Showing papers on "Adaptive algorithm published in 2014"

Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems

Abstract: Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates thanks to the large bandwidth available at mmWave frequencies. To realize sufficient link margin, mmWave systems will employ directional beamforming with large antenna arrays at both the transmitter and receiver. Due to the high cost and power consumption of gigasample mixed-signal devices, mmWave precoding will likely be divided among the analog and digital domains. The large number of antennas and the presence of analog beamforming requires the development of mmWave-specific channel estimation and precoding algorithms. This paper develops an adaptive algorithm to estimate the mmWave channel parameters that exploits the poor scattering nature of the channel. To enable the efficient operation of this algorithm, a novel hierarchical multi-resolution codebook is designed to construct training beamforming vectors with different beamwidths. For single-path channels, an upper bound on the estimation error probability using the proposed algorithm is derived, and some insights into the efficient allocation of the training power among the adaptive stages of the algorithm are obtained. The adaptive channel estimation algorithm is then extended to the multi-path case relying on the sparse nature of the channel. Using the estimated channel, this paper proposes a new hybrid analog/digital precoding algorithm that overcomes the hardware constraints on the analog-only beamforming, and approaches the performance of digital solutions. Simulation results show that the proposed low-complexity channel estimation algorithm achieves comparable precoding gains compared to exhaustive channel training algorithms. The results illustrate that the proposed channel estimation and precoding algorithms can approach the coverage probability achieved by perfect channel knowledge even in the presence of interference.

A Motion-Tolerant Adaptive Algorithm for Wearable Photoplethysmographic Biosensors

Abstract: The performance of portable and wearable biosensors is highly influenced by motion artifact. In this paper, a novel real-time adaptive algorithm is proposed for accurate motion-tolerant extraction of heart rate (HR) and pulse oximeter oxygen saturation (SpO2) from wearable photoplethysmographic (PPG) biosensors. The proposed algorithm removes motion artifact due to various sources including tissue effect and venous blood changes during body movements and provides noise-free PPG waveforms for further feature extraction. A two-stage normalized least mean square adaptive noise canceler is designed and validated using a novel synthetic reference signal at each stage. Evaluation of the proposed algorithm is done by Bland-Altman agreement and correlation analyses against reference HR from commercial ECG and SpO2 sensors during standing, walking, and running at different conditions for a single- and multisubject scenarios. Experimental results indicate high agreement and high correlation (more than 0.98 for HR and 0.7 for SpO2 extraction) between measurements by reference sensors and our algorithm.

Online adaptive algorithm for optimal control with integral reinforcement learning

Abstract: SUMMARY In this paper, we introduce an online algorithm that uses integral reinforcement knowledge for learning the continuous-time optimal control solution for nonlinear systems with infinite horizon costs and partial knowledge of the system dynamics. This algorithm is a data-based approach to the solution of the Hamilton–Jacobi–Bellman equation, and it does not require explicit knowledge on the system's drift dynamics. A novel adaptive control algorithm is given that is based on policy iteration and implemented using an actor/critic structure having two adaptive approximator structures. Both actor and critic approximation networks are adapted simultaneously. A persistence of excitation condition is required to guarantee convergence of the critic to the actual optimal value function. Novel adaptive control tuning algorithms are given for both critic and actor networks, with extra terms in the actor tuning law being required to guarantee closed loop dynamical stability. The approximate convergence to the optimal controller is proven, and stability of the system is also guaranteed. Simulation examples support the theoretical result. Copyright © 2013 John Wiley & Sons, Ltd.

Visual Servoing Trajectory Tracking of Nonholonomic Mobile Robots Without Direct Position Measurement

Abstract: Localization is one of the most difficult and costly problems in mobile robotics. To avoid this problem, this paper presents a new controller for the trajectory tracking of nonholonomic mobile robots using visual feedback without direct position measurement. This controller works on the basis of a novel adaptive algorithm for estimating the global position of the mobile robot online using natural visual features measured by a vision system and its orientation and velocity measured by odometry and Attitude and Heading Reference System (IMU&Compass) sensors. The nonholonomic motion constraint of mobile robots is fully taken into account, compared with most of the existing visual servo controllers for mobile robots. The Lyapunov theory is used to prove that the proposed adaptive visual servo controller gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. A graphical processing unit is adopted to implement the proposed adaptive controller in parallel to achieve real-time detection and tracking of visual features. Experiments on a mobile robot are conducted to validate the effectiveness and robust performance of the proposed controller.

