A new servo drive for electro discharge machining industrial applications is presented in this paper. The development processes of the servo feed drive have passed through three main stages. The first stage focused on design and development of a linear piezoelectric ultrasonic motor. The second one concentrated on development of an electronic driver and its embedded software. The integration, testing, and validation in electro discharge machining system, was the last stage of the development lifecycle. The linear piezoelectric ultrasonic motor consists of three main parts, the stator, rotor, and sliding element. The motor design process, basic configuration, principles of motion, finite element analysis, and experimental examination of the main characteristics are discussed in this paper. The electronic driver of the ultrasonic motor consists of two main stages, the booster and piezoelectric amplifier. The piezo amplifier consists of four output transistors, a push-pull and bridge, connected in order to achieve the necessary electrical parameters to drive and control the motor servo feed drive traveling speed. The essential experimental arrangement to implement and examine the developed ultrasonic servo feed drive in an electro discharge machining system was carried out. The initial results showed that the servo drive is able to provide: a reversible directional of motion, no-load traveling speed equal to 28 mm per s, maximum load of 0.78 N, a resolution <50 μm, and a dynamic time response <10 ms. The electron microscopic micro examination into the machined samples showed that: ultrasonic servo drive showed a clear improvement in the surface profile finish, a notable reduction in the stability, processing time, material removal rate, arcing, and short-circuiting teething phenomena. This was verified by assessing the electrode movements, the variations of the inter electrode gap voltage, current, and feedback control signals.

Recycled carbon fibre (rCF) composites are increasingly being explored for applications such as strain sensing, manufacturing of automobile parts, assistive technologies, and structural health monitoring due to their properties and economic and environmental benefits. The high conductivity of carbon and its wide application for sensing makes rCF very attractive for integrating sensing into passive structures. In this paper, capacitive sensors have been fabricated using rCF composites of varying compositions. First, we investigated the suitability of recycled carbon fibre polymer composites for different sensing applications. As a proof of concept, we fabricated five touch/proximity sensors and three soil moisture sensors, using recycled carbon fibre composites and their performances compared. The soil moisture sensors were realised using rCF as electrodes. This makes them corrosion-resistant and more environmental-friendly, compared to conventional soil moisture sensors realised using metallic electrodes. The results of the touch/proximity sensing show an average change in capacitance (ΔC/C~34) for 20 mm and (ΔC/C~5) for 100 mm, distances of a hand from the active sensing region. The results of the soil moisture sensors show a stable and repeatable response, with a high sensitivity of ~116 pF/mL of water in the linear region. These results demonstrate their respective potential for touch/proximity sensing, as well as smart and sustainable agriculture.

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency.

Innovation in Micro Actuators and Big Data Technology Transform Visually Impaired Daily Life Activities and Improve Their Access to Information Technology Resources

