Signal Enhancement Approach Research Articles

Intelligent condition monitoring with CNN and signal enhancement for undersampled signals

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy.

Double-enhanced sandwich electrochemiluminescence aptasensor based on g-C3N4–Au-luminol nanocomposites and ZnCuS nanosheets for highly sensitive detection of mucin 1

The traditional luminol electrochemiluminescence (ECL) sensing suffers from low signal response and instability issues. Here, an Au/ZnCuS double-enhanced g-C3N4-supported luminol ECL aptasensor is constructed for the sensitive detection of human mucin 1 (MUC1). In this platform, g-C3N4 of a large specific surface area is beneficial to load more luminol illuminants. Au nanoparticles promote the decomposition of H2O2 coreactants to generate more reactive oxygen (•OH and O2•−) intermediates, while ZnCuS can immobilize the aptamer and simultaneously catalyze H2O2 decomposition, realizing the double-wing signal amplification. Under optimal conditions, this sensor shows a good detection capability within 1.0 × 10−4–1.0 × 103 ng mL−1 and a low detection limit of 5.0 × 10−5 ng mL−1, as well as ideal stability, selectivity, and reproducibility. This double-enhanced aptasensor highlights a new signal-enhancement approach for early biomarker detection.

Drone audition: improved Gaussian mixture model Wiener filtering approach for audio signal enhancement

Audio signal enhancement is an integral function of drone audition systems which emerges intelligent audio applications for military and civil environments. Signal enhancement using a drone is challenging due to its emission of significant noise from drone motors and propellers resulting in an extremely low signal-to-drone noise ratio level. A method for signal enhancement from an on-board microphone array on a drone for static flight conditions in adverse noisy environments is proposed. In this paper, we present an improved Gaussian mixture model Wiener filter approach for a noisy drone platform in the presence of interfering noise sources such as wind, and traffic. The key to this method is to exploit the spatial properties of the noise sources by using a set of Gaussian components to derive an accurate estimate of the power spectral densities of both residual drone noise and interfering noise sources to obtain the enhanced target sound source signal. We demonstrate the method's general applicability for both speech, and bird calls using an extensive experimental study, including an experimental recording from a drone acoustics dataset using measured impulse responses, and an outdoor measurement from a hovering drone for a bioacoustic application, respectively.

The speech signal enhancement approach with multiple sub-frames analysis for complex magnitude and phase spectrum recompense

The intended speech must be dealt with in the process of speech communication while under the impact of noise experienced in a variety of situations that degrade speech intelligibility and quality. This work proposes a multiple sub-frames analysis for the elimination of noise variants with compensation of the magnitude and phase spectrum from speech degraded by noise. The clean speech samples are extracted from the ITU-T recommended dataset at a 16 kHz sampling rate and down-sampled to an 8 kHz sampling rate. The noise signal variants are added from the AURORA and NOISEX-92 datasets at diverse input SNR levels (0 dB, 5 dB, 10 dB, 15 dB). The duration of window frames is chosen to be 25 msec in length, together with a shift percentage of 40%, to maintain the continuous nature of frames in speech. The smoothing factor for noise updating in a specific sub-frame is set to 9, and the spectral floor parameter for determining the precise amount of noise elimination in the corrupted spectrum is set to 0.03. The phase spectrum is compensated by incorporating a recompense function that is updated in combination with the sub-frame analysis. The accomplishment of the suggested approach is assessed with regard to objective metrics, including Segmental-Signal-to-Noise-Ratio (SegSNR), Mean-Square-Error (MSE), and Perceptual-Evaluation-of-Speech-Quality (PESQ) scores corresponding to specific sub-frames of speech, respectively. To further analyze the improved quality, simple listening assessment and spectrogram analysis are incorporated, followed by a comparative investigation with prior noise-suppressive algorithms on the corrupted speech corpus.

Pulsed and Continuous Signal Enhancement Based on Improved Noise Power Spectrum Density Estimation in the Passive Underwater Acoustic Data

The pulsed and continuous narrow-band signal enhancement is quite important for the target detection and recognition in a passive sonar system. In this paper, a novel signal enhancement approach is proposed for both pulsed and continuous narrow-band components in the passive underwater acoustic data. The short time Fourier transform (STFT) method is firstly used in the time-domain radiated noise data, and then an improved noise power spectrum density (PSD) estimation is proposed to obtain the accurate noise power by using a two-dimension (2-D) sliding window. Finally, the improved PSD is utilized to enhance both pulsed and continuous narrow-band components. The proposed method has the capability of enhancing pulsed and continuous narrow-band components with avoiding the annoying tuning of the window length. Both simulation and experimental results verify the effectiveness and robustness of the proposed method.

