In an industrial working environment employing multiprocessor communication using UART, noise is likely to affect the data and data may be received with errors. This kind of error occurrence may affect the working of the system resulting in an improper control. Several existing UART designs are incorporating error detection logic. This kind of logic, if detects errors, requires retransmission of corresponding data frames which take additional time for automatic repeat request (ARQ) and retransmission of data. Linear block codes like hamming code have forward error correction (FEC) as well as error detection capability. This paper presents a novel VLSI implementation of UART designed to include (8,4) extended hamming code called SEC-DED code that can correct upto one error and detect upto two errors. This improves the noise immunity of the system optimizing the error free reception of data. The whole design is implemented in Xilinx ISE 12.3 simulator targeted to Xilinx Spartan 6 FPGA.

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Diabetes is a chronic ailment characterized by abnormal blood glucose levels. Diabetes is caused by insufficient insulin synthesis or by cells' insensitivity to insulin activity. Glucose is essential to health since it is the primary source of energy for the cells that make up a person's muscles and tissues. On the condition that if a person has diabetes, his or her body either does not create enough insulin or cannot utilize the insulin that is produced. When there isn't enough insulin or cells stop responding to insulin, many dextroses accumulate in the person's vascular framework. As time passes, this could lead to diseases such as kidney disease, vision loss, and coronary disease. Although there is no cure for diabetes, losing weight, eating nutritious foods, being active, and closely monitoring the diabetes level can all assist. In this research, we used Artificial Neural Network to create a Deep Learning (DL) model for predicting Diabetes. Then it was validated using an accuracy of 92%. In addition, with the help of the MIT website, a mobile application was constructed. This project will now assist in predicting the effects of diabetes and deliver personalized warnings. Early detection of pre-diabetes can be extremely beneficial to patients since studies have shown that symptoms of early diabetic difficulties frequently exist at the time of diagnosis.

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