Search Results (146)

Recursive least-squares (RLS) adaptive algorithms are capable of outperforming least-mean-square (LMS) methods for the identification of long-length impulse responses due to their ability to mitigate the high correlation properties of input signals, such as speech. Despite the encouraging results obtained in terms of tracking speed and accuracy, with respect to LMS methods, most RLS algorithms manifest numerical stability issues. Moreover, when an unknown system changes, the identification process needs to adapt to the new impulse response as soon as possible. The algorithm can require a significant amount of time to generate new accurate results in acoustic echo cancellation (AEC) scenarios. Due to the slow propagation speed of sound, acoustic echo paths are usually modeled using thousands of numerical coefficients, and adaptation energy remains relatively limited. A compromise is usually made between tracking capabilities and steady-state accuracy when choosing the forgetting factor (the most important parameter of the RLS algorithm). This paper analyzes a variable forgetting factor (VFF) RLS type of adaptive filter combined with the conjugate gradient (CG) line search method, which is designed to avoid the classical matrix inversion approach. This VFF-RLS-CG adaptive method is not susceptible to numerical stability issues and is designed to adapt its statistical estimates by determining whether a tracking situation occurs or whether the unknown system is not significantly different. Correspondingly, when necessary, the forgetting factor is decreased for faster adaptation to changes in the working environment. When the filter is estimated to work at steady-state, the above-mentioned parameter’s value is increased in order to boost the accuracy of the adaptive filter. The theoretical model is validated using simulations in AEC scenarios with tracking occurrences and relevant steady-state intervals. Full article

Road noise has become a dominant interior noise source in electrified vehicles, especially at low and medium speeds. In practical active road noise control (ARNC) systems, the error microphones capture not only the road noise component correlated with the reference sensors but also non-coherent disturbances such as wind noise, engine harmonics, and heating, ventilation and air conditioning (HVAC) noise. These disturbances degrade the convergence stability and steady-state attenuation of the conventional filtered-x least mean square (FxLMS) algorithm. This study analyzes FxLMS under non-coherent interference and develops two robustness optimization methods. Under the small-step-size assumption, a statistical convergence model is derived for stationary random inputs, together with the corresponding convergence region and steady-state error expressions. Based on this analysis, a multichannel cascaded controller (MCC) and a bounded variable-step-size (VSS) FxLMS algorithm are proposed. Offline simulations and dSPACE-based experiments are conducted on a single-channel HVAC duct ANC test platform and a vehicle test bench. The vehicle-bench tests use controlled tonal excitations and should be interpreted as an intermediate validation step before real-driving broadband tests. Average noise reduction (ANR) and the norm of the adaptive-filter coefficients are used to evaluate robustness. Both MCC and VSS improve attenuation and reduce coefficient fluctuations under non-coherent interference. Relative to fixed-step FxLMS, the maximum ANR improvement reaches 15.8 dB in simulation and 14.2 dB in the real-time duct experiment. Within the controlled tonal and tonal-plus-white-noise conditions tested here, VSS achieves robustness improvements close to those of MCC with much lower computational cost; therefore, it is a practical candidate for further onboard ARNC evaluation rather than a completed validation under real-driving broadband road noise. Full article

This paper presents a 2-GS/s voltage–time hybrid pipelined analog-to-digital converter (ADC) with a 14-bit digital output, implemented in a 28-nm CMOS process. To alleviate the gain–bandwidth–power trade-off in deeply scaled technologies, the proposed architecture employs a SHA-less front-end and a low-gain inverter-based push–pull RA for energy-efficient coarse quantization. The residue is then transferred to the time domain via a highly linear constant-current voltage-to-time converter (CC-VTC) and digitized by a four-channel time-interleaved gated-ring-oscillator (GRO) TDC. To recover dynamic linearity degraded by low-gain amplification and interleaving mismatches, a multiplier-less digital background calibration engine is implemented. Leveraging mean absolute value (MAV) statistics and dither-injected least-mean-squares (LMS) algorithms, it effectively compensates for inter-channel and interstage errors with minimal hardware overhead. The prototype occupies an active area of 0.16 mm². At 2 GS/s, the ADC achieves a Nyquist SNDR of 63.42 dB and an SFDR of 73.71 dB, corresponding to an ENOB of 10.24 bits. Consuming 86.9 mW from a 1-V supply, it achieves a Walden FoM of 35.9 fJ/conv.-step. Measurement results from multiple chips under a wide range of operating conditions verify the robustness of the proposed ADC. Full article

