Automatic identification technology — Application of two-dimensional code

Author(s):  
Tao Sun ◽  
Di Zhou
2014 ◽  
Vol 513-517 ◽  
pp. 620-623
Author(s):  
Ji Kun Wang ◽  
Xing Zhi Hu ◽  
Xue Zhe Li ◽  
Fei Ji Ding

As the application of the information network technology continuously extended, library management service platform is experiencing a shift to the information service system. In the library information management field, researchers are seeking a library identification system which is more convenient and comprehensive. Under the circumstance of the information technology conditions, Radio Frequency Identification (RFID) technology completes the automatic identification of the electronic identification tags--the two-dimensional code. The RFID technology is a contactless scanning recognition technology. And the core technology of RFID can be described as follows: using two-dimensional code as the filter identification tool according to backup data of the book management.


2021 ◽  
Vol 27 (6) ◽  
pp. 73-96
Author(s):  
Haider A Abass ◽  
Husain Khalaf Jarallah

Pushover analysis is an efficient method for the seismic evaluation of buildings under severe earthquakes. This paper aims to develop and verify the pushover analysis methodology for reinforced concrete frames. This technique depends on a nonlinear representation of the structure by using SAP2000 software. The properties of plastic hinges will be defined by generating the moment-curvature analysis for all the frame sections (beams and columns). The verification of the technique above was compared with the previous study for two-dimensional frames (4-and 7-story frames). The former study leaned on automatic identification of positive and negative moments, where the concrete sections and steel reinforcement quantities the source of these moments. The comparison of the results between the two methodologies was carried out in terms of capacity curves. The results of the conducted comparison highlighted essential points. It was included the potential differences between default and user-defined hinge properties in modeling. The effect of the plastic hinge length and the transverse of shear reinforcement on the capacity curves was also observed. Accordingly, it can be considered that the current methodology in this paper more logistic in the representation of two and three-dimensional structures.  


Author(s):  
Susan A. Vowels

RFID, also known as radio frequency identification, is a form of Auto ID (automatic identification). Auto ID is defined as “the identification of an object with minimal human interaction” (Puckett, 1998). Auto ID has been in existence for some time; in fact, the bar code, the most ubiquitous form of Auto ID, celebrated its 30th year in commercial use in 2004 (Albright, 2004). Barcodes identify items through the encoding of data in various sized bars using a variety of symbologies, or coding methodologies. The most familiar type of barcode is the UPC, or universal product code, which provides manufacturer and product identification. While barcodes have proven to be very useful, and indeed, have become an accepted part of product usage and identity, there are limitations with the technology. Barcode scanners must have line of sight in order to read barcode labels. Label information can be easily compromised by dirt, dust, or rips. Barcodes take up a considerable footprint on product labels. Even the newer barcode symbologies, such as 2D, or two-dimensional, which can store a significant amount of data in a very small space (“Two dimensional…,” 2005) remain problematic. RFID proponents argue that limitations of barcodes are overcome through the use of RFID labeling to identify objects.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Randy M. Sterbentz ◽  
Kristine L. Haley ◽  
Joshua O. Island

AbstractMachine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.


Author(s):  
Susan A. Vowels

RFID, also known as radio frequency identification, is a form of Auto ID (automatic identification). Auto ID is defined as “the identification of an object with minimal human interaction” (Puckett, 1998). Auto ID has been in existence for some time; in fact, the bar code, the most ubiquitous form of Auto ID, celebrated its 30th year in commercial use in 2004 (Albright, 2004). Barcodes identify items through the encoding of data in various sized bars using a variety of symbologies, or coding methodologies. The most familiar type of barcode is the UPC, or universal product code, which provides manufacturer and product identification. While barcodes have proven to be very useful, and indeed, have become an accepted part of product usage and identity, there are limitations with the technology. Barcode scanners must have line of sight in order to read barcode labels. Label information can be easily compromised by dirt, dust, or rips. Barcodes take up a considerable footprint on product labels. Even the newer barcode symbologies, such as 2D, or two-dimensional, which can store a significant amount of data in a very small space (“Two dimensional…,” 2005) remain problematic. RFID proponents argue that limitations of barcodes are overcome through the use of RFID labeling to identify objects.


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