NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Quantifying scale relationships in snow distributions

2007 Summer.Includes bibliographic references.Spatial distributions of snow in mountain environments represent the time integration of accumulation and ablation processes, and are strongly and dynamically linked to mountain hydrologic, ecologic, and climatic systems. Accurate measurement and modeling of the spatial distribution and variability of the seasonal mountain snowpack at different scales are imperative for water supply and hydropower decision-making, for investigations of land-atmosphere interaction or biogeochemical cycling, and for accurate simulation of earth system processes and feedbacks. Assessment and prediction of snow distributions in complex terrain are heavily dependent on scale effects, as the pattern and magnitude of variability in snow distributions depends on the scale of observation. Measurement and model scales are usually different from process scales, and thereby introduce a scale bias to the estimate or prediction. To quantify this bias, or to properly design measurement schemes and model applications, the process scale must be known or estimated. Airborne Light Detection And Ranging (lidar) products provide high-resolution, broad-extent altimetry data for terrain and snowpack mapping, and allow an application of variogram fractal analysis techniques to characterize snow depth scaling properties over lag distances from 1 to 1000 meters. Snow depth patterns as measured by lidar at three Colorado mountain sites exhibit fractal (power law) scaling patterns over two distinct scale ranges, separated by a distinct break at the 15-40 m lag distance, depending on the site. Each fractal range represents a range of separation distances over which snow depth processes remain consistent. The scale break between fractal regions is a characteristic scale at which snow depth process relationships change fundamentally. Similar scale break distances in vegetation topography datasets suggest that the snow depth scale break represents a change in wind redistribution processes from wind/vegetation interactions at small lags to wind/terrain interactions at larger lags. These snow depth scale characteristics are interannually consistent, directly describe the scales of action of snow accumulation, redistribution, and ablation processes, and inform scale considerations for measurement and modeling. Snow process models are designed to represent processes acting over specific scale ranges. However, since the incorporated processes vary with scale, the model performance cannot be scale-independent. Thus, distributed snow models must represent the appropriate process interactions at each scale in order to produce reasonable simulations of snow depth or snow water equivalent (SWE) variability. By comparing fractal dimensions and scale break lengths of modeled snow depth patterns to those derived from lidar observations, the model process representations can be evaluated and subsequently refined. Snow depth simulations from the SnowModel seasonal snow process model exhibit fractal patterns, and a scale break can be produced by including a sub-model that simulates fine-scale wind drifting patterns. The fractal dimensions provide important spatial scaling information that can inform refinement of process representations. This collection of work provides a new application of methods developed in other geophysical fields for quantifying scale and variability relationships

Adaptive Cellular Layout in Self-Organizing Networks using Active Antenna Systems

