A Unified Multi-Functional Dynamic Spectrum Access Framework: Tutorial, Theory and Multi-GHz Wideband Testbed

Dynamic spectrum access is a must-have ingredient for future sensors that are ideally cognitive. The goal of this paper is a tutorial treatment of wideband cognitive radio and radar—a convergence of (1) algorithms survey, (2) hardware platforms survey, (3) challenges for multi-function (radar/communications) multi-GHz front end, (4) compressed sensing for multi-GHz waveforms—revolutionary A/D, (5) machine learning for cognitive radio/radar, (6) quickest detection, and (7) overlay/underlay cognitive radio waveforms. One focus of this paper is to address the multi-GHz front end, which is the challenge for the next-generation cognitive sensors. The unifying theme of this paper is to spell out the convergence for cognitive radio, radar, and anti-jamming. Moore’s law drives the system functions into digital parts. From a system viewpoint, this paper gives the first comprehensive treatment for the functions and the challenges of this multi-function (wideband) system. This paper brings together the inter-disciplinary knowledge

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

An Asynchronous Simulation Framework for Multi-User Interactive Collaboration: Application to Robot-Assisted Surgery

The field of surgery is continually evolving as there is always room for improvement in the post-operative health of the patient as well as the comfort of the Operating Room (OR) team. While the success of surgery is contingent upon the skills of the surgeon and the OR team, the use of specialized robots has shown to improve surgery-related outcomes in some cases. These outcomes are currently measured using a wide variety of metrics that include patient pain and recovery, surgeon’s comfort, duration of the operation and the cost of the procedure. There is a need for additional research to better understand the optimal criteria for benchmarking surgical performance. Presently, surgeons are trained to perform robot-assisted surgeries using interactive simulators. However, in the absence of well-defined performance standards, these simulators focus primarily on the simulation of the operative scene and not the complexities associated with multiple inputs to a real-world surgical procedure. Because interactive simulators are typically designed for specific robots that perform a small number of tasks controlled by a single user, they are inflexible in terms of their portability to different robots and the inclusion of multiple operators (e.g., nurses, medical assistants). Additionally, while most simulators provide high-quality visuals, simplification techniques are often employed to avoid stability issues for physics computation, contact dynamics and multi-manual interaction. This study addresses the limitations of existing simulators by outlining various specifications required to develop techniques that mimic real-world interactions and collaboration. Moreover, this study focuses on the inclusion of distributed control, shared task allocation and assistive feedback -- through machine learning, secondary and tertiary operators -- alongside the primary human operator

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Ambient Excitation Based Model Updating for Structural Health Monitoring via Dynamic Strain Measurements

Structural health monitoring (SHM) technologies continue to be pursued for aerospace structures in the interests of increased safety and, when combined with prognosis, efficiency in life-cycle management. The current work is focused on developing and validating a method for in-situ health monitoring of aerospace structures. In particular, the current framework has been developed for use with response only vibration data using natural operating turbulence to provide the means of excitation. While the framework is general so as to work with a wide suite of sensor options, particular emphasis has been placed on fiber optic strain sensors as a lightweight, low cost, non-intrusive means of monitoring the vibration response. At its core, the developed SHM system actively monitors a network of fiber optic strain sensors and utilizes the transient response data to calculate their associated power spectral densities (PSD). These PSD serve as the fundamental input to the developed SHM algorithm presented in the dissertation whereby comparisons between previously correlated model PSD and the current measured PSD are made. If anomalies between the correlated model and the measured data sets are detected, the developed SHM algorithm seeks to minimize the difference via updating of structural parameters underlying the structural model of interest (in the case of the presented work, a finite element model of the structure). The SHM algorithm itself is an adaption of a statistical least-squares minimization based in concepts of non-linear parameter estimation and model correlation. The algorithm developed uses power spectra based residual error vectors derived from distributed vibration measurements to update a structural model through statistically weighted least-squares minimization. The output of the algorithm is a correlated finite element model which inherently produces estimates of the location, type, and severity of any detected damage as well as the uncertainty associated with these estimates. Throughout the dissertation the developed algorithm was shown, both analytically and experimentally, to successfully detect, locate, and quantify damage present in a structural system

Machine Learning Tools for Radio Map Estimation in Fading-Impaired Channels

In spectrum cartography, also known as radio map estimation, one constructs maps that provide the value of a given channel metric such as as the received power, power spectral density (PSD), electromagnetic absorption, or channel-gain for every spatial location in the geographic area of interest. The main idea is to deploy sensors and measure the target channel metric at a set of locations and interpolate or extrapolate the measurements. Radio maps nd a myriad of applications in wireless communications such as network planning, interference coordination, power control, spectrum management, resource allocation, handoff optimization, dynamic spectrum access, and cognitive radio. More recently, radio maps have been widely recognized as an enabling technology for unmanned aerial vehicle (UAV) communications because they allow autonomous UAVs to account for communication constraints when planning a mission. Additional use cases include radio tomography and source localization.publishedVersio

Intelligent one-point damage localization of an isotropic surface pipeline using Guassian Process regression

Pipelines are subjected to many damaging agents, such as, earthquake, ground movement, and aging which are responsible for important financial expenses. Structural Health Monitoring (SHM) of civil structures using arrays of sensors is promising such that data form the monitoring systems enable us to trace the structural anomalies and performance for early treatments. The need for introducing faster and intelligent methods has helped researchers propose novel approaches for such monitoring procedures. In this study a new method is introduced for monitoring of surface pipelines used primarily for oil and gas. The framework takes the advantage of Gaussian Process Regression Method (GPRM) to create a probabilistic predictive model for damage detection and the subsequent localization of the defect. To this end, an isotropic pipeline is modeled numerically and validated with an experimental setup. Afterwards, the model is extended to the real-life application to establish a meta model. Damages are introduced as small holes at different locations (one at each time). The GPRM is used to map the system responses to the selected statistical features which are utilized as indicators for the existence of the damages and their locations. GPRM reveals more promising results compared with conventional regression analysis. It considers the uncertainties due to lack of observation. In addition, it is an updatable approach with having local effects on the model. In another words, it affects the model in the vicinity of new observations. Moreover, among selected statistical features, number of peaks greater than or equal to 20% and 60% of the maximum peak values show better results corresponding to damage localization. Also the curve length and correlation coefficient of the system response (induced signal) are found to be efficient for damage detection. The novel method has been validated with filed measurements and experimental data and found to work efficiently