Time Domain Research Articles

A Novel FSS Loaded Circular Slot Monopole Antenna for Microwave Breast Imaging Application

Breast cancer continues to be a major contributor to women's global mortality rates, emphasizing the need to explore alternative and supplementary early-stage detection techniques. This study delves into a circular slot monopole ultra-wideband (UWB) antenna equipped with a frequency-selective surface (CSMA-FSS) for early detection microwave imaging applications. To expand the impedance bandwidth of the traditional circular monopole antenna, a circular slit is introduced on a monopole structure. Additionally, a 10 × 10 unit cell FSS structure is integrated into the antenna as a far-field reflecting plane to enhance gain characteristics. The antenna, crafted with dimensions of 50 × 50 × 1.6 mm³ and a 1.6 mm thick FR4 substrate, operates across the ultra-wideband frequency spectrum, spanning from 3.1 GHz to 12 GHz, while consistently maintaining a reflection coefficient (S 11) value of – 10 dB or lower. Notably, the antenna showcases a gain of over 5 dBi across the entire 3.1 GHz to 12 GHz frequency range. The suggested antenna is ideal for microwave imaging applications due to its broad impedance bandwidth, impressive gain, and excellent directivity. Furthermore, it maintains a relatively uniform group delay, with a deviation of ±0.5 ns throughout the UWB band. In the time domain characteristics, the antenna exhibits excellent pulse handling capabilities and strong signal correlation, featuring an S 21 value of less than – 25 dB. The characteristics, observed in both the time and frequency domains, suggest that the proposed antenna holds significant potential for substantially enhancing breast cancer imaging system.

Non-Contact Blood Pressure Estimation from Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14mmHg and -0.20 ±5.50mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

The essential role of climate policy uncertainty in carbon emissions: a fresh insight.

This study assesses the influence of climate policy uncertainty (CPU) on carbon emissions (CE) against the backdrop of economic policy uncertainty (EPU) in the US. The wavelet analysis provides a comprehensive understanding of correlations in the time and frequency domains. The results demonstrate a significant correlation between CPU and CE, which varies across different time periods and frequencies. In the time domain, the results indicate that the CPU and CE move together during certain subperiods. Moreover, there are observable comovements in the frequency domain, particularly in the short to medium range. However, the correlation becomes stronger in the short term when there is no EPU, suggesting a closer interaction between CPU and CE. Therefore, it is crucial for governments to prioritize improving the clarity, credibility, and consistency of climate policies. They should also consider potential economic shocks when designing these policies.

Anisotropic Fourier Heat Conduction and phonon Boltzmann transport equation based simulation of time domain thermo-reflectance experiments

The anisotropic Fourier Heat Conduction Equation (FHCE) and the multidimensional phonon Boltzmann Transport Equation (BTE) were solved numerically in cylindrical coordinates and in time domain to simulate a Time Domain Thermo-Reflectance (TDTR) experimental silicon/aluminum substrate/transducer setup. The out-of-phase response of the probe laser was predicted at various beam offset distances for a pump laser pulse frequency of 80 MHz and modulation frequency of 10 MHz and compared against experimental measurements for a silicon substrate. The isotropic FHCE was also solved for comparison. Results show that the isotropic FHCE with bulk thermal conductivity of 145 W/m/K significantly underpredicts the out-of-phase temperature difference, particularly at smaller beam offsets. With an isotropic thermal conductivity of 105 W/m/K, the computed results match experimental data at smaller beam offsets well, but overpredicts the experimental data at larger beam offsets. An almost-perfect match is obtained by using an anisotropic thermal conductivity wherein the radial (in-plane) thermal conductivity is set to 85 W/m/K and the axial (through-plane) conductivity is set to 130 W/m/K. The multidimensional frequency and polarization dependent phonon BTE is next solved. The BTE results for the out-of-phase temperature difference match experimental observations well at small and intermediate beam offsets, but overpredicts the experimental data at larger beam offsets. FHCE results are fitted to the BTE predictions, and the extracted (best fit) thermal conductivity is found to be 110 W/m/K.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice

