Antenna design considerations for implantable devices in remote telehealth systems Take-Home Messages • Implantable sensors are pivotal to telehealth systems enabling continuous monitoring of a patient's vital health signs wirelessly. • Antennas are an integral element of these systems whose design is a complex task due to harsh and highly volatile in-body environment and requirements of robust and reliable performance while offering miniature structure, patient safety and biocompatibility. • A comprehensive critical review on the antenna design for implantable medical devices highlighting requirements, challenges, antenna types and human body effects on their performance shows that slotted patch antennas operating at higher frequencies can serve the purpose optimally. • The slotted patch antenna designed in the light of recommendations made offers a small size of 7.5×5×0.25 mm 3 with a-10 dB bandwidth of 25 MHz, a near-omnidirectional pattern and a gain of 1.7 dBi. • The paper can serve as a reference for the antenna designers working in the field of implantable devices providing state-of-the-art, current advancements, requirements, challenges as well as design rules.

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Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values.

An optimised design of a radio frequency energy harvesting antenna is presented. The antenna is based on a compact ferrite rod which, together with the electronics, can directly replace batteries in suitable applications. The antenna is optimised such that the energy available for the applications is maximised, while considering constraints such as the device geometry and the Q-factor. That the antenna can power a wireless sensor node is shown from the ambient medium wave transmissions.

As the impacts of global warming have become increasingly severe, oxy-fuel combustion has been widely considered a promising solution for carbon capture and storage (CCS) to reduce carbon dioxide (CO 2 ) to achieve net-zero emissions. In the past few decades, researchers around the world have demonstrated improvements by the application of oxy-fuel combustion to internal combustion (IC) engines. This article presents a comprehensive review of the experimental and simulation studies about oxy-combustion for CCS in IC engines. To give a more comprehensive understanding, it has included a detailed explanation of the essential components contained in an oxy-fuel IC engine and its typical operating parameters. The oxy-fuel IC engine components include the system of oxygen supply, exhaust gas recirculation (EGR), water injection, fuel injection, and CCS. In order to optimise the combustion process, it is required to adopt the appropriate values for the oxygen concentration, EGR rate, ignition timing, compression ratio, fuel injection, and water injection in oxy-fuel engines. The detailed literature review and analysis presented provide a basis for the selection of oxy-fuel combustion for CCS as a prospective solution to reduce carbon emissions in IC engines.

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Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.