Facial expression is one of the most significant elements which can tell us about the mental state of any person. A human can convey approximately 55% of information nonverbally and the remaining almost 45% through verbal communication. Automatic facial expression recognition is presently one of the most difficult tasks in the computer science field. Applications of facial expression recognition (FER) are not just limited to understanding human behavior and monitoring person’s mood and the mental state of humans. It is also penetrating into other fields such as criminology, holographic, smart healthcare systems, security systems, education, robotics, entertainment, and stress detection. Currently, facial expressions are playing an important role in medical sciences, particularly helping the patients with bipolar disease, whose mood changes very frequently. In this study, an algorithm, automated framework for facial detection using a convolutional neural network (FD-CNN) is proposed with four convolution layers and two hidden layers to improve accuracy. An extended Cohn-Kanade (CK+) dataset is used that includes facial images of different males and females with expressions such as anger, fear, disgust, contempt, neutral, happy, sad, and surprise. In this study, FD-CNN is performed in three major steps that include preprocessing, feature extraction, and classification. By using this proposed method, an accuracy of 94% is obtained in FER. In order to validate the proposed algorithm, K-fold cross-validation is performed. After validation, sensitivity and specificity are calculated which are 94.02% and 99.14%, respectively. Furthermore, the f1 score, recall, and precision are calculated to validate the quality of the model which is 84.07%, 78.22%, and 94.09%, respectively.

In this work, the spectral properties of distributed Bragg reflector-based photonic crystal (DBR-PhC) structures were studied for the near-infrared (NIR) range. Different structural properties were varied to study their effect on the quality of the stopband and the appearance of the resonant dips in the reflection spectra of the DBR-PhC structure. The investigated structural features included the depth of PhC holes, hole radius, and number of PhC elements in the DBR structure. The 11-layered DBR structure was designed with a 2.4/1.4 refractive index contrast of alternating layers. The study aimed to achieve optical filtering properties in the DBR-PhC structure, to simplify the structural complexity of Fabry-Pérot filters by eliminating the FP cavity and upper-DBR mirror. The proposed DBR-PhC device can be used in different optical filtering and sensing applications.

An analytical model to predict the surface roughness for the plasma-enhanced chemical vapor deposition (PECVD) process over a large range of temperature values is still nonexistent. By using an existing prediction model, the surface roughness can directly be calculated instead of repeating the experimental processes, which can largely save time and resources. This research work focuses on the investigation and analytical modeling of surface roughness of SiO₂ deposition using the PECVD process for almost the whole range of operating temperatures, i.e., 80 to 450 °C. The proposed model is based on experimental data of surface roughness against different temperature conditions in the PECVD process measured using atomic force microscopy (AFM). The quality of these SiO₂ layers was studied against an isolation layer in a microelectromechanical system (MEMS) for light steering applications. The analytical model employs different mathematical approaches such as linear and cubic regressions over the measured values to develop a prediction model for the whole operating temperature range of the PECVD process. The proposed prediction model is validated by calculating the percent match of the analytical model with experimental data for different temperature ranges, counting the correlations and error bars.

The design of an intracavity spectroscopy based two-mode biomedical sensor involves a thorough investigation of the system. For this purpose, the individual components that are present in the system must be examined. This work describes the principle of two very important gadgets, namely the Fibre Bragg Grating (FBG), and the tunable coupler. We adhere to a Petri network scheme to model and analyze the performance of the FBG, and the results mirror strikingly low difference in the values of Bragg Wavelength during its ascending and descending operational principle, thereby maintaining the accuracy of the sensor’s results. Next, a pseudocode is developed and implemented for the investigation of the optical coupler in LabView. The values of its maximum output power are determined, and the coupling ratio for various values of controlling voltage is determined at three different wavelengths. The hysteresis results mirror an extremely low difference between the forward and reverse values in the results. Both the results of the FBG and the coupler are thereby extremely reliable to use them in the laser system, as evident from the respective intensity noise outcomes, as well as the experimentation on substances of interest (Dichloro Methane and Propofol).

This research work is targeting to demonstrate that micromirror arrays can also be fabricated successfully on flexible substrates—not only on glass substrates. The paper reports on the technological data of a successful process on a flexible substrate for curvilinear surfaces. Polyethylene naphthalate substrate was selected for its low cost, strong resistance against chemicals which are used in fabrication process and sufficient optical transmission for the visible and near infrared spectrum. Finally, electrostatic actuation of the fabricated micromirror structures is proven experimentally.