Efficient Algorithms for Robust One-bit Compressive Sensing

Abstract: While the conventional compressive sensing assumes measurements of infinite precision, one-bit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy one-bit measurements, and develop two novel algorithms with formal theoretical guarantees. First, we propose a passive algorithm, which is very efficient in the sense it only needs to solve a convex optimization problem that has a closed-form solution. Despite the apparent simplicity, our theoretical analysis reveals that the proposed algorithm can recover both the exactly sparse and the approximately sparse vectors. In particular, for a sparse vector with s nonzero elements, the sample complexity is O(s log n/e2), where n is the dimensionality and e is the recovery error. This result improves significantly over the previously best known sample complexity in the noisy setting, which is O(s log n/e4). Second, in the case that the noise model is known, we develop an adaptive algorithm based on the principle of active learning. The key idea is to solicit the sign information only when it cannot be inferred from the current estimator. Compared with the passive algorithm, the adaptive one has a lower sample complexity if a high-precision solution is desired.

Adaptive FEM with Optimal Convergence Rates for a Certain Class of Nonsymmetric and Possibly Nonlinear Problems

Abstract: We analyze adaptive mesh-refining algorithms for conforming finite element discretizations of certain nonlinear second-order partial differential equations. We allow continuous polynomials of arbitrary but fixed polynomial order. The adaptivity is driven by the residual error estimator. We prove convergence even with optimal algebraic convergence rates. In particular, our analysis covers general linear second-order elliptic operators. Unlike prior works for linear nonsymmetric operators, our analysis avoids the interior node property for the refinement, and the differential operator has to satisfy a G\rarding inequality only. If the differential operator is uniformly elliptic, no additional assumption on the initial mesh is posed.

Continuous Mixed $p$ -Norm Adaptive Algorithm for System Identification

Abstract: We propose a new adaptive filtering algorithm in system identification applications which is based on a continuous mixed $p$ -norm It enjoys the advantages of various error norms since it combines p-norms for $1 \leq p \leq 2$ The mixture is controlled by a continuous probability density-like function of $p$ which is assumed to be uniform in our derivations in this letter Two versions of the suggested algorithm are developed The robustness of the proposed algorithms against impulsive noise are demonstrated in a system identification simulation

Adaptive variable step algorithm for missing samples recovery in sparse signals

Abstract: Recovery of arbitrarily positioned samples that are missing in sparse signals recently attracted significant research interest. Sparse signals with heavily corrupted arbitrary positioned samples could be analysed in the same way as compressive sensed signals by omitting the corrupted samples and considering them as unavailable during the recovery process. The reconstruction of the missing samples is done by using one of the well-known reconstruction algorithms. In this study, the authors will propose a very simple and efficient algorithm, applied directly to the concentration measures, without reformulating the reconstruction problem within the standard linear programming form. Direct application of the gradient approach to the non-differentiable forms of measures lead us to introduce a variable step size algorithm. A criterion for changing the adaptive algorithm parameters is presented. The results are illustrated on the examples with sparse signals, including approximately sparse signals and noisy sparse signals.

Critical-Path Analysis and Low-Complexity Implementation of the LMS Adaptive Algorithm

Abstract: This paper presents a precise analysis of the critical path of the least-mean-square (LMS) adaptive filter for deriving its architectures for high-speed and low-complexity implementation. It is shown that the direct-form LMS adaptive filter has nearly the same critical path as its transpose-form counterpart, but provides much faster convergence and lower register complexity. From the critical-path evaluation, it is further shown that no pipelining is required for implementing a direct-form LMS adaptive filter for most practical cases, and can be realized with a very small adaptation delay in cases where a very high sampling rate is required. Based on these findings, this paper proposes three structures of the LMS adaptive filter: (i) Design 1 having no adaptation delays, (ii) Design 2 with only one adaptation delay, and (iii) Design 3 with two adaptation delays. Design 1 involves the minimum area and the minimum energy per sample (EPS). The best of existing direct-form structures requires 80.4% more area and 41.9% more EPS compared to Design 1. Designs 2 and 3 involve slightly more EPS than the Design 1 but offer nearly twice and thrice the MUF at a cost of 55.0% and 60.6% more area, respectively.

