Erfan Dejband

This paper proposes a novel approach to enhance the multichannel fiber optic sensing systems by integrating an Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) to accurately predict sensor responses despite signals overlapping and crosstalk between sensors. The IFFT-DNN leverages both frequency and time domain information, enabling a comprehensive feature extraction which enhances the prediction accuracy and reliability performance. To investigate the IFFT-DNN’s performance, we propose a multichannel water level sensing system based on Free Space Optics (FSO) to measure the water level at multiple points in remote areas. The experimental results demonstrate the system’s high precision, with a Mean Absolute Error (MAE) of 0.07 cm, even in complex conditions. Hence, this system provides a cost-effective and reliable remote water level sensing solution, highlighting its practical applicability in various industrial settings.

In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.

This paper proposes a novel sensing system based on a tunable delay line interferometer. The tunable delay line interferometer has been used to interpret strain, bringing us high accuracy as well as tunability. The shifted wavelength of the fiber Bragg grating (FBG) sensor caused by the applied strain can be visualized by an optical power meter (OPM) instead of an optical spectrum analyzer (OSA) by converting it to a power change using a tunable delay line interferometer (TDI). Different free spectral ranges (FSRs) are assigned to the TDI to investigate the accuracy and operation range of the proposed system. Thus, we achieve high accuracy and sensitivity by adjusting the FSR to 0.47 nm. Experimental results show that the maximum output power variation corresponding to a strain of 10 με is about 0.9 dB when the FSR is set to 0.47 nm. The proposed system is also cost-effective regarding the equipment utilized for interrogation: a tunable delay line interferometer and an optical power meter.

In this paper, an antenna-coupled bias-less pulsed GaN-based photoconductive THz emitter is designed and investigated numerically. The proposed THz emitter is composed of an asymmetric metal-semiconductor-metal structure, which is enhanced by using both plasmonic and piezotronic effects. The top metal electrode in our design consists of nano-slits and nano-gratings, so that the incident wave coupling to the active region, and the output THz radiation of the photoconductor are improved. Benefiting from both the piezoelectric property of GaN, and the designed metal plasmonic structure on the conventional pulsed GaN-based photoconductive THz emitter, we show that the output current can be enhanced by the factor of about 4.37 (from 40μA to 175μA), while the output bandwidth has been increased about 3.8%. The proposed enhanced plasmonic piezotronic THz emitter can lead to emerging a new generation of efficient and tunable THz photoconductive emitters.