The problem of gain-scheduled state feedback control for discrete-time linear systems with time-varying parameters is considered in this paper. The time-varying parameters are assumed to belong to the unit simplex and to have bounded rates of variation, which depend on the values of the parameters and can vary from slow to arbitrarily fast. An augmented state vector is defined to take into acco...

This paper deals with the problem of model (in)validation of discrete-time, causal, LTI stable models subject to Slowly Linear Time Varying structured uncertainly, using freqnency-domain data corrupted by additive noise. It is nell known that in the case of structured LTI uncertainty the problem is NP hard in the number of uncertainty blocks. The main contribution of this paper shows that, on t...

Position determination in biological systems is often achieved through protein concentration gradients. Measuring the local concentration of such a protein with a spatially varying distribution allows the measurement of position within the system. For these systems to work effectively, position determination must be robust to noise. Here, we calculate fundamental limits to the precision of posi...

We provide a new sequential predictors approach for the exponential stabilization of linear time-varying systems. Our method circumvents the problem of constructing and estimating distributed terms in the control laws, and allows arbitrarily large input delay bounds, pointwise time-varying input delays, and uncertainties. Instead of using distributed terms, our approach to handling longer delay...

Diffusion models have become essential for describing the performance and statistics of reaction times in human decision making. Despite their success, it is not known how to evaluate decision confidence from them. I introduce a broader class of models consisting of two partially correlated neuronal integrators with arbitrarily time-varying decision boundaries that allow a natural description o...

It has not been a simple matter to obtain a sound extension of the classical J2 flow theory of plasticity that incorporates a dependence on plastic strain gradients and that is capable of capturing size-dependent behaviour of metals at the micron scale. Two classes of basic extensions of classical J2 theory have been proposed: one with increments in higher order stresses related to increments o...

Fundamental operations of nuclear magnetic resonance (NMR) imaging can be formulated, for a large number of methods, as sampling the object distribution in the Fourier spatial-frequency domain, followed by processing the digitized data to produce a digital image. In these methods, controllable gradient fields determine the points in the spatialfrequency domain which are sampled at any given tim...

We introduce a micro-biochemical administration module (BAM) for generating chemical gradients for use in axonal guidance studies. The device is designed to be simple to use, require minimal packaging, and be operated using only a pipette. A passive pumping mechanism is utilized to pump liquid through a SU-8 microchannel and then the micropore on the Parylene cap of the microchannel. The achie...

We present a novel multi-view reconstruction approach that effectively combines stereo and shape-from-shading energies into a single optimization scheme. Our method uses image gradients to transition between stereo-matching (which is more accurate at large gradients) and Lambertian shape-from-shading (which is more robust in flat regions). In addition, we show that our formulation is invariant ...

We consider the dynamics of fermions with a spatially varying mass which couple to bosons through a Yukawa interaction term and perform a consistent weak coupling truncation of the relevant kinetic equations. We then use a gradient expansion and derive the CP-violating source in the collision term for fermions which appears at first order in gradients. The collisional sources together with the ...