Amplitude-based Method Research Articles

Optical Flow Amplitude-Based Method for Detection and Quantitative Evaluation of Impact Damage in CFRP

Optical Flow Amplitude-Based Method for Detection and Quantitative Evaluation of Impact Damage in CFRP

Metal Thickness Measurement System Based on a Double-Coil Eddy-Current Method With Characteristic Ratio Detection

Eddy-current sensor systems have been employed to determine the real-time thickness distribution of metal films and the thickness at endpoint detection in metal chemical mechanical polishing (CMP) processes, to achieve global planarization and avoid over-polishing or under-polishing. However, the large lift-off distance of 3.5 mm between the sensor coil and metal film gradually decreases with continuous wear of the polishing pad, which has a strong negative impact on the sensitivity and accuracy of the thickness measurement. In this study a double-coil eddy-current sensor system, with novel characteristic ratio detection capability, is proposed for metal film thickness measurement and compared with a traditional amplitude-based method. The linear relationship between the characteristic ratio and metal film thickness that is immune to lift-off distance is demonstrated both theoretically and experimentally. The experimental results show that the proposed method performs better than the amplitude-based method, reducing the effect of lift-off distance variation and obtaining an excellent linear measurement range from 24 to 2095 nm. The measurement accuracy is as high as 2.1 nm with an excitation frequency of 1.39 MHz and a large lift-off distance of 3.5 mm. The relative measurement error is less than 1.2% and the endpoint thickness ranges from 100 to 180 nm in the Cu-CMP process, when the lift-off distance varies from 3.6 to 3.3 mm.

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control. Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure. Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R 2 = 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R 2 = 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability. Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

Image Steganography-based Copyright and Privacy-Protected Image Trading Systems

This paper proposes steganography-based copyright- and privacy-protected image trading systems using image transformation, i.e., either discrete cosine transform (DCT) or Hadamard transform (HT). In the systems, there are a content provider (CP), a consumer, the first trusted third party (TTP), and the second TTP. To protect the copyright of the image, the consumer ID is embedded into the amplitude components of the commercial image by the first TTP using the digital fingerprinting technique, and to protect the consumer's privacy against the first TTP and a malicious third party (s), the image steganography is applied to the commercial image by using image transformation. A color dummy image is used instead of a gray dummy image for security purposes. After applying the image transformation to both images, the coefficient signs of the commercial image are replaced by the coefficient signs of the dummy image pixel-by-pixel so that the inversely transformed commercial image looks like the dummy image instead of the commercial image. Once the consumer receives the fingerprinted image from the first TTP and the coefficient signs of the commercial image from the second TTP, the consumer reconstructs the fingerprinted commercial image without losing the hidden fingerprint at all because of the compatibility of the proposed image steganography method and the amplitude-based fingerprinting method. The experimental results confirm that the stego-images generated by the proposed systems do not look suspicious with higher qualities compared with those generated by existing systems. Moreover, the fingerprinted image quality and the correct fingerprint extracting rate have been improved by the proposed systems.

Skeleton-based reassignment of nonstationary signals spectrogram

Many practical applications require ridge curves detection and enhancement as they allow for instantaneous frequency estimation of nonstationary signals. Unfortunately, the reconstruction of those curves becomes a difficult task whenever multicomponent signals having non-separable modes have to be analyzed. To address this issue, this paper introduces a novel binary model for the spectrogram of multicomponent signals, and applies skeleton to enhance IF curves. To further improve the skeleton-based reassignment, a convolution-deconvolution scheme, that is directly derived from a specific evolution law for signal time-frequency (TF) distribution, is also defined. Experimental results show that the skeleton-based approach can reproduce IF curves of separable modes accurately. In addition, it allows to outperform classical amplitude-based reassignment method in the case of non separable modes.

