Combined Frequency Domain Research Articles

Multi-Sensor Data Fusion for Real-Time Surface Quality Control in Automated Machining Systems.

Multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation.

In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.

A prototype measuring system of soil bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy

A combined-penetrometer sensor prototype (CPSP) for the measurement of topsoil bulk density (BD) was developed and tested under field conditions. The prototype consisted of a standard penetrometer, equipped with a near infrared spectrophotometer (NIRS) (1650–2500 nm) to measure gravimetric moisture content (ω) and a frequency domain reflectometry (FDR) to measure volumetric moisture content (θv), while BD was assessed by the combination of both sensors’ data. The CPSP was tested in situ at five arable and two grassland fields of different soil texture classes in Silsoe, Bedfordshire, UK, during the period from August to December 2013. Artificial neural networks (ANN) were used to predict ω and θv based on data fusion of NIRS diffuse reflectance spectra and FDR output voltage (V), and the predicted values were substituted in a model to predict BD. The CPSP showed more accurate BD assessment in grass fields with root mean square error of prediction (RMSEp) of 0.077 g cm−3, compared to arable fields (RMSEp = 0.104 g cm−3). A collective BD model produced for arable and grass fields provided a moderate accuracy with a RMSEp of 0.102 g cm−3. It can be concluded that the new CPSP can be used successfully to measure BD in the topsoil by combining the NIRS and FDR techniques through ANN-data fusion approach.

Influence of soil moisture content on assessment of bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy under semi field conditions

The potential of combining frequency domain reflectometry (FDR) and visible and near infrared spectroscopy (vis-NIRS) was shown to successfully assess soil bulk density (BD). However, the accuracy of both sensing tools was reported to be influenced by the soil moisture content (MC). The aim of the paper is to evaluate the influence of soil MC level on accuracy of BD assessment under semi field conditions by the combination of FDR and vis-NIRS data. Measurements of volumetric moisture content (θv) in the field and gravimetric moisture content (ω) in the laboratory using a FDR and a vis-NIRS (350–2500nm) techniques were conducted in five arable fields of different soil texture classes in Silsoe, Bedfordshire, UK. In order to account for different MC levels, three measurement campaigns were carried out during the period from July 2011 to October 2012, where a total of 300 soil samples were used, representing three natural MC levels of low (L1), medium (L2) and high (L3). L1, L2 and L3 datasets were subjected to artificial neural network (ANN) analysis to predict ω and θv based on fusion of vis-NIRS spectra and FDR output voltage, and subsequently the predicted values were substituted into a model to assess BD.Results showed that MC has large influence on both the vis-NIRS and FDR sensors for measuring ω and θv, respectively. The accuracy of BD assessment improved with soil MC increase, with root mean square error of prediction (RMSEp) values of 0.079, 0.072 and 0.061 g cm−3, for average ω of 0.106 (L1), 0.197 (L2) and 0.28 (L3) gg−1, respectively. The accuracy of θv measurement with the FDR depended on ensuring good contact with the soil, which is not the case for dry soil conditions, at which accuracy of θv measurement and BD assessment was deteriorated. It is recommended to set an optimal MC range (depending of soil texture), over which precise soil BD estimation can be certain.

Combined Frequency Domain and Time Domain Airborne Data for Environmental and Engineering Challenges

We describe the main properties of the EQUATOR airborne geophysical system and how the system can be employed to solve some engineering and environmental problems. To date, the EQUATOR system has been used for groundwater search in 2014 and 2015 in Siberia. The name “EQUATOR” originates from the intention to EQUATE, or to balance time and frequency domain approaches in airborne electromagnetics, which is why the system was originally designed. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We further explain the difference between the two methods on some numerical and field examples. Also we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of the one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 100 m of the Earth, which are the most relevant for engineering and environmental studies. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface. As an example, we present some case studies obtained over a platform (Siberia) and a crystalline shield (Western Africa) setting. We further suggest using these results in such near-surface studies as sediment thickness estimation, soil state analysis, groundwater search, and geological structural analysis.

