Feature extraction of the first difference of EMG time series for EMG pattern recognition

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2–8%.     

Shape-based Invariant Texture Indexing

This paper introduces a new texture analysis scheme, which is invariant to local geometric and radiometric changes. The proposed methodology relies on the topographic map of images, obtained from the connected components of level sets. This morphological tool, providing a multi-scale and contrast-invariant representation of images, is shown to be well suited to texture analysis. We first make use of invariant moments to extract geometrical information from the topographic map. This yields features that are invariant to local similarities or local affine transformations. These features are invariant to any local contrast change. We then relax this invariance by computing additional features that are invariant to local affine contrast changes and investigate the resulting analysis scheme by performing classification and retrieval experiments on three texture databases. The obtained experimental results outperform the current state of the art in locally invariant texture analysis.     

Underdetermined Convolutive Blind Source Separation via Time–Frequency Masking

In this paper, we consider the problem of separation of unknown number of sources from their underdetermined convolutive mixtures via time-frequency (TF) masking. We propose two algorithms, one for the estimation of the masks which are to be applied to the mixture in the TF domain for the separation of signals in the frequency domain, and the other for solving the permutation problem. The algorithm for mask estimation is based on the concept of angles in complex vector space. Unlike the previously reported methods, the algorithm does not require any estimation of the mixing matrix or the source positions for mask estimation. The algorithm clusters the mixture samples in the TF domain based on the Hermitian angle between the sample vector and a reference vector using the well known k -means or fuzzy c -means clustering algorithms. The membership functions so obtained from the clustering algorithms are directly used as the masks. The algorithm for solving the permutation problem clusters the estimated masks by using k-means clustering of small groups of nearby masks with overlap. The effectiveness of the algorithm in separating the sources, including collinear sources, from their underdetermined convolutive mixtures obtained in a real room environment, is demonstrated.     

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.     

Model independent assertions for integration of heterogeneous schemas

Due to the proliferation of database applications, the integration of existing databases into a distributed or federated system is one of the major challenges in responding to enterprises' information requirements. Some proposed integration techniques aim at providing database administrators (DBAs) with a view definition language they can use to build the desired integrated schema. These techniques leave to the DBA the responsibility of appropriately restructuring schema elements from existing local schemas and of solving inter-schema conflicts. This paper investigates theassertion-based approach, in which the DBA's action is limited to pointing out corresponding elements in the schemas and to defining the nature of the correspondence in between. This methodology is capable of: ensuring better integration by taking into account additional semantic information (assertions about links); automatically solving structural conflicts; building the integrated schema without requiring conforming of initial schemas; applying integration rules to a variety of data models; and performing view as well as database integration. This paper presents the basic ideas underlying our approach and focuses on resolution of structural conflicts.     

Multi-month memory effects on early summer vegetative activity in semi-arid South Africa and their spatial heterogeneity

In semi-arid African regions (annual rainfall between 200 and 600 mm), variability of vegetative activity is mainly due to the rainfall of the current rainy season. In most of South Africa, the rainy season occurs from October to March. On average, vegetative activity lags rainfall by 1 to 2 months. The interannual variability in early summer (December to September) normalized difference vegetation index (NDVI) depends primarily on precipitation at the beginning (October to November) of the rainy season. However, once this primary control is removed, the residual interannual variability in NDVI highlights a double memory effect: a 1-year effect, referred to as Mem₁, and a 7- to 10-month effect, referred to as Mem₂. This article aims at better describing the influence of soil and vegetation characteristics on these two memory effects. The data sets used in this study are as follows: (1) a 19-year NDVI time series from National Oceanic and Atmospheric Administration (NOAA) satellites, (2) rainfall records from a network of 1160 rain-gauge stations compiled by the Water Research Commission (WRC), (3) vegetation types from Global Land Cover (GLC) 2000 and (4) soil characteristics from the soil and terrain database for Southern Africa (SOTERSAF). Results indicate that among 20–30% of NDVI variance that is not explained by the concurrent rainfall, one-third is explained by the two memory effects. Mem₁ is found to have maximum effect in the northwest of our study domain, near the Botswana boundary, in the South Kalahari. Associated conditions are open grasslands growing on Arenosols. Mem₁ is less important in the southeast, particularly in open grassland with shrubs growing on Cambisols. Thus, Mem₁ mainly depends on soil texture. Mem₂ is more widespread and its influence is the greatest in the centre, the south and the east of our domain. It is related to rainfall from January to April, which controls, beyond the intervening dry season, the interannual variations of NDVI (December to September) at the beginning of the next rainy season. Through these new findings, this article emphasizes again the high potential of remote-sensing techniques to monitor and understand the dynamics of semi-arid environments.     

