Signal Enhancement Techniques Research Articles

Performance analysis of single-frequency near electrical resonance signal enhancement (SF-NERSE) defect detection

In the search for ever more sensitive non-destructive testing (NDT) techniques to detect progressively smaller defects in increasingly complex industrial materials, environments and geometries, conventional eddy-current testing (ECT) methods, have reached their sensitivity limit. Fortunately, advanced resonance-based techniques, demonstrated in recent years, promise to help extend that limit, but, before such techniques can be implemented with confidence, a detailed understanding of their sensitivity and stability must be achieved. In this study, statistical probability-of-detection (PoD) analysis was performed to assess the sensitivity of a novel single-frequency, near electrical resonance signal enhancement (SF-NERSE) technique relative to an equivalent conventional probe operation. This study was performed on 36 real fatigue defects in Titanium 6V-4Al (Ti6-4) with defects ranging from 0.10−6.48 mm in surface extent. In addition, a critical evaluation of background noise stability in the SF-NERSE technique (a common concern with NERSE-based methods) was also performed to establish the viability of such a technique in relation to industrial inspection and assessment criteria. The results of this study demonstrate a sensitivity enhancement of up to 20% for the SF-NERSE method over conventional operation and, through controls, confirmed that the effect is a result of the resonance-shifting phenomenon and not just an increase in operational frequency. In addition, an examination of background noise implies that a SF-NERSE method exhibits more stable background noise than conventional excitation. This study validates the SF-NERSE technique and methodology as a viable industrial inspection technique, able to significantly improve detection capabilities.

Autonomous space target tracking through state estimation techniques via ground-based passive optical telescope

The presence of operational satellites or small-body space debris is a challenge for autonomous ground-based space object observation. Although most space objects exceeding 10 cm in diameter have been cataloged, the position of each space object (based on six orbital parameters) remains important and should be updated periodically, as the Earth’s orbital perturbations cause disturbances. Modern ground-based passive optical telescopes equipped with complementary metal-oxide semiconductors have become widely used in astrometry engineering, being combined with image processing techniques for target signal enhancement. However, the detection and tracking performance of this equipment when employed with image processing techniques primarily depends on the size and brightness of the space target, which appears on the monitor screen under variable background interference conditions. A small and dim target has a highly sensitive tracking error compared to a bright target. Moreover, most image processing techniques for target signal enhancement require large computational power and memory; therefore, automatic tracking of a space target is difficult. The present work investigates autonomous space target detection and tracking to achieve high-sensitivity detection and improved tracking ability for non-Gaussian and dynamic backgrounds with a simple system mechanism and computational efficiency. We develop an improved particle filter (PF) using the ensemble Kalman filter (KF) for track-before-detect (TBD) frameworks, by modifying and optimizing the computational formula for our non-linear measurement function. We call this extended version the “ensemble Kalman PF-TBD (EnKPF-TBD).” Three sequential astronomical image datasets taken by the Asia-Pacific Ground-Based Optical Space Objects Observation System (APOSOS) telescope under different conditions are used to evaluate three proposed TBD baseline frameworks. Given an optimal random sample size, the EnKPF-TBD exhibits superior performance to PF-TBD and threshold-based unscented KF with two-dimensional peak search (2dPS). The EnKPF-TBD scheme achieves satisfactory performance for all variable background interference conditions, especially for a small and dim space target, in terms of tracking accuracy and computational efficiency.

A new hybrid adaptive combination technique for ECG signal enhancement

This paper proposes a new de-noising system technique which is composed of Adaptive line enhancer (ALE) with the discrete wavelet transform (DWT) in order to improve the demerit of the ALE. A new adaptive algorithm which depends mainly on the second order resemblance between a signal and its delayed version is also derived and proposed for the ALE. Unlike the conventional DWT process where an estimation of a specific threshold is taken into account, here the ALE based proposed adaptive algorithm is exploited to enhance the detail coefficients. Therefore, the entire system works well for canceling Gaussian and non-Gaussian noise. Some experiments are carried out on an ECG signal to show the effectiveness of the proposed system. It illustrates from the simulations that the proposed technique demonstrates spectacular results for separating various noise types from the contaminated ECG signal. Finally, the proposed adaptive algorithm is compared with the leaky least mean square algorithm of the bases of mean square error. It is found that the performance of the proposed algorithm provides faster convergence rate and lower steady-state error. Consequently, the overall proposed system model represents a workable solution for ECG signal enhancement.

