Cuffless BP measurement system using a single lead electrocardiogram (ECG) and a photoplethysmography (PPG) measurement has been done. The method used is based on the fact that changes in blood pressure (BP) cause changes in Pulse Transit Time (PTT), which usually gets measured as the time difference between the R peak of electrocardiogram (ECG) signal and the peak of a Photoplethysmography (PPG) signal. We have tested the developed BP measurement system with three healthy volunteers and calibrated n subject specific model that predicts blood pressure based on PTT. Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) are measured with a maximum mean error and standard deviation of±2.88 mmHg±1.96 mmHg and±4.7 mmHg±2.5 mmHg respectively

This manuscript presents an essential overview of the implementation of memristor-based logic circuits. Different memristor models such as Linear Drift, Simmons Tunnel, Yakopcic, N on-Linear ion Drift, TEAM, and VTEAM have been taken into consideration and a comparison of results has been done adopting all these models. The logic gates such as NAND, NOT, NOR, AND, OR along with some combinational circuits such as a l-bit comparator, and 2xl multiplexer were designed and implemented using MRL (Memristor Ratioed Logic) in the L TSpice simulation tool. The primary target of this paper is affirming the attributes of memristor at the device level. Here the logic circuits are implemented using Memristor and NMOS devices. All the circuits designed in this paper operate at a supply voltage of lV using HP memristor model. Moreover, the power consumed by the designed circuits has been analyzed and found to be in the order of p W

Introduction: In this work, calibration-free blood pressure estimation using wavelet scalograms of PPG signals using Convolutional Neural Network (CNN) has been proposed. The PPG signal, easily obtained from a subject, serves as a reliable indicator for predicting blood pressure (BP). Methods: The proposed methodology involves employing Continuous Wavelet Transform (CWT) scalograms of the PPG signal as inputs for the CNN. Two distinct architectures for BP estimation are explored: one employing regression with a fully connected neural network and another utilizing CNN with Support Vector Regression (SVR). Results: The results demonstrate superior BP estimation with the CNN-SVR architecture. With the CNN-SVR model, the Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) are estimated with a Root Mean Square Error (RMSE) of 6.7 mmHg and 8.9 mmHg, respectively. Conclusion: The proposed CNN-SVR model gives 52% better estimation error performance in SBP estimation compared to a machine learning model reported in a previous work. © 2024 The Author(s).

An asynchronous duty cycled Medium Access Control (MAC) protocol for wireless sensor networks has been proposed with the transmission power control. The proposed duty-cycled MAC has been implemented in the CC430-based sensor node hardware platform and the empirical energy consumption analysis of the proposed MAC has been done. Compared to the Low Duty Cycled MAC, the reduction in the reception time and the transmission power are the energy conservation factors in the proposed medium access control protocol. The energy consumption has been compared with LDC MAC implementation and it is shown that the proposed Low Duty Cycle MAC with Power Control (LDC-PC) gives more energy conservation for a single hop network.

We present the design and implementation of a hardware accelerator for AES encryption and decryption using AMD Vitis HLS and Xilinx Vivado. The primary objective of this work is to maximize the throughput, with optimizations focusing on resource utilization and power consumption. We detail the design methodology, provide an in-depth analysis of resource utilization, and compare our results with existing AES hardware accelerators. The hardware accelerator has been deployed on a PYNQ-Z2 FPGA board, achieving a throughput of 20.8 Gbps at 650 MHz, Total power consumption of 1.418W. The results show significant performance improvements, demonstrating the viability of high-level synthesis (HLS) for cryptographic applications on FPGAs

A blood flow-based method has been proposed to estimate beat to beat blood pressure parameters from the photoplethysmography (PPG) signal from a single PPG sensor. PPG signal represents the changes in the blood volume. The first derivative of it reflects the blood flow rate. In this work, the features are extracted from the blood flow rate reflected by the first derivative of PPG signal. The proposed method has been validated using Clinical data available in MIMIC II database. The validation shows that the systolic blood pressure and diastolic blood pressure estimated from a single site PPG signal has the mean error ± SD as 0.95 ± 5.14 mmHg for the beat-to-beat Pulse Pressure (PP) and 0.402 ± 4.85 mmHg for beat-to-beat Systolic Blood Pressure (SBP).

A practical implementation of an Asynchronous Wake on Radio(WoR) based medium access control protocol for wireless sensor networks has been reported. Texas Instruments's RF SoC CC430 based wireless node has been used as sensor node. By using the WoR feature of radio core, lower duty cycle of 0.78% has been attained. And overhearing avoidance is also incorporated to further the energy conservation. The comparison of energy consumption of the sensor network with and without the Wake on Radio and overhearing avoidance have been done.