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Wireless sensor networks (WSN), as a solution for buried water pipe monitoring, face a new set of challenges compared to traditional application for above-ground infrastructure monitoring. One of the main challenges for underground WSN deployment is the limited range (less than 3 m) at which reliable wireless underground communication can be achieved using radio signal propagation through the soil. To overcome this challenge, the purpose of this paper is to investigate a new approach for wireless underground communication using acoustic signal propagation along a buried water pipe.

An acoustic communication system was developed based on the requirements of low cost (tens of pounds at most), low power supply capacity (in the order of 1 W-h) and miniature (centimetre scale) size for a wireless communication node. The developed system was further tested along a buried steel pipe in poorly graded SAND and a buried medium density polyethylene (MDPE) pipe in well graded SAND.

With predicted acoustic attenuation of 1.3 dB/m and 2.1 dB/m along the buried steel and MDPE pipes, respectively, reliable acoustic communication is possible up to 17 m for the buried steel pipe and 11 m for the buried MDPE pipe.

Although an important first step, more research is needed to validate the acoustic communication system along a wider water distribution pipe network.

This paper shows the possibility of achieving reliable wireless underground communication along a buried water pipe (especially non-metallic material ones) using low-frequency acoustic propagation along the pipe wall.

In response to food supply constraints resulting from coronavirus disease 2019 (COVID-19) restrictions, in the year 2020, the project developed automated household Aquaponics units to guarantee food self-sufficiency. However, the automated aquaponics solution did not fully comply with data privacy and portability best practices to protect the data of household owners. The purpose of this study is to develop a data privacy and portability layer on top of the previously developed automated Aquaponics units.

Design Science Research (DSR) is the research method implemented in this study.

General Data Protection and Privacy Regulations (GDPR)-inspired principles empowering data subjects including data minimisation, purpose limitation, storage limitation as well as integrity and confidentiality can be implemented in a federated learning (FL) architecture using Pinecone Matrix home servers and edge devices.

The literature reviewed for this study demonstrates that the GDPR right to data portability can have a positive impact on data protection by giving individuals more control over their own data. This is achieved by allowing data subjects to obtain their personal information from a data controller in a format that makes it simple to reuse it in another context and to transmit this information freely to any other data controller of their choice. Data portability is not strictly governed or enforced by data protection laws in the developing world, such as Zimbabwe's Data Protection Act of 2021.

Privacy requirements can be implemented in end-point technology such as smartphones, microcontrollers and single board computer clusters enabling data subjects to be incentivised whilst unlocking the value of their own data in the process fostering competition among data controllers and processors.

The use of end-to-end encryption with Matrix Pinecone on edge endpoints and fog servers, as well as the practical implementation of data portability, are currently not adequately covered in the literature. The study acts as a springboard for a future conversation on the topic.

With the development of integrated circuit and communication technology, digital secure communication has become a research hotspot. This paper aims to design a five-dimensional fractional-order chaotic secure communication circuit with sliding mode synchronous based on microcontroller (MCU).

First, a five-dimensional fractional-order chaotic system for encryption is constructed. The approximate numerical solution of fractional-order chaotic system is calculated by Adomian decomposition method, and the phase diagram is obtained. Then, combined with the complexity and 0–1 test algorithm, the parameters of fractional-order chaotic system for encryption are selected. In addition, a sliding mode controller based on the new reaching law is constructed, and its stability is proved. The chaotic system can be synchronized in a short time by using sliding mode control synchronization.

The electronic circuit is implemented to verify the feasibility and effectiveness of the designed scheme.

It is feasible to realize fractional-order chaotic secure communication using MCU, and further reducing the synchronization error is the focus of future work.

This study aims to collect real-time, in situ data from polymer melt extrusion (ME) 3D printing and use only the collected data to non-destructively identify printed parts that contain defects.

A set of sensors was created to collect real-time, in situ data from polymer ME 3D printing. A variance analysis was completed to identify an “acceptable” range for filament diameter on a popular desktop 3D printer. These data were used as the basis of a quality evaluation process to non-destructively identify spatial regions of printed parts in multi-part builds that contain defects.

Anomalous parts were correctly identified non-destructively using only in situ collected data.

This methodology was developed by varying the filament diameter, one of the most common reasons for print failure in ME. Numerous other printing parameters are known to create faults in melt extruded parts, and this methodology can be extended to analyze other parameters.

To the best of the authors’ knowledge, this is the first report of a non-destructive evaluation of 3D-printed part quality using only in situ data in ME. The value is in improving part quality and reliability in ME, thereby reducing 3D printing part errors, plastic waste and the associated cost of time and material.

The purpose of this paper is to develop a prototype of a wearable medical device in the form of a bandage with a real-time data monitoring platform, which can be used domestically for diabetic patients to identify the possibility of foot ulceration at the early stage.

