A Method to Construct the Mapping to the Feature Space for the Dot Product Kernels

2006 ◽  
pp. 918-929
Author(s):  
Degang Chen ◽  
Qiang He ◽  
Chunru Dong ◽  
Xizhao Wang
Keyword(s):  
Feature Space ◽  
Dot Product

Black-white contrast of dislocation loops

1978 ◽  
Vol 36 (1) ◽  
pp. 328-329
Author(s):  
J. J. Hren ◽  
W. D. Cooper ◽  
L. J. Sykes
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Dislocation Loops ◽  
Major Premise ◽  
Burgers Vector ◽  
Transmission Electron ◽  
Primary Means ◽  
Contrast Analysis ◽  
Dot Product ◽  
Imaging Conditions

Small dislocation loops observed by transmission electron microscopy exhibit a characteristic black-white strain contrast when observed under dynamical imaging conditions. In many cases, the topography and orientation of the image may be used to determine the nature of the loop crystallography. Two distinct but somewhat overlapping procedures have been developed for the contrast analysis and identification of small dislocation loops. One group of investigators has emphasized the use of the topography of the image as the principle tool for analysis. The major premise of this method is that the characteristic details of the image topography are dependent only on the magnitude of the dot product between the loop Burgers vector and the diffracting vector. This technique is commonly referred to as the (g•b) analysis. A second group of investigators has emphasized the use of the orientation of the direction of black-white contrast as the primary means of analysis.

The Utility of an Intermediate Representation of Feature Space: Lessons From Fingerprint Examiners

2012 ◽  
Author(s):  
Tom Busey ◽  
Chen Yu ◽  
Francisco Parada ◽  
Brandi Emerick ◽  
John Vanderkolk
Keyword(s):  
Feature Space ◽  
Intermediate Representation

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

Towards Feature-space Emotional Speech Adaptation for TDNN based Telugu ASR systems

2019 ◽  
Author(s):  
Vishnu Vidyadhara Raju V ◽  
Krishna Gurugubelli ◽  
Mirishkar Sai Ganesh ◽  
Anil Kumar Vuppala
Keyword(s):  
Feature Space ◽  
Emotional Speech

Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

2019 ◽  
Vol 9 (4) ◽  
pp. 497-506 ◽  
Author(s):  
Khamis A. Al-Karawi
Keyword(s):  
Speech Processing ◽  
Speaker Recognition ◽  
System Performance ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Space ◽  
Signal To Noise ◽  
Verification Systems ◽  
Noisy Condition

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.

Construction of Visual Features of Indonesian Digital Poetry

2017 ◽  
Vol 3 (5) ◽  
pp. 1
Author(s):  
Johan Mahyudi ◽  
Djoko Saryono ◽  
Wahyudi Siswanto ◽  
Yuni Pratiwi
Keyword(s):  
Computer Technology ◽  
Visual Art ◽  
Feature Space ◽  
Digital Art ◽  
Visual Features ◽  
Technology Application ◽  
Digital Poetry ◽  
Textual Space ◽  
Neutral Space ◽  
Short Time

In short time, Indonesian digital poetry attracts its audience through a series of visualization features of the digital art. This research uses a short segment analysis on Indonesian videography digital poetry to demonstrate the existence of visual conglomeration practices through the creation of objects, features, a feature of space, measuring distance in feature space, and dimension reduction. These five approaches are proposed by Manovich (2014) in ​​grouping millions of visual artworks based on simple criteria. Of the three common objects are found, Indonesian animators, prefer individuals and texts as the main impression. The movement features are found in cinematic poetry and its rely depend on kinetic texts. Meanwhile, non-movement features can be found in the form of human imitation or part of them, portraits, silhouettes, and comics. Indonesian digital poetry of space features in form of textual space is prioritizing on the kinetics text, the space of time is prioritizing the presentation of objects association of words are spoken, the neutral space is prioritizing the use of computer technology application. The grouping of visual art composition is based on two criteria: the technique of creating and artistic impressions. The dimensional reducing is prominently practiced by Afrizal Malna.

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