Search

In this brief, an optimal active fuzzy fault‐tolerant control (OAFFTC) scheme is proposed for a class of unknown perturbed multi‐input multi‐output (MIMO) nonlinear systems with nonaffine nonlinear actuator faults and time‐varying sensor faults. The control strategy can handle automatically (online updating) with three additives (bias, drift, and loss of accuracy) along with one multiplicative (loss of effectiveness) sensor faults and nonlinear state‐dependent actuator faults. In order to deal with uncertain system dynamics, sensor and actuator faults, and external disturbances, fuzzy systems (FSs) and backstepping techniques were combined to provide the adaptive control term as well as a robust control term. Butterworth filter is used to get rid the algebraic loop problem. The suggested robust term, can deal with approximation errors of the fuzzy systems (FSs). To automatically optimize the adaptive parameters and the starting conditions, particle swarm optimization (PSO) approach is introduced. The Lyapunov approach is employed to demonstrate the closed‐loop system's stability. To assess the efficacy and correctness of the suggested scheme, quadrotor dynamic model is performed on the simulation part. [ABSTRACT FROM AUTHOR]

Pc5 geomagnetic pulsations are ultra-low frequency (ULF) waves whose period lies within 150–600 s. Their detection is crucial for accurate space weather modelling, monitoring, and analysis. Identification of these pulsations using manual approaches is difficult and time-consuming because of the smaller magnitudes and the limited durations within which they occur. To overcome this challenge, we propose a robust Cascade Forward Neural Network (CFNN) model combined with the wavelet technique for automatically detecting Pc5 events from satellite datasets observed at geostationary orbits. The dataset used in this study is the magnetic field vector measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) from 2000 to 2009. Pc5 geomagnetic pulsations were extracted from the toroidal component of the field perturbation using a bandpass Butterworth filter. Continuous wavelet transform (CWT) analysis using the mother Morlet wavelet was utilized to validate the integrity of the extracted signal in the time–frequency domain. The extracted Pc5 signal was decomposed into details and approximations using Daubechies wavelet transform to separate the intelligible signal from the incoherent noise that left their trace on the magnetic field time series. The detail signal was subjected to denoising using the heuristic Stein Unbiased Risk Estimate (SURE) approach with soft thresholding to obtain the denoised Pc5 events. The denoised Pc5 events were utilized as the target in the machine learning whereas the inputs were obtained from 5-dimensional space transformation of the toroidal component of the time series. The developed algorithm performed well and demonstrated a detection accuracy of 80 % and a Root Mean Square Error (RMSE) of 0.13 nT indicating a high performance in detecting Pc5 events. For effective validation of the model, Pc5 events detected by our model were correlated with the Kp index and the amplitude of the Pc5 events observed at geostationary orbit. A good correlation was obtained in both cases, making our model a practical and preferred choice for Pc5 pulsation detection in contrast to conventional frequency analysis tools which take much time for signal processing on large data sets. [ABSTRACT FROM AUTHOR]

In order to solve the hysteresis nonlinearity and resonance problems of piezoelectric stages, this paper takes a three-degree-of-freedom tip-tilt-piston piezoelectric stage as the object, compensates for the hysteresis nonlinearity through inverse hysteresis model feedforward control, and then combines the composite control method of positive acceleration velocity position feedback damping control and high-gain integral feedback controller to suppress the resonance of the system and improve the tracking speed and positioning accuracy. Firstly, the three-degree-of-freedom motion of the end-pose is converted into the output of three sets of piezoelectric actuators and single-axis control is performed. Then, the rate-dependent Prandtl–Ishlinskii model is established and the parameters of the inverse model are identified. The accuracy and effectiveness of parameter identification are verified through open-loop and closed-loop compensation experiments. After that, for the third-order system, the parameters of positive acceleration velocity position feedback damping control and high-gain integral feedback controller are designed as a whole based on the pattern of the Butterworth filter. The effectiveness of the design method is proved by step signal and triangle wave signal trajectory tracking experiments, which suppresses the resonance of the system and improves the bandwidth of the system and the tracking speed of the stage. [ABSTRACT FROM AUTHOR]

The vibration waves generated by pressure fluctuations can substantially impair and jeopardize the structural integrity of roadway anchorage within adjacent rock formations, thereby presenting a significant risk to the safety and operational efficiency of mining activities. In order to address this issue and elucidate the response characteristics of roadway-anchored surrounding rock subjected to P-wave and S-wave influences, this study employs a roadway that is experiencing actual impact instability within a mine situated in Xinjiang as the engineering context. The synchrosqueezing wavelet transform, enhanced by a Butterworth filter, is utilized to isolate and filter seismic wave data, thereby facilitating the extraction of time-frequency signals corresponding to both P-waves and S-waves. Subsequently, a dynamic numerical model is developed to simulate the propagation of these vibration waves. An analysis of the dynamic behavior and response characteristics of P-waves and S-waves is performed, focusing on their interaction with roadway anchoring within the surrounding rock at various stages of propagation. The results indicate that weak rock and plastic zones can absorb vibrational waves, with S-waves exhibiting a stronger absorption effect than P-waves. S-waves contribute to increased stress and displacement in the surrounding rock, leading to the accumulation of elastic energy and an expansion of the plastic zone. The rapid fluctuations in the axial force of bolts along the roadway, caused by S-waves, can result in instability within the roadway. The research findings possess considerable reference value and practical applicability for the design of anti-scour support systems in roadways. [ABSTRACT FROM AUTHOR]

Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model's ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias. [ABSTRACT FROM AUTHOR]

