Present study investigates the historical and contemporary trends in the development of Azerbaija... more Present study investigates the historical and contemporary trends in the development of Azerbaijani national identity. The primary focus here is on the ethnic component which formed the foundation of a distinct national identity of Azerbaijanis. Both natural and situational circumstances affect the development of any ethnicity. The natural circumstances in formation of Azerbaijani ethnic identity were analyzed mainly in ancient and medieval contexts, while the situational circumstances appear in contemporary context, also defining the cause of a modern Azerbaijani statehood. a Dr. Huseynov is a co-founder and general director of Azerbaijani-American Council (AAC);
A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is pre... more A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is presented. The joint timefrequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
… on Neural Networks, 2007. IJCNN 2007, Jan 1, 2007
A model for an infrared (IR) flame detection system using multiple artificial neural networks (AN... more A model for an infrared (IR) flame detection system using multiple artificial neural networks (ANN) is presented. The present work offers significant improvements over our previous design (Huseynov et al., 2005). Feature extraction only in the relevant frequency band using joint time-frequency analysis yields an input to a series of conjugate-gradient (CG) method-based ANNs. Each ANN is trained to distinguish all hydrocarbon flames from a particular type of environmental nuisance and ambient noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by adjustable gain control.
By combining research results from different communities, better systems can be built. This is ex... more By combining research results from different communities, better systems can be built. This is exemplified in our designs of two mobile agent systems on top of distributed shared memory (DSM). Conventional use of DSM causes inadvertent movement of large data, which leads to the belief that DSM is not scalable. In our two designs, we use DSM in two different ways. The first uses explicit agent migration and data distribution where DSM contains only the small agent variables, while in the second all data is placed in DSM, and the agents migrate implicitly to large-sized data. The first design gives the programmers more control whereas the second design provides more transparency. The benefits of both designs include improved efficiency, ease of construction, and ease of use.
A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a... more A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a large number of inputs is presented. Joint time-frequency analysis in the form of Short-Time Fourier Transform was used for extracting the relevant features from infrared sensor signals. After appropriate scaling, this information was provided as an input for the ANN training algorithm based on conjugate-gradient (CG) descent method. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for an industrial hydrocarbon flame detector.
Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to di... more Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to distributed parallel computing. MP is difficult to use, whereas DSM is not scalable. Performance scalability and ease of programming can be achieved at the same time by using navigational programming (NavP). This approach combines the advantages of MP and DSM, and it balances convenience and flexibility. Similar to MP, NavP suggests to its programmers the principle of pivot-computes and hence is efficient and scalable. Like DSM, NavP supports incremental parallelization and shared variable programming and is therefore easy to use. The implementation and performance analysis of real-world algorithms, namely parallel Jacobi iteration and parallel Cholesky factorization, presented in this paper supports the claim that the NavP approach is better suited for general-purpose parallel distributed programming than either MP or DSM.
An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The mode... more An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time–frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method, with input data collected from various flame and non-flame nuisance signals at four different IR wavelengths. The trained ANN connection weights are programmed into an embedded system as part of the filtering scheme for distinguishing flames from nuisance sources. Signal saturation caused by the excessive intensity of some IR sources is resolved by an adjustable gain control mechanism. The model described herein is employed in an industrial hydrocarbon flame detector.
Present study investigates the historical and contemporary trends in the development of Azerbaija... more Present study investigates the historical and contemporary trends in the development of Azerbaijani national identity. The primary focus here is on the ethnic component which formed the foundation of a distinct national identity of Azerbaijanis. Both natural and situational circumstances affect the development of any ethnicity. The natural circumstances in formation of Azerbaijani ethnic identity were analyzed mainly in ancient and medieval contexts, while the situational circumstances appear in contemporary context, also defining the cause of a modern Azerbaijani statehood. a Dr. Huseynov is a co-founder and general director of Azerbaijani-American Council (AAC);
A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is pre... more A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is presented. The joint timefrequency analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an industrial hydrocarbon flame detector.
… on Neural Networks, 2007. IJCNN 2007, Jan 1, 2007
A model for an infrared (IR) flame detection system using multiple artificial neural networks (AN... more A model for an infrared (IR) flame detection system using multiple artificial neural networks (ANN) is presented. The present work offers significant improvements over our previous design (Huseynov et al., 2005). Feature extraction only in the relevant frequency band using joint time-frequency analysis yields an input to a series of conjugate-gradient (CG) method-based ANNs. Each ANN is trained to distinguish all hydrocarbon flames from a particular type of environmental nuisance and ambient noise. Signal saturation caused by the increased intensity of IR sources at closer distances is resolved by adjustable gain control.
By combining research results from different communities, better systems can be built. This is ex... more By combining research results from different communities, better systems can be built. This is exemplified in our designs of two mobile agent systems on top of distributed shared memory (DSM). Conventional use of DSM causes inadvertent movement of large data, which leads to the belief that DSM is not scalable. In our two designs, we use DSM in two different ways. The first uses explicit agent migration and data distribution where DSM contains only the small agent variables, while in the second all data is placed in DSM, and the agents migrate implicitly to large-sized data. The first design gives the programmers more control whereas the second design provides more transparency. The benefits of both designs include improved efficiency, ease of construction, and ease of use.
A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a... more A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a large number of inputs is presented. Joint time-frequency analysis in the form of Short-Time Fourier Transform was used for extracting the relevant features from infrared sensor signals. After appropriate scaling, this information was provided as an input for the ANN training algorithm based on conjugate-gradient (CG) descent method. A classification scheme with trained ANN connection weights was implemented on a digital signal processor for an industrial hydrocarbon flame detector.
Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to di... more Message Passing (MP) and Distributed Shared Memory (DSM) are the two most common approaches to distributed parallel computing. MP is difficult to use, whereas DSM is not scalable. Performance scalability and ease of programming can be achieved at the same time by using navigational programming (NavP). This approach combines the advantages of MP and DSM, and it balances convenience and flexibility. Similar to MP, NavP suggests to its programmers the principle of pivot-computes and hence is efficient and scalable. Like DSM, NavP supports incremental parallelization and shared variable programming and is therefore easy to use. The implementation and performance analysis of real-world algorithms, namely parallel Jacobi iteration and parallel Cholesky factorization, presented in this paper supports the claim that the NavP approach is better suited for general-purpose parallel distributed programming than either MP or DSM.
An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The mode... more An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time–frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method, with input data collected from various flame and non-flame nuisance signals at four different IR wavelengths. The trained ANN connection weights are programmed into an embedded system as part of the filtering scheme for distinguishing flames from nuisance sources. Signal saturation caused by the excessive intensity of some IR sources is resolved by an adjustable gain control mechanism. The model described herein is employed in an industrial hydrocarbon flame detector.
Uploads
Papers by Javid Huseynov