Combined frequency domain photoacoustic and ultrasound imaging for intravascular applications.

Intravascular photoacoustic (IVPA) imaging has the potential to characterize lipid-rich structures based on the optical absorption contrast of tissues. In this study, we explore frequency domain photoacoustics (FDPA) for intravascular applications. The system employed an intensity-modulated continuous wave (CW) laser diode, delivering 1W over an intensity modulated chirp frequency of 4-12MHz. We demonstrated the feasibility of this approach on an agar vessel phantom with graphite and lipid targets, imaged using a planar acoustic transducer co-aligned with an optical fibre, allowing for the co-registration of IVUS and FDPA images. A frequency domain correlation method was used for signal processing and image reconstruction. The graphite and lipid targets show an increase in FDPA signal as compared to the background of 21dB and 16dB, respectively. Use of compact CW laser diodes may provide a valuable alternative for the development of photoacoustic intravascular devices instead of pulsed laser systems.

Synchrophasors data analytics framework for power grid control and dynamic stability monitoring

This article develops a framework for phasor analytics necessary to enable advanced wide-area control and monitoring applications. The key component in this building is the smart phasor measurement unit for control (PMU/C) which from the substation can feed accurate fundamental and low order harmonics phasors to the phasor data concentrator at a higher than the standard rate of one point per cycle. The second piece of the building is the dynamic generator state estimation, which is mandatory to enhance the observability of dynamic phenomena and thus, improves control performance and protection dependability. Following dynamic state estimation, a combined time and frequency domain processing of voltage, angle and frequency measurements based on S-transform is proposed as a good mean for extracting critical features which enable crisper information that are more easily interpretable than the raw phasor time-series. Gluing all the proposed pieces at both substation and supervisory levels, it is possible to build a smart wide-area situational awareness system, able to close the loop through educated and well informed operators for handling geomagnetic disturbance (GMD) impacts on the grid and/or through fast control and automation devices for dealing with stability issues.

Classification of Brainstem Auditory Evoked Potentials Using Artificial Neural Network Based on Time and Frequency Domain Features

This article presents a novel technique of automated classification of brainstem auditory evoked potentials (BAEPs). Brainstem auditory evoked potentials are the evoked potentials used to measure the brain activity that occur in response to the auditory stimulus to the ear and to record the response by placing electrodes on the surface of the scalp. Brainstem auditory evoked potentials reflect neuronal activity in the auditory nerve pathway from the cochlea to the auditory cortex and are used in the diagnosis of neurological diseases leading to hearing loss. In the proposed automated system, the features of BAEPs are extracted in time domain based on latency, and in frequency domain, using fast Fourier transform and discrete wavelet transform. The features extracted from BAEPs are used as variables to build a feed-forward multilayer perceptron, an artificial neural network model for the classification of BAEPs. The stratified 10-fold cross-validation technique is used in training and testing the model to estimate the performance of the system. The combined time and frequency domain features along with physiological parameters as input to artificial neural network resulted in a very good average accuracy of 90.74% in classifying the normal and abnormal BAEP signals.

Analysing the intermittent flapping flight of a Manx Shearwater, Puffinus puffinus, and its sporadic use of a wave-meandering wing-sailing flight strategy

