Design of Turbo Trellis Coding Modulation Scheme of Rate 4/9 for Rician Fading Channel

When the fading channels are encountered during data communication, errors are likely to occur at the receiving end due to multipath propagation. Researchers have been consistently striving to develop Error Correction Schemes that can effectively handle these errors and ensure error-free data reception at the receiver end. Of particular interest are the Forward Error Correction Schemes that can be implemented at the transmitter end itself. However, the implementation of error correction coding through these schemes incurs additional costs in terms of bandwidth expansion, as extra bits need to be added to facilitate error correction. Fortunately, there exists one coding scheme called Trellis Coded Modulation (TCM), which addresses this issue. TCM selects a modulation scheme based on the rate of the convolutional coding scheme. However, this coding technique has limitations in correcting the number of errors, leading to the development of Turbo Coding. This scheme utilizes two coders at the transmitter, arranged in either serial or parallel configuration, and a suitable decoder at the receiver. A design of Turbo Coding scheme has been presented in this paper, that employs convolutional coders having rate 2/3, in a serially concatenated configuration, providing an effective rate of 4/9. This turbo coding scheme is then applied to TCM scheme in order to preserve the bandwidth. Therefore, if using the convolutional coding scheme of rate 2/3, the modulation scheme is 8-QAM and in order to preserve bandwidth after coding, using the Turbo coding scheme of rate 4/9, then the modulation scheme will be 512-QAM. The simulations have been conducted in MATLAB and the error correcting capabilities of the designed scheme in comparison with convolutional coding scheme using the constituent convolutional encoder have also been compared. It has been observed that in the Rician fading channel conditions, the Turbo Trellis Coding Modulation Scheme provides approximately 5 dB gain compared to the convolutional coding scheme

Implementation of Intelligent Smart Heart Health Monitoring System using IOT

There are a lot of severe diseases that are associated with humans, but one of them is cardiac arrest, which, in general terms, we call a heart attack. The already existing heart rate monitoring systems are not mobile, are expensive, and take a little longer to give out the results. So, in this work, we will go for a system called Heart Rate Monitoring system using an ECG sensor and a Raspberry Pi, which actually represents the acquisition and interpretation of a human heart’s data collected with the help of sensors, anywhere and everywhere on the earth, through IOT. We generally consider heart rate while noting the status of the heart, but the oxygen level and body temperature also play a major role in determining the exact heart status. So, the hardware required to implement this heart rate monitoring model consists of different health sensors and a Raspberry Pi configured in a way to communicate with the guardian and the respective doctors over the Internet through an available smart mobile phone. In this work, the sensors configured with the hardware collect the required information about the patient’s health, which includes parameters such as the patient’s heart rate, body temperature, and SPo2 levels. Then, using the collected information from the sensors, the patient’s heart activity is actively observed. Thus, the patient himself or herself can easily identify his or her heart condition with the help of collected data anywhere on the earth through the internet. An alert indicating that their heart status is not good is displayed to the caretakers on the mobile, which shows a message called "abnormal condition to the patient" given the condition that the collected sensors’ values are beyond the threshold information through the GSM module, and also the GPS location of the patient will be sent to the caretakers as well as to the doctors