SAR data and field surveys combination to update rainfall-induced shallow landslide inventory

The Campania region has been recurrently hit by severe landslides in volcanoclastic deposits. The city of Naples, and in particular the Camaldoli and Agnano hills (Phlegraean Fields), also suffered several landslide crises in weathered volcanoclastic rocks as a consequence of intense rainfalls or wildfires. To identify slope failures phenomena occurred in the winter season 2019–2020 an innovative procedure has been proposed. The purpose of this procedure is to highlight areas where major land cover changes occurred within our area of study, which can be potentially related to mass movements. The amplitude of spaceborne SAR images has been exploited for the change detection analysis and the output derived from the segmentation procedure has been compared with field observations. The amplitude-based method has been already applied in the detection of landslides, but never on the event with limited extensions, such as for this application. The achieved outcomes allowed the mapping of 62 new landslides that have been used to update the current landslide inventory database. This type of information is expected to help decision-makers with land planning and risk assessment.

Thickness Measurement Based on Eddy Current Sensor With Coaxial Double Coil for Chemical Mechanical Polishing

Real-time and accurate thickness measurements of copper films on 300-mm silicon wafers are utilized to improve global planarization, control polishing pressure, and avoid dishing and erosion caused by under-polishing and over-polishing during chemical mechanical polishing (CMP) of metals. Nevertheless, the requirements of nanometer-scale measurement sensitivity, wide measurement range from tens of nanometers to 2 μm, and high edge measurement performance make eddy-current thickness measurement extremely challenging. Therefore, we proposed a concise and effective theoretical model to analyze the measurement range and sensitivity in both the amplitude- and phase-based methods. The advantages and disadvantages of the two methods are compared and discussed systematically through simulations and experiments. The effects of the lift-off distance and frequency of excitation signal on measurement sensitivity, range, and edge measurement performance are also examined. The results show that the proposed theory can be used to improve measurement range and sensitivity. And the double-sensor system demonstrated better edge measurement performance than a single coil. Finally, a double-coil eddy current thickness measurement system designed with an average sensitivity of 1.9 mV/nm, a measurement range of 60 nm to 2 μm, and an edge attenuation position of 141.5 mm at a lift-off distance of 3.5 mm can be utilized in the amplitude-based method for advanced Cu-CMP.

Analysis of derivative of instantaneous frequency and its application to voice activity detection

Recently, the use of speech processing in a time–frequency domain that handles the phase spectrum in addition to the amplitude one has been increasing because many studies have revealed the importance of phase. Inspired by this motivation, this paper presents a new speech signal phase processing. The contributions of this paper include the following two points: a detailed analysis of a speech signal by considering a novel phase feature, a derivative of instantaneous frequency (DIF), and demonstrating a new phase-based voice activity detection (VAD) algorithm as one of the DIF’s applications. These contributions develop on our previous work that briefly introduced the DIF into VAD. In the analysis part, we investigate the DIF from the theoretical aspect and research the statistical distribution of the DIF of speech signals under various conditions. We also propose a new phase-based VAD algorithm via the statistical likelihood ratio, and use the DIF as an auxiliary feature to improve a conventional amplitude-based VAD method. The experimental results confirm the efficacy of the phase feature in the VAD application and the possibility of combining the phase and amplitude for better performance.

Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information

A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. First, motor units (MUs) firing information were identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Our approach provides a reliable neural decoding method for dexterous finger movements. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

Clinical evaluation of three respiratory gating schemes for different respiratory patterns on cardiac SPECT.

Respiratory gating reduces respiratory blur in cardiac single photon emission computed tomography (SPECT). It can be implemented as three gating schemes: (a) equal amplitude-based gating (AG); (b) phase or time-based gating (TG); or (c) equal count-based gating (CG), that is, a variant of amplitude-based method. The goal of this study is to evaluate the effectiveness of these respiratory gating methods for patients with different respiratory patterns in myocardial perfusion SPECT. We reviewed 1274 anonymized patient respiratory traces obtained via the Vicon motion-tracking system during their 99m Tc-sestamibi SPECT scans and grouped them into four breathing categories: (a) regular respiration (RR); (b) periodic respiration (PR); (c) respiration with apnea (AR); and (d) unclassified respiration (UR). For each respiratory pattern, 15 patients were randomly selected and their list-mode data were rebinned using the three gating schemes. A preliminary reconstruction was performed for each gate with the heart region segmented and registered to a reference gate to estimate the respiratory motion. A final reconstruction incorporating respiratory motion correction was done to get a final image set. The estimated respiratory motion, the full-width-at-half-maxima (FWHM) measured across the image intensity profile of the left ventricle wall, as well as the normalized standard deviation measured in a uniform cuboid region of the thorax were analyzed. There are 47.1%, 24.3%, 13.5%, and 15.1% RR, PR, AR, and UR patients, respectively, among the 1274 patients in this study. The differences among the three gating schemes in RR were smaller than other respiratory patterns. The AG and CG methods showed statistically larger motion estimation than TG particularly in the AR and PR patterns. Noise of AG varied more in different gates, especially for AR and UR patterns. More than half of the patients reviewed exhibited nonregular breathing patterns. Amplitude-based gating, that is, AG and CG, is a preferred gating method for such patterns and is a robust respiratory gating implementation method given the respiratory pattern of the patients is unknown before data acquisition. Phase gating is also a feasible option for regular respiratory pattern.