This paper presents tunable absorptive dual-notch filters using a stub-loaded coupled line with an inductor. The two-pole Butterworth filter function is modified to attain absorptive responses at a notch frequency. In addition, a stub-loaded coupled line with an inductor is employed to obtain dual-band bandstop responses, the required coupling coefficients, and quality factors based on the modified Butterworth function at two notch frequencies. In the proposed filter, tunable absorptive dual-notch responses are accomplished without using auxiliary circuits, such as an impedance matching network. The two resonant frequencies and the quality factors of the resonators are independently controlled by adjusting the three loading capacitances of the resonator. Three types of filters are designed and then built on a substrate with r = 3.42 and h = 30 mils. The first filter (Filter A) attained a return loss of 11.2-17.1 dB/10.5-24.4 dB at 0.76-0.98 GHz/1.05-1.28 GHz. The measured return loss of the second filter (Filter B) was 12.5-14.8 dB/10.2-12.3 dB at 0.84-1.08 GHz/1.35-1.59 GHz. For Filter C, equipped with a 4-pole response achieved through the cascade connection of two 2-pole filters (Filter B), a return loss of 11.2-15.1 dB/14.2-27.3 dB was obtained at 0.9-1.1 GHz/1.38-1.63 GHz. The application areas of the proposed filters include cognitive radios and carrier aggregation systems requiring two transmit frequencies. [ABSTRACT FROM AUTHOR]

Flexible launch vehicle modes contribute to the deterioration of its stability performance. For this reason, their influence must be considered when designing and validating appropriate solutions for its automatic control system. The first three bending modes are the most relevant for control system design and analysis. Next, considerations regarding the elastic modes of the Vega actuator, used in the simulations performed throughout this study, are presented. The IMU (Inertial Measurement Unit) measurements providing attitude angles and angular rates are strongly corrupted by the local elastic deformations resulting from structural flexibility. These measurements are fed back to the TVC (Thrust Vector Control) system which controls the pitch and the yaw motion of the vehicle. The design methodology for this control system is based on an ℒ1 adaptive technique incorporating a Butterworth filter guarantees good stability performances for wide variations of the launcher’s dynamics parameters. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature. [ABSTRACT FROM AUTHOR]

A Negative Group Delay (NGD) prototype filter design based on the reciprocal transfer function of a low-pass Butterworth filter of a given order, is presented. The out-of-band gain of the prototype transfer function is capped at a finite constant value via multiplication by a transfer function of a low-pass Butterworth filter with 3 dB bandwidth that is wider than the reciprocal function bandwidth. Such synthesized transfer function exhibits maximal magnitude characteristic flatness within the 3 dB bandwidth (Butterworth-like property), while it also exhibits NGD and satisfies Kramers-Kronig relations (causal transfer function). The prototype design achieves an NGD-bandwidth product that in the upper asymptotic limit as the design order increases, is a linear function of out-of-band gain in decibels. This is an improvement compared with previously reported cascaded first-order and second-order designs, which have NGDbandwidth functional dependency of out-of-band gain in decibels to the power of 1/2 and 3/4, respectively. It is shown that the transfer function of the corresponding design transformed to a non-zero center frequency can be exactly implemented with a Sallen-Key topology employing parallel resonators, or approximately implemented with an all-passive ladder topology. An in-band magnitude/phase distortion metric is applied to the prototype designs, evaluated for Gaussian and sinc pulse input waveforms, and compared with values obtained for a well-known commonly used medium. It is also shown that when the specified bandwidth corresponds to the entire bandwidth over which the group delay characteristic is negative, the magnitude characteristic variation approximately equals half the out-of-band gain value in decibels. Therefore, for any NGD design with large out-of-band gain (typically higher than 6 dB), using the entire bandwidth where group delay is negative can result in strong levels of distortion and should be checked for applied waveforms. [ABSTRACT FROM AUTHOR]

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10 − 3 comparing the DigiPen's computed Δ t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR = 60 Δ t ) from the pen accurately correlate with the reference ECG signals. [ABSTRACT FROM AUTHOR]

Background: Human biological rhythms are commonly assessed through physical activity (PA) measurement, but mental activity may offer a more substantial reflection of human biological rhythms. Objective: This study proposes a novel approach based on human-smartphone interaction to compute mental activity, encompassing general mental activity (GMA) and working mental activity (WMA). Methods: A total of 24 health care professionals participated, wearing wrist actigraphy devices and using the "Staff Hours" app for more than 457 person-days, including 332 workdays and 125 nonworkdays. PA was measured using actigraphy, while GMA and WMA were assessed based on patterns of smartphone interactions. To model WMA, machine learning techniques such as extreme gradient boosting and convolutional neural networks were applied, using human-smartphone interaction patterns and GPS-defined work hours. The data were organized by date and divided into person-days, with an 80:20 split for training and testing data sets to minimize overfitting and maximize model robustness. The study also adopted the M10 metric to quantify daily activity levels by calculating the average acceleration during the 10-hour period of highest activity each day, which facilitated the assessment of the interrelations between PA, GMA, and WMA and sleep indicators. Phase differences, such as those between PA and GMA, were defined using a second-order Butterworth filter and Hilbert transform to extract and calculate circadian rhythms and instantaneous phases. This calculation involved subtracting the phase of the reference signal from that of the target signal and averaging these differences to provide a stable and clear measure of the phase relationship between the signals. Additionally, multilevel modeling explored associations between sleep indicators (total sleep time, midpoint of sleep) and next-day activity levels, accounting for the data's nested structure. Results: Significant differences in activity levels were noted between workdays and nonworkdays, with WMA occurring approximately 1.08 hours earlier than PA during workdays (P <.001). Conversely, GMA was observed to commence about 1.22 hours later than PA (P <.001). Furthermore, a significant negative correlation was identified between the activity level of WMA and the previous night's midpoint of sleep (β =–0.263, P <.001), indicating that later bedtimes and wake times were linked to reduced activity levels in WMA the following day. However, there was no significant correlation between WMA's activity levels and total sleep time. Similarly, no significant correlations were found between the activity levels of PA and GMA and sleep indicators from the previous night. Conclusions: This study significantly advances the understanding of human biological rhythms by developing and highlighting GMA and WMA as key indicators, derived from human-smartphone interactions. These findings offer novel insights into how mental activities, alongside PA, are intricately linked to sleep patterns, emphasizing the potential of GMA and WMA in behavioral and health studies. [ABSTRACT FROM AUTHOR]

