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The Gaussian plume model is the core of most regulatory atmospheric dispersion models. The parameters of the model include the source characteristics (e.g. location, strength, size) and environmental parameters (wind speed, direction, atmospheric stability conditions). A sensor network is at disposal to measure the concentration of biological pathogen or chemical substance within the plume. This paper...
Seasonal adjustment plays a significant role in the analysis of current social and economic conditions, particularly in determining the stages of the business cycle at which the economy stands. Nowadays the most popular method for seasonal adjustment in time series forecasting is the X-12-ARIMA algorithm, etc. All of these methods are sophisticated and difficult to understand and not suitable for...
The motivation of this paper is to study the influence of the background EM field (at wireless connection frequencies) on the human body. We have investigated the EM field's distribution in the closed, semi-open and open geometries with the presence of the human model. We studied the EM wave field's nature for some big scenarios considering interaction with an object like a human body. Besides, we...
Many advancement is made in recent days and number of techniques are proposed by different researchers for processing and extracting knowledge from big data. But to evaluate the consistency in extracted model is always questionable. In this paper we are presenting two techniques for measuring the consistency between extracted model and predicting their applicability. In this paper, Meta learning based...
Bio-electrical impedance measures the impedance of living tissues to the flow of a harmless alternating electrical current. For this purpose we need a current source, the most important circuit block of a bio-electrical impedance measurement system. Such a current source is required to have large output impedance and should be able to supply a constant output current in a wide frequency range. In...
Software development cost estimation is an important activity in the early software design phases. The input datasets are primarily taken from the promise repository. Data mining and soft computing techniques are used to assess the software development cost estimation. Each feature in the input dataset is divided, the linguistic terms along with the membership are identified using trapezoidal membership...
As neurobiological evidence points to the neocortex as the brain region mainly involved in high-level cognitive functions, an innovative model of neocortical information processing has been recently proposed. Based on a simplified model of a neocortical neuron, and inspired by experimental evidence of neocortical organisation, the Hierarchical Temporal Memory (HTM) model attempts at understanding...
Electromyogram (EMG) activity from the extensor and flexor muscles of the forearm was sensed with high-density surface electrode arrays and related to the force produced at the four fingertips during constant-posture, slowly force-varying contractions from three healthy subjects. Various electrode montages (spatial filters) and number of electrodes used in the system identification were studied. Average...
A major challenge facing data-mining practitioners in the field of bioinformatics is class imbalance, which occurs when instances of one class (called the majority class) vastly outnumber instances of the other (minority) classes. This can result in models with increased bias towards the majority class (minority-class instances predicted as being in the majority class). Data sampling, a process which...
Quantitative Structure-Activity-Relationships (QSARs) were investigated for cellular uptake of nanoparticles (NPs) using a dataset comprised of 109 NPs of the same iron oxide core but with different surface-modifying organic molecules. QSARs were built using both linear and non-linear model building methods along with a forward descriptor selection from an initial pool of 184 chemical descriptors...
A new concept of passive RFID tag position finding is presented for real world item-level applications and provides automatic guidance for order fulfillment centers and retail stores. The new technique does not require a radio map, reference tags, or any additional hardware. This is accomplished by using precise signal models, a minimum mean-square error estimator and four spatially diverse CP reader...
Ecological niche modeling (ENM) coupled with 3S has become increasingly important for environment monitoring. Chamaecyparis formosensis (Taiwan red cypress, TRC) only grows in Huisun's Shou-Cheng Mountain. We used GIS to overlay physiographic variables and vegetation index with TRC samples. We developed ENMs by using generalized linear model (GLM), maximum likelihood (ML), maximum entropy (MAXENT)...
Peatland in tropical region is a major CO2 emission source because of peat decomposition and forest fire by human induced activities. Remote sensing is effective tool to monitor environmental condition of peatland and forest ecosystem in peatland. A pixel-based approach is one of the most attractive choices for forest type classification or biomass prediction. The traditional method, however, is not...
Among many applications of Shuttle Radar Topography Mission (SRTM) and digital elevation model (DEM), the suitability of this data for simulating potential insolation (PI) has not been fully examined. This study examined the accuracy of potential insolation simulation based on 3 arc-second resolution SRTM. The National Elevation Dataset (NED) was used as reference data. Using a method which does not...
An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect...
The use of accurate 3D spatial network models can enable substantial improvements in vehicle routing. Notably, such models enable eco-routing, which reduces the environmental impact of transportation. We propose a novel filtering and lifting framework that augments a standard 2D spatial network model with elevation information extracted from massive aerial laser scan data and thus yields an accurate...
One-class classification is one of the most challenging topics in the field of machine learning. Recently creating Multiple Classifier Systems for this task has proven itself as a promising research direction. Here arises a problem on how to select valuable members to the committee — so far a largely unexplored area in one-class classification. This paper introduced a novel approach that allows to...
Lymphedema, a chronic disease caused by failure in the lymphatic system, affects nearly 500,000 people in the U.S., and over 2.4 million breast cancer survivors are at-risk for developing this disease at some point in their life. Early detection and management can significantly reduce the potential for symptoms and complications; however, many patients fail to seek medical assistance at the first...
Uncertainties originating from observation data and modelling approaches can affect model accuracy and thus impact on the applicability and reliability of a model. This paper aims to assess the effects of data prevalence (i.e., proportion of presence in the entire data set) on species distribution modelling and habitat preference evaluation using a 0-order genetic Takagi-Sugeno fuzzy model. The effects...
Video and image content has begun to play a growing role in many applications, ranging from video games to autonomous self-driving vehicles. In this paper, we present accelerators for gist-based scene recognition, saliency-based attention, and HMAX-based object recognition that have multiple uses and are based on the current understanding of the vision systems found in the visual cortex of the mammalian...
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