A feasibility study on the use of anthropometric variables to make muscle–computer interface more practical

High classification accuracy has been achieved for muscle–computer interfaces (MCIs) based on surface electromyography (EMG) recognition in many recent works with an increasing number of discriminated movements. However, there are many limitations to use these interfaces in the real-world contexts. One of the major problems is compatibility. Designing and training the classification EMG system for a particular individual user is needed in order to reach high accuracy. If the system can calibrate itself automatically/semi-automatically, the development of standard interfaces that are compatible with almost any user could be possible. Twelve anthropometric variables, a measurement of body dimensions, have been proposed and used to calibrate the system in two different ways: a weighting factor for a classifier and a normalizing value for EMG features. The experimental results showed that a number of relationships between anthropometric variables and EMG time-domain features from upper-limb muscles and movements are statistically strong (average r=0.71?0.80) and significant (p<0.05). In this paper, the feasibility to use anthropometric variables to calibrate the EMG classification system is shown obviously and the proposed calibration technique is suggested to further improve the robustness and practical use of MCIs based on EMG pattern recognition.     

ARIANEXPERT: A knowledge based system to analyze ARIANE's mission data

The ARIANE launcher post mission analysis is done at ARIANESPACE. This activity is called the ‘level 0 post flight analysis’ (PFA) and is carried out after each launch by about 60 engineers who are working together under the leadership of ARIANESPACE.

The PFA is one of the most critical of ARIANE operations, for several reasons:

• - The launch rate (8 a year for ARIANE 4) leaves a very short time to carry out all the verification work. Moreover, the PFA is a mandatory step before authorizing the next launch.

• - The complexity of the ARIANE launcher results in a very high demand on the PFA engineers. Moreover, there are problems of availability of people with relevant expert knowledge (characterized by a substantial staff turn-over during the 10 year life duration of ARIANE 4) which could potentially result in errors or omissions.

It is very important to be able to take into account the experience of the preceding flights and to record the results and the knowledge accumulated for each launch.

• - The quality and the reliability of the PFA mainly depends on the accessibility of data and on the used methodology.

Because the PFA is still largely done manually, and does not benefit from improved methodologies and advanced technologies providing computerized support for data processing and diagnosis, ARIANESPACE has sponsored MATRA ESPACE for the development of a knowledge based system, called ARIANEXPERT, for supporting the PFA activity. This system combines AI techniques and numerical analysis techniques, together with advanced graphical capabilities.

A prototype has been delivered in April 1990 and has been used since 6 months by ARIANESPACE during real PFAs. Several lessons have been drawn from this operational experience and are described in this paper. They concern:

• - The utility and justification of the use of AI techniques mostly coming from the explanation capabilities and the stress put on capturing the expert knowledge.

• - The difficulties associated with the integration of such systems in the exploitation of ARIANE due to the introduction of very new tasks.

• - The user point of view which evolved from reluctant to convinced.

     

Experimental investigation of water droplet–air flow interaction in a non-reacting PEM fuel cell channel

It has been well documented that water production in PEM fuel cells occurs in discrete locations, resulting in the formation and growth of discrete droplets on the gas diffusion layer (GDL) surface within the gas flow channels (GFCs). This research uses a simulated fuel cell GFC with three transparent walls in conjunction with a high speed fluorescence photometry system to capture videos of dynamically deforming droplets. Such videos clearly show that the droplets undergo oscillatory deformation patterns. Although many authors have previously investigated the air flow induced droplet detachment, none of them have studied these oscillatory modes. The novelty of this work is to process and analyze the recorded videos to gather information on the droplets induced oscillation. Plots are formulated to indicate the dominant horizontal and vertical deformation frequency components over the range of sizes of droplets from formation to detachment. The system is also used to characterize droplet detachment size at a variety of channel air velocities. A simplified model to explain the droplet oscillation mechanism is provided as well.