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of individual fingers continuously and concurrently during dexterous finger flexion and extension. Specifically, motor unit (MU) discharge activity was extracted from the surface high-density electromyogram (EMG) signals recorded from the finger extensors and flexors, respectively. The MU information was separated into different groups to be associated with the flexion or extension of individual fingers and was then used to predict individual finger forces during multi-finger flexion and extension tasks. Compared with the conventional EMG amplitude-based method, our method can obtain a better force estimation performance (a higher correlation and a smaller estimation error between the predicted and the measured force) when a linear regression model was used. Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study.

Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time continuous prediction of forces in individual fingers using parallel convolutional neural networks (CNNs). We extracted populational motor unit discharge frequency using CNNs in a parallel structure without spike sorting. The CNN parameters were trained based on two features from high-density electromyogram (HD-EMG), namely temporal energy heatmaps and frequency spectrum maps. The populational motor unit discharge frequency was then used to continuously predict finger forces based on a linear regression model. The force prediction performance was compared with a motor unit decomposition method and the conventional EMG amplitude-based method. Our results showed that the correlation coefficient between the predicted and the recorded forces of the CNN approach was on average 0.91, compared with the offline decomposition method of 0.89, the online decomposition method of 0.82, and the EMG amplitude method of 0.81. Additionally, the CNN based approach showed generalizable performance, with CNN trained on one finger applicable to a different finger. The outcomes suggest that our CNN based algorithm can offer an accurate and efficient force decoding method for human-machine interactions.

Detection of anomalous high-frequency events in human intracranial EEG.

ObjectiveHigh‐frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high‐frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape.MethodWe propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared.ResultIn the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non‐SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude‐based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125).SignificanceADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non‐SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.

Real-time isometric finger extension force estimation based on motor unit discharge information

Objective. The goal of this study was to perform real-time estimation of isometric finger extension force using the discharge information of motor units (MUs). Approach. A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed the neural-drive method. The conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed the EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5 min of isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time. Main results. The simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. The experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time. Significance. The neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information.

Polarization Amplitude-Based Method of Determining the Bearing and Roll Angles of an Object in Motion from Orthogonal Circularly Polarized Signals of a Radio Beacon

The possibility of determining the bearing and roll angles of an object in motion is investigated based on orthogonal left- and right-hand circularly polarized signals simultaneously radiated by a radio beacon from two points with the known coordinates spatially spaced in the horizontal plane. The bearing and roll angles are determined onboard the object in motion using a two-channel receiving system based on polarization amplitude-based processing of resultant vector signals received in a linear polarization basis.

Slow wave detection in sleeping mice: Comparison of traditional and machine learning methods

BackgroundDuring slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial. New methodIn this study, we used the neural network pattern recognition to detect individual slow waves in LFP recorded from mice as well as other commonly used methods that are based on fast frequencies modulation, amplitude, or duration. ResultsThe number and quality of events detected as slow waves depended on the chosen method of detection, level of thresholds, or on combination of methods. Each individual method yields some false-positive and false-negative detections. Typically, the fast frequency-method has a higher false discovery rate, but almost no missing waves; amplitude-based method has relatively high false-positive and false-negative rates; duration-based method has low false-negative rates; neural network pattern recognition approach has the lowest false-positive rate among individual methods, often rejecting waves that were falsely detected by other approaches. Combining all 4 detection methods practically eliminated false-positive errors, but a large number of slow waves remained undetected. ConclusionsThe use of a particular method of slow wave detection needs to be adjusted to the objectives of a given study: to detect all slow waves, but also numerous false positives can be achieved using the fast frequency approach. Neural network pattern recognition method alone can detect slow waves with the lowest false-positive rate, that can be further minimized with the use of combination of other methods.