Non-contact measurement plays a crucial role in monitoring the heart rate of preterm and low birth weight infants in the neonatal intensive care unit (NICU). Addressing the challenges of weak heartbeat signals easily overwhelmed by noise in non-contact heart rate detection for these neonates, this paper proposes a millimeter-wave radar-based heart rate detection method using adaptive subband variable step-size least mean square (LMS) filtering. The innovative approach divides the chest echo signal into multiple subbands, employing an error-based variable step-size update strategy in each subband. By utilizing the abdominal signal as a reference, the heartbeat information is enhanced through adaptive filtering, and the results from various subbands are fused. The heart rate estimation is achieved by combining the fused results with time-frequency analysis using wavelet transform. Experimental results on data collected from multiple preterm infants in the NICU demonstrate that the proposed algorithm can reduce the root mean square error (RMSE) of preterm infant heart rate estimation to below 5 Beats Per Minute (BPM), providing a novel solution for the application of millimeter-wave radar in NICU heart rate monitoring. Full article

In the sensor array signal reception system, improving the accuracy of weak-signal detection is crucial. Traditional fixed-step algorithms struggle to balance the convergence rate (CR) and low steady-state error (SSE) owing to their inherent trade-off limitations. To address this limitation, we propose a novel variable-step-size least-mean-square (VSS-LMS) algorithm based on a modified versoria function, specifically redesigned to enhance curvature characteristics. This approach establishes dynamic coupling between error statistics and step-size factors through nonlinear mapping. It derives closed-loop equations governing parameters ($\alpha$, $\beta$, and $\gamma$) relative to the smoothed instantaneous error correlation function. Consequently, an adaptive feedback system is constructed to achieve real-time adjustment through optimal step-size generation. The optimal parameters ($\alpha$, $\beta$, and $\gamma$) are determined through empirical enumeration and analysis of their impact on algorithmic performance. Comparative evaluations against established VSS-LMS algorithms confirm performance: the proposed algorithm accelerates convergence while maintaining a low SSE, and exhibits robust signal recovery capabilities under low-SNR conditions with diverse interference types. Full article

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments. Full article

This paper proposes a complex fast recursive least-squares (FRLS) channel-estimation algorithm for single-carrier electromagnetic (EM) communications across the seawater–air interface, where severe attenuation and multipath cause strong SNR fluctuations. By redesigning the input-data structure and using forward–backward joint estimation, FRLS reduces the per-iteration complexity from the quadratic cost of classical RLS to a linear form (14L + 20 operations per iteration, where L is the channel length). Simulations under representative one- to three-path channels show that FRLS achieves the lowest steady-state mean-square deviation (MSD) at low SNR, outperforming LMS, IPNLMS, RLS, and PRLS. Offshore experiments further validate the practicality: after MMSE equalization, FRLS yields higher OSNR and improves the BER distribution, demonstrating an effective accuracy–complexity trade-off for hardware-constrained cross-medium EM links. Full article

With the rapid deployment of high-speed maglev transportation systems worldwide, the operational velocity, electromagnetic complexity, and channel dynamics have far exceeded those of conventional rail systems, imposing more stringent requirements on real-time capability, reliability, and interference robustness in wireless communication. In maglev environments exceeding 600 km/h, the channel becomes predominantly line-of-sight with sparse scatterers, exhibiting strong Doppler shifts, rapidly varying spatial characteristics, and severe interference, all of which significantly degrade the stability and convergence performance of traditional beamforming algorithms. Adaptive smart antenna technology has therefore become essential in high-mobility communication and sensing systems, as it enables real-time spatial filtering, interference suppression, and beam tracking through continuous weight updates. To address the challenges of slow convergence and high steady-state error in rapidly varying maglev channels, this work proposes a new Fractional Proportionate Normalized Least Mean Square (FPNLMS) adaptive beamforming algorithm. The contributions of this study are twofold. (1) A novel FPNLMS algorithm is developed by embedding a fractional-order gradient correction into the power-normalized and proportionate gain framework of PNLMS, forming a unified LMS-type update mechanism that enhances error tracking flexibility while maintaining $\mathcal{O}\left(L\right)$ computational complexity. This integrated design enables the proposed method to achieve faster convergence, improved robustness, and reduced steady-state error in highly dynamic channel conditions. (2) A unified convergence analysis framework is established for the proposed algorithm. Mean convergence conditions and practical step-size bounds are derived, explicitly incorporating the fractional-order term and generalizing classical LMS/PNLMS convergence theory, thereby providing theoretical guarantees for stable deployment in high-speed maglev beamforming. Simulation results verify that the proposed FPNLMS algorithm achieves significantly faster convergence, lower mean square error, and superior interference suppression compared with LMS, NLMS, FLMS, and PNLMS, demonstrating its strong applicability to beamforming in highly dynamic next-generation maglev communication systems. Full article