Aptasensors for the detection of infectious pathogens: design strategies and point-of-care testing.

The epidemic of infectious diseases caused by contagious pathogens is a life-threatening hazard to the entire human population worldwide. A timely and accurate diagnosis is the critical link in the fight against infectious diseases. Aptamer-based biosensors, the so-called aptasensors, employ nucleic acid aptamers as bio-receptors for the recognition of target pathogens of interest. This review focuses on the design strategies as well as state-of-the-art technologies of aptasensor-based diagnostics for infectious pathogens (mainly bacteria and viruses), covering the utilization of three major signal transducers, the employment of aptamers as recognition moieties, the construction of versatile biosensing platforms (mostly micro and nanomaterial-based), innovated reporting mechanisms, and signal enhancement approaches. Advanced point-of-care testing (POCT) for infectious disease diagnostics are also discussed highlighting some representative ready-to-use devices to address the urgent needs of currently prevalent coronavirus disease 2019 (COVID-19). Pressing issues in aptamer-based technology and some future perspectives of aptasensors are provided for the implementation of aptasensor-based diagnostics into practical application.

Cascaded and nonlinear DNA assembly amplification for sensitive and aptamer-based detection of kanamycin

Simple, selective and sensitive monitoring of antibiotic residues in food is essential for food safety and human health because of its side effects upon inappropriate usage. Here, with a new label- and enzyme-free significant signal enhancement approach by the coupling of catalytic hairpin assembly (CHA) with nonlinear hybridization chain reaction (nHCR), we developed a fluorescence aptamer sensor for the detection of trace kanamycin in milk samples with high selectivity and sensitivity. The binding of the target kanamycin to the aptamer probe could initiate the CHA between two hairpins for the formation of partial DNA duplexes, which further triggered the nHCR of other three hairpins to yield branched DNA complexes with a multitude of active G-quadruplex structures. The subsequent intercalation of the organic dye, thioflavin T, into G-quadruplex structures resulted in significantly enhanced fluorescence responses for realizing sensitive sensing of kanamycin in the dynamic range of 0.1–300 nM with a detection limit of 46.1 pM. Besides, this strategy could also achieve the monitoring of kanamycin selectively in spiked milk samples. With features of high sensitivity and simplicity in a non-enzyme/label fashion, our signal amplification strategy has high potentials for establishing sensitive and convenient means to monitor various antibiotics.

Ideal ratio mask estimation using supervised DNN approach for target speech signal enhancement

The most challenging process in recent Speech Enhancement (SE) systems is to exclude the non-stationary noises and additive white Gaussian noise in real-time applications. Several SE techniques suggested were not successful in real-time scenarios to eliminate noises in the speech signals due to the high utilization of resources. So, a Sliding Window Empirical Mode Decomposition including a Variant of Variational Model Decomposition and Hurst (SWEMD-VVMDH) technique was developed for minimizing the difficulty in real-time applications. But this is the statistical framework that takes a long time for computations. Hence in this article, this SWEMD-VVMDH technique is extended using Deep Neural Network (DNN) that learns the decomposed speech signals via SWEMD-VVMDH efficiently to achieve SE. At first, the noisy speech signals are decomposed into Intrinsic Mode Functions (IMFs) by the SWEMD Hurst (SWEMDH) technique. Then, the Time-Delay Estimation (TDE)-based VVMD was performed on the IMFs to elect the most relevant IMFs according to the Hurst exponent and lessen the low- as well as high-frequency noise elements in the speech signal. For each signal frame, the target features are chosen and fed to the DNN that learns these features to estimate the Ideal Ratio Mask (IRM) in a supervised manner. The abilities of DNN are enhanced for the categories of background noise, and the Signal-to-Noise Ratio (SNR) of the speech signals. Also, the noise category dimension and the SNR dimension are chosen for training and testing manifold DNNs since these are dimensions often taken into account for the SE systems. Further, the IRM in each frequency channel for all noisy signal samples is concatenated to reconstruct the noiseless speech signal. At last, the experimental outcomes exhibit considerable improvement in SE under different categories of noises.

