Recent progress in innovation is making the life prosper, simpler and easier for common individual. The World Health Organization (WHO) statistics indicate that a large amount of people experience visual losses, because of which they encounter many difficulties in everyday jobs. Hence, our goal is to structure a modest, secure, wearable, and versatile framework for visually impaired to help them in their daily routines. For this, the plan is to make an effective system which will assist visually impaired people through obstacle detection and scenes classification. The proposed methodology utilizes Raspberry-Pi 4B, Camera, Ultrasonic Sensor and Arduino, mounted on the stick of the individual. We take pictures of the scene and afterwards pre-process these pictures with the help of Viola Jones and TensorFlow Object Detection algorithm. The said techniques are used to detect objects. We also used an ultrasonic sensor mounted on a servomotor to measure the distance between the blind person and obstacles. The presented research utilizes simple calculations for its execution, and detects the obstructions with a notably high efficiency. When contrasted with different frameworks, this framework is a minimal effort, convenient, and simple to wear.

People are preoccupied with their own duties in today’s environment, making it tough to oversee and care for disabled people. Paralyzed and disabled people, on the other hand, have a powerful desire to move around freely. Different initiatives have been taken in the past to improve and preserve the self-esteem of such people, and various technologies have also been created to assist them in a better way. The primary advantage of a vision-based autonomous wheelchair is that it can provide greater independence to users with limited mobility. The methodology of other smart wheelchairs varies depending on the specific technology used. Some smart wheelchairs may use sensors to detect obstacles and provide feedback to the user, while others may incorporate GPS and other navigation technologies to assist with indoor and outdoor navigation. Our study proposes a revolutionary implementation of an autonomous system for fully handicapped people, allowing them to drive wheelchairs using just their eyes. The wheelchair’s orientation will be determined by the orientation of the eye. The camera will follow the movement of the eyes. Furthermore, sensors will be installed on the front side of the wheelchair to identify any obstacles along its path. The calibration of the camera with the human eye, without obstructing its vision, is the most challenging task in this research, besides controlling the wheels of the wheelchair.

In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.

We propose a novel deep learning architecture, called XcelNet17, for image classification in remote sensing. Comprising fourteen convolutional and three fully connected layers, XcelNet17 outperforms several benchmark architectures available in the literature in terms of classification accuracy. Additionally, we present BA-ABC, a new hybrid feature selection algorithm that inherits the strengths of the Bat Algorithm (BA) and the Artificial Bee Colony (ABC) algorithm. Together these contributions significantly enhance the performance and accuracy of remote sensing image classification tasks. The proposed framework is thoroughly trained and verified using five benchmark datasets typically used for remote sensing image classification, namely AID, RSSCN7, SIRI-WHU, UC Merced, and WHU RS-19. Our simulation results suggest that in terms of classification accuracy, XcelNet17 outperforms most of the well established networks including AlexNet, VGG16, VGG19, ResNet50, and DarkNet19 by obtaining accuracy values in the range of 94.6% and 99.9%. Furthermore, the proposed features selection method, when integrated with XcelNet17, yields much improved classification accuracy in comparison to various benchmarks including WOA, GWO, BA, ABC, and ACO algorithms. For example, an 8% superior performance on WHU-RS19 dataset has been observed. The attained results are further validated by an in-depth statistical analysis.

The availability of fresh drinkable water and water security is becoming a global challenge for sustainable development. In this regard, solar stills, due to their ease in operation, installation, and utilization of direct sunlight (as thermal energy), promise a better and sustainable future technology for water security in urban and remote areas. The major issue is its low distillate productivity, which limits its widespread commercialization. In this study, the effect of silicon (Si) particles is examined to improve the absorber surface temperature of the solar still absorber plate, which is the major component for increased distillate yield. Various weight percentages of Si particles were introduced in paint and coated on the aluminum absorber surface. Extensive indoor (using a self-made halogen light-based solar simulator) and outdoor testing were conducted to optimize the concentration. The coatings with 15 wt % Si in the paint exhibited the highest increase in temperature, namely, 98.5 °C under indoor controlled conditions at 1000 W/m² irradiation, which is 65.81% higher than a bare aluminum plate and 37.09% higher compared to a black paint-coated aluminum plate. On the other hand, coatings with 10 wt % Si reached up to 73.2 °C under uncontrolled outdoor conditions compared to 68.8 °C for the black paint-coated aluminum plate. A further increase in concentration did not improve the surface temperature, which was due to an excessive increase in thermal conductivity and high convective heat losses.

Production of hydrogen by means of renewable energy sources is a way to eliminate dependency of the system on the electric grid. This study is based on a technique involving coupling of an oxyhydrogen (HHO) electrolyzer with solar PV to produce clean HHO gas as a fuel. One of objectives of this study was to develop a strategy to make the electrolyzer independent of other energy sources and work as a standalone system based on solar PV only. A DC-DC buck convertor is used with an algorithm that can track the maximum power and can be fed to the electrolyzer by PV while addressing its intermittency. The electrolyzer is considered to be an electrical load that is connected to solar PV by means of a DC-DC convertor. An algorithm is designed for this DC-DC convertor that allows maximization and control of power transferred from solar PV to the electrolyzer to produce the maximum HHO gas. This convertor is also responsible for operating the electrolyzer in its optimum operating region to avoid overheating. The DC-DC converter has been tested under simulated indoor conditions and uncontrolled outdoor conditions. Analysis of this DC-DC convertor based on maximum power tracking algorithm showed 94% efficiency.