In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target's GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new waypoint for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.

Delivering a live signature has become laborious for many people in several countries nowadays. Some departments do not accept a scanned signature, which directs people to travel to give a live signature. In this article, a method to design rapid and fluid movements of a universal robot to perform robot writing will be presented for human handwritten signatures. This system will point out the hardware and software design in the real‐time for CNC signature system. The described system can help any person who has a disability and cannot travel or for people who are far away from the department that required a live signature. The technology behind this system is based on the CNC machine pen plotter system. The hardware is a 3D printed part which is consists of seven parts. All parts are printed by 3D printer type “Anet 6.” The CNC machine tools which will be presented in this article are primarily an educational system and can be made with readily available, low‐cost components and single compatible microcontroller and PHPoC Wi‐Fi Shield, acting as a host for CNC software.

An experiment was conducted to determine the performance of a portable thermoelectric fridge used for storing some vaccines powered by a solar panel. The inner size of the cooler box tested was 240 mm × 240 mm × 170 mm with 8kg net weight of the fridge and it can accommodate 9.8 liters of medical supplies. A thermoelectric module ESUMIC DC 12V was used in the test. The thermoelectric hot side was cooled by using air, meanwhile, the cold side of the thermoelectric was connected to the inner heat sink to absorb heat inside the cooler box. The thermoelectric was powered by a portable solar panel and battery directly to each thermoelectric which had a power of 100 WP. The battery controls and maintains the system’s voltage while it is being charged by the portable solar panel. It also supports and supplies the system with enough power. The cooler container was tested for one hour of daylight on 16 April, 21 May, 10 June, and 10 July respectively in 2021. The results showed that the lowest temperature on the thermoelectric cold side and center of the fridge was around 1.5°C and 8.6°C after one hour of working time. The experimental COP obtained were ranging from 0.12 to 0.71. Moreover, solar panels were the potential as power sources for thermoelectric cooling systems.