Adaptive filtering algorithms often suffer from biased parameter estimation and performance degradation in the presence of colored input noise and impulsive observation noise, both of which are common in practical sensor and communication systems. Existing bias-compensated least mean square (LMS) algorithms generally assume white Gaussian input noise, thereby limiting their applicability in real-world scenarios. This paper introduces a robust convex combination bias-compensated LMS (CC-BC-LMS) algorithm designed to address both colored Gaussian input noise and impulsive observation noise. The proposed algorithm achieves bias compensation through robust estimation of the input noise autocorrelation matrix and employs a modified Huber function to mitigate the influence of impulsive noise. A convex combination of fast and slow adaptive filters enables variable step-size adaptation, effectively balancing rapid convergence and low steady-state error. Extensive simulation results demonstrate that the proposed CC-BC-LMS algorithm provides substantial improvements in normalized mean square deviation (NMSD), surpassing state-of-the-art bias-compensated and robust adaptive filtering techniques by $4.48$ dB to $11.4$ dB under various noise conditions. These results confirm the effectiveness of the proposed approach for reliable adaptive filtering in challenging noisy environments. Full article

The air-conditioner noise in a rehabilitation room can seriously affect the mental state of patients. However, the existing single-layer active noise control (ANC) filters may fail to attenuate the complicated harmonic noise, and the deep recursive ANC method may fail to work in real time. To solve the problem, in a bending-pipe model, a new hybrid adaptive self-loading filtered-x least-mean-square (ASL-FxLMS) and convolutional neural network-gate recurrent unit (CNN-GRU) network is proposed. At first, based on the recursive GRU translation core, an improved CNN-GRU network with multi-head attention layers is proposed. Especially for complicated harmonic noises with more or fewer frequencies than harmonic models, the attenuation performance will be improved. In addition, its structure is optimized to decrease the computing load. In addition, an improved time-delay estimator is applied to improve the real-time ANC performance of CNN-GRU. Meanwhile, an adaptive self-loading FxLMS algorithm has been developed to deal with the uncertain components of complicated harmonic noise. Moreover, to achieve balance attenuation, robustness, and tracking performance, the ASL-FxLMS and CNN-GRU are connected by a convex combination structure. Furthermore, theoretical analysis and simulations are also conducted to show the effectiveness of the proposed method. Full article

Structured Light LiDAR is susceptible to lens scattering and temperature fluctuations, resulting in some level of distortion in the captured point cloud image. To address this problem, this paper proposes a high-performance 3D point cloud Least Mean Square filter based on Decision Tree, which is called the D−LMS filter for short. The D−LMS filter is an adaptive filtering compensation algorithm based on decision tree, which can effectively distinguish the signal region from the distorted region, thus optimizing the distortion of the point cloud image and improving the accuracy of the point cloud image. The experimental results clearly demonstrate that our proposed D−LMS filtering algorithm significantly improves accuracy by optimizing distorted areas. Compared with the 3D point cloud least mean square filter based on SVM, the accuracy of the proposed D−LMS filtering algorithm is improved from 86.17% to 92.38%, the training time is reduced by 1317 times and the testing time is reduced by 1208 times. Full article