Abstract Studies of intracranial EEG (iEEG) networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. iEEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal iEEG measures brain activity at baseline and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. iEEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal iEEG across two epilepsy centers to generate iEEG functional connectivity networks. 24 functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing (CAR) or bipolar re-referencing (BR). We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: 1) the introduction of spurious correlations, 2) robustness to incomplete spatial sampling, and 3) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: CAR pipelines, BR pipelines, and relative entropy-based connectivity pipelines. Relative entropy and CAR networks were more robust to incomplete electrode sampling than BR and other connectivity methods (Friedman test, Dunn-Sˇidák test p < 0.0001). BR reduced spurious correlations at non-adjacent channels better than CAR (Δ mean from machine ref = -0.36 vs -0.22), and worse in adjacent channels (Δ mean from machine ref = -0.14 vs -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (BH-corrected p < 0.05, Cohen’s d: 0.60 - 0.76). Finally, we present an interface where users can rapidly evaluate iEEG pre-processing choices to select the optimal preprocessing methods tailored to specific research questions. The choice of preprocessing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their preprocessing methods for deriving iEEG brain networks.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

WBUN: An interpretable convolutional neural network with wavelet basis unit embedded for fault diagnosis

Abstract Convolutional Neural Network (CNN) is extensively applied in mechanical system fault diagnosis. However, the absence of transparent decision mechanisms in CNNs hinders credibility. To address these challenges, this paper proposes an interpretable wavelet basis unit convolutional network (WBUN). This network incorporates meticulously designed wavelet basis unit (WBU) functions into convolutional layer, creating the interpretable wavelet basis unit convolutional (WBUConv) layer. Convolutional kernels with clear physical significance enable the WBUConv layer to extract fault-related features in both time and frequency domains, enhancing diagnostic performance, and interpreting the CNN's attention frequency along with the convolutional kernel's training outcomes. In this paper, three WBU functions are designed to construct the corresponding WBUNs, and their effectiveness and interpretability are verified through three sets of mechanical fault diagnosis experiments. Meanwhile, experimental results demonstrate the WBUConv layer's remarkable advantages in noise robustness, convergence speed, and strong generalization ability.

Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation

A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient’s relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.Graphical abstract

Impact of Obesity on Cardiac Autonomic System Functioning in Military Police Officers.

Cardiac autonomic system functioning may be altered by obesity leading to cardiovascular diseases and associated complications. Military police officers are exposed to traditional and occupational risk factors for the development of CVD, however data on the cardiovascular health in this population is still scarce. In this cross-sectional study, we investigated the impact of obesity on cardiac autonomic modulation and the hemodynamic profile in male active-duty military police officers. The body composition of the volunteers was assessed by octapolar electrical bioimpedance. Participants were classified as non-obese or obese in accordance with their body fat, with further subgroups as physically active obese or insufficiently active obese using International Physical Activity Questionnaire (IPAQ). Cardiac autonomic modulation was assessed by heart rate variability and the automatic oscillometric method allowed us to assess hemodynamic features. 102 military police officers from the state of São Paulo participated in the study. Cardiac autonomic modulation revealed significant impairment in time and frequency domains and non-linear methods in the obese group compared to the non-obese (p < 0.05). A higher physical activity level did not alter these results in the obese group. However, no significant differences in the hemodynamic profile were observed between groups (p > 0.05). These findings suggest a negative association between obesity and cardiac autonomic modulation in military police officers, unaffected by increased physical activity.

