Amplitude Modulation-frequency Modulation Research Articles

Unravelling the physicochemical and antimicrobial mechanisms of human serum albumin/tannic acid coatings for medical-grade polycaprolactone scaffolds

Biofilm-related biomaterial infections are notoriously challenging to treat and can lead to chronic infection and persisting inflammation. To date, a large body of research can be reviewed for coatings which potentially prevent bacterial infection while promoting implant integration. Yet only a very small number has been translated from bench to bedside. This study provides an in-depth analysis of the stability, antibacterial mechanism, and biocompatibility of medical grade polycaprolactone (mPCL), coated with human serum albumin (HSA), the most abundant protein in blood plasma, and tannic acid (TA), a natural polyphenol with antibacterial properties. Molecular docking studies demonstrated that HSA and TA interact mainly through hydrogen-bonding, ionic and hydrophobic interactions, leading to smooth and regular assemblies. In vitro bacteria adhesion testing showed that coated scaffolds maintained their antimicrobial properties over 3 days by significantly reducing S. aureus colonization and biofilm formation. Notably, amplitude modulation-frequency modulation (AMFM) based viscoelasticity mapping and transmission electron microscopy (TEM) data suggested that HSA/TA-coatings cause morphological and mechanical changes on the outer cell membrane of S. aureus leading to membrane disruption and cell death while proving non-toxic to human primary cells. These results support this antibiotic-free approach as an effective and biocompatible strategy to prevent biofilm-related biomaterial infections.

Enhancing nanoscale viscoelasticity characterization in bimodal atomic force microscopy.

Polymeric, soft, and biological materials exhibit viscoelasticity, which is a time dependent mechanical response to deformation. Material viscoelasticity emerges from the movement of a material's constituent molecules at the nano- and microscale in response to applied deformation. Therefore, viscoelastic properties depend on the speed at which a material is deformed. Recent technological advances, especially in atomic force microscopy (AFM), have provided tools to measure and map material viscoelasticity with nanoscale resolution. However, to obtain additional information about the viscoelastic behavior of a material from such measurements, theoretical grounding during data analysis is required. For example, commercially available bimodal AFM imaging maps two different viscoelastic properties of a sample, the storage modulus, E', and loss tangent, tan δ, with each property being measured by a different resonance frequency of the AFM cantilever. While such techniques provide high resolution maps of E' and tan δ, the different measurement frequencies make it difficult to calculate key viscoelastic properties of the sample such as: the model of viscoelasticity that describes the sample, the loss modulus, E'', at either frequency, elasticity E, viscosity η, and characteristic response times τ. To overcome this difficulty, we present a new data analysis procedure derived from linear viscoelasticity theory. This procedure is applied and validated by performing amplitude modulation-frequency modulation (AM-FM) AFM, a commercially available bimodal imaging technique, on a styrene-butadiene rubber (SBR) with known mechanical behavior. The new analysis procedure correctly identified the type of viscoelasticity exhibited by the SBR and accurately calculated SBR E, η, and τ, providing a useful means of enhancing the amount of information gained about a sample's nanoscale viscoelastic properties from bimodal AFM measurements. Additionally, being derived from fundamental models of linear viscoelasticity, the procedure can be employed for any technique where different viscoelastic properties are measured at different and discrete frequencies with applied deformations in the linear viscoelastic regime of a sample.

Towards precise complex AM-FM signals decomposition under strong noise conditions: A novel two-level chirp mode decomposition approach

Many signals in the real world contain noise, which interferes with the analysis of signals. Under harsh environments, the signal will be seriously disturbed by strong noise and overwhelm its changing features. Decomposing the signal with strong noise can effectively reveal the actual change law of original signal. However, the current methods cannot effectively process complex amplitude modulation-frequency modulation (AM-FM) signals with strong noise, and suffer from serious mode aliasing problems. In order to effectively decompose complex AM-FM signals with strong noise, a novel two-level chirp mode decomposition (TCMD) approach is proposed in this paper. TCMD can effectively solve the mode aliasing problem caused by strong noise interference and accurately decompose each sub-signal from original signal with strong noise. TCMD mainly includes two core parts: instantaneous frequency (IF) extraction and signal decomposition. Firstly, we establish a new IF extraction model for strong noise signals called improved parameterized time–frequency transform (IPTFT), which overcomes the shortcomings of current PTFT that cannot effectively handle multi-component signals. IPTFT can effectively filter out the interference of strong noise and accurately extract the initial IFs of multi-component signal. Then, we propose a novel adaptive iterative envelope filter tracking (AIETF) for signal decomposition that adopts the adaptive decomposition idea. AIETF can effectively decompose complex multi-component signals with strong noise, accurately estimate the IFs and reconstruct sub-signals. Decomposition experiments of both simulated and experimental signals with strong noise were carried out to verify proposed TCMD. The results show that TCMD can effectively decompose strong noisy AM-FM signals with close IFs, fast-changing IFs and crossing IFs. Meanwhile, TCMD can also effectively decompose the vibration signals with strong noise of wind turbine gearboxes, aero-engine gearboxes, and faulty bearings under non-stationary conditions to obtain high-quality TFR. Furthermore, TCMD achieves better decomposition effect for strong noise signals than the existing decomposition and TFT methods.

