The scope of this paper is focused on the multidimensional poverty problem in Jordan. Household expenditure and income surveys provide data that are used for identifying and measuring the poverty status of Jordanian households. However, carrying out such surveys is hard, time consuming, and expensive. Machine learning could revolutionize this process. The contribution of this work is the proposal of an original machine learning approach to assess and monitor the poverty status of Jordanian households. This approach takes into account all the household expenditure and income surveys that took place since the early beginning of the new millennium. This approach is accurate, inexpensive, and makes poverty identification cheaper and much closer to real-time. Data preprocessing and handling imbalanced data are major parts of this work. Various machine learning classification models are applied. The LightGBM algorithm has achieved the best performance with 81% F1-Score. The final machine learning classification model could transform efforts to track and target poverty across the country. This work demonstrates how powerful and versatile machine learning can be, and hence, it promotes for adoption across many domains in both the private sector and government.

Audio watermarking has been proposed as an effective solution to solve or mitigate the new challenges of audio ownership verification. These challenges arose from easiness of copying and distributing of audio applications. This paper proposes important performance enhancements to a previously-reported audio ownership verification system based upon utilizing a combination of Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) techniques. A new audio signal framing, DWT matrix formation and embedding methods are proposed and successfully implemented to improve the minimum audio-cover period, quality of the watermarked audio and its robustness against various attacks. Performance evaluation of the developed system demonstrated improvements of 69% in the required minimum audio-cover period, 25% in the Signal-to-Noise Ratio (SNR), and improved robustness against various watermarking benchmark attacks.

In today's digital landscape, affordable Programmable Logic Controllers (PLCs) for industrial automation and applied engineering education are essential, as traditional setups are often too costly for many institutions and individuals. In this paper, a cost-effective, modular PLC lab was designed and developed to enable hands-on learning in industrial automation. The lab accommodates ten pairs of trainees, allowing for simultaneous experimentation using modular simulation kits tailored for various applications, such as traffic light systems, elevators, and automated filling systems. Trainees can prepare, test, and verify their code on these kits in real-time, enhancing their hands-on experience with programmable logic controllers. Each kit offers a set of features, including an integrated input/output interface that supports a variety of input types (e.g., switches, sensors, and push buttons) and output options (e.g., lights, motors, and actuators). This well-rounded setup is specifically designed to meet the educational and training objectives of the lab, promoting an interactive learning environment that develops essential automation skills and builds trainee confidence in applying PLC technology across real-world scenarios.•Offers a cost-effective PLC solution for industrial automation and engineering education, addressing the high cost of traditional systems.•Provides a scalable PLC lab setup developed to facilitate hands-on learning and practical experience in industrial automation, enabling simultaneous experimentation and collaborative learning.•Aligns with educational objectives, fostering an interactive, hands-on environment that builds essential automation skills, by enabling real-code preparation, testing and verification.

Retinal prosthesis is steadily improving as a clinical treatment for blindness caused by retinitis pigmentosa. However, despite the continued exciting progress, the level of visual return is still very poor. It is also unlikely that those utilising these devices will stop being legally blind in the near future. Therefore, it is important to develop methods to maximise the transfer of useful information extracted from the visual scene. Such an approach can be achieved by digitally suppressing less important visual features and textures within the scene. The result can be interpreted as a cartoon-like image of the scene. Furthermore, utilising extravisual wavelengths such as infrared can be useful in the decision process to determine the optimal information to present. In this paper, we, therefore, present a processing methodology that utilises information extracted from the infrared spectrum to assist in the preprocessing of the visual image prior to conversion to retinal information. We demonstrate how this allows for enhanced recognition and how it could be implemented for optogenetic forms of retinal prosthesis. The new approach has been quantitatively evaluated on volunteers showing 112% enhancement in recognizing objects over normal approaches.

License Plate Recognition (LPR) system is a key to many traffic related applications such as road traffic monitoring or parking lots access control. This paper proposes an automatic license plate recognition system for Jordanian license plates. The system presents an algorithm for the detection of license plate and segmentation of characters, and then recognition is done using back propagation neural network. The gain contribution of this work is the improvement of the license plate detection subsystem, which in turn improves the whole LPR system. The performance of the proposed system has been investigated on real images of 240 vehicles captured under various illumination conditions. The results show a robust algorithm with average success rate of 90% for Jordanian license plates.

This paper improves on a recently proposed machine learning approach to assess and monitor the poverty status of Jordanian households. Without a doubt, accurate identification, tracking, and targeting households in poverty is a key to poverty alleviation. This work presents a nontraditional approach to the accurate prediction of poverty for Jordanian households using feature selection and machine learning. This approach can be seen as a support in the decision making process in the government and non-governmental organizations in an ever-shifting socio-economic environment. Using the Jordanian household expenditure and income surveys collected by the Department of Statistics (DoS), LightGBM predictive performance is improved to reach 83% F1-Score using only 41 features in contrast to 81% F1-score and 96 features in our previous work. At the heart of this work is determining which variables of household expenditure and income surveys are most predictive and how they can be most effectively combined. National surveys can now be run with fewer, more targeted questions that can greatly help in assessing the effectiveness of new policies and intervention programs rapidly and cheaply.