ДОСЛІДЖЕННЯ ОСОБЛИВОСТЕЙ ОБРОБЛЕННЯ СИГНАЛІВ ДИНАМІЧНИХ ЦІЛЕЙ В БОРТОВИХ РУХОМИХ ІМПУЛЬСНИХ РАДІОЛОКАТОРАХ

The characteristic features of coherent (quasi-coherent) and incoherent accumulation and detection of signals received from the radio air (reflected from a passive dynamic target) are considered, which are an additive mixture of a useful component and a possible "noise-like" interference with the added "own" noise of the receiving path in airborne mobile pulse radars air base. It is assumed that the random components of the input signal are adequately described by a normal (or close to it) distribution law, and the signal (during the accumulation interval) is harmonic, the period of which is uniquely related to the Doppler frequency shift in the received signal. Necessary and sufficient conditions for coherent (quasi-coherent) and incoherent effective signal accumulation are formulated, analytical and graphical dependencies are given for determining the output ratio "signal /(noise+interference)" at different multiplicities of coherent and incoherent accumulation in both time and spectral domains. A mathematical model based on the system modeling package "System View" was developed and investigated, which demonstrates, by means of adequate simulation and statistical averaging, the peculiarities and quantitative differences of the results of coherent and incoherent accumulations at various, in particular low, but practically significant, input ratios "signal /( noise+interference)", as well as from the multiplicity of accumulation, that is, the number of received signals during the permissible interval of accumulation.

Fidelity susceptibility probes of dynamical quantum criticality

Fidelity susceptibility is a quantum information-based approach that has been an effective tool for detecting and characterizing quantum phase transitions in an equilibrium setting. Motivated by the usefulness of the static counterpart, we propose that it can serve as a versatile tool to identify dynamical quantum phase transitions. Further, we develop linear-scale simulation methods for dynamical fidelity susceptibility calculations, which are based on the Chebyshev polynomial-expansion approach. We study the Aubry–André model as a benchmark for the validation of the computational technique. Numerical simulations show nonanalytic behaviors of the fidelity susceptibility in the time domain, signaling the existence of dynamical quantum phase transitions triggered by the incommensurate potential modulation of quantum quench systems. In addition, we find an excellent scaling collapse of the data onto a single curve in the limiting quench processes, which leads to a single-parameter scaling in the fidelity susceptibility. The feasibility of experimental measurement of the fidelity susceptibility opens up a new avenue toward an understanding of the nonequilibrium transport of quantum systems.

Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series

Carbon emissions have become particularly urgent with the acceleration of industrialization. Currently, AI-based carbon emission modeling provides essential support for solving the problem of predicting, managing, and optimizing carbon emissions. However, there are still challenges in accurately predicting carbon dioxide emissions and establishing an appropriate carbon emission regulatory model to manage carbon dioxide emissions effectively. Firstly, this paper introduces a framework for learning seasonal and trend representations called DeTF (Decoupling representation for Time and Frequency domain of time series). The framework is designed for time series data of CO2 emissions used for power generation from 1973 to 2023. Secondly, we use various data enhancement techniques to improve the robustness of the learned representations. Finally, the prediction results establish a tripartite evolutionary game relationship between the state, regulatory agencies, and enterprises. We use MSE and MAE as evaluation metrics, and our model overall outperforms current state-of-the-art end-to-end algorithms. It outperforms the best performing end-to-end prediction method by 27.08% (MSE) in the prediction results. Besides, the time series prediction results are innovatively combined with the tripartite game model to analyze the main factors determining the optimal strategy.

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

Recent years witness the rapid development of communication and radar technologies, and many transmitters are equipped with both communication and radar functionalities. To keep track of the working state of a target dual-functional transmitter, it is crucial to sense the modulation mode of the emitted signals. In this paper, we propose a deep learning-based intelligent modulation sensing technique for dual-functional transmitters. Different from existing modulation sensing methods, which usually focus on communication signals, we take both communication and radar signals into consideration. Typically, the dominant features of communication signals lie in the time domain, while those of radar signals lie in both time and frequency domains. To enhance the sensing accuracy, we first exploit real and complex value convolution operations to extract both time-domain and frequency-domain features of emitted signals from the target transmitter. Then, we fuse the extracted features by assigning proper weights with the attention mechanism. Simulation results reveal that the proposed technique can improve the sensing accuracy by up to 4% on average compared with benchmarks.

