Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

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Recursive identification of time-varying Hammerstein systems with matrix forgetting

The real-time estimation of the time-varying Hammerstein system by using a noniterative learning schema is considered and extended to incorporate a matrix forgetting factor. The estimation is cast in a variational-Bayes framework to best emulate the original posterior distribution of the parameters within the set of distributions with feasible moments. The recursive concept we propose approximates the exact posterior comprising undistorted information about the estimated parameters. In many practical settings, the incomplete model of parameter variations is compensated by forgetting of obsolete information. As a rule, the forgetting operation is initiated by the inclusion of an appropriate prediction alternative into the time update. It is shown that the careful formulation of the prediction alternative, which relies on Bayesian conditioning, results in partial forgetting. This article inspects two options with respect to the order of the conditioning in the posterior, which proves vital in the successful localization of the source of inconsistency in the data-generating process. The geometric mean of the discussed alternatives then modifies recursive learning through the matrix forgetting factor. We adopt the decision-making approach to revisit the posterior uncertainty by dynamically allocating the probability to each of the prediction alternatives to be combined

Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

This work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models

Randomly Spaced Smart Antennas

The goal of this thesis is to develop adaptive array\u27 features out of randomly spaced antenna elements. Most optimization techniques that have been presented so far for non-equidistant antenna arrays have been restricted to the analysis of symmetric or linear geometries. Several direction of arrival (DOA) and adaptive beamforming al- gorithms are implemented and analyzed for both linear and planar randomly spaced antenna configurations; such as Capon and MUSIC for direction of arrival, and LMS, normalized LMS, leaky LMS, generalized normalized LMS, RLS and variable forget- ting factor RLS algorithms for the adaptive beamforming. The advantages and disadvantages of smart antennas based on random array con- figuration are presented and discussed. Several algorithms have been investigated to study the most suitable ones for optimizing the distribution of the excitation coe- cients. The work discussed herein can be extended to space applications using a cluster of small satellites, UAVs, or any array of sensors that are not aligned together in a standard linear or planar geometrical configuration. Furthermore, the proposed approach can also be extended to standard, two-dimensional linear arrays when one or more elements fail.\u2

Real-time flutter analysis

The important algorithm issues necessary to achieve a real time flutter monitoring system; namely, the guidelines for choosing appropriate model forms, reduction of the parameter convergence transient, handling multiple modes, the effect of over parameterization, and estimate accuracy predictions, both online and for experiment design are addressed. An approach for efficiently computing continuous-time flutter parameter Cramer-Rao estimate error bounds were developed. This enables a convincing comparison of theoretical and simulation results, as well as offline studies in preparation for a flight test. Theoretical predictions, simulation and flight test results from the NASA Drones for Aerodynamic and Structural Test (DAST) Program are compared

Power-Performance Modeling and Adaptive Management of Heterogeneous Mobile Platforms​

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Robust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation

This dissertation provides novel robust and regularized algorithms from linear system identification for parameter estimation with applications in vehicle tractive force prediction and mass estimation

Hyperbolic Deep Reinforcement Learning

We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space. Sequential decision-making requires reasoning about the possible future consequences of current behavior. Consequently, capturing the relationship between key evolving features for a given task is conducive to recovering effective policies. To this end, hyperbolic geometry provides deep RL models with a natural basis to precisely encode this inherently hierarchical information. However, applying existing methodologies from the hyperbolic deep learning literature leads to fatal optimization instabilities due to the non-stationarity and variance characterizing RL gradient estimators. Hence, we design a new general method that counteracts such optimization challenges and enables stable end-to-end learning with deep hyperbolic representations. We empirically validate our framework by applying it to popular on-policy and off-policy RL algorithms on the Procgen and Atari 100K benchmarks, attaining near universal performance and generalization benefits. Given its natural fit, we hope future RL research will consider hyperbolic representations as a standard tool.Comment: Preprin

EXTRACTING NEURONAL DYNAMICS AT HIGH SPATIOTEMPORAL RESOLUTIONS: THEORY, ALGORITHMS, AND APPLICATION

Analyses of neuronal activity have revealed that various types of neurons, both at the single-unit and population level, undergo rapid dynamic changes in their response characteristics and their connectivity patterns in order to adapt to variations in the behavioral context or stimulus condition. In addition, these dynamics often admit parsimonious representations. Despite growing advances in neural modeling and data acquisition technology, a unified signal processing framework capable of capturing the adaptivity, sparsity and statistical characteristics of neural dynamics is lacking. The objective of this dissertation is to develop such a signal processing methodology in order to gain a deeper insight into the dynamics of neuronal ensembles underlying behavior, and consequently a better understanding of how brain functions. The first part of this dissertation concerns the dynamics of stimulus-driven neuronal activity at the single-unit level. We develop a sparse adaptive filtering framework for the identification of neuronal response characteristics from spiking activity. We present a rigorous theoretical analysis of our proposed sparse adaptive filtering algorithms and characterize their performance guarantees. Application of our algorithms to experimental data provides new insights into the dynamics of attention-driven neuronal receptive field plasticity, with a substantial increase in temporal resolution. In the second part, we focus on the network-level properties of neuronal dynamics, with the goal of identifying the causal interactions within neuronal ensembles that underlie behavior. Building up on the results of the first part, we introduce a new measure of causality, namely the Adaptive Granger Causality (AGC), which allows capturing the sparsity and dynamics of the causal influences in a neuronal network in a statistically robust and computationally efficient fashion. We develop a precise statistical inference framework for the estimation of AGC from simultaneous recordings of the activity of neurons in an ensemble. Finally, in the third part we demonstrate the utility of our proposed methodologies through application to synthetic and real data. We first validate our theoretical results using comprehensive simulations, and assess the performance of the proposed methods in terms of estimation accuracy and tracking capability. These results confirm that our algorithms provide significant gains in comparison to existing techniques. Furthermore, we apply our methodology to various experimentally recorded data from electrophysiology and optical imaging: 1) Application of our methods to simultaneous spike recordings from the ferret auditory and prefrontal cortical areas reveals the dynamics of top-down and bottom-up functional interactions underlying attentive behavior at unprecedented spatiotemporal resolutions; 2) Our analyses of two-photon imaging data from the mouse auditory cortex shed light on the sparse dynamics of functional networks under both spontaneous activity and auditory tone detection tasks; and 3) Application of our methods to whole-brain light-sheet imaging data from larval zebrafish reveals unique insights into the organization of functional networks involved in visuo-motor processing