Signal Transformation Research Articles

Reversible addition-fragmentation chain transfer enhanced aggregation signal-on fluorescence detection of alkaline phosphatase.

The instability of the signal intensity of fluorescent biosensors and the false signals have been significant factors affecting the performance of biosensors. Herein, a novel signaling system is devised through the application of reversible addition-fragmentation chain transfer (RAFT) polymerization with monomers containing the tetraphenylethylene (TPE) groups. TPE exhibits an aggregation-induced emission (AIE) phenomenon in certain solvents, mainly due to the blockage of the rotation of its four benzene rings, which also exist in the aggregated state. With this property, a series of molecules are modified based on click chemistry for RAFT polymerization using Fe3O4 magnetic beads as the carriers, and stable aggregated luminescent TPE polymers are formed on the surface of magnetic beads to realize the transformation of fluorescence signal from "0" to "1". In addition, the fluorescence signal demonstrates a positive correlation with alkaline phosphatase (ALP) activity, which can be quantified by measuring the fluorescence intensity. The biosensor exhibits high sensitivity and good linearity in the range of 0.1-5 U/L, with a LOD of 0.079 U/L. Furthermore, the designed strategy demonstrated satisfactory performance in the quantitative determination of ALP activity in serum samples, indicating that the signaling system developed by combining RAFT polymerization and AIE molecules has an important application in the field of fluorescent biosensors.

Engineering the Functional Expansion of Microneedles.

Microneedles (MNs), composed of an array of micro-sized needles and a supporting base, have transcended their initial use to replace hypodermic needles in drug delivery and fluid collection, advancing toward multifunctional platforms. In this review, four major areas are summarized in interdisciplinary engineering approaches combined with MNs technology. First, electronics engineering, the most extensively researched field, enables applications in biomonitoring, electrical stimulation, and closed-loop theranostics through the generation, transmission, and transformation of electrical signals. Second, in electromagnetic engineering, the responsiveness of electromagnetic induction offers prospects for remote and programmable therapeutic applications. Third, photonic engineering endows MNs with novel functionalities, such as waveguiding and photonic manipulation to enhance optical therapeutic capabilities and facilitate the visualization of disease progression and treatment processes. Lastly, it reviewed the role of mechanical engineering in conferring shape adaptability and programmable motion features necessary for various MNs applications. This review focuses on the functionalities that emerge from the intersection of MNs with complementary engineering technologies, aiming to inspire further research and innovation in microneedle technology for biomedical applications.

State Surveillance and Fault Diagnosis of Distillation Columns Using Residual Network-Based Passive Acoustic Monitoring

The operational state of distillation columns significantly impacts product quality and production efficiency. However, due to the complex operation and diverse influencing factors, ensuring the safety and efficient operation of the distillation columns becomes paramount. This research combines passive acoustic monitoring with artificial intelligence techniques, proposed a technology based on residual network (ResNet), which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states. The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform. Ultimately, this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns. Through this approach, the operational states of distillation columns were monitored. Various faults, including flooding, entrainment, dry-tray, etc., were diagnosed with an accuracy of 98.91%. Moreover, an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method. Under the transitional state, the acoustic signals achieved an accuracy of 97.85% on the ResNet, which enables early warnings before faults occur, enhancing the safety of chemical production processes. The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment, particularly distillation columns, ensuring the safety and efficiency.

Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach

Wind turbine wake poses a significant challenge in wind farm operations, affecting power generation efficiency. This study introduces a Dynamic Wake Flow Estimation (DWFE) framework designed to predict wind turbine wake evolution from sparse measurement data. The framework integrates Gaussian Process Regression (GPR), Proper Orthogonal Decomposition (POD), and Long Short-Term Memory (LSTM) networks. Specifically, GPR plays a pivotal role in DWFE by enabling the transformation of flow fields discrete sensor signals into coherent, low-dimensional flow fields which is crucial for accurate flow field predictions, while POD aids in dimensionality reduction and LSTM forecasts temporal wake dynamics, improving both predictive accuracy and computational efficiency. Parametric analysis demonstrates that the robustness and adaptability of the framework improve prediction accuracy with increased sensor density and flexible POD modes. The DWFE framework demonstrates high accuracy in wake flow estimation even with limited data, effectively capturing dynamic wake behavior. Future work will extend this approach to various topographic and climatic conditions, optimizing computational efficiency, and improving interpretability. These advancements will expand the framework's applicability in wind energy and other engineering fields requiring precise flow prediction, emphasizing DWFE's potential in advancing wind farm design, operation and renewable energy optimization.

