Animoto.com is a web application allowing users to automatically create professionally produced video with images and music of their choosing. The resulting video, termed an "Animoto", displays images with high-end motion design determined by Cinematic Artificial Intellience ™ technology. No two Animotos are the same. Even Animotos generated with an identical set of images and music will each have a completely distinct set of motion design. They can be emailed, downloaded, and embedded in pages on websites including social network sites like Facebook and MySpace.
Technology
Cinematic A.I.™ technology analyzes and combines user-selected images and music with the same post-production skills & techniques that are used in television and film. The technology takes into account every nuance of a song - the genre, song structure, energy, rhythm, instrumentation, and vocals.
History
Animoto is a service of Animoto Production, founded in August 2006 by TV & film producers.
Technology
Cinematic A.I.™ technology analyzes and combines user-selected images and music with the same post-production skills & techniques that are used in television and film. The technology takes into account every nuance of a song - the genre, song structure, energy, rhythm, instrumentation, and vocals.
History
Animoto is a service of Animoto Production, founded in August 2006 by TV & film producers.
Holovaty was a renown poet in his day. To his pen are dedicated a number of Ukrainian poems some which became folk songs in the Ukraine. It is known that Taras Shevchenko recorded a number and included them in his kobzar. (Oj Bozhe nash Bozhe) and some sources speculate that it is Holovaty's persona that influenced Shevchenko's choice of the term kobzar for his book of poems.
One of the most often quoted sections of Shevchenko's Kobzar were originally were about A. Holovaty but were edited and changed by Panteleimon Kulish.
Original:
*Nash chubatyj Holovatyj ne vmre, ne zahyne,
Ot de liudy nasha slava, Slava Ukrainy.
*
Our hairy Holovaty will not die, will not perish,
Here people is our glory, the Glory of Ukraine.
Changed to:
*Nasha duma, nasha pisnia, ne vmre ne zahyne,
Ot de liudy nasha slava, Slava Ukrainy.
*
Our duma, our song, will not die and perish
Here people is our glory, the Glory of Ukraine.
The lines were changed by Kulish possibly because of his possible disdain for Holovaty's support of the Russian Administration. Shevchenko on the other hand admired Holovaty such that he also drew a portrait of him.
Because of his noble education he was also well versed in music and was an accomplished bandura player.
One of the most often quoted sections of Shevchenko's Kobzar were originally were about A. Holovaty but were edited and changed by Panteleimon Kulish.
Original:
*Nash chubatyj Holovatyj ne vmre, ne zahyne,
Ot de liudy nasha slava, Slava Ukrainy.
*
Our hairy Holovaty will not die, will not perish,
Here people is our glory, the Glory of Ukraine.
Changed to:
*Nasha duma, nasha pisnia, ne vmre ne zahyne,
Ot de liudy nasha slava, Slava Ukrainy.
*
Our duma, our song, will not die and perish
Here people is our glory, the Glory of Ukraine.
The lines were changed by Kulish possibly because of his possible disdain for Holovaty's support of the Russian Administration. Shevchenko on the other hand admired Holovaty such that he also drew a portrait of him.
Because of his noble education he was also well versed in music and was an accomplished bandura player.
The Bushidokan Federation is a union of autonomous dojos (schools of martial arts) from around the world practicing the art of Dan Zan Ryu Zenyo Bujitsu, operating under agreed-upon principles common to all associated dojos. This system was originated by Senior Professor Herb LaGue, Shihan, a 10th Degree Black Belt of Dan Zan Ryu Zenyo Bujutsu.
Senior Professor Herb LaGue
Prof. Herb LaGue holds the rank of Judan/10th Degree Black Belt and the Title of Shodai in the Bushidokan Federation. He founded the system called Dan Zan Ryu Zenyo Bujutsu in June 2004, and is currently the the head of the Bushidokan Federation Corporation Sole. He has engaged in many activities in support of both the martial arts and conflict resolution for many years by visiting and teaching at dojos and attending peace rallies in many countries around the world.