For active control of impulsive noise, the conventional filtered-x least mean square (FxLMS) algorithm has poor noise reduction performance. To address this issue, this paper designs a robust cost function by embedding the cost function of the FxLMS algorithm into the framework of the adjustable Softsign function, thereby designing a robust Softsign-FxLMS (SFxLMS) algorithm for ANC systems. Furthermore, the parameter λ of the SFxLMS algorithm significantly influences its robustness and convergence speed. Therefore, a variable λ-parameter SFxLMS (VSFxLMS) algorithm is designed to improve the performance of the ANC system. Simulation studies indicate that the proposed SFxLMS algorithm and VSFxLMS algorithm exhibit stronger robustness, faster convergence rates, and better tracking performance compared to several robust FxLMS algorithms. Moreover, the symmetric properties of the proposed Softsign function contribute to balanced error suppression in both positive and negative directions, enhancing the robustness and stability of the ANC system under asymmetric impulsive noise conditions. Full article

The increasing demand for real-time, energy-efficient, and interference-resilient communication in smart healthcare environments has intensified interest in Biomedical Internet of Things (Bio-IoT) systems. However, ensuring reliable wireless connectivity for wearable and implantable biomedical sensors remains a challenge due to mobility, latency sensitivity, power constraints, and multi-user interference. This paper addresses these issues by proposing a reconfigurable massive multiple-input multiple-output (MIMO) antenna architecture, incorporating hybrid analog–digital beamforming and adaptive signal processing. The methodology combines conventional algorithms—such as Least Mean Square (LMS), Zero-Forcing (ZF), and Minimum Variance Distortionless Response (MVDR)—with a novel mobility-aware beamforming scheme. System-level simulations under realistic channel models (Rayleigh, Rician, 3GPP UMa) evaluate signal-to-interference-plus-noise ratio (SINR), bit error rate (BER), energy efficiency, outage probability, and fairness index across varying user loads and mobility scenarios. Results show that the proposed hybrid beamforming system consistently outperforms benchmarks, achieving up to 35% higher throughput, a 65% reduction in packet drop rate, and sub-10 ms latency even under high-mobility conditions. Beam pattern analysis confirms robust nulling of interference and dynamic lobe steering. This architecture is well-suited for next-generation Bio-IoT deployments in smart hospitals, enabling secure, adaptive, and power-aware connectivity for critical healthcare monitoring applications. Full article

This work presents an 80 Gbps photonics-aided millimeter-wave (mm Wave) wireless communication system employing 16-Quadrature Amplitude Modulation (16-QAM) and a 1 × 2 single-input multiple-output (SIMO) architecture with maximum ratio combining (MRC) to achieve robust 87.5 GHz transmission over 4.6 km. By utilizing polarization-diverse optical heterodyne generation and spatial diversity reception, the system enhances spectral efficiency while addressing the low signal-to-noise ratio (SNR) and channel distortions inherent in long-haul links. A blind equalization scheme combining the constant modulus algorithm (CMA) and decision-directed least mean squares (DD-LMS) filtering enables rapid convergence and suppresses residual inter-symbol interference, effectively mitigating polarization drift and phase noise. The experimental results demonstrate an SNR gain of approximately 3 dB and a significant bit error rate (BER) reduction with MRC compared to single-antenna reception, along with improved SNR performance in multi-antenna configurations. The synergy of photonic mm Wave generation, adaptive spatial diversity, and pilot-free digital signal processing (DSP) establishes a robust framework for high-capacity wireless fronthaul, overcoming atmospheric attenuation and dynamic impairments. This approach highlights the viability of 16-QAM in next-generation ultra-high-speed networks (6G/7G), balancing high data rates with resilient performance under channel degradation. Full article

This paper proposes an optimized control filter (OCF) based on the Filtered-x Least Mean Square (FxLMS) algorithm for multi-channel active noise control (ANC) systems. The proposed OCF-McFxLMS algorithm delivers three key contributions. Firstly, even in difficult noise situations such as White Gaussian, Brownian, and pink noise, it greatly reduces error, reaching nearly zero mean squared error (MSE) values across all Microphone (Mic) channels. Secondly, it improves computational efficiency by drastically reducing execution time from 58.17 s in the standard McFxLMS algorithm to just 0.0436 s under White Gaussian noise, enabling real-time noise control without compromising accuracy. Finally, the OCF-McFxLMS demonstrates robust noise attenuation, achieving signal-to-noise ratio (SNR) values of 137.41 dB under White Gaussian noise and over 100 dB for Brownian and pink noise, consistently outperforming traditional approaches. These contributions collectively establish the OCF-McFxLMS algorithm as an efficient and effective solution for real-time ANC systems, delivering superior noise reduction and computational speed performance across diverse noise environments. Full article