Flights of Manx Shearwaters over the Irish Sea were investigated using GPS (n=6) and simultaneous high sample rate triaxial accelerometry (n=1). This pelagic species executes flight through intermittent bursts of flapping flight interspersed with gliding phases while meandering low over the waves. To facilitate the analysis and interpretation of body-mounted accelerometry in these challenging circumstances we introduce a combined time and frequency domain technique allowing accurate separation of flapping from gliding, measurement of wing-beat frequency and determination of flapping duty cycle. Considerable fluctuations in cycle period and time-averaged flapping duty cycle were found. Our approach offered high temporal precision, which was crucial as half the flapping bursts were briefer than 0.8s and half the cycle times shorter than 2.55s. Flapping duty cycles exceeding 38% were likeliest for short range flights and ascending flights. At higher duty cycles, cycle time decreased and wing-beat frequency and amplitude was only moderately elevated. Near-continuous flapping was only observed during steep ascents and strong headwinds. During a long-range foraging flight with good GPS coverage duty cycles between 7% and 63% were observed. We posit that flapping was modulated in order to maintain a steady airspeed in somewhat variable wind and wave conditions as part of a complex wave-meandering wing-sailing flight strategy that was often effective in reducing locomotion costs. Periods of very low duty cycle flight appear to have benefited from instantaneous crosswinds exceeding 10ms−1 with an estimated three-fold reduction in biomechanical power. Accelerometry offers a very practical tool for studying flight performance and the methods herein described can be readily adapted to other species that intermittently beat their wings.

Analysis of slow wave oscillations in cerebral haemodynamics and metabolism following subarachnoid haemorrhage.

Aneurysmal subarachnoid haemorrhage (SAH) causes the greatest loss of productive life years of any form of stroke. Emerging concepts of pathophysiology highlight early abnormalities of microvascular function, including impaired autoregulation of cerebral blood flow and flow-metabolism coupling, as key causes of cerebral ischaemia and poor outcome. Near infrared spectroscopy (NIRS) is a non-invasive optical technique which may help identify cerebral microvascular dysfunction. The aim of this research is to investigate the status of flow-metabolism coupling by examining phase relationships between NIRS-derived concentrations of oxy-haemoglobin ([HbO2]), deoxy-haemoglobin ([HHb]) and cytochrome c oxidase oxidation ([oxCCO]). Eight sedated ventilated patients with SAH were investigated. A combined NIRS broadband and frequency domain spectroscopy system was used to measure [HbO2], [HHb] and [oxCCO] alongside other multimodal neuromonitoring. Wavelet analysis of phase relationships revealed antiphase [HbO2]-[oxCCO] and in-phase [HbO2]-[HHb] oscillations between 0.1Hz-0.01Hz consistent with compromised flow-metabolism coupling. NIRS derived variables might offer unique insights into microvascular and metabolic dysfunction following SAH, and in the future identify therapeutic windows or targets.

Combining frequency domain reflectometry and visible and near infrared spectroscopy for assessment of soil bulk density

This paper introduces a new approach for the assessment of soil bulk density (BD), which relies on an existed model to predict BD as a function of a visible and near infrared spectroscopy (vis-NIRS) measured gravimetric moisture content (ω) and a frequency domain reflectometry (FDR) measured volumetric moisture content (θv). A total of 1013 soil samples collected from England and Wales, from 32 arable and grassland fields with different soil types were measured with a vis-NIR spectrophotometer (LabSpec®Pro Near Infrared Analyzer, Analytical Spectral Devices, Inc., USA) after in situ measurement with a ThetaProbe FDR (Delta-T Device Ltd.). Two calibration methods of the vis-NIRS were tested, namely, partial least squares regression (PLSR) and artificial neural network (ANN). ThetaProbe calibration was performed with traditional methods and ANN. ANN analyses were based on a single-variable input or multiple-variable input (data fusion). During ANN – data fusion analysis, vis-NIRS spectra and ThetaProbe output voltage (V) were fused in one matrix with or without laboratory measured texture fractions and organic matter content (OM). For the vis-NIRS and ThetaProbe traditional calibration, samples were divided into calibration (75%) and prediction (25%) sets, whereas for the ANN analyses these were divided into calibration (65%), test (10%) and independent validation (25%) sets. Results proved that high measurement accuracy can be obtained for ω and θv with PLSR and the best performing traditional calibration method of the ThetaProbe with R2 values of 0.91 and 0.97, and root mean square error of prediction (RMSEp) values of 0.027gg−1 and 0.019cm3cm−3, respectively. However, the ANN – data fusion resulted in improved accuracy (R2=0.98 and RMSEp=0.014gg−1 and 0.015cm3cm−3, respectively). This data fusion approach led to the best accuracy for BD assessment when vis-NIRS spectra and ThetaProbe V only were used as input data (R2=0.81 and RMSEp=0.095gcm−3). It can be concluded that BD can be measured by combining the vis-NIRS and FDR techniques based on ANN-data fusion approach.

