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Quantum computers are a significant threat to the existing cryptography algorithms. The Saber is a lattice-based post-quantum cryptographic or quantum-safe algorithm designed as a quantum computing attack-resistant protocol. The hardness of SABER is based on the Rounding with Learning (LWR) problem. This work presents a hardware implementation of Striding Toom Cook multiplier-based Saber. SABER is selected algorithms of the NIST (National Institute of Standards and Technology) round 3 criteria. After evaluation in the striding school book multiplier, a technique is also introduced to efficiently load the weighted polynomials into registers and multipliers. The implemented design is realized utilizing Cadence digital-flow (industry-standard) and open-source tool flow (OFRS) with CMOS 180nm TSMC and CMOS (130nm/180nm SKY-WATER) process, respectively. The realized chip for the proposed Saber implementation resulted in an area of 12 mm2 and 43.59 mm2/16 mm2 for TSMC CMOS 180nm and SKY-WATER CMOS 180nm/130nm process, respectively. This implementation provided roughly equal latency for Key encryption, decryption, and Key generation when compared to a state-of-the-art design which utilized different techniques for polynomial multiplication whereas this design utilized 2X, 2X, and 2.5X lesser clock cycles required for Key generation, Key encryption, and decryption, respectively, compared to Saber implementation utilizing Striding Toom cook based multiplier for polynomial multiplication.

Autism spectrum disorder (ASD) is a chronic neurological disorder with the severity directly linked to the diagnosis age. The severity can be reduced if diagnosis and intervention are early (age < 2 years). This work presents a novel ear-worn wearable EEG system designed to aid in the early detection of ASD. Conventional EEG systems often suffer from bulky, wired electrodes, high power consumption, and a lack of real-time electrode–skin interface (ESI) impedance monitoring. To address these limitations, our system incorporates continuous, long-term EEG recording, on-chip machine learning for real-time ASD prediction, and a passive ESI evaluation system. The passive ESI methodology evaluates impedance using the root mean square voltage of the output signal, considering factors like pressure, electrode surface area, material, gel thickness, and duration. The on-chip machine learning processor, implemented in 180 nm CMOS, occupies a minimal 2.52 mm² of active area while consuming only 0.87 µJ of energy per classification. The performance of this ML processor is validated using the Old Dominion University ASD dataset.

Objective: A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. Methods: In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP). In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. For fewer computations and accurate measurements of BP, the most significant features are selected using correlation and mutual information measures in the feature engineering part. Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. Results: The proposed approach outperforms the current state-of-the-art in both RR and BP. The RR algorithm results in mean absolute errors (median, 25th-75th percentiles) of 0.4 (0.1–0.7) for CapnoBase dataset and 0.5(0.3-2.8) for BIDMC dataset without discarding any data window. Similarly, BP approach has been validated on a large dataset derived from MIMIC-II ($\sim$1700 records) which has errors (mean absolute, standard deviation) of 5.0(6.3) and 3.0(4.0) for systolic and diastolic BP, respectively. The results meet the American Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) Class A criteria. Conclusion: By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.

Classifying the patient’s depth of anesthesia (LoH) level into a few distinct states may lead to inappropriate drug administration. To tackle the problem, this paper presents a robust and computationally efficient framework that predicts a continuous LoH index scale from 0–100 in addition to the LoH state. This paper proposes a novel approach for accurate LoH estimation based on Stationary Wavelet Transform (SWT) and fractal features. The deep learning model adopts an optimized temporal, fractal, and spectral feature set to identify the patient sedation level irrespective of age and the type of anesthetic agent. This feature set is then fed into a multilayer perceptron network (MLP), a class of feed-forward neural networks. A comparative analysis of regression and classification is made to measure the performance of the chosen features on the neural network architecture. The proposed LoH classifier outperforms the state-of-the-art LoH prediction algorithms with the highest accuracy of 97.1% while utilizing minimized feature set and MLP classifier. Moreover, for the first time, the LoH regressor achieves the highest performance metrics ( $\text{R}^{{{2}}}=0.9$ , MAE = 1.5) as compared to previous work. This study is very helpful for developing highly accurate monitoring for LoH which is important for intraoperative and postoperative patients’ health.

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.