Adaptive determination of fundamental frequency for direct time-domain averaging

Time-domain averaging (TDA), as an effective signal processing technique for periodic signal enhancement has been widely used for fault detection of gearboxes and bearings. TDA normally requires a tachometer signal to provide an accurate estimation of fundamental frequency. However, tachometer is unavailable or difficult to use in many applications. In this paper, an adaptive fundamental-frequency determination method without tachometer signal is proposed. Firstly, the effect of fundamental-frequency estimation error on direct TDA is investigated, and it indicates that minor error of estimation can cause severe deterioration in curve of attenuation coefficient. Secondly, harmonic-to-noise ratio (HNR) is proposed to evaluate the result of direct TDA for a given fundamental frequency, and it is utilized to optimize the fundamental frequency from a number of candidates. Finally, average waveform is obtained by implementing direct TDA with the optimized fundamental frequency. Examples of gear faults are given to verify the proposed method. The derived fundamental frequency is accurate, and average waveforms indicate the presence of gear faults successfully.

Signal enhancement techniques for through-the-earth communication based on multiple references and beamforming

Through-the-Earth Communication (TEC) is very vulnerable to electromagnetic interference (EMI) and the conventional signal enhancement techniques employing local primary and remote reference antennas are unfeasible for downlink signal reception due to the limited space and complicated terrain on the receiving sites. In this paper, a novel magnetic field sensing system for downlink reception, in which multiple reference sensors are deployed locally and orthogonally with the primary receiving sensor, is introduced and the signal model is constructed from a perspective of array reception. A modified minimum variance distortionless response (MVDR) beamformer is designed by exploiting the TEC operating conditions for suppressing the EMIs and enhancing the signal. Real tests with power line harmonic interferences (PLHIs) from household wiring in laboratory and an additional wideband jamming demonstrated a great reduction of the EMIs and thus reliable downlink signal reception could be obtained.

Characterization of Fine Metal Particles Derived from Shredded WEEE Using a Hyperspectral Image System: Preliminary Results.

Waste of electric and electronic equipment (WEEE) is the fastest-growing waste stream in Europe. The large amount of electric and electronic products introduced every year in the market makes WEEE disposal a relevant problem. On the other hand, the high abundance of key metals included in WEEE has increased the industrial interest in WEEE recycling. However, the high variability of materials used to produce electric and electronic equipment makes key metals’ recovery a complex task: the separation process requires flexible systems, which are not currently implemented in recycling plants. In this context, hyperspectral sensors and imaging systems represent a suitable technology to improve WEEE recycling rates and the quality of the output products. This work introduces the preliminary tests using a hyperspectral system, integrated in an automatic WEEE recycling pilot plant, for the characterization of mixtures of fine particles derived from WEEE shredding. Several combinations of classification algorithms and techniques for signal enhancement of reflectance spectra were implemented and compared. The methodology introduced in this study has shown characterization accuracies greater than 95%.

Comparison of signal enhancement techniques using DNA microarrays for screening GM crops

Comparison of signal enhancement techniques using DNA microarrays for screening GM crops

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in matched (or multi-condition) settings where the acoustic conditions of the training data match (or cover) those of the test data. Few studies have systematically assessed the impact of acoustic mismatches between training and test data, especially concerning recent speech enhancement and state-of-the-art ASR techniques. In this article, we study this issue in the context of the CHiME-3 dataset, which consists of sentences spoken by talkers situated in challenging noisy environments recorded using a 6-channel tablet based microphone array. We provide a critical analysis of the results published on this dataset for various signal enhancement, feature extraction, and ASR backend techniques and perform a number of new experiments in order to separately assess the impact of different noise environments, different numbers and positions of microphones, or simulated vs. real data on speech enhancement and ASR performance. We show that, with the exception of minimum variance distortionless response (MVDR) beamforming, most algorithms perform consistently on real and simulated data and can benefit from training on simulated data. We also find that training on different noise environments and different microphones barely affects the ASR performance, especially when several environments are present in the training data: only the number of microphones has a significant impact. Based on these results, we introduce the CHiME-4 Speech Separation and Recognition Challenge, which revisits the CHiME-3 dataset and makes it more challenging by reducing the number of microphones available for testing.

Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithms

In remote health care monitoring, the extraction of high resolution cardiac signals is an important task. For this the cardiac signal (CS) needed to be enhanced. Among various filtering techniques, the adaptive noise cancellation (ANC) is a promising methodology. In adaptive filtering least mean square (LMS) algorithm is the fundamental enhancement algorithm. However, it suffers with slow convergence and weight drift problem in non-stationery environment. In order to improve the performance of ANC this paper proposes to implement an ANC methodology based on variable step size on the normalization of fundamental LMS algorithm for CS enhancement. Based on such strategy this research implements ANC using hybrid algorithm called variable normalized LMS (VNLMS) algorithm. Further, to improve the convergence characteristics, to filter the ability and to minimize computational complexity some versions of VNLMS algorithms are implemented too. Finally, the proposed ANCs are tested using real CS obtained from MIT-BIH data base. The performance evaluation is carried based on signal to noise ratio improvement (SNRI) and excess mean square error (EMSE).

A periodic spatio-spectral filter for event-related potentials

With respect to single trial detection of event-related potentials (ERPs), spatial and spectral filters are two of the most commonly used pre-processing techniques for signal enhancement. Spatial filters reduce the dimensionality of the data while suppressing the noise contribution and spectral filters attenuate frequency components that most likely belong to noise subspace. However, the frequency spectrum of ERPs overlap with that of the ongoing electroencephalogram (EEG) and different types of artifacts. Therefore, proper selection of the spectral filter cutoffs is not a trivial task. In this research work, we developed a supervised method to estimate the spatial and finite impulse response (FIR) spectral filters, simultaneously. We evaluated the performance of the method on offline single trial classification of ERPs in datasets recorded during an oddball paradigm. The proposed spatio-spectral filter improved the overall single-trial classification performance by almost 9% on average compared with the case that no spatial filters were used. We also analyzed the effects of different spectral filter lengths and the number of retained channels after spatial filtering.

Electronic Wheelchair for Physically Disabled Persons

This paper highlights various physical disorders and its associated disabilities. Plenty of wheel chair have been designed so far. A wheel chair which can assist the disabled people in their day to day life became a necessity. It is possible if their other organs of the body assist the electronic gadgets. Proposed method helps the physically challenged people to make their journey through electronic assisted module which works on signal processing over speech and image. This system works via speech or through recognition of hand gesture. To overcome the loss of signal because of real time inputs, different signal enhancement techniques are introduced to achieve high rate of accuracy and stability. The system's response time is very much considerable as the delays of the system are quite reduced. The operating system created to work on gestures and speech was tested on the test bed with different sets of data. Results marked prominent impression over the conventional ones with accuracy rate of 84.66% in case of image processing and 82.33% in speech signal processing.The refinement of the electronic wheelchair is going to definitely help many suffering from the disease.

A Review of Speech Signal Enhancement Techniques

Speech is the most natural and the most effective way of communication between human. During the speech communication, the signals contains some noise so when processing the digital speech signals; speech signal enhancement is very important step. The field speech processing is an applied area of signal processing. The motive of speech enhancement is to enhance the understandability and comprehensibility of speech signal. There are numbers of techniques proposed using which speech signal enhancement is performed. The objective of this paper is to provide an overview of speech enhancement algorithms which are used for enhancement of speech signal.

Design of ANC filter using modified cuckoo search technique for ECG signal enhancement

In this work, the design of an adaptive noise canceller (ANC) filter is presented using modified cuckoo search (MCS) optimization technique. The proposed scheme is applied for de-noising of ECG signals. Our simulation results reveal that the ANC filter based on MCS algorithm provides superior performance than other optimization techniques used to enhance the ECG signal. The performance of ANC filter is compared with other reported algorithms by evaluating the fidelity parameters such as the signal to noise ratio (SNR), maximum error (ME) and mean square error (MSE). The proposed ANC filter design with MCS scheme gives 18% improvement in output SNR, 87% decrease in ME, and 85% reduction in MSE over the recently reported Hilbert Huang Transform (HHT) technique.

