expertise in Inverse Problems are shown in extracting a signal from two received signals that are independently and blindly distorted copies of an original. This is called Blind Equalization Source Recovery (BESR for short)
Another example is Michigan Aerospace’s inversion of atmospheric properties such as wind velocity, temperature and density from interferometric imagery.
Often information is distorted and Michigan Aerospace must unwind the distortions to recover the truth, as demonstrated in the de-blurring example below.
Michigan Aerospace works with the Department of Energy to make wind turbines smarter — to sense when trouble is imminent and actively intervene to prevent catastrophic turbine damage.
We use novel machine learning techniques to fuse information across many scales — from weather reports and geographic data, to turbine sensors and custom LIDAR systems — to build a coherent, real-time picture of turbine health.
The modern battlefield is rife with radars, cell phones, handheld radios, and deliberate RF jamming. Amid this sea of RF noise, it is difficult for the warfighter to detect genuine threats.
As part of its Signals Intelligence (SIGINT) program, we at Michigan Aerospace bring together advanced time/frequency analysis, innovative clustering techniques and sophisticated Bayesian networks to help the U.S. Navy detect, identify, and geolocate military threat radars.
Our SIGINT algorithms isolate radars from background noise, assess their threat level, and locate them on the battlefield, providing the warfighter with heightened situational awareness.
We developed a portable imaging neutron/gamma spectrometer to safely detect radioactive threats.
Nuclear terrorism is among the most frightening threats we face today. Successful detection and interdiction of illicit radioactive materials requires an ability to address the broad range of radioactive signatures. Radiation imaging provides the capability to see the radioactive source via the radiation it produces, allowing responders to pinpoint the location of radioactive materials while remaining at a safe distance.
Spectroscopy is used to identify the radioactive source and estimate whether its threat level. Imaging spectroscopy leverages techniques from modern astrophysics to enable the imaging and identification of radioactive and fissile materials — with potential improvements in detection distance over currently deployed radiation detection systems.
The Portable Neutron/Gamma Spectroscope (NSPECT) uses multiple detection modules to measure the direction and energy of incoming gamma rays or neutrons. NSPECT is the result of a collaboration between Michigan Aerospace Corporation and the University of New Hampshire (UNH) with support from the Defense Threat Reduction Agency of the Department of Defense.
MAKE BETTER DECISIONS
Our data exploitation techniques and products make use of Bayesian networks so that users, particularly companies who thrive on efficiency, can understand all levels of data associated with their operations. As a result, these companies are able to optimize both their prediction- and their decision-making.
We help some companies learn to describe and predict behaviors and preferences of their users, and we enable others to facilitate their clients’ awareness of investment strategies. Our web-based visual interface lets users build and exploit data networks as needed. We can also package applications so that clients can embed them in their own products.