Target-switchable Gd(III)-DOTA/protein cage nanoparticle conjugates with multiple targeting affibody molecules as target selective T1 contrast agents for high-field MRI

Magnetic resonance imaging (MRI) is a non-invasive in vivo imaging tool, providing high enough spatial resolution to obtain both the anatomical and the physiological information of patients. However, MRI generally suffers from relatively low sensitivity often requiring the aid of contrast agents (CA) to enhance the contrast of vessels and/or the tissues of interest from the background. The targeted delivery of diagnostic probes to the specific lesion is a powerful approach for early diagnosis and signal enhancement leading to the effective treatment of various diseases. Here, we established targeting ligand switchable nanoplatforms using lumazine synthase protein cage nanoparticles derived from Aquifex aeolicus (AaLS) by genetically introducing the SpyTag peptide (ST) to the C-terminus of the AaLS subunits to form an ST-displaying AaLS (AaLS-ST). Conversely, multiple targeting ligands were constructed by genetically fusing SpyCatcher protein (SC) to either HER2 or EGFR targeting affibody molecules (SC-HER2Afb or SC-EGFRAfb). Gd(III)-DOTA complexes were chemically attached to the AaLS-ST and the external surface of the Gd(III)-DOTA conjugated AaLS-ST (Gd(III)-DOTA-AaLS-ST) were successfully decorated with either the HER2Afb or the EGFRAfb. The resulting Gd(III)-DOTA-AaLS/HER2Afb and Gd(III)-DOTA-AaLS/EGFR2Afb exhibited high r1 relaxivity values of 57 and 25 mM−1 s−1 at 1.4 and 7 T, respectively, which were 10-fold or higher than those of the clinically used Dotarem. Their target-selective contrast enhancements were confirmed with in vitro cell-based MRI scans and the in vivo MR imaging of tumor-bearing mouse models at 7 T. A target-switchable AaLS-based nanoplatform that was developed in this study might serve as a promising T1 CA developing platform at a high magnetic field to detect various tumor sites in a target-specific manner in future clinical applications.

A Framework for Human Activity Recognition Based on WiFi CSI Signal Enhancement

With the advancement of wireless technologies and sensing methodologies, many studies have shown that wireless signals can sense human behaviors. Human activity recognition using channel state information (CSI) in commercial WiFi devices plays an important role in many applications. In this paper, a framework for human activity recognition was constructed based on WiFi CSI signal enhancement. Firstly, the sensitivity of different antennas to human activity was studied. An antenna selection algorithm was proposed, which can make a choice of the antenna automatically based on their sensitivity in accordance with different activities. Secondly, two signal enhancement approaches, which can strengthen the active signals and weaken the inactive signals, were proposed to extract the active interval caused by human activity. Finally, an activity segmentation algorithm was proposed to detect the start and end time of activity. In order to verify and evaluate the methods, extensive experiments have been conducted in real indoor environments. The experimental results have demonstrated that our solutions can eliminate a large number of redundant information brought by insensitive and inactive signals. Our research results can be put into use to improve recognition accuracy significantly and decrease the cost of recognition time.

Sensitivity Enhancement of Electrochemical Biosensor for Point of Care (POC) Applications: Vi Antigen Detection as a Case Study

A novel approach for signal enhancement of electrochemical biosensors by incorporating mechanical vibrations has been developed. We report the electrochemical study of the ferricyanide and ferrocyanide a redox couple, at room temperature (∼25 °C), under the effect of mechanical vibrations of different frequencies applied to the sensor, for sensitivity enhancement. The experimental results showed a sensitivity enhancement of ∼332% (from to ) at 88.75 Hz of vibration. This novel approach of signal sensitivity enhancement is also validated with antibody immobilization-based Vi antigen detection for typhoid assessment. The sensitivity enhancement up to ∼15% is achieved for Vi antigen detection under the mechanical vibration of 120 Hz. The sensor is fabricated using microfabrication technology. The vibration-assisted ultrasensitive biosensing platform is also prototyped as a portable and Internet of Thing (IoT) enabled device, suitable for point-of-care applications. Detailed features of the prototype along with the test results are elaborated in the paper.

Nanoparticle enhanced laser induced breakdown spectroscopy of liquid samples by using modified surface-enhanced Raman scattering substrates

An assessment of the feasibility of using modified surface enhanced Raman scattering substrates (Ag nanoparticles on indium‑tin-oxide glass) for quantitative nanoparticle-enhanced laser induced breakdown spectroscopy (NELIBS) was carried out. Substrates were prepared with different surface coverage from various nanoparticle sizes, and their laser ablation behaviour was tested in detail. It was found that use of those combinations are most beneficial in terms of the signal enhancement factor, which provide the shortest interparticle distances. With the application of 266 nm laser wavelength, long (ms-range) gate width, and optimized laser pulse energy, the best NELIBS signal enhancement was found to be about a factor of three. By using liquid sample deposition by spraying, which was found to provide an even distribution of liquid samples on the substrate surface, successful calibration for Mn, Zn and Cr was performed. The NELIBS signal repeatability from five repeated measurements was found to be comparable to that of LIBS (5–10% RSD). These observations indicate that the NELIBS signal enhancement approach can be used in quantitative analytical applications for liquid samples, if i) the substrate fabrication procedure has good repeatability, ii) surface coverage and nanoparticle size is tightly controlled, iii) a homogenous liquid sample deposition is achieved.

