Due to the COVID-19 pandemic, the world has faced a significant challenge in the increase of the rate of morbidity and mortality among people, particularly the elderly aged patients. The risk of acquiring infections may increase during the visit of patients to the hospitals. The utilisation of technology such as the “Internet of Things (IoT)” based on Fog Computing and Cloud Computing turned out to be efficient in enhancing the healthcare quality services for the patients. The present paper aims at gaining a better understanding and insights into the most effective and novel IoT-based applications such as Cloud Computing and Fog Computing and their implementations in the healthcare field. The research methodology employed the collection of the information from the databases such as PubMed, Google Scholar, MEDLINE, and Science Direct. There are five research articles selected after 2015 based on the inclusion and exclusion criteria set for the study. The findings of the studies included in this paper indicate that IoT-based Fog Computing and Cloud Computing increase the delivery of healthcare quality services to patients. The technology showed high efficiency in terms of convenience, reliability, safety, and cost-effectiveness. Future studies are required to incorporate the models that provided the best quality services using the Fog and Cloud Computation techniques for the different user requirements. Moreover, edge computing could be used to significantly enhance the provision of health services at home.

Because of a large amount of arithmetic in the standard digital lock-in detection, a high performance processor is needed to implement the algorithm in real time. This paper presents a novel algorithm that integrates oversampling and high-speed lock-in detection. The algorithm sets the sampling frequency as a whole-number multiple of four of the input signal frequency, and then uses the common downsampling technology to lower the sampling frequency to four times of the input signal frequency. It could effectively remove the noise interference and improve the detection accuracy. After that the phase sensitive detector is implemented. It simply does the addition and subtraction on four points in the period of same phase and replaces almost all the multiplication operations to speed up digital lock-in detection calculation substantially. Furthermore, the correction factor is introduced to improve the calculation accuracy of the amplitude, and an error caused by the algorithm in theory can be eliminated completely. The results of the simulation and actual experiments show that the novel algorithm combining digital lock-in detection and oversampling not only has the high precision, but also has the unprecedented speed. In our work, the new algorithm is suitable for the real-time weak signal detection in the general microprocessor not just digital signal processor.

A Tikhonov regularization method in the inverse problem of electrical impedance tomography (EIT) often results in a smooth distribution reconstruction, with which we can barely make a clear separation between the inclusions and background. The recently popular total variation (TV)regularization method including the lagged diffusivity (LD) method can sharpen the edges, and is robust to noise in a small convergence region. Therefore, in this paper, we propose a novel regularization method combining the Tikhonov and LD regularization methods. Firstly, we clarify the implementation details of the Tikhonov, LD and combined methods in two-dimensional open EIT by performing the current injection and voltage measurement on one boundary of the imaging object. Next, we introduce a weighted parameter to the Tikhonov regularization method aiming to explore the effect of the weighted parameter on the resolution and quality of reconstruction images with the inclusion at different depths. Then, we analyze the performance of these algorithms with noisy data. Finally, we evaluate the effect of the current injection pattern on reconstruction quality and propose a modified current injection pattern.The results indicate that the combined regularization algorithm with stable convergence is able to improve the reconstruction quality with sharp contrast and more robust to noise in comparison to the Tikhonov and LD regularization methods solely. In addition, the results show that the current injection pattern with a bigger driver angle leads to a better reconstruction quality.

An appropriate method for calibration set selection is very important for a robust quantitative model, especially for the non-invasive measurement of blood components.

In order to verify the effectiveness of three application methods, an experiment was done to predict the concentration of intra-lipids by single pathlength and multi-pathlength modelling.