A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation

This paper presents an efficient approach to detect, diagnose and estimate the severity of failures in various components of bearings in induction motors using vibration signature analysis. This automated method integrates the Fisher Score feature selection method and an efficient hyperparameter tuning model with machine learning models, including Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Decision Tree (DT), to accurately classify defects in bearings. This approach ensures accurate classification of bearing defects within less computation time. This work is carried out with vibration signals, recorded from a laboratory experimental setup on Machine Fault Simulator (MFS), focusing on ball bearing with defects in inner race, outer race and ball itself. Time and Frequency domain analysis are employed to compute the features for fault investigation in ball bearings using machine learning models. The computed results demonstrate that the proposed feature selection method with hyperparameter tuning achieved remarkable maximum accuracy among X, Y and XY combinations of datasets, with 97% in DT, 94% in SVM and 95.23% in k-NN models during the frequency domain analysis. Notably, these model accuracies improved to 99.04% in DT, 98% in SVM and 98% in k-NN during further analysis with Fisher Score technique. Consequently, the testing loss using this hyperparameter tuning function remains very low. Overall, this paper compares the results of time and frequency domain analysis and introduces a promising approach for both efficient and accurate fault detection and severity estimation in bearings of induction motors, potentially reducing the need for extensive manpower and sensor usage.

Active vibration isolation on electromechanical coupling suspension system of a quarter-vehicle via modal space controller design

The active vibration isolation of a quarter-vehicle suspension system using an electromagnetic damper was achieved by designing the feedback gain of a linear quadratic regulator (LQR) and H-∞ controllers in modal coordinates. The efficacy of vibration isolation was analyzed in the frequency domain, while the performance of vibration suppression and passengers’ comfort quality were evaluated in the time domain. The research results demonstrated that by employing modal coordinate transformation in state space, the designated system’s resonance peaks can be directly suppressed in the frequency domain. The findings also indicate that the active control in modal coordinates can achieve a higher vibration suppression percentage per unit control effort in comparison to traditional active control in physical space. In other words, the modal controller is more efficient in vibration suppression than the controller designed in physical space.

Military Expenditures and Economic Growth: Evidence from NATO and Non-NATO Alliances

ABSTRACT Given its significant socioeconomic implications, the nexus between military expenditures and economic growth has been the subject of an extensive theoretical and empirical debate. The present study focuses on NATO vs. non-NATO member countries (i.e. 141 countries) as a case study to empirically examine the aforementioned complex relationship during the period 1992–2020. Using annual data, we employ a panel model, as well as spectral preliminary analysis that takes into account the nonlinear behaviour of our series, positive or negative causal relationships and their coherence characteristics across both time and frequency domains. Findings reported herein from an extended panel of several states suggest that military expenditure enhances economic growth under the NATO alliance case, whereas it becomes harmful for growth in the case of non-NATO alliances such as the SCO & CSTO. Our study contributes to the understanding on the crucial role of a military alliance membership in the formation of the economic growth and military spending relationship. Hence, our findings can be beneficial for policymakers to account for the existence of possible spillovers that may arise from country participation in a military alliance.

Time distribution of stimulated transition probabilities

Abstract In recent years, an increasing number of experimental results have been published for a wide variety of systems in which high-precision, ultra-fast, and ultra-short laser pulses techniques have been used to study time-resolved dynamical processes. In the absence of a theoretical prediction of the time distribution of the transition probabilities, the lifetimes $\tau$ are generally extracted by fitting the decay time with an exponential, bi-exponential or three-exponential function. In fitting the data, the short-time behavior is generally neglected. The purpose of this study is to show that an explicit formula for the time distribution of the transition probability can be determined rigorously using time-dependent perturbation theory. A formula that perfectly fits an important subset of the experimental results from $t=0$ to $t=\infty$. We show that by following a route different from the usual procedure for deriving transition amplitudes and Einstein coefficients in time-dependent perturbation theory, one ends up with a time-dependent factor, that is, a temporal distribution function of the transition probabilities, and for the Einstein coefficients. The time distribution reported here looks in the time domain similar to the Planck distribution in the frequency domain. In fact, the behavior of the time distribution, with a maximum at $2\tau \ln 2$, and with $\tau$ playing the role of temperature, resembles the behavior of the Planck frequency distribution. We present several examples to demonstrate that the theoretical formula allows for easier adjustment of the experimental results reported in the literature. We also show that the time distribution explains the difference in resonance heights between the experimental and theoretical blue laser spectra.