Combined Time and Frequency Domain Approach to the Interpretation of Bender-Element Tests on Sand

Abstract Shear wave velocities are obtained from bender-element tests on laboratory specimens by analyzing the trigger and response signals. The response signal is a highly distorted translation of the trigger signal, obscuring the identification of the shear wave arrival. This has led to the publication of many criteria to guide subjective decisions on the selection of trigger waveform and frequency and of applicable interpretation methods. Current methods of interpretation result in determination of either the group or phase velocity. As soil and bender-element responses are dispersive, and as the group velocity is only valid in non-dispersive systems, the phase velocity should be measured. A combined time and frequency domain method is presented to allow interpretation of the phase velocity, minimizing subjective input to the interpretation. The method is first demonstrated using simplified synthetic signals and is then applied to laboratory test data. The reproducibility of the results is demonstrated from measurements on ten triaxial specimens of saturated Fraser River sand. The group velocities are shown to be very sensitive to dispersion, not reproducible, and contingent on the selected frequency window, whereas the phase velocities are considerably more repeatable. The combined time and frequency domain method results in the interpretation of a phase velocity using only measured parameters.

Modeling of Shock Wave Generated from a Strong Focused Ultrasound Transducer

A combined time and frequency domain method is developed for the calculation of a shock wave induced by a strong focused ultrasound transducer in the oblate spheroidal coordinate system. The spheroidal beam equation (SBE) is utilized to describe the nonlinear sound propagation; the diffraction and attenuation effects are calculated in the frequency-domain, while the nonlinear effect is calculated in the time-domain. Compared with the traditional frequency-domain method, this method could calculate the shock wave effectively without oscillation at the shock front and with less computation time. When the harmonic number is 200, the computation time by this method is about 1/16 of that used by the traditional frequency-domain method.

GENERATION OF A WIDE-BAND RESPONSE USING EARLY-TIME AND MIDDLE-FREQUENCY DATA THROUGH THE LAGUERRE FUNCTIONS

In this hybrid combined time domain (TD) and frequency domain (FD) approach, one can generate the early-time response using the method of marching-on-in-time (MOT) and use the method of moment (MOM) to generate the middle-frequency response, as the low frequency data may be unstable. The early-time and the middle- frequency data provide the missing low and high frequency response and the late-time response, respectively. Generation of a wide-band response using partial information of the TD data and FD data has been accomplished by the use of the continuous and discrete Laguerre functions.

A Detection Algorithm Combined Frequency Domain Equalization with Partial Parallel Interference Cancellation Based on Energy-Ordering

针对频率选择性信道下的DS-CDMA系统,该文提出了一种联合MMSE(最小均方误差)频域均衡与基于能量排序的部分并行干扰删除(MMSE-EOPPIC)的多用户检测算法。该算法首先采用基于重叠剪切法的MMSE频域均衡代替Rake接收机对各用户的数据信息进行初始估计,然后在干扰删除的每个迭代级根据用户能量由强到弱的顺序依次对用户进行部分干扰删除;为了提高每个迭代级检测的可靠性,该文将当前级已经检测出的能量较强用户的硬判决值用于重构当前级能量较弱用户的多址干扰(MAI)。仿真结果表明MMSE-EOPPIC检测算法可以有效提高系统的比特误码率性能。

In vivo quantitative imaging of normal and cancerous breast tissue using broadband diffuse optical tomography