In situ analysis of gas phase reaction processes within monolithic catalyst supports by applying NMR imaging methods

Measuring spatially resolved concentration distributions in gas phase reaction systems is an important tool to validate simulation calculations, improve the understanding of transport processes within the catalyst, and identify potentials for improvements of monolithic catalyst supports. The commonly used measurement methods for such opaque systems are invasive and, thus, might be misleading due to alteration of the system.To overcome this issue, a 3D magnetic resonance spectroscopic imaging (MRSI) method was developed and implemented on a 7-Tesla NMR imaging system to map the concentration distributions within opaque monolithic catalysts using the ethylene hydrogenation process as case study. The reaction was catalyzed by a coated sponge packing or a honeycomb monolith within an NMR compatible packed bed reactor. Temperatures at the inlet and the outlet of the catalyst beds were simultaneously determined by analyzing the spectra of inserted ethylene glycol filled glass capsules. Steady state concentration profiles and temperature levels were measured at different reaction conditions. In order to prove the plausibility of the measured spatial distributions of compound concentrations, the experimental results were compared to a 1D model of the reactor based on kinetic data from literature. Furthermore, a comparison with integral concentration measurements using a mass spectrometer demonstrated deviations below 5%. The results show that 3D MRSI is a valuable and reliable tool to non-invasively measure spatially resolved process parameters within optically and/or mechanically inaccessible structured monolithic catalyst supports, even if only standard thermal polarization is exploited and the use of expensive and technically challenging signal enhancement techniques (hyperpolarization) is avoided. We expect that 3D MRSI can pave the way toward deeper insight into the interactions between catalyst, catalyst support, and gas phase.

Permafrost Subgrade Condition Assessment Using Extrapolation by Deterministic Deconvolution on Multifrequency GPR Data Acquired Along the Qinghai-Tibet Railway

The Qinghai-Tibet railway (QTR) stretches 1956 km on Tibetan Plateau, the “roof of the world” from Xining City, Qinghai to the City of Lhasa, Tibet, China. About one half ( ${\sim}960\;\text{km}$ ) of the total length of this railway is on permafrost subgrades. Frozen earth hazard and other subgrade problems lead to subgrade instability and affect the normal operation of railway transportation. It is critical to develop a rapid and efficient technique for railway foundation defect detection. Multifrequency ground-penetrating radar (GPR) provides a good tradeoff between imaging depth and resolution for railway subgrades inspection. We have developed a signal fusion method to extrapolate the higher frequency, higher resolution GPR signal into a greater depth based on the lower frequency, greater penetration signal using the extrapolation with deterministic deconvolution (EDD) algorithm. This paper first introduces the principles of EDD and demonstrates the procedure by applying it to a synthetic data set. Next, data preprocessing and filtering are discussed to prepare the field GPR data acquired on QTR to be suitable for carrying out EDD. Finally, the multifrequency radar signal is fused based on EDD to reach a higher resolution and signal-to-noise ratio for the fused radargram profile. Examples of application of this proposed approach at three sections provide a demonstration for characterizing the status of subgrade permafrost and recognizing subgrade problem in different depths beneath the QTR. Our field tests of EDD to multifrequency GPR data demonstrate that it is a promising technique for signal enhancement.