The rapidly growing demand of capacity by wireless services is challenging the mobile industry with a need of new deployment strategies. Besides, the nature of the spatial and temporal distribution of user traffic has become heterogeneous and fluctuating intermittently. Those challenges are currently tackled by network densification and tighter spatial reuse of radio resources by introducing a heterogeneous deployment of small cells embedded in a macro cell layout. Since user traffic is varying both spatially and temporally, a so called busy hour planning is typically applied where enough small cells are deployed at the corresponding locations to meet the expected capacity demand. This deployment strategy, however, is inefficient as it may leave plenty of network resources under-utilized during non-busy hour, i.e., most of the operation time. Such over-provisioning strategy incurs high capital investment on infrastructure (CAPEX) as well as operating cost (OPEX) for operators. Therefore, optimal would be a network with flexible capacity accommodation by following the dynamics of the traffic situation and evading the inefficiencies and the high cost of the fixed deployment approach. The advent of a revolutionizing base station antenna technology called Active Antenna Systems (AAS) is promising to deliver the required flexibility and dynamic deployment solution desired for adaptive capacity provisioning. Having the active radio frequency (RF) components integrated with the radiating elements, AAS supports advanced beamforming features. With AAS-equipped base station, multiple cell-specific beams can be simultaneously created to densify the cell layout by means of an enhanced form of sectorization. The radiation pattern of each cell-beam can be dynamically adjusted so that a conventional cell, for instance, can be split into two distinct cells, if a high traffic concentration is detected. The traffic in such an area is shared among the new cells and by spatially reusing the frequency spectrum, the cell-splitting (sectorization) doubles the total available radio resources at the cost of an increased co-channel interference between the cells. Despite the AAS capability, the realization of flexible sectorization for dynamic cell layout adaptation poses several challenges. One of the challenges is that the expected performance gain from cell densification can be offset by the ensuing co-channel interference in the system. It is also obvious that a self-organized autonomous management and configuration is needed, if cell deployment must follow the variation of the user traffic over time and space by means of a sectorization procedure. The automated mechanism is desired to enhance the system performance and optimize the user experience by automatically controlling the sectorization process. With such a dynamic adaptation scheme, the self-organizing network (SON) facilities are getting a new dimension in terms of controlling the flexible cell layout changes as the environment including the radio propagation characteristics cannot be assumed stationary any longer. To fully exploit the flexible sectorization feature in three-dimensional space, reliable and realistic propagation models are required which are able to incorporate the dependency of the radio channel characteristics in the elevation domain. Analysis of the complex relationship among various system parameters entails a comprehensive model that properly describes the AAS-sectorization for conducting detailed investigation and carrying out precise evaluation of the ensuing system performance. A novel SON algorithm that automates the AAS-sectorization procedure is developed. The algorithm controls the activation/deactivation of cell-beams enabling the sectorization based cell layout adjustment adaptively. In order to effectively meet the dynamically varying network capacity demand that varies according to the spatial user distribution, the developed SON algorithm monitors the load of the cell, the spatial traffic concentrations and adapts the underlying cell coverage layout by autonomously executing the sectorization either in the horizontal or vertical plane. The SON algorithm specifies various procedures which rely on real time network information collected using actual signal measurement reports from users. The particular capability of the algorithm is evading unforeseen system performance degradation by properly executing the sectorization not only where in the network and when it is needed, but also only if the ensuing co-channel interference does not have adverse impact on the user experience. To guarantee the optimality of the network performance after sectorization, a performance metric that takes both the expectable gain from radio resource and impact of the co-channel interference into account is developed. In order to combat the severity of the inter-cell interference problem that arises with AAS-sectorization between the co-channel operated cells, an interference mitigation scheme is developed in this thesis. The proposed scheme coordinates the data transmission between the co-sited cells by the transmission muting principle. To ensure that the transmission muting is not degrading the overall system performance by blanking more data transmission, a new SON algorithm that controls the optimal usage the proposed scheme is developed. To appropriately characterize the spatial separation of the cell beams being activated with sectorization, a novel propagation shadowing model that incorporates elevation tilt parameter is developed. The new model addresses the deficiencies of the existing tilt-independent shadowing model which inherently assumes a stationary propagation characteristics in the elevation domain. The tilt-dependent shadowing model is able to statistically characterize the elevation channel variability with respect to the tilt configuration settings. Simplified 3D beamforming models and beam pattern synthesis approaches required for fast cell layout adaptation and dynamic configuration of the AAS parameters are developed for the realization of various forms of AAS-based sectorization. Horizontal and vertical sectorization are the two forms of AAS-based sectorization considered in this thesis where two beams are simultaneously created from a single AAS to split the underlying coverage layout in horizontal or vertical domain, respectively. The performance of the developed theoretical AAS-sectorization concepts and models are examined by means of system level simulations considering the Long Term Evolution-Advanced (LTE-A) macro-site deployment within exemplifying scenarios. Simulation results have demonstrated that the SON mechanism is able to follow the different conditions when and where the sectorization delivers superior performance or adversely affects the user experience. Impacts on the performance of existing SON operations, like Mobility Robustness Optimization (MRO), which are relying on stationary cell layout conditions have been studied. Further investigations are carried out in combination with the cell layout changes triggered by the dynamic AAS-based sectorization. The observed results have confirmed that proper coordination is needed between the SON scheme developed for AAS sectorization and the MRO operation to evade unforeseen performance degradation and to ensure a seamless user experience. The technical concepts developed in this thesis further have impacted the $3^\textrm{rd}$ Generation Partnership Project (3GPP) SON for AAS Work Item (WI) discussed in the Radio Access Network (RAN)-3 Work Group (WG). In particular, the observed study results dealing with the interworking of the existing SON features and AAS sectorization have been noted in the standardization work

Examining the Active Transportation - Built Environment Relationship in London, Ontario

Research on the relationship between the built environment and active transportation has accelerated and expanded over the past 20 years. This growth is in large part due to continuing evidence of rising rates in obesity and Type-2 diabetes that coincides with decreasing rates of physical activity across all ages in the post-industrial world. Walking more is a simple solution to increasing rates of physical activity. While for most people walking is possible throughout the day, there has been a decrease in the use of walking as a means of transportation. This study examines environmental determinants of active transportation from two perspectives: 1) working adults and 2) elementary school children. It adopts multiple methodologies for identifying travel corridors in geographic information systems (GIS) analysis and tests a novel technique by applying a hexagonal grid to extract built environment measures. Results from this research suggest global positioning system (GPS) tracking is a viable method to capture built environment measures, especially for children. As in previous studies, this study found distance between origin and destination to be the most important determinant to active travel with socio-economic status also playing a key role for adults and children. Results from this research are concurrent with previous literature while employing hexagons as a geographic unit. Examining the active transportation/built environment relationship through the use of GPS and a hexagonal areal unit is a new approach that deserves serious consideration for further research

