Machine learning (ML) has emerged as a pivotal force in enhancing the capabilities of sensing technologies across a broad spectrum of applications, from environmental monitoring and biosensing to agriculture, industrial automation, and so on. This study explores integrating ML techniques with photonic crystal fiber (PCF)-based plasmonic sensing techniques to elevate sensor performance. The PCF has two open channels to augment mode coupling, effectively reducing the gap between the analyte channel and core. Moreover, a thin layer of gold within the open channels of the PCF initiates efficient plasmon generation. The results demonstrate a maximum wavelength sensitivity of 9000 nm/refractive index unit ( RIU ), which can detect a wide range of analyte refractive index (RI) values from 1.33 to 1.40. The sensor exhibits the maximum amplitude sensitivity of 490.41 RIU −1 . It also boasts a resolution of 1.11 × 10 −5 RIU and the maximum figure-of-merit (FOM) achieved is 138.04 RIU −1 at an analyte RI of 1.39. Furthermore, this research introduces a method utilizing generative adversarial networks (GAN) to expand training data for an artificial neural network (ANN) model. This approach substantially improves the prediction of confinement loss across various analytes and wavelengths in a unique geometric configuration. The sensor’s versatility makes it ideal for various applications, including chemical sensing and medical diagnostics. 

Plasmonic sensor using generative adversarial networks integration

Photonic crystal fiber-based SPR biosensor coated with Ag-TiO2 and Au-TiO2 for the detection of skin cancer: a comparison

Plasmonic sensors utilizing surface plasmon resonance (SPR) have emerged as powerful tools for sensitive and label-free detection across a wide range of applications. This study introduces a new dual-core silver-coated plasmonic sensor designed to significantly enhance sensitivity and resolution, making it particularly effective for precise analyte detection in complex environments. A key innovation of this sensor lies in its dual-core architecture, which achieves the highest wavelength sensitivity reported at 30,000 nm/RIU and resolution of 

Dual-Core Silver-Coated Plasmonic Sensor Modelling with Machine Learning

In this paper, a kind of D-type photonic crystal fiber (PCF) sensor with an ultra-wide detection range based on micro-opening gold film coating is proposed. This sensor allows for the sensing detection of the refractive index (RI) of the analyte ranging from 1.30 to 1.42. However, the sensor coated with a micro-opening gold film only achieves an average wavelength sensitivity of 1489 nm/RIU in the x-polarization direction. To improve the performance of the sensor, an attempt was made to replace the micro-opening gold film with MoO2 nanofilm. After simulation calculation, it was found that the RI detection range of the sensor using MoO2 nanofilm became 1.33–1.39. Excitingly, the average wavelength sensitivity in the x-polarized direction reaches 17, 178 nm/RIU, which is 11.5 times better than the original sensor. This implies that the sensor is more sensitive to changes in the RI and can provide more accurate sensing and detection results. It has been demonstrated that the performance of a D-type PCF sensor can be significantly improved by using MoO2 nanofilm. This improvement helps to expand the application domain of sensors and enhance the accuracy of sensing detection. We believe that this research result has important implications for the development of fiber sensor technologies. Ultra-wide refractive index detection range fiber optic sensor Ultra-wide refractive index detection range fiber optic sensor

Ultra-wide measurement range D-shaped photonic crystal fiber sensor based on surface plasmon resonance

Design and Investigation of D-Shaped Photonic Crystal Fiber-Based Plasmonic Biosensor

In this paper, a relatively simple and ultra-sensitive Photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor is proposed for detecting different analyte refractive indices (RIs) ranging from 1.33 to 1.43 over a wide range of wavelength spectrum spanning <inline-formula> <tex-math notation="LaTeX">$0.55~\mu $ </tex-math></inline-formula>m to <inline-formula> <tex-math notation="LaTeX">$3.50~\mu $ </tex-math></inline-formula>m. The comprehensive finite-element simulations indicate that it is possible to achieve remarkable sensing performances such as wavelength sensitivity (WS) and figure of merit (FOM) as high as 123,000 nm/RIU and 683 RIU1, respectively, and extremely low value of wavelength resolution (WR) about <inline-formula> <tex-math notation="LaTeX">$8.13\times 10^{-7}$ </tex-math></inline-formula> RIU. A novel artificial neural network (ANN) model is proposed which helps to accurately predict the RIs by carefully examining the simulation data. The mean square error (MSE) and prediction accuracy (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) values for the ANN model are found about 0.0097 and 0.9987, respectively, indicating the high prediction capability of the proposed ANN model. Due to its exceptional sensitivity and precise detection capabilities, the proposed sensor has the potential to serve as a viable option for sensing analyte RI. Additionally, the sensor could be utilized for identifying cancerous cells and detecting urinary tract infections in humans.

Ultra-Sensitive Visible-IR Range Fiber Based Plasmonic Sensor: A Finite-Element Analysis and Deep Learning Approach for RI Prediction

Application of Chitin Nanofiber as A Nitrogen Source Improves Growth and Yield of BRRI Dhan28

The blind or visually impaired students (VIS) of Bangladesh depend on scribes for appearing in examinations. VIS normally face many difficulties to find out suitable scribe, who is supposed to write the answer in the answer scriptsafter hearing from the VIS in the examination hall. Scribes need to be present throughout the examination for writing the answer scripts. Though there are several approaches were proposed for appearing in the examinations independently without taking help from scribe, very few methods are proposed for Bengali. As an alternative way Bangla Braille Key is proposed with audio feedback of typed words using braille syntax. The Key has six pushbuttons arranged in a 3*2 matrix similar to braille syntax which is interfaced with a computer by using Arduino Uno and CoolTerm software. Theaudio feedback system enables the examinees to hear what they are typing. The written braille codes will be converted into the Bengali letters and automatically be saved into predefined format during the initial setup by examiner or scribe.

Design and Implementation of Bangla Braille Key for Blind Examinees of Bangladesh

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Ultra-Sensitive Visible-IR Range Fiber Based Plasmonic Sensor: A Finite-Element Analysis and Deep Learning Approach for RI Prediction

Application of Chitin Nanofiber as A Nitrogen Source Improves Growth and Yield of BRRI Dhan28

Design and Implementation of Bangla Braille Key for Blind Examinees of Bangladesh