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Inresource-constrained systems, the interference generated by the concurrent execution of multiple controller tasks leads to extra delay and jitter, which degrade control performance and may even jeopardize the stability of the controlled system. This work presents a general methodology that integrates control issues and real-time schedulability analysis to improve the control performance in embed
Time synchronization is crucial for wireless sensor networks (WSNs), where operations often rely on time ordering of events. WSNs are deployed in different scenarios, and therefore their timing requirements are often related to the peculiar characteristics of the specific environment they have to act in. Synchronization is anyway always an issue: transactional applications need monotonicity of the
A linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions, is developed. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torq
The problem of the orbital stabilization of the forced periodic motions of a non-linear all-wheel drive mechanical system is considered within the framework of a model that is widely used in problems of the planning of the motions and feedback design for industrial robotic manipulators. The basic result is the explicit indication of one of the possible redundant sets of coordinates tranverse to th
Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium
Control theory is one of the key intellectual underpinnings that allows us to analyze the interaction of software with the physical world. However, control theory abstracts software actions as actions that take no time to execute. In reality, software execution takes time – and this execution time can be affected by a number of platform issues, including the speed of the processor, the scheduler u
We introduce a scalable observer architecture to estimate the states of a discrete-time linear-time-invariant (LTI) system whose sensors can be manipulated by an attacker. Given the maximum number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel multi-modal Luenberger (MML) observer based on efficient Satisfiability
In this paper two architectures for tunable duplexersare presented. The tuning is accomplished through variablecapacitance and resistance. The architectures are based on athree element series-parallel resonator, with one pass and onestop frequency. Both architectures rely on filtering as well ascancellation for good Tx to Rx isolation while maintaining lowinsertion loss. The first architecture, the
One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a sm
Many standard optimization methods for segmentation and reconstruction compute ML model estimates for ap- pearance or geometry of segments, e.g. Zhu-Yuille [23], Torr [20], Chan-Vese [6], GrabCut [18], Delong et al. [8]. We observe that the standard likelihood term in these formu- lations corresponds to a generalized probabilistic K-means energy. In learning it is well known that this energy has a
Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fiel
We formulate two simple benchmark problems for event-based control, where the optimal solutions in the continuous-time setting turn out to be ordinary PI and PID controllers. The benchmarks can be used to compare the performance of continuous-time, discrete-time, and various event-based controllers with regard to for instance disturbance attenuation, control effort, and average sampling or actuati
We consider a type of optimal switching problems with non-uniform execution delays and ramping. Such problems frequently occur in the operation of economical and engineering systems. We first provide a solution to the problem by applying a probabilistic method. The main contribution is, however, a scheme for approximating the optimal control by limiting the information in the state-feedback. In a
Recently, several convergence rate results for Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) have been presented in the literature. In this paper, we show global linear convergence rate bounds for Douglas-Rachford splitting and ADMM under strong convexity and smoothness assumptions. We further show that the rate bounds are tight for the class of problems und
Robust performance of networks of interconnected heterogenous nonlinear dynamic systems is studied using the theory of integral quadratic constraints. By appealing to recent results on chordal sparsity decompositions of rational transfer matrices, distributed and scalable certificates for performance of interconnections are proposed. The approach is more direct since it does not involve reformulat
Perturbation problems for operators with embedded eigenvalues are generally challenging since the embedded eigenvalues cannot be separated from the rest of the spectrum. In this paper, we study a perturbation problem for embedded eigenvalues for a magnetic Schrödinger operator, when the underlying domain is a cylinder. The magnetic potential is C2 with an algebraic decay rate as the unbounded vari
A method for interpolation of multicomponent streamer data based on using the local directionality structure is presented. The derivative components are used to estimate a vector field that locally describes the direction with the least variability. Given this vector field, the interpolation can be phrased in terms of the solution of a partial differential equation that describes how energy is tra
Previous work on estimating the epipolar geometry of two views relies on being able to reliably match feature points based on appearance. In this paper, we go one step further and show that it is feasible to compute both the epipolar geometry and the correspondences at the same time based on geometry only. We do this in a globally optimal manner. Our approach is based on an efficient branch and bo