A “signal-on” electrochemical biosensor based on DNAzyme-driven bipedal DNA walkers and TdT-mediated cascade signal amplification strategy

In this work, a dual amplified signal enhancement approach based on coupling deoxyribozyme (DNAzyme)-driven bipedal DNA walkers (BDW) and terminal deoxynucleotidyl transferase (TdT)-mediated DNA elongation signal amplifications has been developed for highly sensitive and label-free electrochemical detection of thrombin in human serums. In presence of thrombin, the BDW complex, which is comprised from the target thrombin and two DNAzyme-containing probes, can exhibit autonomous cleavage behavior on the surface of the substrate DNA (SD) modified electrode, and remove the cleaved DNA fragment from the electrode surface. Subsequently, the TdT can catalyze the elongation of the SD with free 3′-OH termini and formation of many G-quadruplex sequence replicates with the presence of 2′-deoxyaguanosine-5′-triphosphate (dGTP) and adenosine 5′-triphosphate (dATP) at a molar ratio of 6:4. These G-quadruplex sequences bind hemin and generate drastically amplified current response for sensitive detection of thrombin in a “signal-on” and completely label-free fashion. Under optimized conditions, the response peak current was linear with the concentration of thrombin in the range from 0.5 pM to 100000 pM with detection limit of 0.31 pM. This research provides us a sustainable idea for the hyphenated multiple amplification strategies and a stable and effective method for the detection of protein biomarkers.

Hybrid approach for ECG signal enhancement using dictionary learning‐based sparse representation

Electrocardiogram (ECG) signal quality enhancement is a crucial task in the computer-based automated processing system. In this study, a dictionary learning (DL)-based sparse representation framework is presented for ECG signal enhancement through denoising. Unlike, traditional filtering techniques, the proposed method removes both low- and high-frequency noises for complete enhancement of the signal quality. The frequency localised DL-based sparse representation approach is applied to remove both baseline wander and power-line interference. The time-localised and signal characteristics based DL-based sparse representation scheme is employed for extraction of clean ECG components from white Gaussian noise and muscle artefact added noisy ECG record. Both qualitative and quantitative performance analyses are carried out using the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) database, QT database and synthetic ECG signals. The efficacy of the proposed method is compared with the existing ECG denoising approaches using standard performance metrics: signal-to-noise ratio, root-mean-square error and percentage RMS difference. It is observed through the detailed study and analysis that the proposed approach outperforms the existing ones and can be served to further enhance the overall quality of ECG in a computer-based automated medical system.

An adaptive chatter signal enhancement approach for early fault diagnosis in machining process

Chatter is a fast-changing machining fault that needs to be timely diagnosed to avoid the deterioration. However, it is a big challenge to recognize the weak chatter component out of the machining process signal that includes strong disturbances caused by the measurement noise, the machining uncertainty in the early stage. In this paper, an adaptive chatter signal enhancement approach is proposed to improve the signal-to-noise ratio (SNR) based on the principle of statistical resonance. A Fokker-Planck model with smoothly quadratic double well potential is proposed and analytically solved without using the adiabatic approximation. Compared with the recorded double-well models, the superiority of this one mainly lies in its better resolvability and enhancement capacity. The cutting force signals are utilized to prove the efficiency of the approach to enhance and highlight the chatter signal out of strong disturbances for early chatter detection.

Gold Nanoparticles Used as Protein Scavengers Enhance Surface Plasmon Resonance Signal.

Although several researchers had reported on methodologies for surface plasmon resonance (SPR) signal amplification based on the use of nanoparticles (NPs), the majority addressed the sandwich technique and low protein concentration. In this work, a different approach for SPR signal enhancement based on the use of gold NPs was evaluated. The method was used in the detection of two lectins, peanut agglutinin (PNA) and concanavalin A (ConA). Gold NPs were functionalized with antibodies anti-PNA and anti-ConA, and these NPs were used as protein scavengers in a solution. After being incubated with solutions of PNA or ConA, the gold NPs coupled with the collected lectins were injected on the sensor containing the immobilized antibodies. The signal amplification provided by this method was compared to the signal amplification provided by the direct coupling of PNA and ConA to gold NPs. Furthermore, both methods, direct coupling and gold NPs as protein scavengers, were compared to the direct detection of PNA and ConA in solution. Compared to the analysis of free protein, the direct coupling of PNA and ConA to gold NPs resulted in a signal amplification of 10–40-fold and a 13-fold decrease of the limit of detection (LOD), whereas the use of gold NPs as protein scavengers resulted in an SPR signal 40–50-times higher and an LOD 64-times lower.

