Joseph P. Frizado

Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average.

This paper presents a GIS-based multi-criteria decision analysis approach for evaluating the suitability for landfill site selection in the Polog Region, Macedonia. The multi-criteria decision framework considers environmental and economic factors which are standardized by fuzzy membership functions and combined by integration of analytical hierarchy process (AHP) and ordered weighted average (OWA) techniques. The AHP is used for the elicitation of attribute weights while the OWA operator function is used to generate a wide range of decision alternatives for addressing uncertainty associated with interaction between multiple criteria. The usefulness of the approach is illustrated by different OWA scenarios that report landfill suitability on a scale between 0 and 1. The OWA scenarios are intended to quantify the level of risk taking (i.e., optimistic, pessimistic, and neutral) and to facilitate a better understanding of patterns that emerge from decision alternatives involved in the decision making process.

The application of Evolutionary Neural Network for bat echolocation calls recognition

An Evolutionary Neural Network (ENN) is developed to identify bats by their vocalization characteristics. This is in an effort to identify local bat species as a large number of bat fatalities near wind turbines have been reported. ENN is based on the Genetic Algorithm, which can be used for optimization of the weight selection of the neural network. We then compare ENN with different classification techniques. In the scope of bat call classification, ENN is a new technique that can be effectively used as a bat-call classifier. This research will help in developing mitigation techniques for reducing bat fatalities. The ENN algorithm is developed in MATLAB.

Avian detection & tracking algorithm using infrared imaging

This paper presents a method for target detection and tracking of IR images in the application of avian surveillance. As there are many reports of avian mortality due to collision with turbine blades, the detection and tracking of birds at turbine sites is an important issue. In this work, three different background subtraction techniques are first applied to detect moving objects. Otsu thresholding method is then extended by incorporating an adaptive variable based on the mean of each frame and certain constant value. Filtering using morphological operations is applied. Results of three different techniques are then compared. Selected technique (RA) followed by thresholding and filtering is then used for tracking and information extraction. Results show that proposed method provides the needed accuracy for IR imagery. This method can be effectively used in different applications of IR imaging.

Implementation of ant clustering algorithm for IR imagery in wind turbine applications

Interaction of avian with turbines has become an important public policy issue, so identification and quantification of avian at turbine sites is crucial.

A novel feature extraction algorithm for classification of bird flight calls

Acoustic monitoring of birds in the vicinity of wind turbines is becoming an important public policy issue. Acoustic monitoring involves preprocessing, feature extraction and classification. A novel Spectrogram-based Image Frequency Statistics (SIFS) feature extraction algorithm has been developed. Features extracted from proposed algorithms were then combined with various classification algorithms such as k-NN, Multilayer Perceptron (MLP) and Hidden Markov Models (HMM) and Evolutionary Neural Network (ENN). SIFS and MMS algorithms, combined with ENN, provided the most accurate results. Proposed algorithms were tested with real data collected during spring migration around Lake Erie in Ohio.

GIS and Economic Development

Geographic information systems (GIS) are used by a wide variety of practitioners to help them solve a broad range of spatially-based problems. In this chapter, we focus on the ways in which GIS can be of utility to economic development practitioners who are charged with the task of developing local economies. We begin by defining local economic development (LED). We then report on the extent to which GIS is used by economic development professionals. This is followed by examples of the application of GIS in economic development work. Specifically, we examine the use of GIS in five areas – economic impact analysis, spatial policymaking, identifying potential cluster regions, identifying critical social relationships, and web-based GIS. We conclude the chapter with a few summary statements.

The BIO-acoustic feature extraction and classification of bat echolocation calls

There are reports that large number of bat fatalities occur near wind turbines. Acoustic characteristics can be employed for bat call recognition to better understand the effects of turbines on different bat species. Acoustic features of bat echolocation calls are extracted based on three different techniques: Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC) and Discrete Wavelet Transform (DWT). These features are fed into an Evolutionary Neural Network (ENN) for their classification at the species level using acoustic features. Results from different feature extraction techniques are compared based on classification accuracy. The technique can identify bats and will contribute towards developing mitigation procedures for reducing bat fatalities.

Acoustic monitoring techniques for avian detection and classification

There are many reports of bird and bat mortality in vicinity of wind turbines [1]. It is important to quantify numbers and species of birds and bats in a given area which is targeted for wind farm development. It is also necessary to assess the behavior of birds and bats in wind farm areas. Acoustic monitoring techniques have been developed in this work for monitoring of birds and bats. Spectrogram-based Image Frequency Statistics (SIFS) is used for feature extraction and Evolutionary Neural Network (ENN) is used for classification purposes. Data was collected near Lake Erie in Ohio during 2011 spring and fall migration periods. Data analysis was performed in accordance to needs of wildlife biologists.

Remote avian monitoring system for wind turbines

A radar and IR based avian monitoring system for an offshore wind turbine application has been designed. The avian monitoring system is capable of capturing radar and IR data. The data is synchronized and sent to a remote computer via 3G system. The IR camera needs to be synchronized with the radar view from a remote location. The system was constructed and successfully tested for remote synchronization of radar and IR camera and transfer of data over the internet. The system is designed to monitor avian activity around offshore wind turbines.

Paragenetic Relationships of Vein Pyrobitumen in the Panel Mine, Elliot Lake Uranium District, Ontario, Canada

Globular blebs of solid pyrobitumen occur in veins that cut uraniferous conglomerates of the Lower Proterozoic Matinenda Formation in the Panel Mine in the Elliot Lake District, Ontario. The blebs are small (1–10mm) and vary in shape from round to discoid, kidney to saddle, twisted or elongate. Their surfaces are shiny and permeated with vesicles. The blebs are composed predominantly of carbon with a H/C ratio of 0.57, a reflectivity (%Rm) of 0.9%, and a δ 13C value of −33‰ (PDB). The paragenetic sequence of minerals and pyrobitumen in the veins is: quartz, pyrite 1, pyrobitumen, sepiolite, pyrite 2, pyrrhotite and galena, and finally calcite. The pyrobitumen blebs in the Panel mine are the result of natural migration and maturation of Precambrian petroleum. Tarry masses in the veins were polymerized to the catagenesis stage by outgasing, water-washing, and thermal cracking.