Mohammad Wadood Majid

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.

Birds/bats movement tracking with IR camera for wind farm applications

Nocturnally migratory birds and bats are at higher risk of colliding with wind turbines. It is important to gather scientific data in an area which have potential of wind farm development. An IR camera recording and its analysis can provide necessary information to wildlife biologists involve with interaction of birds/bats with wind turbines. An efficient IR video processing algorithm has been developed. The proposed algorithm consists of background and consecutive frame subtraction, frame selection, 3-D region labeling and breakpoint recovery. It is then used to process spring 2011 bird migration data that has been collected in Ottawa National Wildlife Refuge in Ohio. Results from this study will be useful for wildlife biologists to make intelligent decision for siting of wind turbines. It will also help policy makers to develop an appropriate public policy for wind farm development in an area with extensive avian activity.

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.

Evolutionary Neural Network parallelization with multicore systems on chip

Evolutionary Neural Network (ENN) has attracted great attention among the researchers in recent years because of its effectiveness at function optimization and, its efficiency in searching large and complex spaces to find nearly global optima. In this work, Parallel Evolutionary Neural Network algorithm is proposed and implemented on Multi-core system on chip. The algorithm is parallelized, partitioned, mapped, and scheduled on multicore. The algorithm is also implemented on single core for comparison. The parallel ENN is developed in C# using .Net framework 4.0. The .Net framework offers comprehensive and flexible threads APIs that allow the efficient implementation of multithreaded applications.

Parallel Implementation of the Wideband Coherent Signal-Subspace (CSS) Based DOA algorithm on single core, multicore and GPU

Computation of Wideband Coherent Signal-Subspace (CSS) Based Direction of Arrival (DOA) has been parallelized and implemented on Multicore (Intel Nehalem Quad Core) and NVIDIAs GPU. This is in an effort to its use for real time applications. The CSS algorithm has been parallelized, partitioned, mapped and scheduled on Multi-Core/GPU. The parallel algorithm is developed in C# and a combination of C and CUDA for Multi-Core and GPU respectively. The algorithm has also been implemented on single core for comparison purposes. Wideband CSS algorithm is implemented assuming 16 and 4 sensors using Uniform Linear Array (ULA).