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In this paper we describe our NLP supported programming-by-demonstration approach to high-level robot programming that allows users to generate skills and robot program primitives for later refinement and re-use. Our ideas incorporate the identification of common user strategies (interaction patterns) in the programming process, which can be exploited to support a human user in establishing common
Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in
A three bit second order delta sigma modulator for audio applications implemented in 130nm CMOS technology is presented. The modulator features two integrators, a flash quantizer and two current steering DACs. In order to minimize the effect of delays in the DACs, excessive loop delay (ELD) compensation is utilized. Using an oversampling ratio (OSR) of 80, the design consumes 2.8mW and achieves a
In this paper we present an audio synthesizer that creates audio signals, intended to be reproduced as sound by a loudspeaker. This paper details the design, implementation and verification of an analog, subtractive audio synthesizer. The synthesizer produces a variety of waveforms spanning approximately 3 octaves. The frequency is controlled by an external voltage and the harmonic content is regu
This thesis addresses problems which require low-rank solutions under convex constraints. In particular, the focus lies on model reduction of positive systems, as well as finite dimensional optimization problems that are convex, apart from a low-rank constraint. Traditional model reduction techniques try to minimize the error between the original and the reduced system. Typically, the resulting re
We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we s
The clinical success of targeted cancer therapies is limited by the emergence of drug resistance often due to pre-existing tumor genetic heterogeneity and acquired, therapy-induced resistance. Targeted therapies have varied success in addressing metastatic disease, due to their ability to penetrate certain physiological compartments. This paper considers an evolutionary cancer model that incorpora
Estimating Cartesian contact forces and torques enables external force supervision for robotic manipulators and even force-controlled applications while avoiding the need for additional external sensing. Redundant manipulators facilitate the problem of Cartesian contact force and torque estimation (CCFE) at the TCP, since an increased amount of joint level information is available for estimating t
In many system identification methods, process model parameters are considered stochastic variables. Several methods do not only yield expectations of these, but in addition their variance, and sometimes higher moments. This paper proposes a method for robust synthesis of the proportional-integral-derivative (PID) controller, taking parametric process model uncertainty explicitly into account. The
A simple form for the optimal H-infinity state feedback of linear time-invariant infinite-dimensional systems is derived. It is applicable to systems with bounded input and output operators and a closed, densely defined, self-adjoint and strictly negative state operator. However, unlike other state-space algorithms, the optimal control is calculated in one step. Furthermore, a closed-form expressi
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
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