A NIR tomography system that combines frequency domain (FD) and continuous wave (CW) measurements was used to image normal and malignant breast tissues. FD acquisitions were confined to wavelengths less than 850 nm because of detector limitations, whereas light from longer wavelengths (up to 948 nm) was measured in CW mode with CCD-coupled spectrometer detection. The two data sets were combined and processed in a single spectrally constrained reconstruction to map concentrations of hemoglobin, water, and lipid, as well as scattering parameters in the breast. Chromophore concentrations were imaged in the breasts of nine asymptomatic volunteers to evaluate their intrasubject and intersubject variability. Normal subject data showed physiologically expected trends. Images from three cancer patients indicate that the added CW data is critical to recovering the expected increases in water and decreases in lipid content within malignancies. Contrasts of 1.5 to twofold in hemoglobin and water values were found in cancers. In vivo breast imaging with instrumentation that combines FD and CW NIR data acquisition in a single spectral reconstruction produces more accurate hemoglobin, water, and lipid results relative to FD data alone.

A Combined Frequency Domain and Tensiometer Sensor for Determining Soil Water Characteristic Curves

There is increasing interest in the development of a technique that can simultaneously measure soil matric potential and water content for rapidly determining a soil water characteristic curve. In this study, we developed a new combined soil water content and potential sensor. The new sensor adapted the stainless steel tube of a conventional water‐filled tensiometer into a single sensing electrode for measuring soil water content. This novel design has the advantage of utilizing the whole length of the tensiometer without adding additional components to the sensor. To verify its feasibility, the sensor was tested on three soils (sand, sandy loam, and clay loam) while they were drying under laboratory conditions to produce the soil water characteristic curves in the matric potential range of 0 to −80 kPa. Water characteristic curves of the three soils were also obtained using standard laboratory techniques (tension table and pressure plate). The results show that water characteristic curves of the three soils from the new sensor are in good agreement with those obtained using the standard methods.

Co-channel Interference Mitigation via Joint Frequency and Space Domains Base Station Cooperation for Multi-Cell OFDMA Systems

Base Station (BS) cooperation has been considered as a promising technology to mitigate co-channel interference (CCI), yielding great capacity improvement in cellular systems. In this paper, by combining frequency domain cooperation and space domain cooperation together, we design a new CCI mitigation scheme to maximize the total utility for a multi-cell OFDMA network. The scheme formulates the CCI mitigation problem as a mixture integer programming problem, which involves a joint user-set-oriented subcarrier assignment and power allocation. A computationally feasible algorithm based on Lagrange dual decomposition is derived to evaluate the optimal value of the problem. Moreover, a low-complexity suboptimal algorithm is also presented. Simulation results show that our scheme outperforms the counterparts incorporating BS cooperation in a single domain considerably, and the proposed low-complexity algorithm achieves near optimal performance.

Near-infrared tomography of breast cancer hemoglobin, water, lipid, and scattering using combined frequency domain and cw measurement

In this study, near-IR tomography was implemented in the wavelength range from 661 to 948 nm to characterize breast tumors in vivo. Frequency-domain measurements provide amplitude and phase transmitted at wavelengths below 850 nm, where photomultiplier tube detection is efficient. Continuous-wave detection at additional longer wavelengths (903, 912, and 948 nm) was collected using a CCD-based spectrometer. Phantom validation experiments showed improved accuracy in hemoglobin and water concentrations using this technique. Three women with malignant breast tumors were studied. The addition of cw data at longer wavelengths increased the recovered contrast of water in the tumor region relative to surrounding tissue and allowed quantification of lipid.

Bootstrapping spectra: Methods, comparisons and application to knock data

The problem of confidence interval estimation for spectra is addressed. Unlike asymptotic techniques, bootstrap techniques provide accurate measures of confidence in that they maintain the preset level, in particular for small sample sizes and non-Gaussian data. We investigate some recently proposed methods, a time domain bootstrap approach, the so-called tapered block bootstrap and a combined time domain and frequency domain approach, the so-called autoregressive-aided periodogram bootstrap. We compare the methods with a well-established frequency domain residual based bootstrap technique in view of confidence accuracy. Confidence interval estimates for spectra of knock data are shown.