Ali Raza Barket
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ali Raza Barket has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Weidong Hu
Beijing Institute of Technology
2 shared publications Bing Wang
Beijing Institute of Technology
1 shared publication- Gulab Shah
College of Electronics and Information Engineering NUAA. Nanjing 210016, People's Republic of China
1 shared publication Jabir Shabbir Malik
Beijing Institute of Technology
1 shared publication- Qasim Ali
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
1 shared publication Waseem Shahzad
Beijing Institute of Technology
1 shared publication
Latest Publications
- Open Access
- 18 January 2024
- Published by MDPI AG in Journal of Sensor and Actuator Networks
- Vol. 13 (1) , 8
Varactor-Based Tunable Sensor for Dielectric Measurements of Solid and Liquid Materials
In this article, a tunable RF sensor is presented for the measurement of dielectric materials (liquids and solids) based on a metamaterial resonator. The proposed novel configuration sensor has a microstrip line-loaded metamaterial resonator with tunable characteristics by utilizing a single varactor diode in the series of the resonator. CST Microwave studio is employed for 3D simulations of the tunable sensor, and the desired performance is attained by optimizing various structural parameters to enhance the transmission coefficient ( magnitude) notch depth performance. The proposed RF sensor can be tuned in L and S-bands using the varactor diode biasing voltage range of 0–20 V. To validate the performance of the sensor, the proposed design has been simulated, fabricated, and tested for the dielectric characterization of different solid and liquid materials. Material testing is performed in the bandwidth of 1354 MHz by incorporating a single metamaterial resonator-based sensor. Agilent’s Network Analyzer is used for measuring the S-parameters of the proposed sensor topology under loaded and unloaded conditions. Simulated and measured S-parameter results correspond substantially in the 1.79 to 3.15 GHz frequency band during the testing of the fabricated sensor. This novel tunable resonator design has various applications in modulators, phase shifters, and filters as well as in biosensors for liquid materials.- 18 January 2024
- Published by MDPI AG in Journal of Sensor and Actuator Networks
- Vol. 13 (1) , 8
Open AccessVaractor-Based Tunable Sensor for Dielectric Measurements of Solid and Liquid Materials
In this article, a tunable RF sensor is presented for the measurement of dielectric materials (liquids and solids) based on a metamaterial resonator. The proposed novel configuration sensor has a microstrip line-loaded metamaterial resonator with tunable characteristics by utilizing a single varactor diode in the series of the resonator. CST Microwave studio is employed for 3D simulations of the tunable sensor, and the desired performance is attained by optimizing various structural parameters to enhance the transmission coefficient ( magnitude) notch depth performance. The proposed RF sensor can be tuned in L and S-bands using the varactor diode biasing voltage range of 0–20 V. To validate the performance of the sensor, the proposed design has been simulated, fabricated, and tested for the dielectric characterization of different solid and liquid materials. Material testing is performed in the bandwidth of 1354 MHz by incorporating a single metamaterial resonator-based sensor. Agilent’s Network Analyzer is used for measuring the S-parameters of the proposed sensor topology under loaded and unloaded conditions. Simulated and measured S-parameter results correspond substantially in the 1.79 to 3.15 GHz frequency band during the testing of the fabricated sensor. This novel tunable resonator design has various applications in modulators, phase shifters, and filters as well as in biosensors for liquid materials. - 13 June 2022
- Published by Optica Publishing Group in Optics Express
- Vol. 30 (13) , 23398-23416
Selection criteria of image reconstruction algorithms for terahertz short-range imaging applications
Terahertz (THz) imaging has been regarded as cutting-edge technology in a wide range of applications due to its ability to penetrate through opaque materials, non-invasive nature, and its increased bandwidth capacity. Recently, THz imaging has been extensively researched in security, driver assistance technology, non-destructive testing, and medical applications. The objective of this review is to summarize the selection criteria for current state-of-the-art THz image reconstruction algorithms developed for short-range imaging applications over the last two decades. Moreover, we summarize the selected algorithms’ performance and their implementation process. This study provides an in-depth understanding of the fundamentals of image reconstruction algorithms related to THz short-range imaging and future aspects of algorithm processing and selection.
- 13 June 2022
- Published by Optica Publishing Group in Optics Express
- Vol. 30 (13) , 23398-23416
Selection criteria of image reconstruction algorithms for terahertz short-range imaging applications
Terahertz (THz) imaging has been regarded as cutting-edge technology in a wide range of applications due to its ability to penetrate through opaque materials, non-invasive nature, and its increased bandwidth capacity. Recently, THz imaging has been extensively researched in security, driver assistance technology, non-destructive testing, and medical applications. The objective of this review is to summarize the selection criteria for current state-of-the-art THz image reconstruction algorithms developed for short-range imaging applications over the last two decades. Moreover, we summarize the selected algorithms’ performance and their implementation process. This study provides an in-depth understanding of the fundamentals of image reconstruction algorithms related to THz short-range imaging and future aspects of algorithm processing and selection.