Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

In the era of Industry 4.0, remote monitoring and controlling appliance/equipment at home, institute, or industry from a long distance with low power consumption remains challenging. At present, some smart phones are being actively used to control appliances at home or institute using Internet of Things (IoT) systems. This paper presents a novel smart automation system using long range (LoRa) technology. The proposed LoRa based system consists of wireless communication system and different types of sensors, operated by a smart phone application and powered by a low-power battery, with an operating range of 3–12 km distance. The system established a connection between an android phone and a microprocessor (ESP32) through Wi-Fi at the sender end. The ESP32 module was connected to a LoRa module. At the receiver end, an ESP32 module and LoRa module without Wi-Fi was employed. Wide Area Network (WAN) communication protocol was used on the LoRa module to provide switching functionality of the targeted area. The performance of the system was evaluated by three real-life case studies through measuring environmental temperature and humidity, detecting fire, and controlling the switching functionality of appliances. Obtaining correct environmental data, fire detection with 90% accuracy, and switching functionality with 92.33% accuracy at a distance up to 12 km demonstrated the high performance of the system. The proposed smart system with modular design proved to be highly effective in controlling and monitoring home appliances from a longer distance with relatively lower power consumption.

Vehicles on the road are rising in extensive numbers, particularly in proportion to the industrial revolution and growing economy. The significant use of vehicles has increased the probability of traffic rules violation, causing unexpected accidents, and triggering traffic crimes. In order to overcome these problems, an intelligent traffic monitoring system is required. The intelligent system can play a vital role in traffic control through the number plate detection of the vehicles. In this research work, a system is developed for detecting and recognizing of vehicle number plates using a convolutional neural network (CNN), a deep learning technique. This system comprises of two parts: number plate detection and number plate recognition. In the detection part, a vehicle’s image is captured through a digital camera. Then the system segments the number plate region from the image frame. After extracting the number plate region, a super resolution method is applied to convert the low-resolution image into a high-resolution image. The super resolution technique is used with the convolutional layer of CNN to reconstruct the pixel quality of the input image. Each character of the number plate is segmented using a bounding box method. In the recognition part, features are extracted and classified using the CNN technique. The novelty of this research is the development of an intelligent system employing CNN to recognize number plates, which have less resolution, and are written in the Bengali language.

The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU ⁻¹ , respectively. The sensor shows an excellent spectral resolution of the highest value of $5.26\times 10^{-6}$ RIU, ensuring the capability of identifying a very small change in analyte refractive index (RI) within the range of 1.33 to 1.42. The performance investigation is also carried out altering the diameter of air holes, pitch, and gold layer thickness to explore the variation in phase matching conditions due to the change in structural parameters. As the sensor is adept at detecting the sample with high sensitivity and sensing resolution, the proposed sensor can be highly efficient in detecting Pseudomonas bacteria as well as other organic compounds, and biological analytes.

As a promising optical method used in a variety of applications surface plasmon resonance (SPR) sensors are employed over a wide range of boundaries. This research proposes a highly sensitive SPR based sensor with a novel hybrid structure using transition metal dichalcogenides (e.g. WSe2, PtSe2) along with black phosphorene (BP) through comprehensive numerical study. To analyze and evaluate the performances of the proposed sensor, the widely used transfer matrix method (TMM) was used. The performances of the sensor were measured in terms of reflectivity, sensitivity, detection accuracy (DA), and figure of merit (FOM). The sensor structure was optimized by changing different structural parameters of the hybrid architecture to obtain better performances. The results revealed that insertion of PtSe2 with WSe2 and BP over a gold layer of the conventional structure improved the performance of the sensor and the maximum sensitivity of the sensor was measured as 200 deg/RIU with a FOM of 17.70 RIU-1. As well, the light penetration through the optimized sensor is investigated using the finite element method (FEM) based software. With this kind of high sensing capabilities, it may be convinced that the proposed sensor can be applied in different fields of biosensing to detect liquid biological and biochemical samples or analytes.

The ease of controlling waveguide properties through unparalleled design flexibility has made the photonic crystal fiber (PCF) an attractive platform for plasmonic structures. In this work, a dual analyte channel’s highly sensitive PCF bio-sensor is proposed based on surface plasmon resonance (SPR). In the proposed design, surface plasmons (SPs) are excited in the inner flat portion of two rectangular analyte channels where gold (Au) strip is deposited. Thus, the surface roughness that might be generated during metal deposition on circular surface could be effectively reduced. Considering the refractive index (RI) change in the analyte channels, the proposed sensor is designed and fully characterized by the finite element method based COMSOL Multiphysics software. Improved sensing characteristics including wavelength sensitivity (WS) of 186,000 nm/RIU and amplitude sensitivity (AS) of 2,792.97 RIU-1 in the wide RI range of 1.30 to 1.43 is obtained. In addition, the proposed sensor exhibits excellent resolution of 5.38 × 10-7, signal to noise ration (SNR) of 13.44 dB, figure of merits (FOM) of 2188.23, detection limit (DL) of 101.05 nm, and detection accuracy (DA) of 0.0204 nm-1. Outcomes of the analysis indicate that the proposed sensor could be suited for accurate detection of organic chemicals, bio-molecules, and biological analytes.