The prototype can measure blood volumetric change and temperature variation in the forefoot area simultaneously. The waveform extracted using a pulsatile-blood-flow signal was used to assess blood perfusion-related information, and hence, predict ischemic ulcers. The temperature difference between ulcerated and the reference was used to predict neuropathic ulcers. The medical device can be used as a bandage during the application wherein the sensory module is placed inside the hollow pocket of the bandage. A platform was developed through a mobile application where doctors can extract real-time information, and hence, determine the possibility of ulceration.

The height of the peaks in the pulsatile-blood-flow signal measured from the subject with foot ischemic ulcers is significantly less than that of the subject without ischemic ulcers. In the presence of ischemic ulcers, the captured waveform flattens. Therefore, the blood perfusion from arteries to the tissue of the forefoot is considerably low for the subject with ischemic ulcers. According to the temperature difference data measured over 25 consecutive days, the temperature difference of the subject with neuropathic ulcers occasionally exceeded the 4 °F range but mostly had higher values closer to the 4 °F range. However, the temperature difference of the subject who had no complications of neuropathic ulcers did not exceed the 4 °F range, and the majority of the measurements occupy a narrow range from −2°F to 2 °F.

The proposed prototype of wearable medical apparatus can monitor both temperature variation and pulsatile-blood-flow signal on the forefoot simultaneously and thereby predict both ischemic and neuropathic diabetes using a single device. Most importantly, the wearable medical device can be used domestically without clinical assistance with a real-time data monitoring platform to predict the possibility of ulceration and the course of action thereof.

Currently, several studies have been published using sensorized insoles for estimating ground reaction force using plantar pressure. However, information on design parameters, manufacturing techniques and guidelines for developing insoles is scarce, often leaving gaps that do not allow reproducing the insole. This study aims to empirically investigate the main parameters of constructing a sensorized insole for application in human gait.

Two devices were built to evaluate the force sensors. The first focuses on the construction of the sensors with different settings: the density of the sensor’s conductive trails (thickness and distance of the trails) and the inertia of the sensors (use of spacers to prevent unwanted readings). The second device focuses on the data capture and processing system: resolution of the analog–digital converter, acquisition rate and sensor activation level.

The resolution increase of the analog–digital converter and acquisition rate do not contribute to noise increase. Reducing the sensors’ coverage area can increase sensorized insole capacity. The inertia of the sensors can be adjusted using spacers without changing the electrical circuit and acquisition system.

Most sensorized insoles use commercial sensors. For this reason, it is not possible a full customization. This paper maps the main variables to manufacture custom sensors and data acquisition systems. This work also presents a case study where it is possible to see the influence of the parameters in the correlation between the sensorized insole and an instrumented treadmill with a force platform.

Induction heating applications aided by power electronic control have become very attractive in the recent past. For cooking applications, power electronics circuits are very suitable to feed power to multi loads with an appropriate control technique. The purpose of this paper is to develop a three leg inverter to feed power to three loads simultaneously and independently.

Pulse density modulation control technique is used to control the output power independently with constant switching frequency.

Multi-load handling converter with independent power control is achieved with reduced number of switching devices (two switches/per load) with simple control strategy.

The proposed system is simulated in MATLAB/Simulink, and the thermal analysis is carried out in COMSOL multi-physics software. The hardware realisation is performed for a 1 kW prototype with 20 kHz switching frequency and 10 kHz pulse density modulation frequency. PIC16F877A microcontroller is used to validate the experimental results for various values of control signals (D_PDM). The simulation and experimental results are in good agreement and validates the developed system.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper healthcare, education, living conditions, wages and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

The ability to characterise highly conducting objects, that may also be highly magnetic, by the complex symmetric rank–2 magnetic polarizability tensor (MPT) is important for metal detection applications including discriminating between threat and non-threat objects in security screening, identifying unexploded anti-personnel landmines and ordnance and identifying metals of high commercial value in scrap sorting. Many everyday non-threat items have both a large electrical conductivity and a magnetic behaviour, which, for sufficiently weak fields and the frequencies of interest, can be modelled by a high relative magnetic permeability. This paper aims to discuss the aforementioned idea.

The numerical simulation of the MPT for everyday non-threat highly conducting magnetic objects over a broad range of frequencies is challenging due to the resulting thin skin depths. The authors address this by employing higher order edge finite element discretisations based on unstructured meshes of tetrahedral elements with the addition of thin layers of prismatic elements. Furthermore, computer aided design (CAD) geometrical models of the non-threat and threat object are often not available and, instead, the authors extract the geometrical features of an object from an imaging procedure.

The authors obtain accurate numerical MPT characterisations that are in close agreement with experimental measurements for realistic physical objects. The assessment of uncertainty shows the impact of geometrical and material parameter uncertainties on the computational results.

The authors present novel computations and measurements of MPT characterisations of realistic objects made of magnetic materials. A novel assessment of uncertainty in the numerical predictions of MPT characterisations for uncertain geometry and material parameters is included.