Distributed Diffusion-Based LMS for Node-Specific Adaptive Parameter Estimation

Abstract: A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest, parameters of common interest to a subset of nodes and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local, common or global parameters. Coupled with the estimation of the different sets of parameters, the implementation of each LMS algorithm is only undertaken by the nodes of the network interested in a specific set of local, common or global parameters. The study of convergence in the mean sense reveals that the proposed algorithm is asymptotically unbiased. Moreover, a spatial-temporal energy conservation relation is provided to evaluate the steady-state performance at each node in the mean-square sense. Finally, the theoretical results and the effectiveness of the proposed technique are validated through computer simulations in the context of cooperative spectrum sensing in Cognitive Radio networks.

Effective decomposition of large-scale separable continuous functions for cooperative co-evolutionary algorithms

Abstract: In this paper we investigate the performance of cooperative co-evolutionary (CC) algorithms on large-scale fully-separable continuous optimization problems. We have shown that decomposition can have significant impact on the performance of CC algorithms. The empirical results show that the subcomponent size should be chosen small enough so that the subcomponent size is within the capacity of the subcomponent optimizer. In practice, determining the optimal size is difficult. Therefore, adaptive techniques are desired by practitioners. Here we propose an adaptive method, MLSoft, that uses widely-used techniques in reinforcement learning such as the value function method and softmax selection rule to adapt the subcomponent size during the optimization process. The experimental results show that MLSoft is significantly better than an existing adaptive algorithm called MLCC on a set of large-scale fully-separable problems.

Actuator fault estimation for a class of nonlinear descriptor systems

Abstract: This article proposes an actuator fault estimation approach for a class of nonlinear descriptor systems. The radial basis function RBF neural networks are utilised to model the actuator faults. The adaptive fault estimation observer is designed by exploiting the on-line learning ability of RBF neural networks to approximate the actuator fault. The adaptive algorithm of the RBF networks is established by the Lyapunov theory, and the design of the proposed observer is reformulated as a set of linear matrix inequalities LMIs, which can be conveniently solved by standard LMI tools. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed fault diagnosis method.

Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking

Abstract: Most common cameras use a CCD sensor device measuring a single color per pixel. The other two color values of each pixel must be interpolated from the neighboring pixels in the so-called demosaicking process. State-of-the-art demosaicking algorithms take advantage of inter-channel correlation locally selecting the best interpolation direction. These methods give impressive results except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, they create interpolation artifacts. We introduce a new algorithm involving non-local image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous. The proposed algorithm introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of. Comparison shows that the proposed algorithm gives state-of-the-art methods in several image bases.

Exploiting redundancy for cost-effective, time-constrained execution of HPC applications on amazon EC2

Abstract: The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides a fixed-cost option (called on-demand) and a variable-cost, auction-based option (called the spot market). The spot market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning.We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost by exploiting redundancy in the EC2 spot market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to 7x cheaper than using the on-demand market and up to 44% cheaper than the best non-redundant, spot-market algorithm.

Adaptive Backtracking Search Algorithm for Induction Magnetometer Optimization

Abstract: Backtracking search algorithm (BSA) is a novel evolutionary algorithm (EA) for solving real-valued numerical optimization problems. In this paper, an adaptive BSA (ABSA) is proposed to solve the optimization problem of an induction magnetometer (IM). In the adaptive algorithm, the probabilities of crossover and mutation are varied depending on the fitness values of the solutions to refine the convergence performance. The proposed ABSA will also be compared with basic BSA and other widely used EA algorithms. Simulation results show that ABSA is better able to solving the IM optimization problems.

State-space architecture of the partitioned-block-based acoustic echo controller

Abstract: Acoustic echo cancellation has traditionally employed basically all variants known from deterministic adaptive filter design, such as least mean-square (LMS), recursive least-squares (RLS), and frequency-domain adaptive filters (FDAF). More recently, a stochastic adaptive filter design based on the concept of acoustic state-space modeling of the echo path has been introduced to accommodate for an ever sought unification of adaptive filtering and adaptation control. The corresponding Kalman filter theory has been formulated for single-channel, multi-channel, and nonlinear echo cancellation problems. This paper closes an important gap by formulating the state-space model and the corresponding adaptive algorithm for the partitioned-block filtering structure which is especially relevant in practice. This structure allows for the use of significantly longer filter lengths in comparison to previous work, and for the flexible design and implementation of acoustic echo cancellers for widely differing acoustic conditions.

