scholarly journals Characterization of the Identification Process in RFID Systems

10.5772/7987 ◽  
2010 ◽  
Author(s):  
J. Vales-Alonso ◽  
M. V. ◽  
Bueno Delgado ◽  
E. Egea-Lpez ◽  
J.J. Alcaraz-Espn ◽  
...  
Keyword(s):  
Identification Process ◽  
Rfid Systems

scholarly journals Experimental Investigation of an Electrical Model for Sodium–Nickel Chloride Batteries

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2652
Author(s):  
Roberto Di Rienzo ◽  
Gianluca Simonte ◽  
Ian Biagioni ◽  
Federico Baronti ◽  
Roberto Roncella ◽  
...  
Keyword(s):  
Nickel Chloride ◽  
Battery Management ◽  
Experimental Characterization ◽  
Identification Process ◽  
Battery Model ◽  
Battery Management Systems ◽  
Set Up ◽  
Battery Technology ◽  
Validation Tests

This work describes the experimental characterization of a commercial sodium–nickel chloride battery and the investigation on a state-of-the-art model that represents the battery behavior. This battery technology is considered very promising but it has not fully been exploited yet. Besides improvements on the technological side, accurate models of the battery should be found to allow the realization of Battery Management Systems with advanced functions. This achievement may extend the battery exploitation to its best. The paper describes the experimental set-up and the model parameter identification process, and discusses the identified parameters and the model validation tests. The comparison between model simulations and experiments shows that the model is rather accurate for low-current rates, but it loses accuracy and it is not able to reproduce with fidelity the battery behavior at low states of charge or at high current rates. Further research efforts and refinements of the model are necessary to make available a sodium–nickel chloride battery model accurate in any operating condition.

A Fast and Accurate Tag Estimation Algorithm for Dynamic Framed-Slotted ALOHA Based on M/D/1 Queue in RFID Systems

2013 ◽  
Vol 340 ◽  
pp. 732-736
Author(s):  
Hui Song
Keyword(s):  
Estimation Algorithm ◽  
Slotted Aloha ◽  
Tag Identification ◽  
Current Frame ◽  
Frame Size ◽  
Identification Process ◽  
Time Consumption ◽  
Rfid Systems ◽  
Lower Complexity ◽  
Framed Slotted Aloha

A novel tag estimation algorithm named DFSA-MD1 is proposed, and applied to the field of tag identification that aims at decreasing tag collision number in RFID systems. The proposed algorithm regarded the tag identification process of a frame of the dynamic frame slotted ALOHA algorithm (DFSA) as an M/D/1 queuing system. The reader can estimate tag number of next frame from success slots number and collision slot number in current frame and current frame size according to M/D/1 queuing model.The experimental results show that DFSA-MD1 algorithm requires less time consumption and lower complexity for tag identificaiton and achieves the best performance of the DFSA tags identification protocols.

scholarly journals EXPERIMENTAL CHARACTERIZATION OF RFID SYSTEMS FOR PROCESS CONTROL IN INDUSTRIAL MARBLE MACHINES

2010 ◽  
pp. 70-79
Author(s):  
Sergio Saponara ◽  
Fabrizio Iacopetti ◽  
Luca Fanucci ◽  
Bruno Neri
Keyword(s):  
Process Control ◽  
Experimental Test ◽  
Ad Hoc ◽  
Wireless Systems ◽  
Cutting Process ◽  
Experimental Characterization ◽  
Rfid Systems ◽  
Working Environments ◽  
Marble Industry

The paper presents an experimental characterization of wireless systems, specifically RFID technologies, applied to polishing/cutting process control in the marble industry. The application of RFID systems has the final aim of allowing the automatic and contact-less detection of the presence of a marble slab in different points of a marble machine, outside and/or inside, trying to overcome some limitations of the currently used proximity detectors. Slab detection is needed for the process control in order to properly activate the abrasive or cutting heads of the machine. Four RFID systems at 125 kHz, 13.56 MHz, 868 MHz and 2.45 GHz have been tested in different set-ups representative of those found in marble machines. Starting from commercially available tags, readers and antennas, ad-hoc developed or customized hardware and/or software have been used for the experimental test campaign, that has been carried out also considering dirty and wet working environments representative of those found in real applications.

scholarly journals Towards an Efficient Identification Process for Large-Scale RFID Systems

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2350 ◽  
Author(s):  
Leonardo Sanchez ◽  
Victor Ramos
Keyword(s):  
Radio Frequency Identification ◽  
Large Scale ◽  
Traditional Approach ◽  
Identification Process ◽  
Advantages And Disadvantages ◽  
Rfid Systems ◽  
Distributed Approach ◽  
Implementation Costs ◽  
Frequency Identification ◽  
Time Required

Radio Frequency Identification (RFID) is one of the most widely used wireless communications technologies nowadays. Among the numerous processes executed within an RFID system, the identification processis the most important one. There have been several proposals to efficiently execute such a mechanism, which are based on the use of an RFID identification method. Besides, one of the most studied scenarios comprises one reader and a set of RFID tags, which we call the centralized approach. Recent work shows that executing the identification process in a distributed or parallel way may be of great benefit for applications with high requirements on time and resources usage, i.e., applications where the time required to execute the identification process needs to be low. In this paper, we focus is on large RFID systems and compare two identification mechanisms, one based on the centralized approach and the other based on the distributed approach. Our aim is to find the advantages and disadvantages of each approach for general RFID scenarios. We observe that the distributed approach is very promising compared to the traditional approach since considerable improvements are found in identification delay, and also the implementation costs would be highly reduced.

scholarly journals Automated Microfossil Identification and Segmentation Using a Deep Learning Approach

2019 ◽  
Author(s):  
L.E Carvalho ◽  
G. Fauth ◽  
S. Baecker Fauth ◽  
G. Krahl ◽  
A. C. Moreira ◽  
...  
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
Computational Analysis ◽  
Extraction Process ◽  
Oil Extraction ◽  
High Rate ◽  
Learning Approach ◽  
3D Images ◽  
Identification Process

AbstractThe applicability of computational analysis to paleontological images ranges from the study of the animals, plants and evolution of microorganisms to the simulation of the habitat of living beings of a given epoch. It also can be applied in several niches, such as oil exploration, where there are several factors to be analyzed in order to minimize the expenses related to the oil extraction process. One factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, extraction of samples for petrophysical components evaluation, the correlation with logs of other drilling wells and so on. In the samples extraction part the Computed Tomography (CT) is of importance because it preserves the sample and makes it available for several analyzes. Based on 3D images generated by CT, several analyzes and simulations can be performed and processes, currently performed manually and exhaustively, can be automated. In this work we propose and validate a method for fully automated microfossil identification and extraction. A pipeline is proposed that begins in the scanning process and ends in an identification process. For the identification a Deep Learning approach was developed, which resulted in a high rate of correct microfossil identification (98% of Intersection Over Union). The validation was performed both through an automated quantitative analysis based upon ground truths generated by specialists in the micropaleontology field and visual inspection by these specialists. We also present the first fully annotated MicroCT-acquired publicly available microfossils dataset.

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