54 IMMUNOLOCALIZATION OF STEROID SULFATASE AND ESTROGEN-SPECIFIC SULFOTRANSFERASE IN BOVINE FOLLICLES

Steroid hormones are regulators in the fine-tuned mechanism of follicular development in cattle. Their concentration and property can be modulated via different processes. Sulfoconjugation via sulfotransferases (SULT) changes them from being hydrophobic to hydrophilic molecules, thereby preventing them from diffusing freely across the lipid bilayer and necessitating a transport system like the sodium-dependent organic anion transporter (SOAT; SLC10A6). In addition, sulfated steroids can no longer bind to their nuclear receptors, rendering them biologically inactive. Steroid sulfatase (STS) removes the sulfate moiety from conjugated steroids, transforming them to the free active forms. Data about the concentration of sulfated steroids in follicular fluid and the expression of the related enzymes are limited in horses and cattle. Recently, transcripts of the corresponding enzymes have been detected in cumulus cells of immature bovine cumulus-oocyte complex. Furthermore, it has been shown that small amounts of sulphated steroids are present in bovine follicular fluid. The objective of the study was to investigate the steroid metabolizing enzymes at the protein level via immunohistochemistry (IHC). Bovine ovaries collected at a slaughterhouse were categorized according to their oestrus cycle stage. Three pairs of ovaries of each cycle stage (proestrus, oestrus, postestrus, interestrus) were collected. Morphological criteria (ovaries: presence/absence of follicles and/or CL, size and number of follicles; closure of the cervix, amount of mucus) were employed to categorize them. Samples for IHC were fixed in formalin. After washing and dehydration, the samples were finally embedded in paraffin and mounted onto blocks. Indirect immunoperoxidase staining methods were applied using the streptavidin-biotin technique for signal enhancement following standard procedures. Tissue sections of 3 µm were mounted onto glass slides, and a polyclonal rabbit anti-human STS and a polyclonal rabbit anti-bovine SULT1E1 primary antibody were used to detect STS or SULT1E1, respectively. Negative controls were included using nonspecific rabbit IgG. The evaluation of the staining was descriptive in relation to a positive control (bovine placenta). Immunostaining for STS was detected in granulosa cells of antral and secondary follicles and in the endothelium of blood vessels, irrespective of the stage of the oestrus cycle. Moreover, staining for SULT1E1 was restricted to granulosa cells in antral follicles, again irrespective of the stage of the oestrus cycle. However, staining intensity for STS and SULT1E1 was only weak. These data indicate for the first time the presence of the steroid metabolising enzymes STS and SULT1E1 in bovine ovaries at the protein level. However, due to the low expression level, further studies are needed to clarify the function. We gratefully acknowledge the financial support of the German Research Foundation (DFG; FOR 1369, WR 154/3–1).

A biomemory chip composed of a myoglobin/CNT heterolayer fabricated by the protein-adsorption-precipitation-crosslinking (PAPC) technique

In this study, a biomemory chip consisting of a myoglobin/carbon nanotube (CNT) heterolayer is fabricated via the protein-adsorption-precipitation-crosslinking (PAPC) technique for electrochemical signal enhancement, long-term stability, and improved memory function. The PAPC technique is used to fabricate a myoglobin/CNT heterolayer with a CNT core and a high-density myoglobin-shell structure to achieve efficient heterolayer formation and improved performance of the heterolayer. The fabricated myoglobin/CNT heterolayer is immobilized onto a Au substrate through a chemical linker. The surface morphology of the deposited heterolayer is investigated via transmission electron microscopy and atomic force microscopy. The redox properties of the myoglobin/CNT heterolayer are investigated by cyclic voltammetry, and the memory function of the heterolayer, including the “write step” and “erase step,” is measured by chronoamperometry. Compared with the myoglobin monolayer without CNT, the myoglobin/CNT heterolayer fabricated by the PAPC technique exhibits greater electrochemical signal enhancement, long-term stability at room temperature, and improved memory function. The results suggest that the proposed myoglobin/CNT heterolayer produced via the PAPC technique can be applied as a platform for bioelectronic devices to achieve improved signal intensity and durability.

Oxidized Porous Silicon Nanostructures Enabling Electrokinetic Transport for Enhanced DNA Detection

Nanostructured porous silicon (PSi) is a promising material for the label‐free detection of biomolecules, but it currently suffers from limited applicability due to poor sensitivity, typically in micromolar range. This work presents the design, operation concept, and characterization of a novel microfluidic device and assay that integrates an oxidized PSi optical biosensor with electrokinetic focusing for a highly sensitive label‐free detection of nucleic acids. Under proper oxidation conditions, the delicate nanostructure of PSi can be preserved, while providing sufficient dielectric insulation for application of high voltages. This enables the use of signal enhancement techniques, which are based on electric fields. Here, the DNA target molecules are focused using an electric field within a finite and confined zone, and this highly concentrated analyte is delivered to an on‐chip PSi Fabry–Pérot optical transducer, prefunctionalized with capture probes. Using reflective interferometric Fourier transform spectroscopy real‐time monitoring, a 1000‐fold improvement in limit of detection is demonstrated compared to a standard assay, using the same biosensor. Thus, a measured limit of detection of 1 × 10−9 m is achieved without compromising specificity. The concepts presented herein can be readily applied to other ionic targets, paving way for the development of other highly sensitive chemical and biochemical assays.