One of the key elements of the future smart cities is automation and utilisation of robotic system technology. It is believed that not in the far future that home automation, healthcare, telecare, digitised technology and assisted living technology will make a real impact in our quality of life, especially for older adults with long term condition. Older adults to live independently and have a good quality of life is quite a challenge, since the majorly of them have long term health condition diseases. Health services providers across EU and overall the world, informal and formal carer plays major roles in providing the necessary services and support. Diseases on older adults society are one of the leading causes of death, from which thousands of people die every year. Many of the non-communicable diseases can be prevented by tackling associated risk factors. The cost of treatment of such diseases in the EU only is estimated to be over 70% of the Health Service budget. Treatment includes home-care, medication, consultation and many other relevant services. However, these services are still not adequate, due to the lack of implemented automation and robotic systems technology that enable the older adults to manage their daily life activities independently, taking medications, receive the necessary health services on time, which, in many cases leads to loss of lives and waste of NHS resources. Daily life activities management and telehealth remote monitoring system is one of the potential innovative approaches, to improve the older adult’s quality of life, help live independently, improve NHS services, sustain its economic growth and improve social development. It is a rapidly developing concept where daily life activities, health condition, medical information is transferred through interactive data, and audio visual media and shared between services provider, informal and formal carer. In this 4^th issue and 2^nd issues the journal a selection of some of these technological challenges facing the automation industry are presented and some of the inventive researcher pioneering solutions and findings are introduced. The first paper by Hafez Fouad and Hesham Farouk (Microelectronics Department, Electronics Research Institute, Cairo, Egypt) presents Performance Optimization of Healthcare Mobile Applications using Embedded System Design and Implementation. The paper introduces An Embedded device which is an object that contains a special-purpose computing system. The system, which is completely enclosed by the object, may or may not be able to connect to the Internet. Embedded systems have extensive applications in consumer, commercial, automotive, industrial and healthcare markets. Generally, an embedded device's operating system will only run a single application which helps the device to do its job. The heart of electronics system and also coordinator in communication is embedded system managing biometric data from numerous stationary and on body. This simply means that the device's software does not have a user interface (UI). In such cases, an in-circuit emulator (ICE) is temporarily installed between the embedded device and an external computer to debug or update the software. Because embedded systems have limited computing resources and strict power requirements, writing software for embedded devices is very specialized field that requires knowledge of both hardware components and programming. This work describes the design of mobile medical system that can be used to monitor the human temperature and heart beat using a stand-alone microcontroller. The system hardware architecture consists of temperature and heart beat sensors, signal conditioning circuits (SCC), single chip microcontroller, LCD display and GSM modem. An embedded software algorithm acquires temperature, heart beat and pressure, processes, transmits, displays and stores it in the built-in EPROM of the microcontroller. Yasser Fouad (Alexandria University, Egypt) introduces Classification Process using Hybrid Model of Rough Neural Networks and Gene Expression Programming. The paper presents a new model of rough neural networks based on learning using gene expression programming for classification support. The Objective of gene expression programming rough neural networks approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds. Saman Fattahi, Islam Shyha, Hamid Baseri (Islamic Azad University, Iran & Northumbria University, UK & Babol University of Technology, Iran) presents Optimisation of dry electrical discharge machining of high speed steel using Grey-relational Analysis. The paper introduces Dry electrical discharge machining (DEDM) is considered as a green format of the conventional oil dielectric EDM where a gas is used as the dielectric medium. This paper details experimental results when DEDM of high speed steel workpieces using multi-response optimisation. Experimental trials were performed to investigate the influence of current, pulse on-time, duty factor, gas pressure and spindle rotational speed on material removal rate (MRR), surface roughness (Ra), and radial over cut (ROC). Grey relational analysis (GRA) based on Taguchi approach was employed for multi-objective optimisation of DEDM responses. Confirmation trials were subsequently conducted and results showed that process responses can be successfully optimised using GRA. Results showed that current, duty factor and air pressure were found to be statistically significant factors at 90% confidence level, with percentage contribution ratios (PCRs) of 72.8%, 4.3%, and 3.7%, respectively. Micro pores and minor surface cracks were visible in the majority of specimens. The paper by Komlanvi Moglo, Mahmoud Shafik, Mfortaw Elvis Ashu (College of Engineering and Technology, University of Derby, UK) is focused on the design and development of a 3D sustainable renewable micro power station for smart grid industrial applications. It demonstrates the need of the supply of clean energy and how its security is becoming a global issue for all countries across the world, due to the limitations of fossil fuels resources usages for energy generations, the relative high dependency on imported fuels, their ever climbing prices and its environmental impacts. Power supply must increase as rapidly as demand to ensure sustained growth. This is the rationale upon which Governments, international organizations, researchers are accelerating investments in expanding the power system, its generation and transmission. This paper presents the preliminary research undertaken to design and develop a 3Dimentional (3D) sustainable renewable micro power station model for smart grid industrial applications. It introduces a solution to challenges in the energy generation sector which do not only refrain only to the safe supply of clean Energy. A major importance for the theoretical study of hybrid systems, based on renewable energy (photovoltaic, wind, hydro system) is the availability of the models that can be utilized to study the performance of hybrid systems and most important, computer aided design simulation tools. As the available tools are quite limited, this paper would present the most current and up to date model which can be used for the simulation purposes of the 3D sustainable renewable micro power station for smart grid applications as well as for educational purposes. The final paper of this issue is focused on the Quarter Car Railway Vehicle cutting edge technology. Iqbal M. Ahmed, Md. Yusof Hazlina, M. M. Rashid (International Islamic University Malaysia, Malaysia) presents the Modelling of a Small-Scale Test Rig of Quarter Car Railway Vehicle Suspension System. The paper focused on the Suspension system which is a mechanism that isolates the vehicle body from road shocks and unwanted vibrations. A basic suspension system usually consists of spring, damper, rods, and ball joints in both automotive and railway transportation. The primary aim of this project is to design and develop a small-scale test rig of quarter car railway vehicle suspension system. This paper describes component selection strategy, technical design procedure, and research investigation of test rig and suspension system. The rig design and motion analysis is conducted by using Solidworks motion study tool where dynamic characteristic of the system, such as, required torque, acceleration and displacement of vehicle body and bogie are investigated based on selected parameters and track inputs. We hope that you find the issue beneficial and has also given you a good on-site of the ongoing applied research in this fascinating fast-evolving area of innovation and technology.We look forward to receive your contribution to the future journal issues.

Many of the Ambient Assisted Living Technologies (AALT) available in the market to the end-users with long term health condition have no common inter-operational protocol. Each product has its own communication protocols, different interfaces and interoperation which limits their solution reliability, flexibility and efficiency. This paper presents assisted living platform solution for elderly people with long term health condition based on wireless sensors networking technology. The system includes multi feedback sensor arrangements for monitoring, such as: blood pressure, heart rate and body temperature. Each sensor has been integrated with the necessary near real time embedded and wireless protocols that allow data collection, transfer and interoperate in ad-hoc bases. The data will be communicated wirelessly to central data base system and shared though cloud network. The collected data will be processed and relevant intelligent algorithms will be deployed to ensure certain actions taken place when health condition warnings arise. These warnings to be communicated to relevant carer, General Practitioner (GP) and health authority to take the necessary action and steps to handle such end user health condition warnings. The proposed solution system will provide the flexibility to analyse most of the health conditions based on near real time monitoring technology. It will enable the population of elderly with long term health condition to manage their daily life activities within multiple environments i.e. from their comfort home, care centres and hospitals. The data and information will be treated with high confidentiality to ensure end-users integrity and dignity have been maintained.