A novel reconstruction method based on changes in phase for subsurface large sloped dielectric target using GPR

In ground-penetrating radar (GPR) subsurface target reconstruction, various techniques based on amplitude (or energy) information of echoes from metal target with small size can work well. However, for environmental and geological applications, the quantitative analysis of the target's geometric features, like location, shape and size, is exactly what we are concerned about. Amplitude-based reconstruction method faces challenges in these applications. A large sloped target under the surface may lead to abundant virtual image energy and cause position deviation. In addition, the echoes from the inner part of the penetrable dielectric target may be too weak to be detected. However, changes in phase may highlight the effects of echoes from the target on that from the surroundings, even if the effect is small due to the weak energy. In this paper, a novel method based on changes in phase is proposed to reconstruct subsurface large sloped dielectric target. To remove the virtual image, the main idea is based on the geometric relationship between the recorded signal plotted beneath the receiving antenna and the reflected signal emanated from the target position which is “ahead” or “behind” of the receiving antenna. Furthermore, the electromagnetic (EM) wave propagating through the penetrable target will change its velocity and result in advancing or lagging related to the geometric shape of the target. In this case, the weak echoes from the back surface of the target can be compensated according to the advancing or lagging. With the proposed method, the virtual image is eliminated and both front and back surface of the target are reconstructed. Results from the laboratory experiments demonstrate the validity of the proposed method.

Object localization using the statistical behavior of volume speckle fields

Speckle noise presents challenges in object localization using reconstructed wavefronts. Here, a technique for axial localization of rough test objects based on a statistical algorithm that processes volume speckle fields is demonstrated numerically and experimentally. The algorithm utilizes the standard deviation of phase difference maps as a metric to characterize the object wavefront at different axial locations. Compared with an amplitude-based localization method utilizing energy of image gradient, the technique is shown to be robust against speckle noise.

An Amplitude-Based Modeling Method and its Application on the Impedance Inversion in Heterogeneous Paleokarst Carbonate Reservoirs

For the modeling of complex reservoirs with strong heterogeneity, for instance the deeply buried paleokarst reservoirs in the Tarim Basin, the traditional method by lateral interpolation and extrapolation of measured logs between well locations with the guiding of interpreted seismic horizons is driven by distance and often leads to non-geologic solutions, while the past improvements via adding seismic velocities or attributes information are still not accurate due to the resolution limitation or AVO (amplitude versus offset) effects contamination. In this paper, we present an amplitude-based modeling method by utilizing the heterogeneous information from seismic data to guide the geological model construction, based on the inverted pure P-wave data which have removed the AVO effects. The proposed method is applied in the impedance inversion of the paleokarst carbonate reservoirs in the Tarim Basin, where the reservoirs are characterized by substantial heterogeneity. Both the constructed Low frequency model (LFM) and the inverted impedance results of proposed method are more correlative with drilling data than that of traditional method. This method is more beneficial for strong heterogeneous reservoirs description especially in well insufficient or absent areas, suggested by the comparisons with traditional methods in the ZG8 area.

Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods

This paper proposes a method for achieving a high performance of N200 and P300 classification by applying independent component analysis to select the channels, which deliver brain signals with large N200 and P300 potentials and small artifacts. In this study, the authors find out the relationship between the highest accuracy and the weights of the independent components and use this relationship to predict the optimal channels of each individual subject. They compare five channel selection methods: the ICA-based method and the curve-fitting-based method proposed in this paper, the amplitude-based method, the experiential optimal 8 channel combination and all 30 channel combination methods. The comparative studies show that the ICA-based method achieves an average accuracy of 99.3% across four subjects, which is superior to the other four methods.