Background: The development of wearable solutions for tracking upper limb motion has gained research interest over the past decade. This paper provides a systematic review of related research on the type, feasibility, signal processing techniques, and feedback of wearable systems for tracking upper limb motion, mostly in rehabilitation applications, to understand and monitor human movement. Objective: The aim of this article is to investigate how wearables are used to capture upper limb functions, especially related to clinical and rehabilitation applications. Methods: A systematic literature search identified 27 relevant studies published in English from 2011 to 2023, across 4 databases: ACM Digital Library, IEEE Xplore, PubMed, and ScienceDirect. We included papers focusing on motion or posture tracking for the upper limbs, wearable devices, feedback given to end users, and systems having clinical or rehabilitation purposes. We excluded papers focusing on exoskeletons, robotics, prosthetics, orthoses, or activity recognition systems; reviews; and books. Results: The results from this research focus on wearable devices that are designed to monitor upper limb movement. More specifically, studies were divided into 2 distinct categories: clinical motion tracking (15/27, 56%) and rehabilitation (12/27, 44%), involving healthy individuals and patients, with a total of 439 participants. Among the 27 studies, the majority (19/27) used inertial measurement units to track upper limb movement or smart textiles embedded with sensors. These devices were attached to the body with straps (mostly Velcro), providing flexibility and stability. The developed wearable devices positively influenced user motivation through the provided feedback, with visual feedback being the most common owing to the high level of independence provided. Moreover, a variety of signal processing techniques, such as Kalman and Butterworth filters, were applied to ensure data accuracy. However, limitations persist and include sensor positioning, calibration, and battery life, as well as a lack of clinical data on the effectiveness of these systems. The sampling rate of the data collection ranged from 50 Hz to 2000 Hz, which notably affected data quality and battery life. In addition, several findings were inconclusive, and thus, further future research is needed to understand and improve upper limb posture to develop progressive wearable systems. Conclusions: This paper offers a comprehensive overview of wearable monitoring systems, with a focus on upper limb motion tracking and rehabilitation. It emphasizes the various types of available solutions; their efficacy, wearability, and feasibility; and proposed processing techniques. Finally, it presents robust findings regarding feedback accuracy derived from experiments and outlines potential future research directions. [ABSTRACT FROM AUTHOR]

Filters are electrical circuits or networks that filter out unwanted signals. In these circuits, signals are permeable in a certain frequency range. Attenuation occurs in signals outside this frequency range. There are two types of filters: passive and active. Active filters consist of passive and active components, including transistors and operational amplifiers, but also require a power supply. In contrast, passive filters only consist of resistors and capacitors. Therefore, active filters are capable of generating signal gain and possess the benefit of high-input and low-output impedance. In order for active filters to be more functional, the parameters of the resistors and capacitors in the circuit must be at optimum values. Therefore, the active filter is discussed in this study. In this study, the tree seed algorithm (TSA), a plant-based optimization algorithm, is used to optimize the parameters of filters with tenth-order Butterworth and Bessel topology. In order to improve the performance of the TSA for filter parameter optimization, opposition-based learning (OBL) is added to TSA to form an improved TSA (I-TSA). The results obtained are compared with both basic TSA and some algorithms. The experimental results show that the I-TSA method is applicable to this problem by performing a successful prediction process. [ABSTRACT FROM AUTHOR]

The aims of this study are to characterize the contamination of EMG signals by artifacts generated by the delivery of spinal cord transcutaneous stimulation (scTS) and to evaluate the performance of an Artifact Adaptive Ideal Filtering (AA-IF) technique to remove scTS artifacts from EMG signals. $\textit {Methods}$ : In five participants with spinal cord injury (SCI), scTS was delivered at different combinations of intensity (from 20 to 55 mA) and frequencies (from 30 to 60 Hz) while Biceps Brachii (BB) and Triceps Brachii (TB) muscles were at rest or voluntarily activated. Using a Fast Fourier Transform (FFT), we characterized peak amplitude of scTS artifacts and boundaries of contaminated frequency bands in the EMG signals recorded from BB and TB muscles. Then, we applied the AA-IF technique and the empirical mode decomposition Butterworth filtering method (EMD-BF) to identify and remove scTS artifacts. Finally, we compared the content of the FFT that was preserved and the root mean square of the EMG signals (EMGrms) following application of the AA-IF and EMD-BF techniques. Results: Frequency bands of ~2Hz width were contaminated by scTS artifact at frequencies nearby the main frequency set for the stimulator and its harmonics. The width of the frequency bands contaminated by scTS artifacts increased with current intensity delivered using scTS ($\text{p} < 0.001$), was lower when EMG signals were recorded during voluntary contractions compared to rest ($\text{p} < 0.05$), and was larger in BB muscle compared to TB muscle ($\text{p} < 0.001$). A larger portion of the FFT was preserved using the AA-IF technique compared to the EMD-BF technique (96±5% vs. 75±6%, $\text{p} < 0.001$). Conclusion: The AA-IF technique allows for a precise identification of the frequency bands contaminated by scTS artifacts and ultimately preserves a larger amount of uncontaminated content from the EMG signals. [ABSTRACT FROM AUTHOR]

