Kevin R. Wheeler

Gestures as input: neuroelectric joysticks and keyboards

EMG (electromyogram) technology helps capture gestures as input for virtual joysticks and keyboards and thus could lead to new applications in flight control, space, and the video game industry.

Collective intelligence for control of distributed dynamical systems

We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, The American Economic Review, 84 (1994) 406; D. Challet and Y. C. Zhang, Physica A, 256 (1998) 514). We view it as an instance of the general problem of how to configure the nodal elements of a distributed dynamical system so that they do not work at cross purposes, in that their collective dynamics avoids frustration and thereby achieves a provided global goal. We summarize a mathematical theory for such configuration applicable when (as in the bar problem) the global goal can be expressed as minimizing a global energy function and the nodes can be expressed as minimizers of local free energy functions. We show that a system designed with that theory performs nearly optimally for the bar problem.

General principles of learning-based multi-agent systems

We consider the problem of how to design large decentralized multiagent systems (MAS’s) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into one of how to automatically set/update the reward functions for each of the agents so that the global goal is achieved. In particular we do not want the agents to “work at cross-purposes” as far as the global goal is concerned. We use the term artificial COllective INtelligence (COIN) to refer to systems that embody solutions to this problem. In this paper we present a summary of a mathematical framework for COINs. We then investigate the real-world applicability of the core concepts of that framework via two computer experiments: we show that our COINs perform near optimally in a difficult variant of Arthur’s bar problem [1] (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINs in the leader-follower problem.

Gesture-based control and EMG decomposition

This paper presents two probabilistic developments for the use with electromyograms (EMGs). First described is a neuroelectric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMGs into individual motor unit action potentials (MUAPs). This Bayesian decomposition method allows for distinguishing individual muscle groups with the goal of enhancing gesture recognition. All examples presented rely upon sampling EMG data from a subjects forearm. The gesture-based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models, which are used to recognize the gestures as they are being performed in real time from moving averages of EMGs. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMGs do not provide an easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups, we present a Bayesian algorithm to separate surface EMGs into representative MUAPs. The algorithm is based on differential variable component analysis, which was originally developed for electroencephalograms. The algorithm uses a simple forward model representing a mixture of MUAPs as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data were obtained using a custom linear electrode array designed for this study

Near real-time, autonomous detection of marine bacterioplankton on a coastal mooring in Monterey Bay, California, using rRNA-targeted DNA probes

A sandwich hybridization assay (SHA) was developed to detect 16S rRNAs indicative of phylogenetically distinct groups of marine bacterioplankton in a 96-well plate format as well as low-density arrays printed on a membrane support. The arrays were used in a field-deployable instrument, the Environmental Sample Processor (ESP). The SHA employs a chaotropic buffer for both cell homogenization and hybridization, thus target sequences are captured directly from crude homogenates. Capture probes for seven of nine different bacterioplankton clades examined reacted specifically when challenged with target and non-target 16S rRNAs derived from in vitro transcribed 16S rRNA genes cloned from natural samples. Detection limits were between 0.10-1.98 and 4.43- 12.54 fmole ml(-1) homogenate for the 96-well plate and array SHA respectively. Arrays printed with five of the bacterioplankton-specific capture probes were deployed on the ESP in Monterey Bay, CA, twice in 2006 for a total of 25 days and also utilized in a laboratory time series study. Groups detected included marine alphaproteobacteria, SAR11, marine cyanobacteria, marine group I crenarchaea, and marine group II euryarchaea. To our knowledge this represents the first report of remote in situ DNA probe-based detection of marine bacterioplankton.

A Model-Based Probabilistic Inversion Framework for Characterizing Wire Fault Detection Using TDR

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable handheld devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S -parameter-based forward-modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Device control using gestures sensed from EMG

In this paper, we present neuro-electric interfaces for virtual device control. The examples presented rely upon sampling electromyogram data from a participants forearm. This data is then set into pattern recognition software that had been trained to distinguish gestures from a given gesture set. The pattern recognition software consists of hidden Markov models, which are used to recognize the gestures as they are being performed in real-time. two experiments were conducted to examine the feasibility of this interface technology. The first replicate a virtual joystick interface and the second replicated a keyboard.

An Adaptive Nonlinear Aircraft Maneuvering Envelope Estimation Approach for Online Applications

A nonlinear aircraft model is presented and used to develop an overall unified approach to online trim and maneuverability envelope estimation with uncertainty quantification without any requirement for active input excitation. The concept of time scale separation makes this method suitable for the adaptive characterization of altered safe maneuvering limitations based on aircraft performance after impairment. The results can be used to provide pilot feedback and/or be combined with flight planning, trajectory generation, and guidance algorithms to help maintain safe aircraft operations in both nominal and off-nominal scenarios.

Safe maneuvering envelope estimation based on a physical approach

This paper discusses a computationally efficient algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. This approach differs from others since it is physically inspired. This more transparent approach allows interpreting data in each step, and it is assumed that these physical models based upon flight dynamics theory will therefore facilitate certification for future real life applications.

The Environmental Sample Processor (ESP) - An Autonomous Robotic Device for Detecting Microorganisms Remotely using Molecular Probe Technology

We are developing an instrument to conduct molecular biological analyses below the ocean surface, autonomously. The device is known as the Environmental Sample Processor, or ESP. The system is based on a modular design consisting of a core sample processor (the ESP), analytical modules and sampling modules. The core ESP provides the primary interface between the environment and a set of DNA and antibody-based tests that are carried out onboard the instrument in real-time. In addition, the ESP can be used to archive samples for a variety of analyses after the instrument is returned to a laboratory. Sampling modules are devices external to the core ESP that can be added to meet specialized needs, such as operating in the deep-sea (etc). Analytical modules are conceived of as stand-alone devices that can be added to the core ESP to impart different suites of analytical functions downstream of common sample processing operations. At the time of this writing we have worked most extensively on the core ESP and detection chemistries that involve DNA probe and protein arrays. The ESP has been deployed successfully in coastal ocean surface waters. We are also developing a sample collection module and pressure housing suitable for deploying the ESP at depths to 1000m. This version of the instrument is known as the deep-sea ESP, or D-ESP. The long-term objective of the D-ESP program is to provide a molecular analytical capability at deep-sea hot vents and cold seeps. The D-ESP will be deployed using an ROV and later transitioned to benthic moorings and a cabled observatory. Finally, we are just starting work to incorporate a microfluidic analytical module to support assays that require DNA purification and amplification