Automated Identification and Measurement of Objects via Populations of Medial Primitives, with Application to Real Time 3D Echocardiography

1999 ◽  
pp. 84-97 ◽  
Author(s):  
George D. Stetten ◽  
Stephen M. Pizer
Keyword(s):  
Real Time ◽  
3D Echocardiography ◽  
Automated Identification

Relation of Left Atrial Maximal Volume Measured by Real-Time 3D Echocardiography to Demographic, Clinical, and Doppler Variables

2008 ◽  
Vol 101 (9) ◽  
pp. 1347-1352 ◽  
Author(s):  
Stefano De Castro ◽  
Stefano Caselli ◽  
Emanuele Di Angelantonio ◽  
Sara Del Colle ◽  
Francesca Mirabelli ◽  
...  
Keyword(s):  
Real Time ◽  
Left Atrial ◽  
3D Echocardiography ◽  
Maximal Volume

Assessment of the Tricuspid Valve Morphology by Transthoracic Real-Time-3D-Echocardiography

2005 ◽  
Vol 22 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Renate Schnabel ◽  
Alexander V. Khaw ◽  
Ralph Stephan von Bardeleben ◽  
Christina Strasser ◽  
Thorsten Kramm ◽  
...  
Keyword(s):  
Real Time ◽  
Tricuspid Valve ◽  
3D Echocardiography ◽  
Valve Morphology

scholarly journals False Positive RFID Detection Using Classification Models

2019 ◽  
Vol 9 (6) ◽  
pp. 1154 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Bohan Yoon ◽  
Jongtae Rhee
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Time ◽  
Outlier Detection ◽  
Radio Frequency Identification ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Automated Identification ◽  
Rfid Data

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

Oral Everolimus for Treatment of a Giant Left Ventricular Rhabdomyoma in a Neonate-Rapid Tumor Regression Documented by Real Time 3D Echocardiography

2015 ◽  
Vol 32 (12) ◽  
pp. 1876-1879 ◽  
Author(s):  
Robert Wagner ◽  
Frank Thomas Riede ◽  
Hiroshi Seki ◽  
Frauke Hornemann ◽  
Steffen Syrbe ◽  
...  
Keyword(s):  
Real Time ◽  
Tumor Regression ◽  
Left Ventricular ◽  
3D Echocardiography
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