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R 0 , R 1 , C 1 , and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively. [ABSTRACT FROM AUTHOR]

The original pavement surface must meet roughness specifications to ensure the quality of resurfacing or reconstruction projects. Therefore, it is crucial to use appropriate roughness detection equipment and evaluation indicators to represent the surface's roughness characteristics. This study adopted the shot blasting and milling machine to treat the cement concrete base surface at four levels increased with the shot blasting times and texture depth respectively. The 3D laser detection equipment with 0.55 mm measurement accuracy captured raw data from the surface. The 8-neighbor cyclic filling algorithm was applied to correct invalid data, followed by Butterworth filtering to reduce noise. The Kriging interpolation method was utilized to reconstruct the 3D model of the surface. The surface roughness characteristics were assessed through three categories of indicators: geometric, spectral, and fractal indicators. A comprehensive roughness index (T PCA) based on these indicators was performed using the principal component analysis. The three categories of roughness indicators exhibit varying degrees of change with the variation of the treatment parameters. At the second level of shot-blasting treatment, the T PCA reached its peak, showing a 240.1 % increase over the control group. Under the fourth milling treatment level, T PCA achieved its highest recorded value, marking a 1357.8 % increase compared to the control. [Display omitted] ● 3D laser detection captured roughness of cement concrete base post shot blasting and milling treatments. ● Three types of surface roughness indicators—geometric, spectral, and fractal—were measured and analysed. ● The T PCA evaluation index was proposed, which can accurately reflect comprehensive texture information of the interface. [ABSTRACT FROM AUTHOR]

This paper provides a valuable method to perform transient numerical simulations for hybrid rocket motors (HRMs). A two-dimensional transient numerical simulation model based on Butterworth filter and dynamic mesh is established. The Butterworth filter adopted in this project is a second-order low-pass filter. Then a firing test is arranged to prove the accuracy of the numerical simulation model of the HRMs. This rocket motor adopts 98% hydrogen peroxide (HP) and hydroxyl-terminated polybutadiene (HTPB) fuel containing aluminum as the propellants. The results demonstrate that the Butterworth filter can significantly improve the smoothness of the combustion surface compared with non-filter. And the combustion chamber pressure and thrust of simulation and test results are in good agreement. The average deviation of combustion chamber pressure between the simulation and test is 0.9%, and the average deviation of thrust between the simulation and test is 3.3%, which verifies the feasibility of the numerical simulation model for this HRM. Comparing the regression rate and filtered regression rate along with axial distance over time, it is indicated that the high-frequency oscillations are filtered by the Butterworth filter. In order to compare the port of simulation and test, the combustion chamber is scanned by computed tomography (CT) after the test. The results demonstrate that the simulation and test results are in good agreement, and the average deviation of simulation and test results is 4.5% mainly due to the end-burning of grain and nitrogen blowing. Then, the long-time simulations that adopted the Butterworth filter and non-filter were conducted. The results demonstrate that the use of the Butterworth filter can significantly smooth the combustion surface and the model has the ability to perform long-time simulations. This project can make the simulation for HRMs closer to the test results and provide a valuable guide for HRMs simulation. • A simulation model of hybrid rocket motor based on Butterworth filter is established. • The Butterworth filter is adopted to smooth the combustion surface. • A firing test of hybrid rocket motor with fuel containing aluminum is carried out. • Simulated combustion surface is compared with the computed tomography scan results. • A long-time simulation is conducted to validate model accuracy. [ABSTRACT FROM AUTHOR]

• A torsional vibration adaptive control algorithm was developed. • The influence of external interference on torsional vibration was studied. • Torsional vibration adaptive control algorithm improves anti-interference performance. • Compensation algorithms improve the adaptability of torsional vibration control. For planetary multi-speed hybrid power systems, addressing the problem of integrating of control effect, anti-interference, stability, and real-time performance for torsional vibration adaptive control during motor drive processes under disturbance uncertainty, this paper proposes a hybrid adaptive torsional vibration control algorithm. This algorithm includes torque reconstruction and feedback control, online adaptive adjustment of control parameters, and interference adaptive compensation, aiming to improve the torsional vibration of the system, enhance the adaptability of the algorithm to operating conditions, and suppress external interference. First, a torque reconstruction and feedback control algorithm was developed based on the theory of non-identical phase superposition as the basic method for torsional vibration adaptive control. Second, using methods such as offline optimization, torque location identification, and weighted parameter calculation, a multi-condition control parameter adaptive adjustment method was proposed. Subsequently, an adaptive compensation algorithm for interference was designed based on the physical realization of the nominal inverse model and Butterworth filter. Simulation and hardware-in-the-loop test results show that the proposed hybrid adaptive torsional vibration control algorithm can significantly reduce the maximum amplitude of the longitudinal acceleration and motor wheel equivalent speed difference for the sudden changes of motor torque in tip-in process, with improvement rates of 96.9% and 54.9% respectively, demonstrating good working condition adaptability and anti-interference capability by verification of different working conditions for tip-in/tip-out, gear shifting, regenerative braking and measurement noise interference. [ABSTRACT FROM AUTHOR]

In this article, a single operational transresistance amplifier (OTRA) based realization of second order low‐pass (LP), high‐pass (HP), band‐pass (BP), band‐reject (BR), and all‐pass (AP) filter and their individual performance analysis: noise, temperature, and worst‐case analysis have been presented. The proposed circuit can be reconfigured as Butterworth and Bessel filter by changing the values of passive components. The presented filters are suitable for MOS‐C implementation, which facilitates tuning of cut‐off/center frequency and quality factor electronically. The proposed circuits are analyzed theoretically, simulated and experimentally verified in laboratory. All the simulations are performed with CMOS model of OTRA (180 nm) in SPICE and experimentally verified with AD844AN IC. The experimental and simulation results are matching closely. The result obtained from post‐layout simulation of the proposed Butterworth LP filter confirms the feasibility of the design. Finally working of the proposed all‐pass filter is established by the design of a buffer and quadrature oscillator. [ABSTRACT FROM AUTHOR]

