A Procedure for Validating Impedance Parameters of HF/UHF RFID Transponder Antennas

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

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RF signal fluctuation based passive UHF RFID tag localization

2021 International Conference on Communications, Information System and Computer Engineering (CISCE) ◽

10.1109/cisce52179.2021.9445992 ◽

2021 ◽

Author(s):

Yang Zhao ◽

Xianhui Liu ◽

Lingyun Li ◽

Xiaoxia Zhao

Keyword(s):

Uhf Rfid ◽

Rfid Tag ◽

Passive Uhf Rfid ◽

Tag Localization ◽

Signal Fluctuation ◽

Rf Signal

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Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

Sensors ◽

10.3390/s21124129 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4129

Author(s):

Sisay Mebre Abie ◽

Ørjan Grøttem Martinsen ◽

Bjørg Egelandsdal ◽

Jie Hou ◽

Frøydis Bjerke ◽

...

Keyword(s):

Phase Angle ◽

Short Term Memory ◽

Cell Damage ◽

Electrical Impedance Spectroscopy ◽

Electric Impedance ◽

Freezing And Thawing ◽

Short Term ◽

Long Short Term Memory ◽

Impedance Parameters ◽

Deep Learning Model

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

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