Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem. We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

This article presents a new approach to designing brain–computer interfaces (BCIs) that explicitly accounts for both the uncertainty of neural signals and the important role of sensory feedback. This approach views a BCI as the means by which users communicate intent to an external device and models intent as a string in an ordered symbolic language. This abstraction allows the problem of designing a BCI to be reformulated as the problem of designing a reliable communication protocol using tools from feedback information theory. Here, this protocol is given by a posterior matching scheme. This scheme is not only provably optimal but also easily understood and implemented by a human user. Experimental validation is provided by an interface for text entry and an interface for tracing smooth planar curves, where input is taken in each case from an electroencephalograph during left- and right-hand motor imagery.

This paper presents an interface that allows a human pilot to remotely teleoperate an unmanned aircraft flying at a fixed altitude with input only from an electroen-cephalograph (EEG), which is used in this case to distinguish between left- and right-hand motor imagery in the brain. The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for the aircraft. The underlying problem is then to design a communication protocol by which the pilot can, with vanishing error probability, specify a string in this language using a sequence of bits sent through a binary symmetric channel in the presence of noiseless feedback. Such a protocol is provided by the combination of arithmetic coding as a method of lossless data compression with posterior matching as a capacity-achieving channel code. Preliminary hardware experiments demonstrate the feasibility of this approach.

There are applications where microphone arrays must be integrated into/onto a structure. As an example there are efforts to instrument soldiers' helmets and vehicles with sensors in order to detect and localize noise events. In these cases the sound will be diffracted around the support platform. In this paper a spherical diffracting platform is used and is shown to improve low frequency performance. The sphere was chosen as its diffraction can be modeled analytically and it is similar to a helmet. Specifically, this paper will compare both theoretical and experimental results for spherical free field and diffracting arrays of identical geometries to examine the effect diffraction has on the array performance. Singular values of the array manifold matrix will be compared to show that diffracting arrays outperform free field arrays, especially at lower frequencies.

We propose a complementary approach to the design of neural prosthetic interfaces that goes beyond the standard approach of estimating desired control signals from neural activity. We exploit the fact that the for a user's intended application, the dynamics of the prosthetic in fact impact subsequent desired control inputs. We illustrate that changing the dynamic response of a prosthetic device can make specific tasks significantly easier to accomplish. Our approach relies upon principles from stochastic control and feedback information theory, and we illustrate its effectiveness both theoretically and experimentally - in terms of spelling words from a menu of characters using binary surface electromyography classification.

We present a scene recognition framework for a novel automatic view management system in video conferencing. The system aims to provide the best viewpoints to the viewer based on the integration of both audio and video information. Distinguished from previous approaches, our system is aware of the 'scenes' going on, thus applying an appropriate view management policy for different scenarios is made possible. In this paper, we describe the framework of the view management system, with emphasis on the scene recognition based view management and the portability of the system. Preliminary experiments are performed for the evaluation of our approach.

Localization of acoustic sources has been an area of research starting primarily with underwater acoustics. Recently, localization of sources in air has become a topic of interest for automatic detection, surveillance and tracking for military applications. Other application include novel teleconference devices, specifically automatic focusing on the person currently speaking. Current technology relies on free-field localization arrays, and there is a plethora of literature on array beam-forming of these arrays. This paper introduces a new technology of diffracting arrays and novel signal processing methods for characterization of these arrays and localization of acoustic sources. The theory and experimental implementation of an acoustic diffracting array (ADA) is presented, including singular value decomposition and the effect of robustness. The results indicate that ADA results in increased localization accuracy, and more directional point spread functions due to increased magnitude and phase differences compared to a free-field array