In recent years, the world has witnessed a significant increase in the number of elderly who often suffer from chronic diseases, and has witnessed in recent months a major spread of the new coronavirus (COVID-19), which has led to thousands of deaths, especially among the elderly and people who suffer from chronic diseases. Coronavirus has also caused many problems in hospitals, where these are no longer able to accommodate a large number of patients. This virus has also begun to spread between medical and paramedical teams, and this causes a major risk to the health of patients staying in hospitals. To reduce the spread of the virus and maintain the health of patients who need a hospital stay, home hospitalization is one of the best possible solutions. This paper proposes a home hospitalization system based on the Internet of Things (IoT), Fog computing, and Cloud computing, which are among the most important technologies that have contributed to the development of the healthcare sector in a significant way. These systems allow patients to recover and receive treatment in their homes and among their families, where patient health and the hospitalization room environmental state are monitored, to enable doctors to follow the hospitalization process and make recommendations to patients and their supervisors, through monitoring units and mobile applications developed for this purpose. The results of evaluation have shown great acceptance of this system by patients and doctors alike.

PH measurements are widely used in agriculture, biomedical engineering, the food industry, environmental studies, etc. Several healthcare and biomedical research studies have reported that all aqueous samples have their pH tested at some point in their lifecycle for evaluation of the diagnosis of diseases or susceptibility, wound healing, cellular internalization, etc. The ion-sensitive field effect transistor (ISFET) is capable of pH measurements. Such use of the ISFET has become popular, as it allows sensing, preprocessing, and computational circuitry to be encapsulated on a single chip, enabling miniaturization and portability. However, the extracted data from the sensor have been affected by the variation of the temperature. This paper presents a new integrated circuit that can enhance the immunity of ion-sensitive field effect transistors (ISFET) against the temperature. To achieve this purpose, the considered ISFET macro model is analyzed and validated with experimental data. Moreover, we investigate the temperature dependency on the voltage-current (I-V). Accordingly, an improved conditioning circuit is designed in order to reduce the temperature sensitivity on the measured pH values of the ISFET sensor. The numerical validation results show that the developed solution accurately compensates the temperature variation on the measured pH values at low power consumption.

The radio frequency identification (RFID) system is one of the most important technologies of the Internet of Things (IoT) that tracks single or multiple objects. This technology is extensively used and attracts the attention of many researchers in various fields, including healthcare, supply chains, logistics, asset tracking, and so on. To reach the required security and confidentiality requirements for data transfer, elliptic curve cryptography (ECC) is a powerful solution, which ensures a tag/reader mutual authentication and guarantees data integrity. In this paper, we first review the most relevant ECC-based RFID authentication protocols, focusing on their security analysis and operational performances. We compare the various lightweight ECC primitive implementations designed for RFID applications in terms of occupied area and power consumption. Then, we highlight the security threats that can be encountered considering both network attacks and side-channel attacks and analyze the security effectiveness of RFID authentication protocols against such types of attacks. For this purpose, we classify the different threats that can target an ECC-based RFID system. After that, we present the most promising ECC-based protocols released during 2014–2021 by underlining their advantages and disadvantages. Finally, we perform a comparative study between the different protocols mentioned regarding network and side-channel attacks, as well as their implementation costs to find the optimal one to use in future works.

This paper presents a comparative synthesis of the suitability of three memristive device technologies and their corresponding spike-timing-dependent plasticity (STDP) learning windows for neuromorphic applications. The physical mechanisms behind the nonlinear switching memristive dynamics of ReRAM, based on titanium dioxide, ferroelectric tunnel junctions, and phase change memory are analyzed towards the development of accurate and computationally efficient compact models which are implemented as a Verilog-A description. The developed Verilog-A compact models are separately validated and compared with the measurement data. Moreover, the asynchronous STDP learning rule is implemented using the above mentioned memristive devices as artificial synapse for spike-based neuromorphic computing. The considered memristive technologies are compared and discussed towards their integration in fast and/or large-scale circuit implementations.