Experimental analysis of a motorcycle semi-active rear suspension

The topic of this paper is the experimental analysis and development of a control system for a semi-active suspension in a 2-wheel vehicle. The control system is implemented via a semi-active electro-hydraulic damper located in the rear suspension of a motorbike. The entire design and analysis procedure is carried out: the semi-active damper is characterized; a wide range of control strategies is recalled and an innovative semi-active algorithm (Mix-1-Stroke) based on a single-sensor layout is proposed. The strategies are then implemented in the Electronic Control Unit of the motorbike. Tests, both on test-bench and on-road, are presented. The result is the comparative analysis of a wide portfolio of different suspension control strategies, which shows the effectiveness of the Mix-1-Sensor rationale.     

MODIS vegetation metrics as indicators of hydrological response in watersheds of California Mediterranean-type climate zones

Vegetation characteristics of a watershed can be important in estimating hydrological response variables (HRVs) such as streamflow (Q), evapotranspiration (ET), and river yield (Q/P). Quantifying the relationship between satellite-derived vegetation metrics and hydrological response to precipitation (P) has the potential to facilitate prediction of HRVs for ungauged watersheds, and/or aid in the assessment of watershed similarity as an initial phase of hydrological regionalization. The utility of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to estimate HRVs of watersheds at the regional scale (southern and central California) is tested in this study. An exhaustive statistical regression analysis was conducted to quantify the relationships between MODIS vegetation metrics and HRVs using both ordinary least squares and spatially varying parameter models. Additionally a confirmatory analysis was conducted to test the effect precipitation and potential evaporation have on the exploratory regression results. Results from both the exploratory and confirmatory analyses suggest that (1) while there are limitations in the water balance approach to estimating ET (errors associated with changes in storage and meteorological data are unknown), moderate statistical relationships exist between MODIS vegetation metrics and HRVs; (2) these relationships are heavily influenced by vegetation-precipitation relationships and general precipitation magnitudes; (3) relationships between MODIS metrics and precipitation/HRVs are strongest when drought conditions prevail; and (4) LAI has the strongest relationship with precipitation and HRVs compared to other MODIS vegetation metrics.     

Combining per-pixel and object-based classifications for mapping land cover over large areas

A plethora of national and regional applications need land-cover information covering large areas. Manual classification based on visual interpretation and digital per-pixel classification are the two most commonly applied methods for land-cover mapping over large areas using remote-sensing images, but both present several drawbacks. This paper tests a method with moderate spatial resolution images for deriving a product with a predefined minimum mapping unit (MMU) unconstrained by spatial resolution. The approach consists of a traditional supervised per-pixel classification followed by a post-classification processing that includes image segmentation and semantic map generalization. The approach was tested with AWiFS data collected over a region in Portugal to map 15 land-cover classes with 10 ha MMU. The map presents a thematic accuracy of 72.6 ± 3.7% at the 95% confidence level, which is approximately 10% higher than the per-pixel classification accuracy. The results show that segmentation of moderate-spatial resolution images and semantic map generalization can be used in an operational context to automatically produce land-cover maps with a predefined MMU over large areas.     

Multitemporal MERIS images for land-cover mapping at a national scale: a case study of Portugal

Medium-spatial-resolution satellite images have already proved to be successful in automatic production of global land-cover maps. However, their operational use for land-cover mapping at a national scale has not yet been well established. We find that the reasons for this are not data-source dependent, but are due to the land-cover nomenclatures properties adopted, regional landscape specificities and the methodological approaches used. The aim of this paper is to evaluate the suitability for national applications of land-cover maps derived from automatic classification of medium-spatial-resolution satellite images. To tackle this issue, we produce a land-cover map of Continental Portugal from multitemporal MEdium Resolution Imaging Spectrometer (MERIS) full-resolution satellite images of 2005 and evaluate its accuracy. For the accuracy assessment of the final map, we compute unbiased estimates of overall, user and producer accuracies using an independent testing sample collected through a stratified random sampling design. The overall accuracy of the final map is 80%, with an absolute precision of 2% at the 95% confidence level. High independent accuracy assessment results demonstrate that medium-spatial-resolution satellite images can be used on an operational basis for annual production of land-cover maps suitable for national applications.     

