Corrosion occurs in all sectors including oil pipelines, drinking water and sewerage in the majority of cases linked to corrosion of steel. Good corrosion management includes optimising corrosion control actions and minimising lifecycle corrosion costs whilst meeting environmental goals. The toxicity of commonly used synthetic inhibitors are the subject of recent legislations (REACH and PARCOM) have led to search on more eco-friendly corrosion inhibitors. Extensive research is conducted to assess the corrosion inhibition rate of diverse green inhibitors. However, it was not adequately

In this work, we are proposing a silicon (Si) based concentric tube broadband absorber. The proposed broadband absorber is composed of consecutive concentric tubes of intrinsic Si and doped-Si (D-Si) layers. The structure exhibits a broadband performance within a wide range of mid-IR wavelength spectrum extending from 3 to 7 µm with an absorption peak that varies between 0.88 and 0.97 in the case of S-polarized incident light. We report that light coupling to the proposed concentric tube metamaterial absorber structure over a broad wavelength range is a result of exhibiting multiple resonance

Recently, unmanned aerial vehicles (UAVs), also known as drones, have gained widespread interest in civilian and military applications, which has led to the development of novel UAVs that can perform various operations. UAVs are aircraft that can fly without the need of a human pilot onboard, meaning they can fly either autonomously or be remotely piloted. They can be equipped with multiple sensors, including cameras, inertial measurement units (IMUs), LiDAR, and GPS, to collect and transmit data in real time. Due to the demand for UAVs in various applications such as precision agriculture

This paper presents a real-time vision framework that detects and tracks vehicles from stationary camera. It can be used to calculate statistical information such as average traffic speed and flow as well as in surveillance tasks. The framework consists of three main stages. Vehicles are first detected using Haar-like features. In the second phase, an adaptive appearance-based model is built to dynamically keep track of the detected vehicles. This model is also used in the third phase of data association to fuse the detection and tracking results. The use of detection results to update the

In the era of digital transformation, a mass disruption in the global industries have been detected. Big data, the Internet of Things (IoT) and Artificial Intelligence (AI) are just examples of technologies that are holding such digital disruptive power. On the other hand, retailing is a high-intensity competition and disruptive industry driving the global economy and the second largest globally in employment after the agriculture. AI has large potential to contribute to global economic activity and the biggest sector gains would be in retail. AI is the engine that is poised to drive the

Up to 50% of amputees abandon their prostheses, partly due to rapid degradation of the control systems, which require frequent recalibration. The goal of this study was to develop a Kalman filter-based approach to decoding motoneuron activity to identify movement kinematics and thereby provide stable, long-term, accurate, real-time decoding. The Kalman filter-based decoder was examined via biologically varied datasets generated from a high-fidelity computational model of the spinal motoneuron pool. The estimated movement kinematics controlled a simulated MuJoCo prosthetic hand. This clear-box

This paper tries to alleviate some challenges facing blind and visually impaired people in public transportation systems by providing them with in-station navigation information and real-time schedule information. Novel system architecture for the Intelligent Transportation Systems (ITS) navigation for blind and visually impaired people based on recent Radio Frequency Identification (RFID) localization technologies, commonly used in robotics, is proposed. Furthermore, a live timetable using a new ZigBee network broadcasting protocol with detailed frame structure is used for provision of real

In this paper, we describe a method for Scale-Adaptive visual tracking using compressive sensing. Instead of using scale-invariant-features to estimate the object size every few frames, we use the compressed features at different scale then perform a second stage of classification to detect the best-fit scale. We describe the proposed mechanism of how we implement the Bayesian Classifier used in the algorithm and how to tune the classifier to address the scaling problem and the method of selecting the positive training samples and negative training samples of different scales. The obtained

Robots nowadays, are introduced to many domains and fields. One of these fields is education. We introduce integrating robots and games in education. We have designed a humanoid robot tutoring biology. Our robot is interacting with a student to play a game to enhance and examine the student's knowledge. In our game, we developed cognitive capabilities for the robot. We analyzed the features that both the robot and the game have to possess, and we developed a system for organ detection and recognition with the highest possible accuracy and lowest processing time. Our game introduces a multi

The present research revisits rare experiments which determined elastic shakedown (SD) limit pressures of full scale radial and oblique nozzles partially penetrating spherical vessels. The experiments were conducted at Berkeley Nuclear Laboratories in England [1]. The SD limit pressures were determined via conducting consecutive series of internal pressure cycles and observing cyclic strain variation recorded by strain gauges cemented at predetermined various critical locations within the junctions’ vicinities. The Nonlinear Superposition Method (NSM), formulated for computing elastic SD limit