Circuits, Systems and Signal Processing最新文献

Accurately estimating the direction of arrival (DOA) of wideband signals with a sensor array is critical in communications, radar, and the Internet of Things. This paper proposes two single-source DOA estimation methods for wideband linear frequency modulation signals: time-delay mixing multiple signal classification (TDM-MUSIC) and enhanced self-mixing MUSIC (ESM-MUSIC). TDM-MUSIC employs time-delay mixing of the received signal to construct an equivalent single-frequency signal model, thereby enhancing estimation accuracy while maintaining reasonable computational efficiency. ESM-MUSIC improves the conventional self-mixing model by adding frequency correction steps, resulting in excellent DOA estimation performance at the expense of computational complexity. Unlike conventional methods that rely on approximate models, our methods establish more accurate equivalent models. A key advantage of our methods is that they allow flexible adjustment of the optimal sensor inter-element spacing in arrays based on the equivalent signal model rather than the actual signal model, simplifying engineering fabrication and reducing mutual coupling between sensors. The paper establishes the Cramér–Rao bounds for both proposed methods and demonstrates their superiority over existing methods through comprehensive numerical simulations. Further, the experiment using a TI-AWR2243 multi-sensor array radar system confirms that our methods are feasible for practical engineering applications.

This letter proposes a two-dimensional direction-of-arrival (2-D DOA) estimation method for conjugate augmented spatial-temporal L-shaped arrays based on the rank-reduction principle. The basic idea of the proposed method is to utilize the spatial-temporal 2-D structure of the data received by the array and the conjugate symmetry of the signal delay auto-correlation function to construct a conjugate augmented spatial-temporal cross-correlation matrix. Then, the properties of the matrix Kronecker product can be utilized to decouple the steering vector of the 2-D angle of arrival and utilize the rank-reduction and one-dimensional spectral peak search to automatically pair the 2-D DOA. The proposed method can handle the 2-D angle-estimation problem under underdetermined cases, and the effectiveness of the proposed method was verified by computer simulations.

Classification of cardiac diseases from electrocardiogram signals is essential for enhancing patient results to minimize healthcare costs, early detection and accurate diagnosis. This research investigates the integration of dimensionality reduction methods with various deep learning classifiers to improve the accuracy and efficient classification of cardiac disease. Uniform Manifold Approximation and Projection combined with Principal Component Analysis is used for dimensionality reduction, that captures both global and local data structures. Deep learning classifiers with convolutional neural networks, capsule networks, recurrent neural networks, graph neural networks, deep long short-term memory and automatical attention-based convolutional neural networks are employed for classification. The Adaptive spiral Flying Sparrow Search algorithm optimizes classifier parameters for enhance accuracy. Performance is evaluated through various metrics, with area under the receiver operating characteristic curve, accuracy, F1-Score, precision and recall. The proposed method's outcomes are compared with and without optimization to demonstrate the efficiency and attains 92.16%, 96.15%, 91.95%, 94.65%, 91.45% and 92.85% accuracy respectively for each classification method.

Phase noise (PN) is a prevalent challenge in oscillator-driven systems, leading to spectral dispersion of the power spectral density (PSD) around a Dirac delta function. This paper addresses the task of estimating a communication channel affected by additive white Gaussian noise (AWGN) and phase noise. Traditional estimation methods such as the least mean square (LMS) and mean square error (MSE) criteria are deemed inadequate due to the unique characteristics of phase noise. In this study, we propose a novel approach for PN channel estimation utilizing information-theoretic learning (ITL) principles, specifically focusing on the maximum correntropy criterion (MCC). By employing MCC, our method enhances the robustness of the channel estimator in steady-state conditions, thereby improving the accuracy of parameter estimation. Additionally, to expedite the convergence rate of our algorithm, we introduce a novel mixed-LMS approach that amalgamates elements of both MSE and MCC. This hybrid technique leverages the strengths of each criterion, resulting in a more efficient and accurate estimation of the PN-affected channel. Through comprehensive analysis and experimentation, our proposed method demonstrates its effectiveness in mitigating the impact of phase noise on channel estimation.

The projection order of the affine projection algorithm (APA) affects not only the convergence performance but also the steady-state behavior. Therefore, for solving the fixed projection order of the conventional APA, we develop a new scheme of variable projection order (VPO) that enables the evolution from the maximum projection order to the minimum one by resorting to the adaptation of the mixing factor, thereby yielding a novel APA with VPO. Moreover, for dealing with sparse systems with fast convergence, we extend the proposed VPO scheme to proportionate APA and develop a VPO-based memory proportionate APA. Simulation results in the scenarios of system identification and acoustic echo cancellation have shown that the proposed algorithms have a faster convergence rate and lower steady-state error as compared to their original counterparts and the existing VPO algorithms.