Prof. LaGue was born in Reno, NV on June 12, 1941. His father,
Senior Professor Herb LaGue
Prof. Herb LaGue holds the rank of Judan/10th Degree Black Belt and the Title of Shodai in the Bushidokan Federation. He founded the system called Dan Zan Ryu Zenyo Bujutsu in June 2004, and is currently the the head of the Bushidokan Federation Corporation Sole. He has engaged in many activities in support of both the martial arts and conflict resolution for many years by visiting and teaching at dojos and attending peace rallies in many countries around the world.
Prof. LaGue was born in Reno, NV on June 12, 1941. His father,
"Open Heart" is a research project for the automatic diagnosis of Heart diseases through ECG. It is being carried out at Pakistan Institute of Engineering and Applied Sciences (PIEAS) , Islamabad, Pakistan.
The major incentive behind the development of this project is cardiac disease being one of the leading causes of death all over the world. With the inception of fast signal processing and computing hardware, techniques for the automatic detection of cardiac disorders through ECG has stemmed up as one of the most promising methodologies in Clinical Decision Support Systems. Such a system can offer rapid, accurate and reliable diagnosis to a variety of cardiac diseases and can reduce the work load for cardiac experts along with providing a facility for the simultaneous monitoring of multiple patients. In this project the objective is to develop techniques for the automatic processing and analysis of the ECG. The work is divided into three major parts: Part-I involving study and implementation of methods for removal of artifacts from the ECG. These include baseline and noise removal techniques. In this work different baseline removal techniques, such as use of digital FIR and IIR filters and 3 different polynomial fitting approaches have been compared to conclude that the use of a two stage first order polynomial fitting based method introduces least distortion in the ECG while effectively compensating the ECG baseline. For Noise removal, a comparison of three different techniques, i.e. Use of Digital filters, Independent Component Analysis (ICA) and Local Nonlinear Projective Filtering has been carried out leading to the conclusion that nonlinear projective filtering performs well in removing noise from the ECG, whereas the potential of ICA for this purpose has been explored.
Part-II involves the segmentation of different ECG components, i.e. P, QRS and T-waves using methods based on digital filters, Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform. A new method for QRS detection and delineation through CWT has been developed which compares well with existing research offering Sensitivity/Specificity of ~99.8% for detection of QRS with ~10ms error in determining its onset and offset on the QT Database available at Physionet. The accuracy of an existing DWT based method has been improved through the use of Genetic Algorithms (GA). We conclude that the use of DWT with parameter optimization through GA proves to be the most effective technique for ECG Segmentation giving equally good accuracy in terms of detection and delineation.
Part-III is concerned with the classification of different types of heart rhythms (Normal, Atrial Premature Beats, Ventricular Premature Beats, Paced Rhythms, Left and Right Bundle Branch Blocks) and the detection of ST Segment deviations connected to Ischemic Heart Disease. For the purpose of classification of different arrhythmias DWT based features have been compared with those obtained from the Discrete Fourier Transform (DFT) to conclude that DWT is more effective in the classification of different types of heart rhythms. An accuracy of 99.1% has been achieved through implementation of a DWT based technique for feature extraction and using k-Nearest Neighbor classifiers. These results have been compared with those obtained through the use of Probabilistic Neural Networks (PNN) and Learning Vector Quantization (LVQ) Neural Networks. A comparison of the performance of different types of feature extraction and classification techniques for the detection of ischemic ST deviation episodes, such as time-domain features with a rule based classifier, use of Principal Component Analysis (PCA) based features with a Backpropagation Neural Network, a Neural Network Ensemble and a Support Vector Machine (SVM) ensemble classifier, has been established. A Sensitivity/Positive Predictivity of ~90% has been achieved with the use of a novel Neural Network Ensemble which uses lead specific principal components as features. These results are highest in terms of accuracy when compared with the existing literature with the novelty lying in the use of lead specific KLT Bases and Ensemble Neural Classifiers for each lead.
The work reported in this thesis can be used to establish the foundations of a practical stand-alone system for patient monitoring and the design of a multiple patient monitoring system as required in hospitals. Currently the project focus is on the development of hardware for real-time system application.
The project team welcomes any collaborative research offers.
List of Publications:
Afsar, F.A. and M. Arif, QRS Detection and Delineation Techniques for ECG Based Robust Clinical Decision Support System Design, in National Science Conference. 2007: Lahore, Pakistan.