Novel Noise-Source-Identification Technique Combining Acoustic Modal Analysis and a Coherence-Based Noise-Source-Identification Method

A novel technique is presented, which combines the fields of coherence-based noise-source identification with acoustic modal analysis. Coherence-based noise-source-identification techniques apply the coherence function, estimated between pairs of dynamic pressure sensors, to identify the relative contributions of one or several noise sources to the total noise measured at a location of interest. Acoustic-modal-analysis techniques use arrays of microphones to decompose the pressure field at an axial location of a duct into constituent modal amplitudes. Conditional Spectral Analysis: Modal is the primary novel technique presented herein, and uses conditional spectral analysis to allow the beneficial capabilities of each of these well-regarded fields to be combined into one method. The technique allows the total modal pressure field measured in a duct to be decomposed into parts associated with contributing uncorrelated noise sources. The techniques’ efficacy is validated with a two noise-source-region experimental rig. A second novel technique is also presented: Signal Enhancement: Modal, which is in essence a modal version and enhancement of the now classic signal-enhancement technique, which allows the influence of extraneous noise to be removed from acoustic modal measurements. The modal signal enhancement was developed as part of the modal conditional spectral analysis, but can in fact be used independently in conditions in which only one correlated noise source is measured by the sensors in the duct. The modal-conditional-spectral-analysis technique represents a significant advancement in noise-source identification, and may be applied to a full-scale aeroengine to identify the specific contributions of core-noise sources within the engine to the amplitudes of the acoustic modes propagating through the engine exhaust. This added acoustic modal information can be used to predict the relative contribution of these noise sources to the total noise radiated from the engine and into the far field. Knowledge of the contributions of each core-noise-source contribution to the modal content propagating through the engine exhaust also allows for better mitigation through optimized liner designs and location.

Lighting up the Raman signal of molecules in the vicinity of graphene related materials.

Surface enhanced Raman scattering (SERS) is a popular technique to detect the molecules with high selectivity and sensitivity. It has been developed for 40 years, and many reviews have been published to summarize the progress in SERS. Nevertheless, how to make the SERS signals repeatable and quantitative and how to have deeper understanding of the chemical enhancement mechanism are two big challenges. A strategy to target these issues is to develop a Raman enhancement substrate that is flat and nonmetal to replace the conventional rough and metal SERS substrate. At the same time, the newly developed substrate should have a strong interaction with the adsorbate molecules to guarantee strong chemical enhancement. The flatness of the surface allows better control of the molecular distribution and configuration, while the nonmetal surface avoids disturbance of the electromagnetic mechanism. Recently, graphene and other two-dimensional (2D) materials, which have an ideal flat surface and strong chemical interaction with plenty of organic molecules, were developed to be used as Raman enhancement substrates, which can light up the Raman signals of the molecules, and these substrates were demonstrated to be a promising for microspecies or trace species detection. This effect was named "graphene enhanced Raman scattering (GERS)". The GERS technique offers significant advantages for studying molecular vibrations due to the ultraflat and chemically inert 2D surfaces, which are newly available, especially in developing a quantitative and repeatable signal enhancement technique, complementary to SERS. Moreover, GERS is a chemical mechanism dominated effect, which offers a valuable model to study the details of the chemical mechanism. In this Account, we summarize the systematic studies exploring the character of GERS. In addition, as a practical technique, the combination of GERS with a metal substrate incorporates the advantages from both conventional SERS and GERS. The introduction of graphene to the Raman enhancement substrate extended SERS applications in a more controllable and quantitative way. Looking to the future, we expect the combination of the SERS concept with the GERS technology to lead to the solution of some important issues in chemical dynamics and in biological processes monitoring.