End-effects mitigation in empirical mode decomposition using a new correlation-based expansion model

The real-time analysis of time-varying data has become extremely significant in mechanical systems. The Hilbert–Huang transform (HHT) is a recent development in signal processing based on time–frequency distribution for analyzing complex signals. The HHT permits instantaneous attributes to be used to study signals that display complex behavior and stochastically vary with time. This transform combines empirical mode decomposition (EMD) and Hilbert transform (HT). The EMD converts a signal of time into various intrinsic mode functions (IMF), which are subsequently used by the HT to insert into the same time–frequency space. However, the restriction of the end-effect is an important problem when employing the EMD method. End-effect causes several issues such as changing the shapes of envelopes, deviating IMF, computational instabilities at the signal borders, and modal aliasing. To mitigate the end-effects, this study presents a correlation-based expansion model at both ends of the signal. Motivated by end-effects mitigation, data outside of the signal boundaries are predicted based on the similarity of the signal’s start and end segments with the signal's inner segment. Correlation techniques are a reliable method to find signal pattern similarities. Seven simulated signals are used to verify the method's performance. The seven signals include six monovarietal signals with various amplitude modulation-frequency modulation behaviors and a one-channel functional near-infrared spectroscopy (fNIRS) signal. The comparison results of the proposed technique with standard EMD and EMD coupled with other expansion methods show that the end-effect in the EMD method by correlation expansion can effectively reduce.

Multiple Sclerosis Disease Evolution Assessment in Brain MRI Lesions Based on Texture and Multi-Scale Amplitude Modulation-Frequency Modulation (AM-FM) Features

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background</i> : Monitoring disease evolution in Multiple sclerosis (MS) subjects may aid in decision making for personalizing treatment and disease evolution prediction. We investigate the use of disability progression, using clinical features, the expanded disability status scale (EDSS), and their relationship with texture features and Amplitude Modulation-Frequency Modulation (AM-FM) features extracted from MRI MS detectable lesions for the prognosis of future disability on magnetic resonance imaging (MRI). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods</i> : MS detectable brain lesions from N=38 symptomatic untreated subjects diagnosed with clinically isolated syndrome (CIS), were manually segmented, by an experienced MS neurologist, on transverse T2-weighted (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> W) images obtained from serial brain MRI scans at the baseline (Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0M</sub> ) and the repeat (Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6-12M</sub> ) examinations. The subjects were separated into two different groups based on their EDSS: (G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> : 1≤EDSS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Y</sub> ≤3.5 (N=26) and G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> : 3.5<EDSS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2Y</sub> ≤8.5 (N=12) and were monitored over ten years’ time (Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10Y</sub> ). After intensity normalization and image registration, texture and AM-FM features were extracted from all MS lesions at Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0M</sub> and Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6-12M</sub> . The extracted features were used to develop models that correlated with the disease progression in Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10Y</sub> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : We found statistically significant differences for features extracted from the two different groups (G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> vs G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> at Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10Y</sub> ) and these might be used to predict the development and or the severity of the MS disease. The best model for classifying G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> vs G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> subjects at Time <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10Y</sub> included information taken from the MS lesion images, texture features and AM-FM features extracted from those MS lesion images (with a correct classification score of %CC=94). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions</i> : The proposed methodology may contribute to additional factors for predicting the development and assessing the severity of the MS disease. However, a larger scale study is needed to establish the application in clinical practice and for computing additional features that may provide information for better and earlier differentiation between normal tissue and MS lesions.