Afsar, F.A. and M. Arif, Detection of ST Segment Deviation Episodes in the ECG using KLT with an Ensemble Neural Classifier, in International Conference on Emerging Technologies (ICET 2007). 2007: Islamabad, Pakistan.
----
--Fayyaz.A.Afsar 12:46, 3 November 2007 (UTC)
The major incentive behind the development of this project is cardiac disease being one of the leading causes of death all over the world. With the inception of fast signal processing and computing hardware, techniques for the automatic detection of cardiac disorders through ECG has stemmed up as one of the most promising methodologies in Clinical Decision Support Systems. Such a system can offer rapid, accurate and reliable diagnosis to a variety of cardiac diseases and can reduce the work load for cardiac experts along with providing a facility for the simultaneous monitoring of multiple patients. In this project the objective is to develop techniques for the automatic processing and analysis of the ECG. The work is divided into three major parts: Part-I involving study and implementation of methods for removal of artifacts from the ECG. These include baseline and noise removal techniques. In this work different baseline removal techniques, such as use of digital FIR and IIR filters and 3 different polynomial fitting approaches have been compared to conclude that the use of a two stage first order polynomial fitting based method introduces least distortion in the ECG while effectively compensating the ECG baseline. For Noise removal, a comparison of three different techniques, i.e. Use of Digital filters, Independent Component Analysis (ICA) and Local Nonlinear Projective Filtering has been carried out leading to the conclusion that nonlinear projective filtering performs well in removing noise from the ECG, whereas the potential of ICA for this purpose has been explored.
Part-II involves the segmentation of different ECG components, i.e. P, QRS and T-waves using methods based on digital filters, Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform. A new method for QRS detection and delineation through CWT has been developed which compares well with existing research offering Sensitivity/Specificity of ~99.8% for detection of QRS with ~10ms error in determining its onset and offset on the QT Database available at Physionet. The accuracy of an existing DWT based method has been improved through the use of Genetic Algorithms (GA). We conclude that the use of DWT with parameter optimization through GA proves to be the most effective technique for ECG Segmentation giving equally good accuracy in terms of detection and delineation.
Part-III is concerned with the classification of different types of heart rhythms (Normal, Atrial Premature Beats, Ventricular Premature Beats, Paced Rhythms, Left and Right Bundle Branch Blocks) and the detection of ST Segment deviations connected to Ischemic Heart Disease. For the purpose of classification of different arrhythmias DWT based features have been compared with those obtained from the Discrete Fourier Transform (DFT) to conclude that DWT is more effective in the classification of different types of heart rhythms. An accuracy of 99.1% has been achieved through implementation of a DWT based technique for feature extraction and using k-Nearest Neighbor classifiers. These results have been compared with those obtained through the use of Probabilistic Neural Networks (PNN) and Learning Vector Quantization (LVQ) Neural Networks. A comparison of the performance of different types of feature extraction and classification techniques for the detection of ischemic ST deviation episodes, such as time-domain features with a rule based classifier, use of Principal Component Analysis (PCA) based features with a Backpropagation Neural Network, a Neural Network Ensemble and a Support Vector Machine (SVM) ensemble classifier, has been established. A Sensitivity/Positive Predictivity of ~90% has been achieved with the use of a novel Neural Network Ensemble which uses lead specific principal components as features. These results are highest in terms of accuracy when compared with the existing literature with the novelty lying in the use of lead specific KLT Bases and Ensemble Neural Classifiers for each lead.
The work reported in this thesis can be used to establish the foundations of a practical stand-alone system for patient monitoring and the design of a multiple patient monitoring system as required in hospitals. Currently the project focus is on the development of hardware for real-time system application.
The project team welcomes any collaborative research offers.
List of Publications:
Afsar, F.A. and M. Arif, QRS Detection and Delineation Techniques for ECG Based Robust Clinical Decision Support System Design, in National Science Conference. 2007: Lahore, Pakistan.
Afsar, F.A. and M. Arif, Detection of ST Segment Deviation Episodes in the ECG using KLT with an Ensemble Neural Classifier, in International Conference on Emerging Technologies (ICET 2007). 2007: Islamabad, Pakistan.
----
--Fayyaz.A.Afsar 12:46, 3 November 2007 (UTC)