Manjeet Kumar

Researchers have focused their attention toward meminductors after the development of memristors. Due to the difficulties of obtaining such elements as discrete components in the market, researchers intend to mimic them. This article introduces a novel Second‐Generation Current Conveyor (CCII) based grounded meminductor emulator. It consists of three CCIIs, two MOS capacitors, one NMOS, and two grounded resistors. The proposed circuit avoids analog multiplier circuits. The suggested meminductor can maintain pinched hysteresis curve between flux and current effectively within a 90 MHz frequency. The designed meminductor emulator circuit is capable of demonstrating both hard‐ and smooth‐switching behaviors without altering the circuit architecture. The functionalities are validated utilizing SPICE in conjunction with the TSMC 180 nm technology parameter. The power consumption of the proposed meminductor is 1.27 mW. Experimental validation is also performed using IC AD844 and IC HCF4007UBE. Further, the impact of nonidealities on the designed meminductor emulator is examined. Additionally, other critical attributes, including nonvolatility testing, temperature variations, supply voltage fluctuations, transistor size variations, and process corner simulations, have also been demonstrated. A comparison of the proposed meminductor with existing counterparts in the literature has been made, focusing on various performance parameters. Moreover, a meminductor‐based chaotic oscillator is designed as an application of the proposed meminductor.

This study aims to examine the new labels for the significant characterization of sleep disorder, in the context of sleep microstructure, as revealed by cyclic alternating pattern (CAP). The observation of sleep disorders through ceaseless recording and monitoring of electrocardiogram (ECG) signal assist to take protective care upon cardiovascular diseases (CVDs). Differences between normal subject and sleep-disordered subject are scrutinized by estimating the heart rate. The variation of CAP events is then studied in distinct scales by the use of wavelet analysis. The essence of CAP events is considered in terms of electrocardiography complexity analysis for various types of events. Few assumptions regarding the microstructure difference in sleep disorders including quality sleep, asymmetric activation-deactivation patterns, and high duration of desynchronization phases, produces complexity in CVD analysis. This study expands the existing holdings between the increased CAP rate and the instable characteristics of sleep and highlights the differences in sleep disorders with categorization analysis. This study creates a new aspect for the interpretation of the CAP events through energy-based spectrum. The detection of sleep disorders including insomnia, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), rapid eye movement (REM) sleep behavior disorder (RBD), and sleep disordered breathing (SDB) with normal is performed. The proposed methodology employed adaptive superresolution transform with Inception-v3 deep neural network (DNN) attained an accuracy of 98.2% using CAP sleep dataset for sleep disorder detection. The proposed methodology outperforms the state-of-the-art methods namely short time Fourier transform (STFT) and continuous wavelet transform (CWT) in heart rate estimation.

Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.

In recent years, memristor emulators have been used in a wide range of applications including neuromorphic computing, security, analog signal processing, and other related fields. This article introduces a novel electronically tunable dual flux-controlled floating memristor emulator using single current-controlled differential difference current conveyor (CCDDCC), a MOS capacitor, and one pMOS transistor. The core of the suggested emulator features an electronically tunable CCDDCC-based feedback circuit. The proposed circuit avoids passive components, diodes, and analog multiplier circuits. The suggested memristor has the ability to tune the area of the pinch hysteresis loop (PHL) without altering the input frequency, input voltage, MOS capacitance, and active component. The circuit operates effectively within a 300-MHz frequency range and demonstrates a well-defined pinched hysteresis loop. Its performance is evaluated using 180-nm TSMC parameters, with a compact layout area of $758.12~\mu $ m2. In addition, the robustness of the circuit is thoroughly assessed through analyses of temperature variations, supply voltage fluctuations, transistor size variations, and process corner simulations.

Continuous hemoglobin (Hb) monitoring can diagnose conditions such as polycythemia, anemia, and various cardiovascular diseases. Current Hb measurement techniques are still invasive, often causing discomfort and pain for the user. Non-invasive Hb measurement is a newer technology that is gaining popularity among researchers. This paper proposes a new non-invasive Hb measurement approach using multiwavelength PPG signals. The transmissive mode PPG sensor and the reflective mode PPG sensor collected real-time PPG signals at four wavelengths: 560 nm, 660 nm, 880 nm, and 940 nm. A novel twenty-six feature vector such as perfusion density (PD), PPG absorbance (ABR), molar extinction coefficient (MEC), magnitude of prominent peak (PPM), frequency of prominent peak (PPF), and absorbance ratio are first time computed in this work from the real-time acquired PPG signal. The performance of the proposed framework is analyzed by utilizing the optimized four different machine learning (ML) regression models such as the AdaBoost model, XGBoost model, BPNN model, and the CatBoost model. Further, this work utilized Shapley Additive Explanations (SHAP) values to visualize the importance of each feature in Hb estimation, enabling a clear understanding of how different features contribute to the prediction process. This SHAP value helps to select the most relevant features for improving the accuracy and interpretability of the Hb estimation model. Results indicate that the BPNN model exhibited superior performance, yielding a mean absolute error (MAE) of 0.421 g/dL, 0.624 g/dL, and 1.57 g/dL for Dataset-1, Dataset-2, and In-house dataset respectively. Additionally, the proposed framework is implemented on an embedded platform featuring an STM32F67ZI microcontroller, showcasing its effectiveness for real-time execution.

This paper reports a reconfigurable positive and negative immittance circuit using single topology built with Operational Transconductance Amplifier (OTA). The purpose of this paper is to introduce a reconfigurable positive and negative immittance circuit using minimum number of active and passive components. Changing the gate voltage of MOS switches makes it simple to flip between positive and negative operating modes. The designed circuit employs only one grounded capacitor as a passive component, which makes it compatible with current integration technologies. Layout area of the designed immittance circuit is 50.1[ ][ ]m. Nonideal impact of OTA on the proposed circuit is also evaluated mathematically. In addition, resistorless single topology-based reconfigurable multifunction filter and quadrature oscillator circuits are also designed using proposed immittance circuits. The values of cut-off frequency of multifunction filter and oscillating frequency of quadrature oscillator may be tuned via modifying the transconductance of OTA. Monte Carlo and PVT analyses of the proposed circuits are illustrated to show the robustness of the designed circuits. The functionality of the designed circuits is demonstrated by SPICE simulation based on the 90[ ]nm CMOS process parameter.