This study presents a cross-coupled, fully differential cascode operational amplifier (op-amp) featuring resistive common-mode feedback (CMFB) and rail-to-rail input/output (RRIO) stages. The architecture is implemented in 90 nm CMOS and uses single Miller capacitors for frequency compensation. The op-amp runs on 900 mV power supply and has an open-loop gain of 84 dB, a power supply rejection ratio (PSRR) of 100 dB at 10 kHz, and a common-mode rejection ratio (CMRR) of 96 dB at 10 kHz. The input-referred noise density is 96.28 nV/√Hz at 10 kHz, with a total power consumption of 637 μW, and the unity-gain bandwidth (UGBW) is 615 kHz. These performance parameters, together with the capacity to handle sub-μA quiescent current, make the design highly compatible with precision biopotential capture, such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals, where microvolt-level sensitivity is very critical. Integrating high open-loop gain, robust supply, CMRR, and ultra-low power dissipation, the proposed op-amp serves as a high-performance analog front-end (AFE) for next-generation wearable biomedical sensors and energy-constrained IoT devices.
This review addresses the problem of detecting and assessing the intensity of pain events in newborns to inform the development of effective treatment methods in neonatal units. The study investigates automated methods for detecting pain in newborns by analyzing electroencephalographic (EEG) signals using machine learning models, particularly neural networks. This approach improves the accuracy of pain detection in newborns, who are nonverbal patients, and helps physicians adopt effective treatment strategies and minimize potential long-term consequences. This paper reviews current methods for processing and analyzing EEG signals in newborns, as well as machine learning approaches, including deep learning technologies, for analyzing pain markers in newborns. The review of existing methods for digital processing of EEG signals includes approaches to identifying diagnostic features of pain in newborn EEGs and to developing feature sets using various machine learning methods. The analysis was conducted using various neural network architectures, optimizing the feature set, and determining the importance of pain markers. The paper presents an analysis of the results from applying different types of machine learning models, tested on available databases of newborn EEG signals linked to pain events. An approach is proposed for building automated systems to analyze EEG signals from newborns to detect pain events and to support diagnostic decision-making in anesthesiology and intensive care units.
The purpose of this paper is to formulate and prove a local matrix criterion that makes it possible, without the need for a complete solution of the system of linear equations, to determine the exact equipotentiality of a specified group of internal nodes in a passive reciprocal linear circuit with real or complex admittances. The resistive case is a special case of this broader statement. For complex admittances, the result applies to a fixed frequency of a steady-state harmonic mode. The object of analysis is the admittance matrix of a circuit with a block partition of nodes into a set of external nodes and a set of internal nodes, for which the possibility of exactly merging nodes into a single equivalent node is examined. In contrast to the general Kron reduction, which eliminates internal nodes via the Schur complement, this paper considers a special case that is verified before the reduction, in which the equipotentiality of the internal group is established without computing the inverse admittance matrix. It has been proved that this condition is necessary and sufficient for the equipotentiality of all nodes in the group for an arbitrary voltage vector at external nodes. As a result, if the criterion is satisfied, the reduced admittance matrix with respect to external nodes is independent of the internal inter-node admittances. The input impedances of external nodes in the specified operating modes are also independent of these admittances, but only in those modes that are calculated from this reduced matrix. At the same time, the full admittance matrix and its determinant generally depend on the internal inter-node admittances. The practical value of this result lies in the ability to perform a preliminary check of equipotential groups, reduce large linear circuits, verify models with branches between internal nodes, and simplify the admittance matrix without losing its properties with respect to external nodes. The conditions for extending the criterion to frequency-dependent admittance matrices are also considered.
This paper analyzes the current state and development perspectives of phase-pulse modulators. We show that a key component is the link that provides the pulse-shift shape, which depends on the modulating process. We present the principle for developing a phase-pulse modulator using an acousto-optic delay line. A physical-mathematical interpretation of the device’s operation is developed. Here, the pulse delay depends on the frequency of the voltage-controlled oscillator. We discuss the mechanism underlying systematic inaccuracy in the acousto-optic phase-pulse modulator and propose a method to measure it. The proposed method has been verified through numerical analysis and experiments. The feasibility of creating an acousto-optic phase-pulse modulator has been experimentally verified. Experimental results are presented that confirm the operational capability of the proposed design. Verification was done through numerical analysis and experiments, with results shown in tables and graphs to validate the modulator’s feasibility and performance.
Ultra-wideband (UWB) Time-Difference-of-Arrival (TDoA) is a popular approach for indoor system localization due to its simplicity, efficiency, high performance, and low energy consumption. To solve the nonlinear multilateration problem associated with determining the transmitter’s coordinates based on data from synchronized receivers, the least-squares method with low computational complexity or its improved variants, in particular, the two-stage weighted least squares (WLS) method, are often used. However, these methods perform poorly on ill-conditioned matrices due to a specific mutual arrangement between the receiver and the transmitter. This paper proposes a parametric weighted least squares algorithm that is effective under these conditions and enables the determination of an entire family of solutions. It provides accurate and reliable position estimates even under complex conditions associated with system rank reduction. The efficiency of the proposed algorithm is analyzed under various scenarios, including Gaussian noise, synchronization errors at the reference node, and multipath interference, for different receiver configurations arranged in a square grid.
