Frame Work on Automatic RFID Tag Detection

Kumar, Ailla Goutham (2015) Frame Work on Automatic RFID Tag Detection. MTech thesis.



RFID technology is the well developed technology. Which is having so many applications in real life. But along with large number of applications it is also having some disadvantages. Like this technology fails with cartons containing metal, water or any liquid content. This is due to absorption of radio waves by liquid content. Along with this there is one more problem which is low detection rate. RFID Detection is nothing but successful identification of rfid tag. Using high signal strength reader we can increase the detection rate but it is up to small level. For better improvement we are going for intelligent method. [1] Detecting of RFID transponder with help of RFID reader is most significant in the radio frequency identification systems [2]. For development of RFID technology successful tag detection is compulsory. The major considerations effecting the successful tag detection by RFID interrogator contain transponder position and relative position of the interrogator and reader field area [1][27]. In this project we examine the features of tag identification on the two dimensional plane by an experiment approach depending on the received signal strength from the tag. We perform a process for calculating identification linked directly to the strength of transponder with help of artificial neural networks and Support vector machine. The main advantage of this method is to prevent time consumption and decrease the price by immediate detection of transponder [3] [4] [5] [1]. No human intervention is required [2]. Many experiments revealed that the method can forecast the transponder recognition with an accuracy of 94% for different reader antenna positions. This method is mainly helpful in finding out the best transponder identification changing feature conditions.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Radio Frequency Identification Technology,Support Vector Machine, Artificial Neural Networks, Cross Validation, K Fold Algorithm, Data Acquisition,
Subjects:Engineering and Technology > Electronics and Communication Engineering > Wireless Communications
Engineering and Technology > Electronics and Communication Engineering > Artificial Neural Networks
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:7002
Deposited By:Mr. Sanat Kumar Behera
Deposited On:06 Mar 2016 16:20
Last Modified:06 Mar 2016 16:20
Supervisor(s):Ari, S

Repository Staff Only: item control page