Digital noise reduction schemes are proposed for minimizing the output of the hearing aid in the existence of noise. That oversimplification does not observe the reality that individual perception and preference determine which sounds are required and which sounds are not required. Though numerous approaches have been endorsed to diminish the noise level, there is a necessity of development in the noise reduction systems. This paper offered a hybrid method to shrink the commotion in the signals. First, the robust signal is fetched from the database and that signal is subjected to pre-reconstruction with the assistance of empirical mode decomposition. After that the input to Bionic wavelet transform (BWT) is set as the resultant reconstructed signal to derive the de-noised signal. Butterworth filter is used for fine tuning de-noised signal. Consequently, inverse BWT is utilized to derive the final de-noised signal. The recommended methodology is implemented in the MATLAB platform. This method is assessed utilizing more number of noises; furthermore, the technique's performance is matched with the prevailing noise removal techniques. [ABSTRACT FROM AUTHOR]

The design and realization of fractional-order low-pass Butterworth filter (FOLPBF) for order (1 + α), with 0 < α < 1, optimized using a speed enhanced series combination of two meta-heuristic algorithms, namely cuckoo search algorithm (CSA) and interior search algorithm (ISA), is presented in this paper. This method optimizes both the gain and phase for the FOLPBF with respect to the ideal second-order Butterworth filter. The improvement in CSA → ISA series algorithm is established over standalone optimization using CSA and ISA on the basis of gain error, roll-off error, 3 dB frequency error, magnitude error [including passband error (PE) and stopband error (SE)], phase error, and convergence rate. The performance of the series optimization is demonstrated to be superior to the optimization techniques used in previous literature in terms of roll-off error, 3 dB frequency error, PE, SE, phase error and speed. The transient responses achieved with CSA → ISA series optimization is compared with the ideal response using Simulink simulations. [ABSTRACT FROM AUTHOR]

Copyright of Techno.com is the property of Universitas Dian Nuswantoro, Fakultas Ilmu Komputer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Medical images such as Magnetic Resonance Images (MRI) and Computed Tomography (CT) are the two most emerging imaging technologies. Processing these are useful in identification of disease and also in diagnosing a specific organ. In this paper, we have proposed a procedure for the detection of brain tumour from MRI images of brain using Image Processing techniques such as filtering in Frequency Domain, Thresholding, and Morphological Reconstruction. These techniques are implemented on MRI images using MATLAB Image Processing toolbox. Image is converted into Frequency Domain and Butterworth High pass filters are applied on the image and then opening-closing by reconstruction are applied on the image to detect the tumour present in the image and also the area of the tumour is identified. [ABSTRACT FROM AUTHOR]

Two solutions to the polynomial filter's transfer function synthesis problem are considered for comparison in the frequency and time domain: the broad class of filters with a critical monotonic amplitude characteristic (CMAC) in the passband and filters which use Chebyshev (C) polynomials. To complete the synthesis procedure for linear phase applications, group delay correctors are considered, for which a convenient approximation procedure is proposed here. Comparisons of the original functions and the corrected ones are performed in the frequency and time domain. It is shown that when CMAC and C are compared as such, the latter is by no means preferable from the selectivity point of view, while the opposite stands when the comparison is based on passband amplitude distortions. When phase-corrected filtering functions are compared, based on circuit complexity and time domain performance, the CMAC are shown to be preferable. [ABSTRACT FROM AUTHOR]

We present a set of treatment techniques for GMOS/IFU data cubes, including correction of the differential atmospheric refraction; Butterworth spatial filtering, to remove high spatial-frequency noise; instrumental fingerprint removal; Richardson–Lucy deconvolution, to improve the spatial resolution of the observations. A comparison with HST images shows that the treatment techniques preserve the morphology of the spatial structures in the data cubes, without introducing distortions or ghost features. The treatment procedure applied to the data cube of the central region of the late-type galaxy NGC 2835 allowed the detection of a nuclear emitting region with emission-line ratios indicating a probable LINER, although the classifications of Transition Object or Seyfert galaxy cannot be discarded. Such a detection was not possible without the data treatment. This shows that the benefits provided by this treatment procedure are not merely cosmetic, but actually affect the quality of different types of analyses, like the determination of the fraction of galaxies, of different morphological types, with a central Seyfert/LINER/Transition object emission. [ABSTRACT FROM AUTHOR]

Abstract In this paper the realization of nth order (n ≥ 3) fully-differential current-mode filters using Current Differencing Current Conveyors (CDCC) has been presented which results in circuits employing all grounded passive elements. In contrast to earlier known realizations of fully-differential filters which invariably require more than one capacitors per pole, the proposed realization employs only one capacitor per pole. The cut-off frequency of the realized filter can be electronically tuned when all the grounded resistors associated with the integrators are implemented by identical CMOS grounded voltage-controlled-resistors (VCR) driven by a common control voltage. The methodology has been illustrated by realizing a fifth order Butterworth filter as a specific example whose workability has been verified using SPICE simulations in 0.18 µm TSMC technology. A reduced-component-version of the designed fifth order Butterworth filter has also been presented which also employs all grounded RC components but does not have electronic-tunability. Some representative simulation results have been included. [ABSTRACT FROM AUTHOR]