Massive MIMO for Dependable Communication

Cellular communication is constantly evolving; currently 5G systems are being deployed and research towards 6G is ongoing. Three use cases have been discussed as enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC). To fulfill the requirements of these use cases, new technologies are needed and one enabler is massive multiple-input multiple-output (MIMO). By increasing the number of antennas at the base station side, data rates can be increased, more users can be served simultaneously, and there is a potential to improve reliability. In addition, it is possible to achieve better coverage, improved energy efficiency, and low-complex user devices. The performance of any wireless system is limited by the underlying channels. Massive MIMO channels have shown several beneficial properties: the array gain stemming from the combining of the signals from the many antennas, improved user separation due to favourable propagation -- where the user channels become pair-wise orthogonal -- and the channel hardening effect, where the variations of channel gain decreases as the number of antennas increases. Previous theoretical works have commonly assumed independent and identically distributed (i.i.d.) complex Gaussian channels. However, in the first studies on massive MIMO channels, it was shown that common outdoor and indoor environments are not that rich in scattering, but that the channels are rather spatially correlated. To enable the above use cases, investigations are needed for the targeted environments. This thesis focuses on the benefits of deploying massive MIMO systems to achieve dependable communication in a number of scenarios related to the use cases. The first main area is the study of an industrial environment and aims at characterizing and modeling massive MIMO channels to assess the possibility of achieving the requirements of URLLC in a factory context. For example, a unique fully distributed array is deployed with the aim to further exploit spatial diversity. The other main area concerns massive MIMO at sub-GHz, a previously unexplored area. The channel characteristics when deploying a physically very large array for IoT networks are explored. To conclude, massive MIMO can indeed bring great advantages when trying to achieve dependable communication. Although channels in regular indoor environments are not i.i.d. complex Gaussian, the model can be justified in rich scattering industrial environments. Due to massive MIMO, the small-scale fading effects are reduced and when deploying a distributed array also the large-scale fading effects are reduced. In the Internet-of-Things (IoT) scenario, the channel is not as rich scattering. In this use case one can benefit from the array gain to extend coverage and improved energy efficiency, and diversity is gained due to the physically large array

An investigation into estimating productivity, above ground biomass and leaf area index of Eucalyptus grandis using remotely sensed data and a process-based model.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritburg, 2007.South Africa depends largely on afforestation programs for its timber supplies due to the great demands for fiber and wood products. This has brought discomfort to other water users who have advocated that the effects of afforestation on water resources are detrimental to the country as a whole since South Africa is known as a water scarce country. This study has undertaken to integrate a process-based model and remote sensing data to estimate water use and productivity of Eucalyptus grandis in the Zululand areas of South Africa. The remote sensing techniques and recently developed "process based model" that is 3PG-S were used to estimate water use and productivity of Eucalyptus grandis, an economically important plantation species grown in the summer rainfall areas of South Africa. The study utilized monthly Landsat Thematic Mapper datasets and climatic data as inputs into the 3PG-S model, determined the Leaf Area Index (LAI) and Specific Leaf Area (SLA) through direct (destructive sampling) and indirect measurements (LiCor- 2000) and assessed the relationships between various vegetation indices (VI's) using correlation and regression analyses. The results suggest that all the indices, except the ratio VI, correlated significantly with LiCor-determined and destructively measured LAI values with both normalized difference vegetation index (NDVI) and Ratio Vegetation Index (RVI) (r=0.86,

Science Tools to Implement Ecosystem Based Management in Massachusetts (DRAFT)

In this report we provide a framework for implementing ecosystem based management (EBM) and suggest a range of science information tools and their appropriate application to the decision making process. These tools can be broadly classified as modeling tools, decision analysis tools, and indicators. Modeling tools allow the user to organize data, communicate scientific findings to management and stakeholder audiences, and test alternative management scenarios. When used unwisely, however, models can preclude options, present unusable scenarios, generate results in scales that differ from management needs, and impose huge time, data, and technical requirements (Manno et al., 2008). Decision analysis tools can inform management decisions but should not be relied upon solely; they are valuable aids in the process and provide opportunities for all-stakeholder input, visualization, and scenario analysis. Indicators are scientific measurements of ecological or socio-economic phenomena that provide data for monitoring and evaluating the systems being managed. While indicators are by and large widely accepted, their selection is based on expert opinion and involves a level of subjectivity. Inappropriately selected indicators can misinform management decisions. By helping to identify and mitigate lack of information, these science tools can be of great value in the shift to ecosystem based management