application of iterative techniques to adaptive detection processes by A. Clements

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Thesis(Ph.D.) - Loughborough University of Technology 1976.

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Statementby A. Clements.
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Open LibraryOL20007149M

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Among the applications considered in this book are joint equalization and decoding, turbo codes, multiuser detection and decoding, broadband wireless channel equalization, and applications to two-dimensional storage and imaging systems. Audience: Iterative Detection: Adaptivity, Complexity Reduction, and Applications provides an accessible and detailed reference for researchers.

The application of iterative techniques to adaptive detection processes Author: the received signal elements of a group are detected simultaneously in an iterative process, a separate detection process being used for each received group of elements.

At transmission rates of up to bits-per-second over voice frequency channels, a Author: Alan Clements. Adaptive and Iterative Signal Processing in Communications Adaptive signal processing (ASP) and iterative signal processing (ISP) are important tech-niques in improving the performance of receivers in communication systems.

Using exam-ples from practical transceiver designs, this book describes the fundamental theory and. In the previous chapter, we have introduced different adaptive techniques and discussed their applications to process monitoring and fault detection issues.

The essential idea behind these adaptive methods is the real-time update of the parameters in the monitoring and detection system to match the possible changes in the process under : Steven X. Ding. “To implement the method proposed in ’An Adaptive Iterative Learning Control Algorithm With Experiments on an Industrial Robot’[1] first in simulation, and then on a linear mo-tor system.

The proposed method uses Kalman filtering techniques to estimate the system’s position, despite the presence of measurement noise. However, the matrices D and M are non-singular, so that setting DM −1 f = 0yieldsf (x) = 0, which is a contradiction. 1D iterative adaptive RBF methods The iterative adaptive RBF edge detection method uses the difference in the rate of growth or decay of the RBF coeffi- cients.

Iterative reconstruction techniques were introduced almost 4 decades ago 4 as an alternative approach for improving CT image quality by reducing quantum noise and artifacts associated with FBP.

Recent progress in computing power now enables iterative image reconstruction, within a clinically acceptable time frame. Iterative, adaptive & incremental as a life cycle is explained here.

I would like to understand the difference between iterative & overlapping phase & the definition of iterative phase. I came across these terms in PMBOK’s page 19 as well as in 1 practice question. IBM Watson considers the four key advantages as “Adaptive,” “Interactive,” “Iterative and Stateful,” and “Contextual.” Adaptive: Cognitive testing is adaptive because it enables the system to learn as information changes and as both goals and requirements evolve.

For instance, the system can suggest a number of test iterations. Adaptive medical detection system: An iterative averaging method for automated detection analysis using DMFBs Abstract: In recent years a new generation of droplet based lab-on-chip device termed as Digital Microfluidic Biochip(DMFB) has found wide applications in the field of clinical diagnostics, DNA sequencing, drug design and environmental.

Based on adaptive iterative detection in the presence of the parameter uncertainty [79], the authors presented algorithms for an adaptive SISO decoder in the presence of carrier phase uncertainty.

An adaptive detection scheme is proposed for radar imaging. The proposed detector is a postprocessing scheme derived for one- two- and three-dimensional data, and applied to through-the-wall imaging using synthetic aperture radar. The target image statistics depend on the target three-dimensional orientation and position.

The statistics can also vary with the standoff distance of the. For UXO detection and discrimination, models are fit to survey electromagnetic or magnetometer data using indirect (iterative) inverse methods.

These all follow the common process in which (1) model parameters are input to a forward model, (2) output is compared against observed data and used to find a. Adaptive and Iterative Optimization Techniques for Data-Driven Fault Diagnosis.

Front Matter. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book.

Data-driven Techniques Fault Detection and Isolation Fault. applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a non-parametric and user parameter-free weighted least-squares algorithm, was recently shown to offer.

In fact, it has been observed that when detection relies on linear processing techniques such as MMSE, the benefits of iterative reception become rather marginal [23].

Consequently, in this work. Approaches to Adaptive Iteration: a comparative review – Markus Fietz 2 3 VARIANTS OF THE ADAPTIVE ITERATION APPROACH Adaptive Iteration is an iterative approach involving four core phases (Figure 1).

Variants of Adaptive Iteration have been developed over the years to explain and guide experiential learning and adaptive action. Space-domain suppression techniques apply beamforming methods to deal with the high power interferences.

The adaptive optimum beamformer for passive radar application has been investigated in [2, 3]. Methods which utilize the eigen-structure of the spatial correlation matrix have been proposed in [4, 5].

Such sparseness often occurs in practical circuits and systems, as demonstrated, for example, by the ISCAS 89/93 benchmarks. The application of the adaptive technique to design-space exploration (synthesis) is also demonstrated by developing automated search techniques for scheduling iterative data-flow graphs.

Study of Adaptive Activity-Aware Iterative Detection Techniques for Massive Machine-Type Communications Roberto B. Di Renna, Student Member, IEEE, and Rodrigo C. de Lamare, Senior Member, IEEE Abstract This work studies the uplink of grant-free low data-rate massive machine-to-machinecommunications (mMTC).

A key issue in paper-machine Cross-Directional (CD) Control is alignment. Typically, this mapping problem is a non-linear and slowly time-varying phenomenon for individual machines.

The first part of this thesis specifically focuses on different causes of the misalignment and reviews recent developments in CD mapping.

It summarizes the results of a comprehensive survey of different alignment. Detect clusters of circular objects by iterative adaptive thresholding and shape analysis Viewed 5k times I have been developing an application to count circular objects such as bacterial colonies from pictures.

