In this paper, we derive the bit error rate (BER) and pairwise error probability (PEP) for massive multipleinput multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems for different M -ary modulations based upon the approximate noise distribution after channel equalization. The PEP is used to obtain the upper-bounds for convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. In addition, complexity analysis of the log-likelihood ratio (LLR) values is performed using the approximate noise probability density function (PDF). The derived LLR computations can be timeconsuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced using Newton's interpolation with different polynomial orders and the results are compared with the exact simulations. The Neumann large matrix approximation is used to design the receiver for a zero-forcing equalizer (ZFE) by reducing the number of operations required in calculating the channel matrix inverse. Simulations are used to demonstrate that the results obtained using the derived equations match closely the Monte-Carlo simulations.

Continuous monitoring of breathing activity plays a major role in detecting and classifying a breathing abnormality. This work aims to facilitate detection of abnormal breathing syndromes, including tachypnea, bradypnea, central apnea, and irregular breathing by tracking of thorax movement resulting from respiratory rhythms based on ultrasonic radar detection. This paper proposes a non-contact, non-invasive, low cost, low power consumption, portable, and precise system for simultaneous monitoring of normal and abnormal breathing activity in real-time using an ultrasonic PING sensor and microcontroller PIC18F452. Moreover, the obtained abnormal breathing syndrome is reported to the concerned physician’s mobile telephone through a global system for mobile communication (GSM) modem to handle the case depending on the patient’s emergency condition. In addition, the power consumption of the proposed monitoring system is reduced via a duty cycle using an energy-efficient sleep/wake scheme. Experiments were conducted on 12 participants without any physical contact at different distances of 0.5, 1, 2, and 3 m and the breathing rates measured with the proposed system were then compared with those measured by a piezo respiratory belt transducer. The experimental results illustrate the feasibility of the proposed system to extract breathing rate and detect the related abnormal breathing syndromes with a high degree of agreement, strong correlation coefficient, and low error ratio. The results also showed that the total current consumption of the proposed monitoring system based on the sleep/wake scheme was 6.936 mA compared to 321.75 mA when the traditional operation was used instead. Consequently, this led to a 97.8% of power savings and extended the battery life time from 8 h to approximately 370 h. The proposed monitoring system could be used in both clinical and home settings.

<p class="MDPI17abstract"><span>In this paper, the air temperature and humidity levels in the infant' incubator are monitored remotely by means of Arduino microcontroller with different sensors and an open source internet of things (IoT) applications. The system is connected to a network via a wireless fidelity (Wi-Fi) connection to be linked to an application on the smart phone or to the computer. The system is designed using Arduino microcontroller, DHT11/DHT22 sensor for measuring the body parameters, such as the temperature and the humidity, LCD monitor, ESP8266 WiFi modules, and NodeMCU-v3.The results have shown that real time updated medical records can be transferred to the medical staff utilizing ThingSpeak IoT applications. </span></p>

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Wearable devices used to monitor patients are classified as part of mobile health, one of the branches of e-health. They are widely used to monitor the vital signs of patients outside of the health institutions environment. The aim of this paper is to design a device that can be worn at low cost and of small size to provide comfort to the patient. The accuracy of this device should be high compared to the benchmark. Also, this study takes into account real-time remote monitoring based on the wireless sensor network and cloud computing where cloud computing is integrated with the Internet of Things to solve the problem of the flow of the huge amount of data. The Wearable Remote Vital Signs Monitoring System (WRVSMS) was manufactured by a printed circuit board, where the ESP01, MAX30100, NTC, OLED, and Li-ion battery were used. The WRVSMS was connected to the cloud server via the HTTP protocol where the data was stored and analysed. The WRVSMS works on basis the combining data of vital signs through which the stakeholders to whom the alert is sent will be assigned based on the patient‘s case where the alert will be sent, which is short message to the stakeholders that are important in rescuing the patient when the patient‘s vital signs are outside threshold. The results showed that the device is 99.37% accurate and statistical analysis was performed to test the error.