Egram Based SVD Method for Gear Fault Diagnosis

The performance of singular value decomposition (SVD) mainly relies on selection of the effective singular values. However, most of the existing methods for selecting the effective singular values have some problems such as unsatisfactory performance of denoising. For selecting the effective singular values more accurately, a novel energy diagram (Egram) based SVD method is proposed. Novelties of the proposed method are divided into two parts. Firstly, the form of the effective singular values is determined by selection criteria of embedding dimension of Hankel matrix in SVD. The selection criteria are defined based on the special relationship between the form of the effective singular values and the embedded dimension of Hankel matrix for the amplitude modulation-frequency modulation (AM-FM) signal, and the special relationship is found that, when appropriate embedding dimension is selected, the AM-FM signal is mainly decomposed into two adjacent singular values with approximately equal numerical magnitude, which are called singular value pairs ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SVP</i> ). Secondly, the Egram is developed to adaptively locate effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SVP</i> by estimating the energy of frequency band and to detect gear fault. The Egram-based SVD method is verified using the simulated signal and experiment signal that measured from the gearbox test bench, and analysis results show that the form and location of the effective singular values that contains gear fault can be determined. Comparisons with the difference spectrum, relative change rate of singular values, relative change rate of singular kurtosis and fast Kurtogram show that the Egram-based SVD has much better ability to detect gear fault.

Investigation on turbulent characteristic of crossflow under suction in a T-junction using Holo-Hilbert spectral analysis

To investigate the crossflow of ventilation in high-speed train, a T-junction experimental setup is built and velocity data of crossflow under different conditions and positions are measured. Specifically, the conditions include the different velocities of crossflow and different intensities of suction, and the positions include upstream, mid-centre, and downstream. To give full information of the crossflow characteristics, the Holo-Hilbert spectral analysis (HHSA) method is used to analyse these velocity data. The results show that the components of frequency modulation (FM) and amplitude modulation (AM) can be found in the crossflow suffered from the suction. Furthermore, there exists an obvious difference between the crossflow with and without vanes. The crossflow without vanes has more components of FM modulation and AM modulation with high frequency. The increase of the velocity and velocity ratio both can influence the turbulent characteristics of crossflow, which presents high energy with a higher frequency of AM and FM. The crossflow at the upstream and the downstream has similar components presented in the AM–FM spectrum, while the crossflow at the mid-centre has more high-frequency components. In addition, the turbulent intensity at the mid-centre is inversely proportional to the distance from the suction.

Quantitative nanomechanical properties evaluation of a family of β-sheet peptide fibres using rapid bimodal AFM

Self-assembling peptides have become important building blocks for materials design (e.g. hydrogels) and play a crucial role in a range of diseases including Alzheimer and Parkinson. In this context, accessing the nanomechanical properties of ubiquitous β-sheet rich nanofibres (e.g.: amyloids) is key to the formulation of materials and design of therapies. Although the bulk mechanical properties of hydrogels can easily be accessed using common techniques and equipment, the mechanical properties of their constituent fibres, in particular if with radii in the nanometre scale, are more challenging to measure and estimate. In this work we show for the first time how the rapid nanomechanical mapping technique: amplitude modulation-frequency modulation (AM-FM), can be used to determine the heights, Young's moduli and viscosity coefficients of a series of β-sheet peptide nanofibres with high statistical confidence. Our results show how peptide sequence and in particular length, charge and interaction with the substrate affect the viscoelastic properties of the peptide fibres.

Effect of F-Actin Organization in Lamellipodium on Viscoelasticity and Migration of Huh-7 Cells Under pH Microenvironments Using AM-FM Atomic Force Microscopy

