Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

/pdf/hyperspectral-unmixing-overview-geometrical-statistical-and-2lb11fr9l4.pdf

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.

A Viking Lander 1 image was modeled as mixtures of reflectance spectra of palagonite dust, gray andesitelike rock, and a coarse rocklike soil. The rocks are covered to varying degrees by dust but otherwise appear unweathered. Rocklike soil occurs as lag deposits in deflation zones around stones and on top of a drift and as a layer in a trench dug by the lander. This soil probably is derived from the rocks by wind abrasion and/or spallation. Dust is the major component of the soil and covers most of the surface. The dust is unrelated spectrally to the rock but is equivalent to the global-scale dust observed telescopically. A new method was developed to model a multispectral image as mixtures of end-member spectra and to compare image spectra directly with laboratory reference spectra. The method for the first time uses shade and secondary illumination effects as spectral end-members; thus the effects of topography and illumination on all scales can be isolated or removed. The image was calibrated absolutely from the laboratory spectra, in close agreement with direct calibrations. The method has broad applications to interpreting multispectral images, including satellite images.

Spectral mixture modeling - A new analysis of rock and soil types at the Viking Lander 1 site. [on Mars]

Digital Coding of Waveforms

http://www.eis.mdx.ac.uk/staffpages/juanaugusto/CookAJ2009.pdf

Review: Ambient intelligence: Technologies, applications, and opportunities

Background: Researchers and medical practitioners have long sought the ability to continuously and automatically monitor patients beyond the confines of a doctor’s office. We describe a smart home monitoring and analysis platform that facilitates the automatic gathering of rich databases of behavioral information in a manner that is transparent to the patient. Collected information will be automatically or manually analyzed and reported to the caregivers and may be interpreted for behavioral modification in the patient. Method: Our health platform consists of five technology layers. The architecture is designed to be flexible, extensible, and transparent, to support plug-and-play operation of new devices and components, and to provide remote monitoring and programming opportunities. Results: The smart home-based health platform technologies have been tested in two physical smart environments. Data that are collected in these implemented physical layers are processed and analyzed by our activity recognition and chewing classification algorithms. All of these components have yielded accurate analyses for subjects in the smart environment test beds. Conclusions: This work represents an important first step in the field of smart environment-based health monitoring and assistance. The architecture can be used to monitor the activity, diet, and exercise compliance of diabetes patients and evaluate the effects of alternative medicine and behavior regimens. We believe these technologies are essential for providing accessible, low-cost health assistance in an individual’s own home and for providing the best possible quality of life for individuals with diabetes.

/pdf/smart-home-based-health-platform-for-behavioral-monitoring-kul59uvgah.pdf

Smart Home-Based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients:

Autonomous single-pass endmember approximation using lattice auto-associative memories

This paper presents a novel, three-stage, auto-associative memory based on lattice algebra. The first two stages of this memory consist of correlation matrix memories within the lattice domain. The third and final stage is a two-layer feed-forward network based on dendritic computing. The output nodes of this feed-forward network yield the desired pattern vector association. The computations performed by each stage are all lattice based and, thus, provide for fast computation and avoidance of convergence problems. Additionally, the proposed model is extremely robust in the presence of noise. Bounds of allowable noise that guarantees perfect output are also discussed.

/pdf/a-new-auto-associative-memory-based-on-lattice-algebra-32bds57sn6.pdf

A New Auto-associative Memory Based on Lattice Algebra

This paper presents a design for the parallel processing of synthetic aperture radar data using one or more Field Programmable Gate Arrays (FPGAs). Our design supports real-time computation of a two-dimensional image from a matrix of echo pulses and their corresponding response values. Components of this design include: (a) central processing pipeline to perform back projection calculations, (b) pre-fetch cache to minimize external memory access latency, (c) memory bridge that serves as the primary on-chip storage for pulse data, and (d) a pixel queue to direct image data in and out of the pipeline. Design parameters may be adjusted to achieve optimum performance, and multiple instances of this design may be replicated on-chip to achieve prespecified performance objectives. We provide a complexity analysis as a function of the input and output parameters. Simulation results based on an implementation of this design show that our design achieves 160 GFLOPs per instance on a simulated Altera Stratix III EP3SL150 FPGA, and scales well for output image size ranging from 500 × 500 pixels to 5,000 × 5,000 pixels.

/pdf/parallel-processing-techniques-for-the-processing-of-4kdf3b5fk5.pdf

Parallel processing techniques for the processing of synthetic aperture radar data on FPGAs

The thin-client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small-footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin-client computing. Firstly, a mathematical performance model is derived for wireless thin-client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user-specified quality of service (QoS). Secondly, a proxy-based adaptation framework is developed for wireless thin-client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule-based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet-based image coding to provide scalable QoS that can degrade gracefully. Our thin-client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright © 2008 John Wiley & Sons, Ltd.

Mark S. Schmalz

Papers

Smart Home-Based Health Platform for Behavioral Monitoring and Alteration of Diabetes Patients:

Autonomous single-pass endmember approximation using lattice auto-associative memories

A New Auto-associative Memory Based on Lattice Algebra

Parallel processing techniques for the processing of synthetic aperture radar data on FPGAs

Adaptive wireless thin-client model for mobile computing