This paper presents a smart irrigation system suitable for use in places where water scarcity is a challenge. In many parts of Africa, even when irrigation is practiced, it is manually operated. Smart irrigation system is thereby believed to be a major solution. The paper therefore presents a smart irrigation system that optimizes the available water in the water reservoir thus providing an efficient and effective water usage solution for the irrigation system. The irrigation system is able to automatically start/stop water pumps on the irrigation site based on the soil moisture content acquired from the moisture content sensor as well as the ultrasonic sensor measuring the water level in the reservoir. The measured sensor values are sent to the Arduino microcontroller for configuring the control algorithm. The system prioritizes irrigation operation by determining the number of pumps to be operated at any instance as well as their locations. In this way, different crops can be watered depending on their varying water requirements. In order to implement the design, a laboratory scale architectural model depicting a farm setting with reservoir, direct current (DC) pumps and the control unit was constructed. Experimental results revealed good performance which makes the developed system a suitable tool for studies on irrigation.

Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.

In a nuclear power plant (NPP), the used tools are visually inspected to ensure their integrity before and after their use in the nuclear reactor. The manual inspection is usually performed by qualified technicians and takes a large amount of time (weeks up to months). In this work, we propose an automated tool inspection that uses a classification model for anomaly detection. The deep learning model classifies the computed tomography (CT) images as defective (with missing components) or defect-free. Moreover, the proposed algorithm enables incremental learning (IL) using a proposed thresholding technique to ensure a high prediction confidence by continuous online training of the deployed online anomaly detection model. The proposed algorithm is tested with existing state-of-the-art IL methods showing that it helps the model quickly learn the anomaly patterns. In addition, it enhances the classification model confidence while preserving a desired minimal performance.