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.

We demonstrate a novel method that employs a frame-accumulation and shaped-function technique to improve an image sensor’s detection sensitivity using probability statistics. It can obtain the unbiased estimate for the low-light-level image signal, thus upgrading the signal-to-noise ratio to a high degree. It was verified by an experiment in a sealed box. By the help of a variable light beam–shaped function saw-tooth signal in front of a camera, the low-light-level shadow image that was invisible to the camera could be revealed clearly from the frame accumulation data. The method can surpass an image sensor’s detection limitation.

The rapid development of industrial technologies continuously increases the heavy metal pollution of water resources. Recently, portable electrochemical analysis-based devices for detecting heavy metal ions have attracted much attention due to their excellent performance and low fabrication costs. However, it has proven difficult to accommodate complex testing needs in a cost-effective manner. To address these limitations, we propose a new system for the in situ detection of heavy metals in wastewater using an organic light-emitting diode-based panel to display data in real time and Bluetooth to transmit data to a smartphone for rapid analysis. The fabricated device integrates an in situ signal analysis circuit, a Bluetooth chip, a photocured 3D-printed shell, and an electrode sleeve interface. In addition, a fully screen-printed functional electrode plate containing chitosan/PANi–Bi nanoparticle@graphene oxide multi-walled carbon nanotubes is utilized for the rapid detection of heavy metal ions. This device can perform wireless data transmission and analysis and in situ signal acquisition and processing. The sensor exhibits a high sensitivity (Hg²⁺: 88.34 μA ppm⁻¹ cm⁻²; Cu²⁺: 0.956 μA ppm⁻¹ cm⁻²), low limit of detection (Hg²⁺: 10 ppb, Cu²⁺: 0.998 ppm) and high selectivity during the detection of copper and mercury ions in tap water under non-laboratory conditions, and the results of real-time tests reveal that parameters measured in the field and laboratory environments are identical. Hence, this small, portable, electrochemical sensor with a screen-printed electrode can be effectively used for the real-time detection of copper and mercury ions in complex water environments.

High-precision spectral analysis for complex liquid components has received much attention in recent years. However, the lack of systematic strategies of measurement and modeling notably appears when multiple internal and external factors vary—e.g., the sample parameters, instruments, and external environment. Many methods have been developed to eliminate these effects. According to the characteristics of error sources and its propagation rules, “M + N” theory integrates and classifies the internal and external factors that affect the analysis of complex liquid components into M factors and N factors systematically. “M” and “N” refers to the M components in a liquid and N interference factors, respectively, and “+” means that the spectral is a response to the interaction of M and N. In this review, “M + N” theory sums up five measurement and modeling strategies used to reduce the effect of various factors on spectral quantitative analysis of complex liquids. Finally, this review summarized the strategies used to improve the accuracy of quantitative analysis of complex liquids and discussed future research directions.