A novel combination of neural networks and FFT for frequency estimation of SAW resonators’ responses
M. M. Jazini, M. Khoshakhlagh, and N. Masoumi, in Proc. 2019 5th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pp. 1-5, Dec. 2019.
In this paper, we introduce an innovative method that combines neural networks with Fast Fourier Transform (FFT) to improve the accuracy of frequency estimation in Surface Acoustic Wave (SAW) resonator sensors. This approach, known as FFTNN, is particularly robust against noise, making it ideal for challenging environments and long channels. Through experimental validation, we demonstrated that our method significantly enhances the precision of frequency detection, offering potential advancements in various applications requiring high-resolution measurement.
Md A. Momin, M. Jazini, M. J. Rahman, and T. Mieno, Analysis & Sensing, e202400062, 2024.
Our study explores innovative foot pressure sensors using carbon nanotube (CNT)-coated cotton fibers, designed to detect and analyze human activities. These sensors demonstrate sensitivity, repeatability, and durability, confirmed through Python simulations and rigorous testing. They respond nonlinearly to pressure, influenced by fiber contact area, and withstand long-term outdoor exposure while retaining conductivity. The sensors can monitor activities like walking, running, and even muscle movements, showing potential for health monitoring and artificial voice synthesis. Using the MRMR algorithm, we also achieve activity recognition, and a piezoelectric sensor within the system estimates harvested power, advancing self-powered wearable technology for healthcare and assistive applications.
A new frequency detection method based on FFT in the application of SAW resonator sensor
M. M. Jazini, M. Khoshakhlagh, and N. Masoumi, in Proc. Iranian Conference on Electrical Engineering (ICEE), pp. 232-237, May 2018.
Our research highlights the advantages of passive Surface Acoustic Wave (SAW) sensors in wireless, battery-free measurement systems, powered by pulse signals from an interrogation unit. The SAW sensor emits a noisy, damped sine wave in response, resonating at a frequency that varies based on sensing parameters. The challenge lies in accurately detecting this frequency to measure the parameter precisely. While traditional methods like FFT, zero crossing, and peak detection are commonly used, they suffer from limitations in accuracy and speed. To address this, we developed a new frequency detection approach based on FFT, which improves performance and reduces the need for high ADC sampling rates in the interrogation unit. Our method also accurately detects the initial phase and amplitude of the response, offering a robust solution for SAW sensor applications.
Limitations of Fourier Transform Analysis in the Wireless SAW Sensor Systems
M. M. Jazini and N. Masoumi, in Proc. 2019 Sixth Iranian Conference on Radar and Surveillance Systems, Mar. 2020.
This study explores the unique capabilities of Surface Acoustic Wave Resonator (SAWR) sensors, known for their wireless, battery-free operation in measuring temperature, pressure, and torque across various environments. We conduct a theoretical analysis of SAWR measurement using Fast Fourier Transform (FFT) and validate our findings experimentally. The paper also reviews key components of the measurement unit, focusing on the frequency response of SAWR signals processed by an ADC and FFT-capable processor. Our results provide guidance for selecting optimal local oscillator frequencies in RF systems, enhancing measurement accuracy while reducing costs. Unlike previous approaches that chose intermediate frequencies (IF) experimentally, we propose new formulae to define theoretical bounds for the IF frequency region.