What make it easy is the fact that the objects are generally well distinct from the background. I therefore. Adaptive signal processing (ASP) and iterative signal processing (ISP) are important techniques in improving receiver performance in communication systems.

Using examples from practical transceiver designs, this book describes the fundamental theory and practical aspects of both methods, providing a link between the two where possible.

This letter proposes a novel channel-tracking scheme to improve the performance of the dedicated short-range communication (DSRC) systems affected by rapid fluctuations in channel envelopes. The proposed technique is called “iterative (turbo) compensation.” It utilizes additional information extracted from the receivers output to further improve the accuracy of the channel estimation.

We do not intend to provide an in-depth discussion on existing procedures. Rather, we present practical applications stemming from some of these through a simple iterative framework that was devised (Srivastava, ) to engage with the process of continuous meaning-making and progressive focusing inherent to analysis processes.

Adaptive management includes the basic principles of agile project management, such as iterative processes and creative business environments. In addition, adaptive management involves the active use of quantitative methods to measure project performance and apply learning to improve decisions.

Some examples corresponding to both non-adaptive and adaptive crack detection systems are presented in Figure It is remarkable the amount of false positives that appear due to bad parameter setting.

The RMSE of the non-adaptive crack detection system is whereas the RMSE of the adaptive approach is (reduced by a factor of ). To evaluate dose reduction and image quality of abdominopelvic computed tomography (CT) reconstructed with model-based iterative reconstruction (MBIR) compared to adaptive statistical iterative reconstruction (ASIR).

In this prospective study, 85 patients underwent referential- low- and ultralow-dose unenhanced abdominopelvic CT. Images were reconstructed with ASIR for low-dose (L. Automatic detection of objects is critical to video tracking systems. One of the simplest techniques for detection is background subtraction (BS).

BS refers to the process of segmenting moving regions from image sequences. The BS process involves building a model of the background and extracting regions of the foreground (moving objects). In this paper, we propose an extended cluster BS (CBS. An adaptive iterative strategy is also proposed to adjust the number of iterations in each scale, which makes the algorithm perform robustly and fast.

Extensive experimental results on real blurred images demonstrate that the proposed algorithm performs well in both quantitative and visual evaluations against the state-of-the-art methods with. The detection and decoding may be iterated a number of times.

During the iterative detection and decoding process, the reliability of the bit decisions is improved with each iteration. The iterative detection and decoding process described herein may be used to combat frequency selective fading as well as flat fading.

@article{osti_, title = {ADAPTIVE ANNEALED IMPORTANCE SAMPLING FOR MULTIMODAL POSTERIOR EXPLORATION AND MODEL SELECTION WITH APPLICATION TO EXTRASOLAR PLANET DETECTION}, author = {Liu, Bin}, abstractNote = {We describe an algorithm that can adaptively provide mixture summaries of multimodal posterior distributions.

The parameter space of the involved. Robust and Adaptive Control Workshop Adaptive Control: Introduction, Overview, and Applications Nonlinear Dynamic Systems and Equilibrium Points • A nonlinear dynamic system can usually be represented by a set of n differential equations in the form: – x is the state of the system – t is time •If f does not depend explicitly on time.

These cover a variety of techniques, ranging from the mathematical morphology based methods, the deformable models up to thresholding based methods.

However, one of the problems of these iterative techniques is the stopping criterion, for which many strategies have been proposed [Vincent & Soille, ; Cheriet et. al., ; Chenyang et. Comparison of image quality between filtered back-projection and the adaptive statistical and novel model-based iterative reconstruction techniques in abdominal CT for renal calculi Varut Vardhanabhuti, Sumaira Ilyas, Catherine Gutteridge, Simon J.

Freeman, and Carl A. Roobottom. Comparison between human and model observer performance in low-contrast detection tasks in CT images: application to images reconstructed with filtered back projection and iterative algorithms I Hernandez-Giron, MSc, 1, 2 A Calzado, PhD, 2 J Geleijns, PhD, 3 R M S Joemai, PhD, 3 and W J H Veldkamp, PhD 3.

Model reduction approaches have been shown to be powerful techniques in the numerical simulation of very large dynamical systems. The presence of multiple inputs and outputs (MIMO systems) makes the reduction process even more challenging.

We consider projection-based approaches where the reduction of complexity is achieved by direct projection of the problem onto a rational Krylov subspace.

Huge online community of Project Managers offering o how-to articles, templates, project plans, and checklists to help you do your job. Fractional processes are widely found in science, technology and engineering systems.

In Fractional Processes and Fractional-order Signal Processing, some complex random signals, characterized by the presence of a heavy-tailed distribution or non-negligible dependence between distant observations (local and long memory), are introduced and examined from the ‘fractional’ perspective using.

Definition. Agile projects are iterative insofar as they intentionally allow for “repeating” software development activities, and for potentially “revisiting” the same work products (the phrase “planned rework” is sometimes used; refactoring is a good example).

They are iterative in a third, less essential sense, in being most often structured around a series of iterations of fixed.

In this study, a novel sparsity-based space–time adaptive processing algorithm based on the complex-valued Homotopy technique is proposed for airborne radar applications.

The proposed algorithm firstly extends the existing standard real-valued Homotopy method to a more general complex-valued application using the gradient approaches.

By exploiting the sparsity of the clutter spectrum in the.Adaptive management (AM), also known as adaptive resource management (ARM) or adaptive environmental assessment and management (AEAM), is a structured, iterative process of robust decision making in the face of uncertainty, with an aim to reducing uncertainty over time via system this way, decision making simultaneously meets one or more resource management .Coding and Iterative Detection for Magnetic Recording Channels - Ebook written by Zining Wu.

Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Coding and Iterative Detection for .

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