Library-based adaptive observation through a sparsity-promoting adaptive observer

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper proposes an adaptive observer for a class of nonlinear system with linear parametrization. The main novelty of the technique is that the regressor vector is considered to be unknown. Instead, a library of candidate non-linear functions is implemented, which transforms the original parameter vector into a new one that is characterized by being sparse. In such problem, it is shown that standard adaptive observers cannot recover the original vector due to a lack of persistence of excitation. Instead, a parameter-adaptation with an implicit l1 regularization is implemented. It is shown that this new observer can recover the parameter vector under standard assumptions of sparse signal recovery. The results are validated in a numerical simulation.This work has been partially funded by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656), by the project DOVELAR (ref. RTI2018-096001-B-C32) and by the PTI FLOWBAT 2021 project (ref. 642 201980E101).Peer ReviewedPostprint (author's final draft

A Generic Adaptive Model in Adaptive Hypermedia

For adaptive hypermedia there is a strong model in form of the AHAM model and the AHA! system. This model, based on the Dexter Model, however is limited to application in hypermedia systems. In this paper we propose a new Generic Adaptivity Model. This statemachine based model can be used as the basis for adaptation in all kinds of applications

A self-adaptive segmentation method for a point cloud

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%

A robust adaptive robot controller

A globally convergent adaptive control scheme for robot motion control with the following features is proposed. First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Second, the controller does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Third, we derive for the unknown parameter design a relationship between compensator gains and closed-loop convergence rates that is independent of the robot task. A simulation example of a two-DOF manipulator featuring some aspects of the control scheme is give

A spiral model for adding automatic, adaptive authoring to adaptive hypermedia

At present a large amount of research exists into the design and implementation of adaptive systems. However, not many target the complex task of authoring in such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck for authoring of adaptive hypermedia. One such solution is the reuse of automatically generated metadata. In our previous work we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT ( My Online Teacher), and a semantic desktop environment, indexed by Beagle++. A prototype, Sesame2MOT Enricher v1, was built based upon this integration approach and evaluated. After the initial evaluations, a web-based prototype was built (web-based Sesame2MOT Enricher v2 application) and integrated in MOT v2, conforming with the findings of the first set of evaluations. This new prototype underwent another evaluation. This paper thus does a synthesis of the approach in general, the initial prototype, with its first evaluations, the improved prototype and the first results from the most recent evaluation round, following the next implementation cycle of the spiral model [Boehm, 88]

Constrained Adaptive Sensing

Suppose that we wish to estimate a vector x∈Cn from a small number of noisy linear measurements of the form y=Ax+z, where z represents measurement noise. When the vector x is sparse, meaning that it has only s nonzeros with s≪n, one can obtain a significantly more accurate estimate of x by adaptively selecting the rows of A based on the previous measurements provided that the signal-to-noise ratio (SNR) is sufficiently large. In this paper we consider the case where we wish to realize the potential of adaptivity but where the rows of A are subject to physical constraints. In particular, we examine the case where the rows of A are constrained to belong to a finite set of allowable measurement vectors. We demonstrate both the limitations and advantages of adaptive sensing in this constrained setting. We prove that for certain measurement ensembles, the benefits offered by adaptive designs fall far short of the improvements that are possible in the unconstrained adaptive setting. On the other hand, we also provide both theoretical and empirical evidence that in some scenarios adaptivity does still result in substantial improvements even in the constrained setting. To illustrate these potential gains, we propose practical algorithms for constrained adaptive sensing by exploiting connections to the theory of optimal experimental design and show that these algorithms exhibit promising performance in some representative applications

Dynamic Channel Allocation Techniques Using Adaptive Modulation and Adaptive Antennas

This contribution studies the impact of adaptive quadrature amplitude modulation (AQAM) on network performance when applied to a cellular network, using adaptive antennas in conjunction with both fixed channel allocation (FCA) and locally distributed dynamic channel allocation (DCA) schemes. The performance advantages of using adaptive modulation are investigated in terms of the overall network performance, mean transmitted power, and the average network throughput. Adaptive modulation allowed an extra 51% of users to be supported by an FCA 4-QAM network, while in conjunction with DCA, an additional 54% user capacity was attained. Index Terms—Adaptive antennas, adaptive modulation, adaptive arrays, beam-steering, DCA, dynamic channel allocation