A data driven compressive sensing approach for time-frequency signal enhancement

Signals with the time-varying frequency content are generally well represented in the joint time-frequency domain; however, the most commonly used methods for time-frequency distributions (TFDs) calculation generate unwanted artifacts, making the TFDs interpretation more difficult. This downside can be circumvented by compressive sensing (CS) of the signal ambiguity function (AF), followed by the TFD reconstruction based on the sparsity constraint. The most critical step in this approach is a proper CS-AF area selection, with the CS-AF size and shape being generally chosen experimentally, hence decreasing the overall reliability of the method. In this paper, we propose a method for an automatic data driven CS-AF area selection, which removes the need for the user input. The AF samples picked by the here-proposed algorithm ensure the optimal amount of data for the sparse TFD reconstruction, resulting in higher TFD concentration and faster sparse reconstruction algorithm convergence, as shown on examples of both synthetical and real-life signals.

Target-triggered catalytic hairpin assembly and TdT-catalyzed DNA polymerization for amplified electronic detection of thrombin in human serums

Specific and sensitive detection of protein biomarkers is of great importance in biomedical and bioanalytical applications. In this work, a dual amplified signal enhancement approach based on the integration of catalytic hairpin assembly (CHA) and terminal deoxynucleotidyl transferase (TdT)-mediated in situ DNA polymerization has been developed for highly sensitive and label-free electrochemical detection of thrombin in human serums. The presence of the target thrombin leads to the unfolding and capture of a significant number of hairpin signal probes with free 3′-OH termini on the sensor electrode. Subsequently, TdT can catalyze the elongation of the signal probes and formation of many G-quadruplex sequence replicates with the presence of dGTP and dATP at a molar ratio of 6:4. These G-quadruplex sequences bind hemin and generate drastically amplified current response for sensitive detection of thrombin in a completely label-free fashion. The sensor shows a linear range of 0.5pM–10.0nM and a detection limit of 0.12pM for thrombin. Moreover, the developed sensor can selectively discriminate the target thrombin against other non-target proteins and can be employed to monitor thrombin in human serum samples.

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

This paper proposes a novel approach to periodic fault signal enhancement in rotating machine vibrations with a tristable mechanical vibration amplifier (TMVA) by exploiting stochastic resonance (SR). The TMVA is a nonlinear physical structure system that consists of a cantilever beam and a magnet system. Through the TMVA, the periodic weak signal can be amplified with the assistance of noise in the regime of SR. Benefitting from a wider interwell spacing and a smoother potential curve, the TMVA produces a more regular output waveform with lower noise in a wider operating bandwidth as compared to the monostable and bistable amplifiers. Different from the traditional signal enhancement approach which is based on digital signal processing (DSP) techniques, the designed physical structure can realize signal enhancement in a simple, intuitive, effective and adaptive way without too much complex operations. The effectiveness and efficiency of the proposed approach are validated by a simulated fault signal and the practical bearing and gearbox fault signals, in comparison with a traditional DSP-based SR method. The principle of the proposed approach shows potential applications on rotating machine fault diagnosis area and other areas related to weak periodic signal enhancement.

Flow-modulated targeted signal enhancement for volatile organic compounds.

Comprehensive two-dimensional gas chromatography is a technique that is becoming more widespread within the analytical community, especially in the separation of complex mixtures. Modulation in comprehensive two-dimensional gas chromatography can be achieved by manipulating temperature or flow and offers many advantages such as increased separation power, but one underutilized advantage is increased detectability due to the reduction of peak width from the use of a modulator. A flow modulator was used to selectively target analytes for increased detectability with a standard flame ionization detector operated at 100 Hz, without the need for cryogens or advanced modulation software. By the collection of the entire peak volume followed by peak transfer rather than further separation, an increase of 12 times in peak height and detectability was realized for the analytes tested using an internal loop modulator configuration. An external loop flow modulator configuration allowed for more volatile analytes (with k < 5), and demonstrated an analyte detectability enhancement factor of at least 6. The collection loop size can be readily increased with an external loop configuration to accommodate for these naturally broader peaks.This novel flow modulated targeted signal enhancement approach was applied to industrially significant analyses like the analysis of methanol in a hydrocarbon streams. Methanol was detected at 7 ppb with a conventional flame ionization detector and without the need for pre-concentration.