The underwater environment introduces uncertainty into the acoustic velocity, which affects the performance of traditional direction of arrival (DOA) estimation methods. This research proposes a cascaded neural network based underwater DOA estimate approach to address this issue. In this method, the cascade neural network is composed of a velocity regressor and a velocity classifier. To determine the estimated value of acoustic velocity, the velocity classifier first breaks down the input data into distinct velocity domains. It then regulates the velocity regression process. Then, the array steering matrix predicted by the blind source separation algorithm is utilized to determine the angle, and the acoustic velocity is modiffed by the cascaded neural network. Eventually, it is possible to derive the DOA estimation value under the calculated acoustic velocity. The suggested method has a high estimation accuracy especially when the acousitc velocity is unknown, as shown by the simulation results.

In this article, two accurate nonlinear methods are proposed to calculate non-symmetrical locking ranges of the Injected Cross-Coupled Oscillator (ICCO) with the parallel RLC tank and series RL with a parallel C tank for both weak and strong injection levels. By writing governing differential equations of circuit elements of the ICCO, graphical presenting of current vectors, and using the averaging method for solving nonlinear equations, equations of the ICCO are simplified. Then, exact non-symmetrical locking ranges are calculated using the iterative method. Moreover, the describing function of the oscillator’s nonlinear part, an inverse tangent function, is applied to the model. The inverse tangent function generates complicated governing differential equations of circuit elements that are accurate. Then, it is solved to ICCO for the first time and has novel results for calculating non-symmetrical locking ranges. There is a good agreement between theoretical and simulation results. The proposed non-symmetrical locking ranges are accurate in both weak and strong injections. The absolute percent of errors for various levels of the injection signal is less than 20%. In the bargain, proposed locking ranges are the most accurate compared to previously published results.

The advancement of emerging technologies favors the proliferation of multi-valued logic design as it offers enhancement of circuit performance parameters with increased level of integration. This work has presented carbon nanotube field effect transistor (CNTFET) based ternary shift register designs which are realized by employing single-edge triggered ternary D-flip-flop cells with reset input. The dependency of threshold voltage on carbon nanotube physical dimensions is used for the realization of multiple threshold voltages in ternary logic designs. The D-flip flop design with reset capability implementation is performed using multiplexer based positive and negative latches arranged in master–slave architecture. Further, the D-flip-flop cells with reset input are combined to construct Ternary logic serial input serial output (SISO), parallel input parallel output (PIPO) and parallel input serial output (PISO) registers. The latching of the input across the output happens only if the reset input is high otherwise no latching is performed. The PISO register is operating in two modes of loading and shifting realized using NAND logic. The proposed ternary shift register designs using CNTFETs are simulated using HSPICE considering the 32 nm Stanford CNTFET model. The results demonstrate that for 4-bit register design, power and PDP improvements of more than 70% are achieved for SISO designs and a maximum of 90% is attained for PIPO and PISO register designs as compared to recent counterparts. The Monte-Carlo simulation results indicate robust and stable operation of the proposed designs when subjected to process variations.

In order to solve the problem of deteriorating performance of the conventional subband adaptive filtering algorithm when processing the EIV model with impulsive noise, this paper proposes the robust Total Least Mean M-Estimate normalized subband filter (TLMM-NSAF) adaptive algorithm based on the M-estimation function. In addition, we conduct a detailed theoretical performance analysis of the TLMM-NSAF algorithm, which allows us to determine the stable step size range and theoretical steady-state mean squared deviation of the algorithm. To further improve the algorithm's performance, we propose a new variable step size method. Finally, we compared the algorithm with other competition algorithms in applications of system identification and acoustic echo cancellation. Simulation results have demonstrated the superiority of our proposed algorithm, as well as the consistency between the theoretical values and the simulated values.

The mathematical modeling of the real-time system results in large-dimensional ordinary or partial differential equations, which are challenging to employ for investigation and control synthesis. Finding a comparable system of the same kind in a lower-order dimension that can maintain the core characteristics of the higher-order system (HOS) is essential. This article discusses a novel technique for HOS’s reduced order approximation and control design. The proposed method modifies the Schur method for balanced truncation. The method circumvents the requirement of balancing transformation and steady-state gain (SSG) deviation as required in the truncated model reduction algorithm. The offered technique eliminates the SSG deviation without altering the dynamical behavior compared to the HOS by appending a gain enhancement factor to the numerator polynomial of the reduced system transfer matrix. The offered technique ensures that the reduced system retains the HOS stability, passivity, SSG, and all critical characteristics. The recommended method’s findings are compared to the outcomes of recently published work’s lower-order models. Further, using the proposed method, the reduced model has been used to design a PID controller for the flexible-missile control model and an automatic voltage regulator system. The controller produced using the simplified model provides almost the exact time domain specifications (TDS) as the controller made using the HOS, and its design is also relatively more straightforward. Step response and TDS assess lower- and higher-order controller performance.