Highlights • Non-ideal lowpass filters are implemented to the MWC scheme for wideband monitoring. • All imperfections of the non-ideal filters are detailed: “amplitude” and “phase”. • Digital post-treatment method has been proposed to compensate non-ideal lowpass filter imperfections. • Simulations with several parameters of the MWC scheme result in their probability of good reconstruction and false alarm. • The obtained outputs of practical lowpass filters are as good as ideal filter output. Abstract This paper focuses on non-ideal filters in a Modulated Wideband Converter (MWC) scheme. The MWC is a system that can sample a sparse wideband signal at sub-Nyquist rate. Generally, the output of the ideal MWC components will ensure a perfect reconstruction. In practice, the reconstruction should be based on the output of non-ideal components, especially filters. The impact of non-ideal filters will trigger to a bad reconstruction. In this paper, a detailed study on non-ideal lowpass filters imperfection used in compressed sensing MWC scheme is synthesized. A digital post-treatment scheme with amplitude and phase compensation is proposed after real lowpass filtering step in order to have the filtered output as close as the ideal lowpass filter output. At last, reconstruction spectra obtained from different simulated lowpass filters are compared with different parameters of MWC. [ABSTRACT FROM AUTHOR]

Currently, most microwave filters are designed using sophisticated computer-aided design (CAD) tools, which primarily based on the insertion loss method. The image parameter method (IPM), which can provide an effective control for the passband and stopband of microwave filters without any use of lumped prototypes, is paid little attention to microwave filter design. In this paper, the detailed design procedures for synthesizing microwave low-pass filter (LPF) and optimizing the performance of Butterworth filter with IPM are proposed. Based on the IPM, the improved infinite attenuation unit (IAU) cell can be designed firstly, which can attain higher selectivity and better stopband rejection of LPF while maintain good performance in passband. Then a 2 GHz ten-order high-performance LPF and an optimized Butterworth LPF are designed using improved IAU cell. Experiment results of the fabricated LPF agree well with the full-wave EM simulation, which shows the proposed approach can provide improved characteristics for both passband and stopband of LPFs. [ABSTRACT FROM AUTHOR]

In this letter, a symmetric quartic potential well is considered for use as a low-pass filter in BPAM receivers. By using the BPAM input to force a change in the shape of the well, the output can be considered as the position of a particle in the well. The potential well has been designed to pass the BPAM signal and suppress the background noise. Within the paper, DC gain and cut-off frequency are defined where the potential well is subject to a noise free single pulse. The effects of the background noise are also discussed. These initial results are applicable to other pulse modulation methods. Moreover, it is revealed that the cut-off frequency plays a significant role on the BPAM detector. Finally, a new result for the probability of error is introduced which clearly shows that the potential well filters out the background noise as much as a lowpass Butterworth filter does. [ABSTRACT FROM AUTHOR]

In this study, electronically-tunable, current-mode, square-root-domain, third-order low-pass filter is proposed. The study is carried out with three circuit designs. First circuit is third-order low-pass Butterworth filter, second circuit is third-order low-pass Chebyshev filter and the last circuit is third-order low-pass elliptic filter. All the input and output values of the filter circuit are current. Only grounded capacitors and MOSFETs are required in order to realize the filter circuit. Additionally, natural frequency of the current-mode filter can be adjusted electronically using outer current sources. To validate the theory and to demonstrate the performance of third-order filter, frequency and time domain simulations of PSPICE program are used. To that end, TSMC 0.35m Level 3 CMOS process parameters are utilized to realize the simulations of the filter. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive N-path filter design technique is presented. The filter designed by this method can be reconfigured to different shapes, e.g., Butterworth or Chebyshev with variable bandwidth (BW), based on the user requirements. In addition, an analytical synthesis procedure is introduced to realize high-order bandpass filters (BPFs) based on N-path architecture with a prescribed set of specifications. A proof-of-concept BPF is fabricated and measured in a 65-nm CMOS process. The filter can be reconfigured to realize different filter shapes by changing coupling capacitors. The BW of the proposed filter is also tunable. The passband of the filter is tunable from 0.2 to 1.2 GHz with a gain of 14.5–18 dB, a noise figure of 3.7–5.5 dB, and a total power consumption of 37.5–52.7 mW. The channel BW can be varied from 20 to 40 MHz and the filter out-of-band third-order intercept point ( $\Delta f=50$ MHz) is 25 dBm. [ABSTRACT FROM AUTHOR]

Infrared moving point target detection is a challenging task in infrared tracking systems. In this paper, an anisotropic spatial-temporal fourth-order diffusion filter (ASTFDF) is proposed for background prediction from infrared frame images. Using it, we can detect infrared point targets by subtracting the predicted background from the original image. Experiments in detecting targets from several different infrared image frame sequences indicated that the proposed ASTFDF can predict the background effectively and that the target can be detected precisely while achieving a higher background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG) compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As resistance spot welding is a crucial and widely used metal joining technique nowadays, a cheap and highly accurate online quality monitoring scheme is strongly demanded in industry. In this paper, a novel framework for welding quality examination is proposed by using advanced signal processing and artificial intelligence techniques. Our proposed framework consists of two objective schemes, a general welding quality classification scheme and a more detailed and advanced welding quality estimation scheme. To achieve a fast, convenient and cheap monitoring strategy, only the easily obtained electrical signals are monitored for data acquisition. For the welding quality classification, a self-organizing map under a windowed feature extraction is applied. Results of the spot welding experiment from a portable welding machine show that the classification accuracy can be 92.9%. For welding quality estimation, variation of the welding time and detailed aspects of welding quality are introduced. In particular, a modified recurrent neural network is utilized for the size estimation of the heat affected zone, while a novel self-organizing map type classifier is used to detect the expulsion condition along with its occurrence time. During the spot welding experiments, the average error percentage of the heat affected zone size estimation is around 6.7% and the accuracy on expulsion detection reaches 93.3%. [ABSTRACT FROM AUTHOR]