Cytoskeleton is responsible for fundamental cellular processes and functions. The filamentous actin (F-actin) is a key constituent of the cytoskeleton system which is intrinsically viscoelastic and greatly determines the mechanical properties of cells. The organization and polymerization of F-actin are relevant to the viscoelasticity distribution and the migration of living cells responding to pH microenvironments. Recently, progression in various diseases such as cancers have been found that cellular migration is related to the alterations in the viscoelasticity of lamellipodium. However, the correlation among F-actin organization, viscoelastic properties and cellular migration of living cancer cells under different pH microenvironments are still poorly understood. Conventional experimental methods of optical microscopy and atomic force microscopy (AFM) can neither break the trade-off between resolution and rate in cytoskeleton imaging, nor achieve the structural characterization and the mechanical measurement simultaneously. Although multifrequency AFM with amplitude modulation-frequency modulation (AM–FM) enables us to probe both the surface topography and the viscoelasticity distribution of cells, it is difficult to image the cytoskeletal filaments with the diameter down to the scale of tens of nanometers. Here, we have improved the AM-FM AFM by employing the high damping of cell culture medium to increase the signal-to-noise ratio and achieve a stable imaging of F-actin with the resolution down to 50 nm underin situmicroenvironment. The approach that can successfully visualize the structures of cytoskeletal filaments and measure the distribution of mechanical properties simultaneously enable us to understand the relationship between the organization of F-actin and the viscoelasticity of living Huh-7 cancer cells under different pH values. Our experimental results have demonstrated that, unlike the randomly distributed F-actin and the homogeneous viscoelasticity at the normal pH level of 7.4, the living Huh-7 cancer cells with the reduced pH level of 6.5 show highly oriented and organized F-actin along the lamellipodium direction associated with the significant gradient increase both in elasticity and viscosity, which are confirmed by immunofluorescence confocal microscopy. The F-actin organization and the gradient viscoelasticity of lamellipodium provide structural and mechanical understanding on the adhesion and migration of living cancer cells that undergo metastasis and malignant transformation.

Performance Improvement of Dual STO Reading With AM–FM Combined Detection for 3-D Magnetic Recording

3-D magnetic recording using microwave-assisted magnetic recording (MAMR) and a spin-torque oscillator (STO) for reading has been proposed as a candidate for prospective recording technologies. In 3-D magnetic recording using STO as a reading, the temporal magnetization dynamics of the STO has the fluctuations for frequency and amplitude which depend on the recorded pattern. In our previous work, we have constructed the STO signal model considering both amplitude and frequency fluctuations to develop a combined signal processing using the amplitude and frequency fluctuation detections. We have applied the STO signal model to the low-density parity-check (LDPC) coding and iterative decoding system with the individual detectors for amplitude modulation (AM) and frequency modulation (FM). Furthermore, we have reported that the dual STO reading system with only AM detection provides the improvement of the bit error rate (BER) performance. In this study, we propose the dual STO reading system with AM-FM combined detection in the hope that the finite impulse response (FIR) filter output distribution will be easier to detect with the decoder. We evaluate the BER performances of the proposed system for carrier-to-noise ratio (CNRS) of the system noise under an areal recording density of 1 Tbits/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> specification with bit-pattern medium stricture by computer simulation. The results show that the FIR filter output distribution for our proposed dual STO reading system at an FM detector output has become sharper, and the proposed system achieves the “No errors” with CNRS less by about 3.5 dB than the single STO reading system.

Medical Image Analysis Using AM-FM Models and Methods.

Medical image analysis methods require the use of effective representations for differentiating between lesions, diseased regions, and normal structure. Amplitude Modulation-Frequency Modulation (AM-FM) models provide effective representations through physically meaningful descriptors of complex non-stationary structures that can differentiate between the different lesions and normal structure. Based on AM-FM models, medical images are decomposed into AM-FM components where the instantaneous frequency provides a descriptor of local texture, the instantaneous amplitude captures slowly-varying brightness variations, while the instantaneous phase provides for a powerful descriptor of location, generalizing the traditionally important role of phase in the Fourier Analysis of images. Over the years, AM-FM representations have been used in a wide variety of medical image analysis applications based on a vastly reduced number of features that can be easily learned by simple classifiers. The paper provides an overview of AM-FM models and methods, followed by applications in medical image analysis. We also provide a summary of emerging trends and future directions.

A Bilateral Second-Order Synchrosqueezing Transform and Application to Vibration Monitoring of Aerospace Engine

The synchrosqueezing transform (SST) and its ameliorated methods can enhance the energy concentration of the vibration signals in time-frequency domain, but it does not show enough effect for the weak feature extraction. Besides, many large-scale rotating machines such as aerospace engines and gas engines often work under complex vibrations, and this increases the difficulty for health monitoring based on vibration signals. This article presents a time-frequency analysis (TFA) named bilateral second-order SST (BFSST2). It can sharpen the instantaneous frequencies (IFs) like second-order SST (FSST2), and more importantly, it can extract the weak features such as amplitude-modulation frequency-modulation IFs and small-amplitude time-varying IFs which are easily ignored on time-frequency plane. To achieve the aforementioned target, the IFs are first obtained by employing FSST2, and then a bilateral function representing a novel negative-positive TFA is structured by combining the hyperbolic tangent function, exponential function, and sign function together. After that the large-amplitude IFs are transformed into small-negative amplitude ones by the product with the bilateral function, and this can not only enhance the original weak feature but also avoid its confusion with the transformed small-negative amplitude ones. The proposed BFSST2 is validated by a numerical simulation. At last, two case studies are given to illustrate its effectiveness in aerospace engine vibration monitoring.

