Unified radio and network control across heterogeneous hardware platforms

Experimentation is an important step in the investigation of techniques for handling spectrum scarcity or the development of new waveforms in future wireless networks. However, it is impractical and not cost effective to construct custom platforms for each future network scenario to be investigated. This problem is addressed by defining Unified Programming Interfaces that allow common access to several platforms for experimentation-based prototyping, research, and development purposes. The design of these interfaces is driven by a diverse set of scenarios that capture the functionality relevant to future network implementations while trying to keep them as generic as possible. Herein, the definition of this set of scenarios is presented as well as the architecture for supporting experimentation-based wireless research over multiple hardware platforms. The proposed architecture for experimentation incorporates both local and global unified interfaces to control any aspect of a wireless system while being completely agnostic to the actual technology incorporated. Control is feasible from the low-level features of individual radios to the entire network stack, including hierarchical control combinations. A testbed to enable the use of the above architecture is utilized that uses a backbone network in order to be able to extract measurements and observe the overall behaviour of the system under test without imposing further communication overhead to the actual experiment. Based on the aforementioned architecture, a system is proposed that is able to support the advancement of intelligent techniques for future networks through experimentation while decoupling promising algorithms and techniques from the capabilities of a specific hardware platform

Building a Self-Contained Search Engine in the Browser

JavaScript engines inside modern web browsers are capa-ble of running sophisticated multi-player games, rendering impressive 3D scenes, and supporting complex, interactive visualizations. Can this processing power be harnessed for information retrieval? This paper explores the feasibility of building a JavaScript search engine that runs completely self-contained on the client side within the browser—this in-cludes building the inverted index, gathering terms statistics for scoring, and performing query evaluation. The design takes advantage of the IndexDB API, which is implemented by the LevelDB key–value store inside Google’s Chrome browser. Experiments show that although the performance of the JavaScript prototype falls far short of the open-source Lucene search engine, it is sufficiently responsive for interac-tive applications. This feasibility demonstration opens the door to interesting applications and architectures

Assessment of polarimetric SAR data for discrimination between wet versus dry soil moisture conditions

Radarsat-2 imagery from extreme dry versus wet conditions are compared in an effort to determine the value of using polarimetric synthetic aperture radar (SAR) data for improving estimation of fuel moisture in a chronosequence of Alaskan boreal black spruce ecosystems (recent burns, regenerating forests dominated by shrubs, open canopied forests, moderately dense forest cover). Results show strong distinction between wet and dry conditions for C-HH and C-LR polarized backscatter, and Freeman–Durden and van Zyl surface bounce decomposition parameters (35–65% change for all but the dense spruce site). These four SAR variables have high potential for evaluation of within site surface soil moisture, as well as for relative distinction between wet and dry conditions across sites for lower biomass and sparse canopy forested sites. However, for any given test site except the shrubby regrowth site, van Zyl volume, surface, and double bounce scattering all result in similar percentage increases from dry to wet soil condition. This indicates that for most of these test sites/cases moisture enhances the magnitude of the return for all scattering mechanisms evaluated. Thus, differences in scattering from the interaction of biomass, surface roughness, and moisture condition across sites remains an issue and backscatter due to surface roughness or biomass cannot be uniquely estimated. In contrast, the Cloude–Pottier C-band decomposition variables appear invariant to soil moisture, but may instead account for variations in ecosystem structure and biomass. Further investigation is needed, as results warrant future research focused on evaluation of multiple polarimetric parameters in algorithm development

Evaluation of polarimetric Radarsat-2 SAR data for development of soil moisture retrieval algorithms over a chronosequence of black spruce boreal forests

C-band Radarsat-2 data were used to develop multi-parameter algorithms to retrieve soil moisture across a chronosequence (recently burned, shrubby regrowth and mature forest) of fire-disturbed Alaskan boreal black spruce forests using polarized backscatter intensity, polarimetric decomposition and discriminator parameters as independent variables. To account for complex interactions of surface roughness, biomass and soil moisture, multiple polarimetric variables were evaluated empirically in algorithm development using a time series dataset that included the wet and dry extreme conditions. Results indicate that polarimetric discriminators, such as maximum degree of polarization, fractional polarization or coefficient of variation, may be instrumental in accounting for the type of scattering occurring or the complexity or heterogeneity of the distributed target. Using the maximum degree of polarization combined with one or more variables on scattered intensity (e.g. C-HV, C-HH, or maximum scattering intensity) improved multi-linear moisture retrieval algorithms by 27–33% over the single, dual and quad polarized backscatter intensity algorithms. For all sites combined, algorithms were developed with low SE (7.0% volumetric soil moisture), moderately high R2 (0.77) and low RMSE (6.7% volumetric soil moisture). However, the best models were produced by separating the recently burned forests (strongest algorithm: R2 0.94, SE 5.9, RMSE 7.4) from the shrubby and mature forests (best algorithm: R2 0.85, SE 4.7, RMSE 7.3). Results are limited to sites with biomass below 3 kg/m2