This paper presents a novel online diagnostics method capable of detecting winding deformations in two-winding single-phase transformers. The main idea is to identify changes in the short-circuit impedance. The combination of 3-D Lissajous curve methods with a Butterworth low-pass filter allows for the accurate determination of winding deformation of large power transformers in real time. The method is very robust and capable of detecting deformations at the early stage even when the measurements are noisy. Only information already available to the differential protection relay is needed. The proposed diagnostics method has been validated with circuit and finite-element simulations plus a lab experiment. The results show that the proposed online diagnostics technique has the ability to identify winding deformation problems under severe conditions, such as nonsinusoidal input, nonlinear loading, and measurement noise. Under ideal conditions (no signal noise), the inductive identification error of the proposed online diagnostics method identifies the parameters with less than 0.09% error. When accepting a measurement noise of 1%, the error on the identification of inductance is less than 0.13%. [ABSTRACT FROM PUBLISHER]

This paper presents a specific data processing flow to be applied to marine multichannel seismic reflection data collected by a streamer in order to use them to perform prestack adjoint waveform inversion of ocean's thermohaline properties. The overall goal is to increase the signal-to-noise ratio (SNR) of the weak reflections generated at the small impedance contrasts within the water layer while preserving the direct wave. The processing flow focuses on increasing the SNR of the shot gather records by forcing noise amplitudes to fall inside a range of physical plausible values for water layer reflections. This processing step is applied in two independent branches of the workflow; one dealing with the water layer reflections and the second with the direct wave, which are separated by applying a singular value decomposition. To test the performance of the processing flow, we combine actual noise field recordings with a synthetic seismic data set. We apply the proposed data processing flow to quantify differences between noise-free and processed record sections, and we then compare with the results obtained by applying a Butterworth filter (BF). For offsets smaller than 1500 m, the BF processing produces a signal with SNR < 0.1; the proposed workflow allows to retrieve the seismic signal with 0.1 < SNR < 2.4. For offsets larger than 1500 m, the BF processing allows obtaining the SNR up to 1.4, while the proposed workflow increases the SNR up to 5. We finally demonstrate that the processed data can be used to perform waveform inversion with an accuracy of ~0.02 m/s. [ABSTRACT FROM AUTHOR]

This paper presents the first uniformly processed comprehensive database on strong motion acceleration records for the extensive regions of western Himalaya, northeast India, and the alluvial plains juxtaposing the Himalaya. This includes 146 three components of old analog records corrected for the instrument response and baseline distortions and 471 three components of recent digital records corrected for baseline errors. The paper first provides a background of the evolution of strong motion data in India and the seismotectonics of the areas of recording, then describes the details of the recording stations and the contributing earthquakes, which is finally followed by the methodology used to obtain baseline corrected data in a uniform and consistent manner. Two different schemes in common use for baseline correction are based on the application of the Ormsby filter without zero pads (Trifunac 1971) and that on the Butterworth filter with zero pads at the start as well as at the end (Converse and Brady 1992). To integrate the advantages of both the schemes, Ormsby filter with zero pads at the start only is used in the present study. A large number of typical example results are presented to illustrate that the methodology adopted is able to provide realistic velocity and displacement records with much smaller number of zero pads. The present strong motion database of corrected acceleration records will be useful for analyzing the ground motion characteristics of engineering importance, developing prediction equations for various strong motion parameters, and calibrating the seismological source model approach for ground motion simulation for seismically active and risk prone areas of India. [ABSTRACT FROM AUTHOR]

The evaluation of complexity in univariate signals has attracted considerable attention in recent years. This is often done using the framework of Multiscale Entropy, which entails two basic steps: coarse-graining to consider multiple temporal scales, and evaluation of irregularity for each of those scales with entropy estimators. Recent developments in the field have proposed modifications to this approach to facilitate the analysis of short-time series. However, the role of the downsampling in the classical coarse-graining process and its relationships with alternative filtering techniques has not been systematically explored yet. Here, we assess the impact of coarse-graining in multiscale entropy estimations based on both Sample Entropy and Dispersion Entropy. We compare the classical moving average approach with low-pass Butterworth filtering, both with and without downsampling, and empirical mode decomposition in Intrinsic Multiscale Entropy, in selected synthetic data and two real physiological datasets. The results show that when the sampling frequency is low or high, downsampling respectively decreases or increases the entropy values. Our results suggest that, when dealing with long signals and relatively low levels of noise, the refine composite method makes little difference in the quality of the entropy estimation at the expense of considerable additional computational cost. It is also found that downsampling within the coarse-graining procedure may not be required to quantify the complexity of signals, especially for short ones. Overall, we expect these results to contribute to the ongoing discussion about the development of stable, fast and robust-to-noise multiscale entropy techniques suited for either short or long recordings. [ABSTRACT FROM AUTHOR]

Fractional-order low-pass and high-pass Butterworth and Chebyshev filters with cut-off frequencies determined independently of their order are approximated in this work. The already available schemes in the literature have a cut-off frequency pre-determined by the pole frequency value, and hence the filter's order. The derivation of rational integer-order approximation functions, which approximate the frequency behavior of the filters, is performed through the utilization of a magnitude curve-fitting based method. The form of the transfer function is common for all filter functions, providing design versatility. Implementation is performed using just one active element, leading to a structure with minimized active component count. The evaluation of the accuracy of the employed approximation method is performed through numerical and experimental results, while the evaluation of the behavior of the active circuit topology is done using circuit simulations. [ABSTRACT FROM AUTHOR]

The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs. [ABSTRACT FROM PUBLISHER]

