Non-invasive cholesterol assessment: current methods and future perspectives Ravisankar Dakupati, P N S B S V Prasad V, Syed Ali Hussain, Pradyut Kumar Sanki Analytical Methods, 2026 Optical spectroscopy combined with chemometric modeling enables non-invasive cholesterol quantification through spectral fingerprinting of skin tissue. This review evaluates analytical performance, challenges, and trans-lational potential.
Non-Invasive Cholesterol Detection Leveraging FTIR Spectroscopy Ravisankar Dakupati, B.S.S. Tejesh, Syed Ali Hussain, Pradyut Kumar Sanki, M. Ramakrishnan 2025 5th IEEE International Conference on Applied Electromagnetics Signal Processing and Communication Aespc 2025, 2025
Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression P. N. S. B. S. V. Prasad V, Ali Hussain Syed, Mudigonda Himansh, Biswabandhu Jana, Pranab Mandal, et al. Scientific Reports, 2024 Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body’s cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.
Efficient in situ learning of hybrid LIF neurons using WTA mechanism for high-speed low-power neuromorphic systems Syed Ali Hussain, P N S B S V Prasad V, Pradyut Kumar Sanki Physica Scripta, 2024 The emerging market for hardware neuromorphic systems has fulfilled the growing demand for fast and energy-efficient computer architectures. Memristor-based neural networks are a viable approach to meet the need for low-power neuromorphic devices. Spiking neural networks (SNNs) are widely recognized as the best hardware solution for mimicking the brain’s efficient processing capabilities. To build the SNN model, we have designed an energy-efficient hybrid Leaky Integrated and Fire (LIF) neuron model using Carbon Nano Tube Field Effect Transistors (CNTFET) and memristors. This hybrid neuron operates at 3.89 MHz, with 1.047nW and 0.257fJ of power and energy per spike with a constant power supply (V dd ) and an excitation voltage of 0.5V, under the ideal conditions. When the intrinsic constraints of CNTFETs and memristors, such as parasitic elements and hysteresis effects, are taken into consideration, the operating frequency is lowered to 3.45 MHz (an 11.5% decrease), and energy consumption rises to 0.317 fJ per spike (a 23.3% increase). Despite these limitations, our design outperforms with existing works. On the other hand the development of in situ, Spike Timing Dependent Plasticity (STDP) learning through memristors as synapses results in a computational challenge. In this paper, we adopt a potent technique capable of carrying out both learning and inference. The weight modulation is accomplished using a linear memristor model, resulting in high speed and reduced power consumption. We intend to apply the winner-takes-all (WTA) mechanism within the SNN architecture, which incorporates recurrently connected proposed neurons in the output layer, for real-time pattern recognition. The proposed design has been implemented and the performance metrics superseded the existing works in terms of power, energy, and accuracy. Furthermore, the design is capable of classifying 50×104 images per second.
An SoC System for Real-Time Edge Detection Vanama Yamini, Syed Ali Hussain, G. Chandra Sekhar, P. Avinash Kumar, P. Lehitha, et al. Journal of Electronic Materials, 2024
Smart Point-of-Care Application for Automated Wound Segmentation Biswabandhu Jana, Amrit K. Singha, Subhasis Mahata, Mahua Bhattacharya, Pradyut K. Sanki 8th International Symposium on Innovative Approaches in Smart Technologies Isas 2024 Proceedings, 2024
