Performance loss of multivariate detection algorithms due to covariance estimation

2009 ◽  
Author(s):  
Charles E. Davidson ◽  
Avishai Ben-David
Keyword(s):  
Covariance Estimation ◽  
Performance Loss ◽  
Detection Algorithms ◽  
Multivariate Detection

Linguistic Competence in Aphasia

2008 ◽  
Vol 17 (3) ◽  
pp. 87-92
Author(s):  
Leonard L. LaPointe
Keyword(s):  
Language Use ◽  
Linguistic Competence ◽  
Careful Study ◽  
Feral Children ◽  
Performance Factors ◽  
Performance Loss ◽  
Linguistic Rules ◽  
Communication Goals ◽  
Aphasia Treatment ◽  
And Performance

Abstract Loss of implicit linguistic competence assumes a loss of linguistic rules, necessary linguistic computations, or representations. In aphasia, the inherent neurological damage is frequently assumed by some to be a loss of implicit linguistic competence that has damaged or wiped out neural centers or pathways that are necessary for maintenance of the language rules and representations needed to communicate. Not everyone agrees with this view of language use in aphasia. The measurement of implicit language competence, although apparently necessary and satisfying for theoretic linguistics, is complexly interwoven with performance factors. Transience, stimulability, and variability in aphasia language use provide evidence for an access deficit model that supports performance loss. Advances in understanding linguistic competence and performance may be informed by careful study of bilingual language acquisition and loss, the language of savants, the language of feral children, and advances in neuroimaging. Social models of aphasia treatment, coupled with an access deficit view of aphasia, can salve our restless minds and allow pursuit of maximum interactive communication goals even without a comfortable explanation of implicit linguistic competence in aphasia.

Structured Robust Covariance Estimation

2015 ◽  
Author(s):  
Ami Wiesel ◽  
Teng Zhang
Keyword(s):  
Covariance Estimation

A nanometric view on performance-loss mechanisms in perovskite/c-Si multi-junction solar cells

2019 ◽  
Author(s):  
Quentin Jeangros ◽  
Christophe Ballif ◽  
Peter Fiala ◽  
Ricardo A.Z. Razera ◽  
Daniel A. Jacobs ◽  
...  
Keyword(s):  
Solar Cells ◽  
Performance Loss ◽  
Loss Mechanisms

Analysis of Induced End-of-Life Failures in SRAM Through Nanoprobing

2018 ◽  
Author(s):  
Oberon Dixon-Luinenburg ◽  
Jordan Fine
Keyword(s):  
End Of Life ◽  
Soft Errors ◽  
Cause Analysis ◽  
New Approach ◽  
Performance Loss ◽  
Root Cause ◽  
Long Period ◽  
Atomic Force ◽  
Stress Resultant ◽  
Voltage Stress

Abstract In this paper, we demonstrate a novel nanoprobing approach to establish cause-and-effect relationships between voltage stress and end-of-life performance loss and failure in SRAM cells. A Hyperion II Atomic Force nanoProber was used to examine degradation for five 6T cells on an Intel 14 nm processor. Ten minutes of asymmetrically applied stress at VDD=2 V was used to simulate a ‘0’ bit state held for a long period, subjecting each pullup and pulldown to either VDS or VGS stress. Resultant degradation caused read and hold margins to be reduced by 20% and 5% respectively for the ‘1’ state and 5% and 2% respectively for the ‘0’ state. ION was also reduced, for pulldown and pullup respectively, by 4.5% and 5.4% following VGS stress and 2.6% and 33.8% following VDS stress. Negative read margin failures, soft errors, and read time failures all become more prevalent with these aging symptoms whereas write stability is improved. This new approach enables highly specific root cause analysis and failure prediction for end-of-life in functional on-product SRAM.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

Survey on Fast CFAR Detection Algorithms for SAR Image Targets

2012 ◽  
Vol 38 (12) ◽  
pp. 1885 ◽  
Author(s):  
Ming-Bo ZHAO ◽  
Jun HE ◽  
Qiang FU
Keyword(s):  
Sar Image ◽  
Cfar Detection ◽  
Detection Algorithms
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