The geothermal prospect located at Gunung Endut was investigated in 1980s based on reconnaissance surveys, primarily on geological and geochemical aspects. Then in 2006 a gravity measurement with 247 stations was carried out. However, due to the distribution of the gravity station was less than optimal, interpretation of gravity data was not suitable with the geological situation that support geothermal system. To ensure this, we have carried out a new gravity survey with 134 random stations distribution but remained concentrated on hot spring locations. Data from both measurements were processed by the same treatment starting from pre-processing with some standar corrections to obtain complete Bouguer anomaly, then continue by filtering process to separate regional and residual gravity anomaly using Butterworth filter in a wavenumber domain. Residual gravity anomaly map obtained by random position of stations is more resemble to the geological features developed in the prospect area. The contrast boundaries of the residual gravity anomaly value coincide with the fault-line structures recorded in the surface geological data. This result is better when compared with previous measurements. This study shows that there is a significant influence of the distribution pattern of the gravity stations which affects the conformity with the geological data. Based on the new interpretation result, the structural features in the prospect area are characterized by the complex fault structures that intersected each other mainly close to the surface manifestations. This intersection area is expected to be an enhanced permeability zone so that the hydrothermal fluid can flow to the surface. The interpretation of new gravity data combined with the results of geochemical analysis can be used for more understanding of the geothermal system beneath Gunung Endut prospect area. [ABSTRACT FROM AUTHOR]

In this paper, the use of MOS-only active filters in the implementation of area-effective low-IF image reject filters is discussed. For this purpose, complex filter design procedure is considered regarding MOS-only filter realization techniques and a new second-order filter topology which favors filter’s area-effective implementation is proposed. Owing to the presented approach, a new complex image-reject filter with reduced chip area, which can be used in dual-band GNSS receivers, is obtained. Issues related to IQ imbalance are briefly discussed and a calibration imbalance circuit is proposed. By virtue of the current-mode design technique, the IQ imbalance circuit is embedded with the complex filter, simplifying the system topology. In order to illustrate the feasibility of the filter, post-layout simulation results of a fourth-order Butterworth-type image reject filter designed for dual-band GNSS receivers are provided using SPECTRE simulation tool in CADENCE design environment. [ABSTRACT FROM AUTHOR]

Many studies have examined the contribution of different spectral bands to speech intelligibility, measuring recognition scores of filtered speech stimuli. For a given filter bandwidth, the influence of filter properties on such experiments has been studied mainly with respect to transition band slopes. The objective of the present study was to determine whether nominal transition band slope is a sufficient characterization of filter properties. Several types of filters, both finite impulse response and infinite impulse response types were examined in three experiments to determine if details of the transition band behavior, as well as group delay properties, had any influence on recognition scores. The results of a total of 72 participants showed that for 1/3 octave passbands, differences between filters having the same nominal transition band slopes, but of different types, were large and statistically significant. Linearity of phase response, however, did not influence the results. Further experiments using passband widths of 1/2 and 2/3 octaves revealed that only for the latter the difference in recognition scores between filter types ceased to be significant. These results have important implications for studies which involve filtered speech as well as models that employ different types of filters to emulate peripheral auditory processing. [ABSTRACT FROM AUTHOR]

Recently, many active filter circuits that use only MOS transistors were presented in the literature. In these circuits, instead of passive resistors, transconductances of MOSFETs operating in saturation region are used. Moreover, their parasitic gate to source capacitances are employed instead of passive capacitors. These circuits provide circuit implementation with fewer transistors and eliminate the need for external passive elements. In this study, a MOS-only third order Butterworth filter for high frequency applications has been proposed. The circuit leads to a compact circuit having fewer number of transistors compared to classical active block-based analog counterparts. Simulation results agree well with the theoretical study. [ABSTRACT FROM AUTHOR]

The behavior of noise in asynchronous spectrum in generalized two-dimensional (2D) correlation spectroscopy is investigated. Mathematical analysis on the noise of 2D spectra and computer simulation on a model system show that the fluctuation of noise in a 2D asynchronous spectrum can be characterized by the standard deviation of noise in 1D spectra. Furthermore, a new approach to improve a signal-to-noise ratio of 2D asynchronous spectrum by a Butterworth filter is developed. A strategy to determine the optimal conditions is proposed. Computer simulation on a model system indicates that the noise of 2D asynchronous spectrum can be significantly suppressed using the Butterworth filtering. In addition, we have tested the approach to a real chemical system where interaction between berberine and ß-cyclodextrin is investigated using 2D UV-vis asynchronous spectra. When artificial noise is added, cross peaks that reflect intermolecular interaction between berberine and ß-cyclodextrin are completely masked by noise. After the method described in this article is utilized, noise is effectively suppressed, and cross peaks are faithfully recovered. The above result demonstrates that the approach described in this article is applicable in real chemical systems. [ABSTRACT FROM AUTHOR]

Given that the incipient fault is too weak for extraction, a novel approach that is based on sparse optimization is proposed for incipient fault diagnosis. The proposed optimization method consists of three steps: First, autocorrelation analysis is utilized to filter broadband random noise. Then, the weighted sparsity-based denoising method is proposed to extract periodic impulses. The prior knowledge that periodic impulses are sparse is used to constitute a penalty term; thus a novel weighted sparse optimization model is established. The majorization-minimization method is used to solve the optimization model. The high-pass filter in quadratic fidelity term is constructed by a Butterworth filter based on banded matrices, thus effectively improving computational efficiency. Lastly, the interval of periodic impulses, which corresponds to the fault frequency of rolling bearing, is obtained. Moreover, simulation and experimental results show that the proposed approach can successfully extract fault features from the signals of low signal to noise ratio. [ABSTRACT FROM AUTHOR]