Multiphysics simulations of a cylindrical waveguide optical switch using phase change materials on silicon

This work presents the design and multiphysics simulation of a cylindrical waveguide-based optical switch using germanium-antimony-tellurium (GST) as an active phase change material. The innovative cylindrical architecture is theoretically analyzed and evaluated at 1550 nm wavelength for telecommunication applications. The dispersion relation is derived analytically for the first time to model the optical switch, while finite element method (FEM) and finite difference time domain (FDTD) techniques are utilized to simulate the optical modes, light propagation, and phase change dynamics. The fundamental TE01 and HE11 modes are studied in detail, enabling switching between low-loss amorphous and high-loss crystalline GST phases. Increasing the GST thickness is found to increase absorption loss in the crystalline state but also slows down phase transition kinetics, reducing switching speeds. A 10 nm GST layer results in competitive performance metrics of 0.79 dB insertion loss, 13.47 dB extinction ratio, 30 nJ average power consumption, and 3.5 Mb/s bit rate. The combined optical, thermal, and electrical simulation provides comprehensive insights towards developing integrated non-volatile photonic switches and modulators utilizing phase change materials.

Surface-Enhanced Raman Spectroscopy of Ammonium Nitrate Using Al Structures, Fabricated by Laser Processing of AlN Ceramic

This work presents results on laser-induced surface structuring of AlN ceramic and its application in Surface-Enhanced Raman Spectroscopy (SERS). The laser processing is performed by nanosecond pulses in air and vacuum. Depending on the processing conditions, different surface morphology can be obtained. The ablation process is realized by ceramic decomposition as the formation of an aluminium layer is detected. The efficiency of the fabricated structures as active substrates in SERS is estimated by the ability of the detection of ammonium nitrate (NH4NO3). It is conducted for Raman spectrometer systems that operate at wavelengths of 514 and 785 nm where the most common commercial systems work. The obtained structures contribute to enhancement of the Raman signal at both wavelengths, as the efficiency is higher for excitation at 514 nm. The limit of detection (LOD) of ammonium nitrate is estimated to be below the maximum allowed value in drinking water. The analysis of the obtained results was based on the calculations of the near field enhancement at different conditions based on Finite Difference Time Domain simulation and the extinction spectra calculations based on Generalized Mie scattering theory. The structures considered in these simulations were taken from the SEM images of the real samples. The oxidation issue of the ablated surface was studied by X-ray photoelectron spectroscopy. The presented results indicated that laser structuring of AlN ceramics is a way for fabrication of Al structures with specific near-field properties that can be used for the detection of substances with high social impact.

On a stabilization of the Ingard-Myers impedance boundary condition and its time domain implementation

It has been well-known that the Ingard-Myers impedance condition, while simple to apply, is subject to the hydrodynamic Kelvin-Helmholtz-type instability due to its use of a vortex sheet in modeling the flow at the liner boundary. Recently, in the development of a time domain boundary element method for acoustic scattering by treated surfaces, it was found that by neglecting a certain second-order spatial derivative term in the Ingard-Myers formulation, the hydrodynamic instability can be avoided. The present paper aims to provide further analysis of this modified condition, hereby referred to as the Truncated Ingard-Myers Impedance Boundary Condition (TIMIBC). It will be shown, based on the dispersion relations of linear waves, that the instability intrinsic to the Ingard-Myers condition is eliminated in the proposed new formulation. Quantitative assessments on the accuracy of the TIMIBC for scattering of acoustic waves by lined surfaces are carried out, and its effectiveness is demonstrated by a numerical example. It is found that the TIMIBC provides a good approximation to the original Ingard-Myers condition for flows of low to mid subsonic Mach numbers. As such, the proposed TIMIBC can offer a practical solution for overcoming the intrinsic instability associated with the Ingard-Myers condition. Moreover, time domain implementation of the TIMIBC is also discussed and illustrated with a numerical example using a finite difference scheme. In particular, a minimization procedure for finding the poles and coefficients of a broadband multipole expansion for the impedance function is formulated by which, unlike the commonly used vector-fitting method, passivity of the model is ensured.