Array processing has been a subject of intense research for several decades, but most successful methods depend on relatively large arrays of sensors, narrowband processing of stationary sources, fewer sources than sensors, and simple acoustic environments. The capabilities of human and other animal hearing systems demonstrate that these constraints can be overcome. Our prior work has developed new bio-inspired binaural algorithms for the localization and separation of desired sources from a cluttered acoustic environment, such as in a restaurant or cocktail party, that show remarkable performance improvements over conventional techniques while using only two to four microphones. Current research is developing meta-adaptive algorithms that automatically and blindly learn the characteristics of complex acoustic environments (such as reverberation or many nonstationary interferers) and adapt or auto-calibrate to them to maximize performance. Four-dimensional sound recording, transmission, and playback that preserves the directional characteristic of the sound are being developed. These new methods are being applied to a number of applications such as advanced hearing aids, hands-free telephony, advanced multimedia systems, automotive and military applications, noise suppression for speech recognition systems, and highly accurate direction-finding of RF sources with small antennas.

Animals have many types of sensory systems that provide vital information about their environment. In collaboration with biologists and MEMS engineers who develop new forms of sensors, we are developing novel bio-inspired sensing systems with fundamentally new capabilities. Recent work has developed an artificial lateral line that detects and locates moving objects in the water surrounding the array, an artificial weak electro sense that performs a similar function using a self-generated electric field, and bio-inspired algorithms for high-resolution acoustic source localization and separation using very small arrays. Current work is extending these methods to localization of neurons, new tactile and vibrissal (whisker) sensing systems, and an "electronic bat" 4-D imaging system.

Reducing energy dissipation and size of wireless sensing systems could enable many high-impact applications such as intelligent materials, smart objects that are aware of and adjust to their surrounding conditions, and enriched human sensory and monitoring interfaces as well as military systems. We believe that only dynamically self-optimizing systems that adaptively select the right information, data resolution and rate, signal representation, communication bandwidth, and data latency over a broad dynamic range can provide the needed functionality at an absolute minimal energy cost. As part of the Multiscale Systems Center, we are developing "attentional" signal processing systems that optimally adapt all system components to the current demands of the environment and the application to maximize the expected long-term system utility within a very limited overall energy budget. This includes highly scalable detection and estimation algorithms that trade-off performance and energy over orders of magnitude to dynamically match current system needs, as well as system management algorithms that maximize the total system utility.