Development of a Device for Remote Monitoring of Heart Rate and Body Temperature

We present a new integrated, portable device to provide a convenient solution for remote monitoring heart rate at the fingertip and body temperature using Ethernet technology and widely spreading internet. Now a days, heart related disease is rising. Most of the times in these cases, patients may not realize their actual conditions and even it is a common fact that there are no doctors by their side, especially in rural areas, but now a days most of the diseases are curable if detected in time. We have tried to make a system which may give information about one's physical condition and help him or her to detect these deadly but curable diseases. The system gives information of heart rate and body temperature simultaneously acquired on the portable side in real time and transmits results to web. In this system, the condition of heart and body temperature can be monitored from remote places. Eventually, this device provides a low cost, easily accessible human health monitor solution bridging the gaps between patients and doctors

BB-ML: Basic Block Performance Prediction using Machine Learning Techniques

Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction at a much finer granularity, namely at the Basic Block (BB) level, which are single entry, single exit code blocks that are used for analysis by the compilers to break down a large code into manageable pieces. We extrapolate the basic block execution counts of GPU applications and use them for predicting the performance for large input sizes from the counts of smaller input sizes. We train a Poisson Neural Network (PNN) model using random input values as well as the lowest input values of the application to learn the relationship between inputs and basic block counts. Experimental results show that the model can accurately predict the basic block execution counts of 16 GPU benchmarks. We achieve an accuracy of 93.5% in extrapolating the basic block counts for large input sets when trained on smaller input sets and an accuracy of 97.7% in predicting basic block counts on random instances. In a case study, we apply the ML model to CUDA GPU benchmarks for performance prediction across a spectrum of applications. We use a variety of metrics for evaluation, including global memory requests and the active cycles of tensor cores, ALU, and FMA units. Results demonstrate the model's capability of predicting the performance of large datasets with an average error rate of 0.85% and 0.17% for global and shared memory requests, respectively. Additionally, to address the utilization of the main functional units in Ampere architecture GPUs, we calculate the active cycles for tensor cores, ALU, FMA, and FP64 units and achieve an average error of 2.3% and 10.66% for ALU and FMA units while the maximum observed error across all tested applications and units reaches 18.5%.Comment: Accepted at the 29th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2023

Interfacial and Aggregation Behavior of Dicarboxylic Amino Acid-Based Surfactants in Combination with a Cationic Surfactant

The interfacial and micellization behavior of three dicarboxylic amino acid-based anionic surfactants, abbreviated as AAS (N-dodecyl derivative of -aminomalonate, -aspartate, and -glutamate) in combination with hexadecyltrimethylammonium bromide (HTAB) were investigated by surface tension, conductance, UV-vis absorption/emission spectroscopy, dynamic light scattering (DLS), and viscosity studies. Critical micelle concentration (CMC) values of the surfactant mixtures are significantly lower than the predicted values, indicating associative interaction between the components. Surface excess, limiting molecular area, surface pressure at the CMC, and Gibbs free energy indicate spontaneity of the micellization processes compared to the pure components. CMC values were also determined from the sigmoidal variation in the plot of micellar polarity and pyrene UV vis absorption/emission intensities with surfactant concentration. The aggregation number, determined by static fluorescence quenching method, increases with decreasing mole fraction of the AAS (alpha(AAS)), where the micelles are mainly dominated by the HTAB molecules. The size of the micelle increases with decreasing alpha(AAs), leading to the formation of larger and complex aggregates, as also supported by the viscosity studies. Micelles comprising 20-40 mol % AAS are highly viscous, in consonance with their sizes. Some of the mixed surfactant systems show unusual viscosity (shear thickening and increased viscosity with increasing temperature). Such mixed surfactant systems are considered to have potential in gel-based drug delivery and nanoparticle synthesis