Coherence between atmospheric teleconnections and Mackenzie River Basin lake levels

Lake Athabasca (LA), Great Slave Lake (GSL) and Great Bear Lake (GBL) lie within the Mackenzie River Basin (MRB), with GBL and GSL being the ninth and tenth largest lakes in the world by volume. How these lake levels fluctuate in time is important in management of the Peace-Athabasca delta, the ecology of these lakes, and for estimating sediment flux. The understanding of how the MRB lake levels interact with atmospheric teleconnections at different time scales may permit enhanced prediction of MRB water levels. Here we compare five teleconnections (North Pacific (NP), Pacific North American (PNA), Pacific Decadal (PDO), El Niño (MEI) and Arctic Oscillations (AO)) with lake water levels using a squared coherence analysis to determine over what timescales these teleconnections play statistically significant roles within the basin. The relevance of these interactions is then examined using power spectral analysis. We find PNA plays a considerable role in the southern half of the MRB over the interdecadal timescale. In contrast, PDO, despite having large interdecadal fluctuations, plays little role in the interdecadal lake water level fluctuations. Over the 1.1-3 year timescale, several teleconnections also show coherence with lake levels but are of less importance due to small water level fluctuations over that timescale. The coherence between LA and GSL water levels is also reduced over the 1.1-3 year timescale and may be related to flow regulation by the W. A. C. Bennett dam.     

Interactive comparison of scalar fields based on largest contours with applications to flow visualization

Understanding fluid flow data, especially vortices, is still a challenging task. Sophisticated visualization tools help to gain insight. In this paper, we present a novel approach for the interactive comparison of scalar fields using isosurfaces, and its application to fluid flow datasets. Features in two scalar fields are defined by largest contour segmentation after topological simplification. These features are matched using a volumetric similarity measure based on spatial overlap of individual features. The relationships defined by this similarity measure are ranked and presented in a thumbnail gallery of feature pairs and a graph representation showing all relationships between individual contours. Additionally, linked views of the contour trees are provided to ease navigation. The main render view shows the selected features overlapping each other. Thus, by displaying individual features and their relationships in a structured fashion, we enable exploratory visualization of correlations between similar structures in two scalar fields. We demonstrate the utility of our approach by applying it to a number of complex fluid flow datasets, where the emphasis is put on the comparison of vortex related scalar quantities.     

Generic-type hierarchical multi digital signal processor system for hard-field tomography

This article introduces the design and implementation of a hierarchical multi digital signal processor system aimed to perform parallel multichannel measurements and data processing of the type widely used in hard-field tomography. Details are presented of a complete tomography system with modular and expandable architecture, capable of accommodating a variety of data processing modalities, configured by software. The configuration of the acquisition and processing circuits and the management of the data flow allow a data frame rate of up to 250 kHz. Results of a case study, guided path tomography for temperature mapping, are shown as a direct demonstration of the system's capabilities. Digital lock-in detection is employed for data processing to extract the information from ac measurements of the temperature-induced resistance changes in an array of 32 noninteracting transducers, which is further exported for visualization.     

Bayesian analysis of Lidar signals with multiple returns

Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterisation of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far range depth imaging.     

Learning with Nearest Neighbour Classifiers

This paper introduces a learning strategy for designing a set of prototypes for a 1-nearest-neighbour (NN) classifier. In learning phase, we transform the 1-NN classifier into a maximum classifier whose discriminant functions use the nearest models of a mixture. Then the computation of the set of prototypes is viewed as a problem of estimating the centres of a mixture model. However, instead of computing these centres using standard procedures like the EM algorithm, we derive to compute a learning algorithm based on minimising the misclassification accuracy of the 1-NN classifier on the training set. One possible implementation of the learning algorithm is presented. It is based on the online gradient descent method and the use of radial gaussian kernels for the models of the mixture. Experimental results using hand-written NIST databases show the superiority of the proposed method over Kohonen's LVQ algorithms.