Eliminating the end effect of empirical mode decomposition using a cubic spline based method

The empirical mode decomposition (EMD) is a method that is commonly applied to extract the intrinsic mode functions (IMFs) of a signal by a sifting process, which requires imposing the extended extrema at both ends of the signal (i.e., the end condition). The imposition of extended extrema can cause an error, which is often presented by the changing shapes of original envelopes and distort extracted IMFs, which is described as the end effect. An important issue during the application of the EMD is restricting the end effect. This paper reveals the decisive factors that can restrict the end effect by determining the uniqueness of the envelope, and provides an interpretation of the end effect in terms of the differences between the original envelope and the extended envelope based on the cubic spline theory. Two principles that are important to the design of an end condition method are provided. The first principle is that the domain of the extended envelope needs to cover the original signal; the second is that the ordinate value of the extended local maxima is greater than or equal to that of the extended local minima. Following these two principles, a new end condition method, the cubic spline based method (CSBM), is proposed in this study. The novelty of the CSBM is that the extended envelope maintains the shape of the original envelope in their intersection domain, and the end effect can be restricted in a limited domain during the sifting process of EMD for each different input signal. Six signals are used to demonstrate the performance of the CSBM by comparing them with two other end condition methods, the extreme method and the improved slope based method (ISBM). The six signals include: a damped sinusoid signal, four monovariate signals with various amplitude modulation-frequency modulation (AM-FM) behaviors, and a one-channel functional near-infrared spectroscopy (fNIRS) signal. Results show that the CSBM in general performs better than the other two methods.

AF-MNS: A Novel AM-FM Based Measure of Non-Stationarity

Robust signal processing in any application depends on the representation and the understanding of the signals in hand. There is a plethora of signal processing methods, wherein choosing a method over others would depend on the attributes of the signals and their intended application. As of now, only spectral stationarity is discussed for the deterministic signals. This letter presents a novel amplitude-modulation-frequency-modulation (AM-FM) based measure of non-stationarity of the deterministic signals by extracting AM-FM components using the Fourier decomposition method (FDM). FDM generates analytic signal representation, where the real and imaginary parts are the Hilbert transform pairs. Simple and intuitive measures of amplitude and frequency non-stationarity are presented to quantitatively assess the extent of non-stationarity. Both these measures are zero for stationary signals, while at least one of them is positive for the non-stationary signals. Important observations regarding the stationarity of a signal are made with the help of some examples. The utility of the concept is demonstrated via an application of EEG signal classification.

A Study on AM–FM Combined Detection for Magnetic Recording Using STO Reading

A 3-D magnetic recording using microwave-assisted magnetic recording (MAMR) and a spin-torque oscillator (STO) as a reading sensor has been proposed as a candidate for the prospective recording technologies. In the STO reading system, the temporal magnetization dynamics of the STO vary not only in amplitude but also in frequency, depending on the recorded pattern. Therefore, we have constructed the STO signal model which considers both amplitude and frequency fluctuations to develop a combined signal processing using the detection of amplitude and frequency fluctuations. Furthermore, we have applied the STO signal model to the low-density parity-check (LDPC) coding and iterative decoding system with the detectors for amplitude modulation (AM) and frequency modulation (FM) and have proposed the AM-FM combined detection. Then, we have evaluated the bit error rate (BER) performances by computer simulation. The results show that the system with AM-FM combined detection improves the performance of the LDPC coding and iterative decoding system, which achieves “no errors” at the carrier-to-noise ratio for system noise CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> = 15.9$ dB, whereas the AM and FM detections require CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> = 21.2 and 17.2 dB.