Local joint information based active fault tolerant control for reconfigurable manipulator

Abstract: This paper is concerned with the active fault tolerant control problem for reconfigurable manipulator actuator based on local joint information. It is considered that the entire reconfigurable manipulator system consists of a couple of independent joint modules as subsystems, which are controlled using unified radial basis function neural network adaptive algorithm using local joint information when actuators are fault free. For the subsystem in actuator fault situation, fault detection is achieved through comparing the user defined threshold to the residual between actual velocity value and nonlinear velocity observation value. The unknown input state observer is exploited for fault identification. Based on the information aforementioned, a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault. The advantages of the presented scheme are that unlike the complex control structure in centralized control, this scheme possesses simple control structure, as well as could isolate and tolerant the fault in subsystem. Furthermore, it can be easily applied to different configurations without any parameters modification. It means that the local fault could not affect the joint in normal situation. In order to demonstrate the effectiveness of the proposed method, two different 2-DOF reconfigurable manipulators are employed for simulation.

Adaptive Multi-rate Wavelet Method for Circuit Simulation

Abstract: In this paper a new adaptive algorithm for multi-rate circuit simulation encountered in the design of RF circuits based on spline wavelets is presented. The circuit ordinary differential equations are first rewritten by a system of (multi-rate) partial differential equations (MPDEs) in order to decouple the different time scales. Second, a semi-discretization by Rothe's method of the MPDEs results in a system of differential algebraic equations (DAEs) with periodic boundary conditions. These boundary value problems are solved by a Galerkin discretization using spline functions. An adaptive spline grid is generated, using spline wavelets for non-uniform grids. Moreover the instantaneous frequency is chosen adaptively to guarantee a smooth envelope resulting in large time steps and therefore high run time efficiency. Numerical tests on circuits exhibiting multi-rate behavior including mixers and PLL conclude the paper.

Quasi-optimal convergence rates for adaptive boundary element methods with data approximation, part I: weakly-singular integral equation

Abstract: We analyze an adaptive boundary element method for Symm's integral equation in 2D and 3D which incorporates the approximation of the Dirichlet data $$g$$ g into the adaptive scheme. We prove quasi-optimal convergence rates for any $$H^{1/2}$$ H 1 / 2 -stable projection used for data approximation.

Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks

Abstract: A distributed adaptive algorithm is proposed to solve a node-specific parameter estimation problem where nodes are interested in estimating parameters of local interest and parameters of global interest to the whole network. To address the different node-specific parameter estimation problems, this novel algorithm relies on a diffusion-based implementation of different Least Mean Squares (LMS) algorithms, each associated with the estimation of a specific set of local or global parameters. Although all the different LMS algorithms are coupled, the diffusion-based implementation of each LMS algorithm is exclusively undertaken by the nodes of the network interested in a specific set of local or global parameters. To illustrate the effectiveness of the proposed technique we provide simulation results in the context of cooperative spectrum sensing in cognitive radio networks.

Robust adaptive sliding mode control of MEMS gyroscope using T–S fuzzy model

Abstract: In this paper, a multi-input multi-output Takagi–Sugeno (T–S) fuzzy model is proposed to represent the nonlinear model of micro-electro mechanical systems (MEMS) gyroscope and improve the tracking and compensation performance. A robust adaptive sliding mode control with on-line identification for the upper bounds of external disturbances and an adaptive estimator for the model uncertainty parameters are proposed in the Lyapunov framework. The adaptive algorithm of model uncertainty parameters could compensate the error between the optimal T–S model and the designed T–S model, and decrease the chattering of the sliding surface. Based on Lyapunov methods, these adaptive laws can guarantee that the system is asymptotically stable. For the purpose of comparison, the designed controller is also implemented on the nonlinear model of MEMS gyroscope. Numerical simulations are investigated to verify the effectiveness of the proposed control scheme on the T–S model and the nonlinear model.

Fast, Accurate and Robust Adaptive Finite Difference Methods for Fractional Diffusion Equations: The Size of the Timesteps does Matter

Abstract: The computation time required by standard finite difference methods with fixed timesteps for solving fractional diffusion equations is usually very large because the number of operations required to find the solution scales as the square of the number of timesteps. Besides, the solutions of these problems usually involve markedly different time scales, which leads to quite inhomogeneous numerical errors. A natural way to address these difficulties is by resorting to adaptive numerical methods where the size of the timesteps is chosen according to the behaviour of the solution. A key feature of these methods is then the efficiency of the adaptive algorithm employed to dynamically set the size of every timestep. Here we discuss two adaptive methods based on the step-doubling technique. These methods are, in many cases, immensely faster than the corresponding standard method with fixed timesteps and they allow a tolerance level to be set for the numerical errors that turns out to be a good indicator of the actual errors.