Antimicrobial modification of PLA scaffolds with ascorbic and fumaric acids via plasma treatment

An optimal medical scaffold should be biocompatible and biodegradable and should have adequate mechanical properties and scaffold architecture porosity, a precise three-dimensional shape, and a reasonable manufacturing method. Polylactic acid (PLA) is a natural biodegradable thermoplastic aliphatic polyester that can be fabricated into nanofiber structures through many techniques, and electrospinning is one of the most widely used methods. Medical fiber mat scaffolds have been associated with inflammation and infection and, in some cases, have resulted in tissue degradation. Therefore, surface modification with antimicrobial agents represents a suitable solution if the mechanical properties of the fiber mats are not affected. In this study, the surfaces of electrospun PLA fiber mats were modified with naturally occurring l-ascorbic acid (ASA) or fumaric acid (FA) via a plasma treatment method. It was found that 30 s of radio-frequency (RF) plasma treatment was effective enough for the wettability enhancement and hydroperoxide formation needed for subsequent grafting reactions with antimicrobial agents upon their decomposition. This modification led to changes in the surface properties of the PLA fiber mats, which were analyzed by various spectroscopic and microscopic techniques. FTIR-ATR confirmed the chemical composition changes after the modification process and the surface morphology/topography changes were proven by SEM and AFM. Moreover, nanomechanical changes of prepared PLA fiber mats were investigated by AFM using amplitude modulation-frequency modulation (AM-FM) technique. A significant enhancement in antimicrobial activity of such modified PLA fiber mats against gram-positive Staphylococcus aureus and gram-negative Escherichia coli are demonstrated herein.

Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal

Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.

Multicomponent AM–FM signal analysis based on sparse approximation

The authors propose in this work to formulate multicomponent amplitude modulation (AM)-frequency modulation (FM) representations as a sparse approximation (SA) problem, which is solved using orthogonal matching pursuits (OMP) and least angle regression (LARS). The approach appears to be an efficient tool to separate the frequency content of linear and non-linear signals and behaves in a better way like the well-known data-driven empirical mode decomposition (EMD). Contrary to related works where AM-FM models and/or EMD are used to improve sparsity or learned dictionaries, they propose instead a sparse approach framework in AM-FM modelling and EMD. Recasting EMD as a SA problem provides a new framework towards its comprehension, formulation, and convergence since OMP and LARS converge. Doing so presents an alternative to getting rid of the interpolation process that EMD terribly suffers from. Conducted experiments show noticeable improvements in terms of frequency separation of linear and non-linear signals, tone separation capability and robustness in a noisy environment.

An empirical mode decomposition based hidden Markov model approach for detection of Bryde's whale pulse calls.

This letter proposes an empirical mode decomposition (EMD) based hidden Markov model (HMM) approach for the detection of mysticetes' pulse calls such as the Bryde's whales. The HMM detection capabilities depend on the deployed feature extraction (FE) technique. The EMD is proposed as a performance efficient alternative to the popular Mel-scale frequency cepstral coefficient (MFCC) and linear predictive coefficient (LPC) FE techniques. The amplitude modulation-frequency modulation components derived from the EMD process are modified to form feature vectors for the HMM. Also, the ensemble EMD (EEMD) is adapted in a similar way as the EMD. These proposed EMD-HMM and EEMD-HMM approaches achieved better performance in comparison to the MFCC-HMM and LPC-HMM approaches.

Nanoscale chemical and mechanical heterogeneity of human dentin characterized by AFM-IR and bimodal AFM

Human dentin, as an important calcified tissue in the body, plays significant roles in withstanding masticatory forces and has a complex hierarchical organization. Understanding the composition and ultrastructure of dentin is critical for elucidating mechanisms of biomineralization under healthy and pathological states. Here, atomic force microscope infrared spectroscopy (AFM-IR) and AFM-based amplitude modulation-frequency modulation (AM-FM) techniques were utilized to detect the heterogeneity in chemical composition and mechanical properties between peritubular and intertubular dentin at the nanoscale. AFM-IR spectra collected from peritubular and intertubular dentin contained similar vibrational bands in the amide regions (I, II and III), suggesting that collagen may exist in both structures. A distinctive band at 1336 cm−1 indicative of SO stretching vibrations was detected only in peritubular dentin. AFM-IR imaging showed an uneven distribution of chemical components at different locations, confirming the heterogeneity of dentin. The Young’s modulus of peritubular dentin was higher, and was associated to a higher mineral content. This study demonstrated distinctive chemical and mechanical properties of peritubular dentin, implying the different development and mineralization processes between peritubular and intertubular dentin. AFM-IR is useful to provide compositional information on the heterogeneity of human dentin, helping to understand the mineral deposition mechanisms of dentin.