Adaptive regularization, linearization, and discretization and a posteriori error control for the two-phase Stefan problem

Abstract: We consider in this paper the time-dependent two-phase Stefan problem and derive a posteriori error estimates and adaptive strategies for its conforming spatial and backward Euler temporal discretizations. Regularization of the enthalpy-temperature function and iterative linearization of the arising systems of nonlinear algebraic equations are considered. Our estimators yield a guaranteed and fully computable upper bound on the dual norm of the residual, as well as on the L2(L2) error of the temperature and the L2(H1) error of the enthalpy. Moreover, they allow to distinguish the space, time, regularization, and linearization error components. An adaptive algorithm is proposed, which ensures computational savings through the online choice of a sufficient regularization parameter, a stopping criterion for the linearization iterations, local space mesh refinement, time step adjustment, and equilibration of the spatial and temporal errors. We also prove the efficiency of our estimate. Our analysis is quite general and is not focused on a specific choice of the space discretization and of the linearization. As an example, we apply it to the vertex-centered finite volume (finite element with mass lumping and quadrature) and Newton methods. Numerical results illustrate the effiectiveness of our estimates and the performance of the adaptive algorithm.

Implementation of a real-time adaptive digital shaping for nuclear spectroscopy

Abstract: This paper presents the structure, design and implementation of a new adaptive digital shaper for processing the pulses generated in nuclear particle detectors. The proposed adaptive algorithm has the capacity to automatically adjust the coefficients for shaping an input signal with a desired profile in real-time. Typical shapers such as triangular, trapezoidal or cusp-like ones can be generated, but more exotic unipolar shaping could also be performed. A practical prototype was designed, implemented and tested in a Field Programmable Gate Array (FPGA). Particular attention was paid to the amount of internal FPGA resources required and to the sampling rate, making the design as simple as possible in order to minimize power consumption. Lastly, its performance and capabilities were measured using simulations and a real benchmark.

An Adaptive Multiscale Finite Element Method

Abstract: This work is devoted to an adaptive multiscale finite element method (MsFEM) for solving elliptic problems with rapidly oscillating coefficients. Starting from a general version of the MsFEM with oversampling, we derive an a posteriori estimate for the $H^1$-error between the exact solution of the problem and a corresponding MsFEM approximation. Our estimate holds without any assumptions on scale separation or on the type of the heterogeneity. The estimator splits into different contributions which account for the coarse grid error, the fine grid error, and the oversampling error. Based on the error estimate, we construct an adaptive algorithm that is validated in numerical experiments.

Adaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors

Abstract: Compared to traditional distributed computing like grid system, it is non-trivial to optimize cloud task’s execution performance due to its more constraints like user payment budget and divisible resource demand. In this paper, we analyze in-depth our proposed optimal algorithm minimizing task execution length with divisible resources and payment budget: 1) We derive the upper bound of cloud task length, by taking into account both workload prediction errors and hostload prediction errors. With such state-of-the-art bounds, the worst-case task execution performance is predictable, which can improve the quality of service in turn. 2) We design a dynamic version for the algorithm to adapt to the load dynamics over task execution progress, further improving the resource utilization. 3) We rigorously build a cloud prototype over a real cluster environment with 56 virtual machines, and evaluate our algorithm with different levels of resource contention. Cloud users in our cloud system are able to compose various tasks based on off-the-shelf web services. Experiments show that task execution lengths under our algorithm are always close to their theoretical optimal values, even in a competitive situation with limited available resources. We also observe a high level of fair treatment on the resource allocation among all tasks.

An adaptive steganographic algorithm for 3D polygonal models using vertex decimation

Abstract: Most 3D steganographic algorithms emphasize high data capacity, low distortion, and correct data extraction. However, their disadvantage is in the existence of the same embedding capacity for each data-embedded vertex in the 3D models. Embedding the same capacity in the vertex located on the surface with different properties may cause obvious distortion, making it difficult to achieve the initial goal of information-hiding techniques. This study proposes a new and adaptive 3D steganographic algorithm that considers the surface complexity. To increase the accuracy of the complexity estimation for each embedding vertex, the proposed algorithm adopts a vertex decimation process to determine its referencing neighbors. Thereafter, different amounts of the secret messages are embedded according to the surface properties of each vertex. This approach preserves important shape features and produces a more imperceptible result. Experimental results show that the proposed adaptive algorithm can achieve more accurate estimation results with a higher data capacity and acceptable distortion. The proposed technique is feasible in 3D steganography.