Reflexive neural circuits and the origin of language and music codes

Conscious activity is grounded on the reflexive self-awareness in sense perception, through which the codes signifying sensual perceptive events operate and constrain human behavior. These codes grow via the creative generation of hypertextual statements. We apply the model of Vladimir Lefebvre (Lefebvre, V.A., 1987, J. Soc. Biol. Struct. 10, 129-175) to reveal the underlying structures on which the perception and creative development of language and music codes are based. According to this model, the reflexive structure of conscious subject is grounded on three thermodynamic cycles united by the control of the basic functional cycle by the second one, and resulting in the internal action that it turn is perceived by the third cycle evaluating this action. In this arrangement, the generative language structures are formed and the frequencies of sounds that form musical phrases and patterns are selected. We discuss the participation of certain neural brain structures and the establishment of reflexive neural circuits in the ad hoc transformation of perceptive signals and show the similarities between the processes of perception and of biological self-maintenance and morphogenesis. We trace the peculiarities of the temporal encoding of emotions in music and musical creativity, as well as the principles of sharing musical information between the performing and the perceiving individuals.

End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers

End-to-End Electrocardiogram Signal Transformation from Continuous-Wave Radar Signal Using Deep Learning Model with Maximum-Overlap Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Network Layers

Novel method to determine bedrock from a single sensor based seismic reflection signal using Haar wavelet transform

The determination of bedrock depth is crucial across various earth sciences and related fields. Geophysical techniques, notably the continuous wavelet transform, are increasingly employed to map subsurface bedrock structures. This method allows the transformation of 1D time domain seismic reflection signals into a 2D image, providing time–frequency representations for feature extraction. Leveraging the advantages of wavelet theory, particularly the Haar wavelet known for its simplicity and compact support, this study utilizes continuous Haar wavelet local maxima lines to enhance bedrock analysis. Seismic reflection data is acquired using an extremely sensitive geophone as a receiver along with a high dynamic range data logger. The Common Midpoint (CMP) approach, coupled with optimized offset distances, is utilized to ensure robust data quality and signal strength. Simple sources, such as a sledgehammer, generate sound waves for data collection prior to borehole drilling, correlating acquired data with lithogeological information. This research emphasizes the significance of adjusting parameters like sampling frequency, choice of wavelet, and optimal offset distances. The methodology employed, involving a single geophone, a one-channel data recorder, and a basic sledgehammer as a sound source, offers an inexpensive, straightforward, and non-invasive approach. The study demonstrates the effectiveness of Haar wavelet local maxima lines in accurately determining bedrock depth, producing results closely aligned with both predicted and actual depths.

Simultaneous detection of membrane protein and mRNA at single extracellular vesicle level by droplet microfluidics for cancer diagnosis

IntroductionSimultaneous detection of proteins and mRNA within a single extracellular vesicle (EV) enables comprehensive analysis of specific EVs subpopulations, significantly advancing cancer diagnostics. However, developing a sensitive and user-friendly approach for simultaneously detecting multidimensional biomarkers in single EV is still challenging. ObjectivesTo facilitate the analysis of multidimensional biomarkers in EVs and boost its clinical application, we present a versatile droplet digital system facilitating the concurrent detection of membrane proteins and mRNA at the single EV level with high sensitivity and specificity. MethodsThe antibody-DNA conjugates were firstly prepared for EVs protein biomarkers recognition and signal transformation. Coupling with the assembled triplex droplet digital PCR system, a versatile droplet digital analysis assay for simultaneous detection of membrane protein and mRNA at a single EV level was developed. ResultsOur new droplet digital system displayed high sensitivity and specificity. Additionally, its clinical application was validated in a breast cancer cohort. As expected, this assay has demonstrated superior performance in distinguishing breast cancer from healthy individuals and benign controls through combined detection of EVs protein and mRNA markers compared to any single kind marker detections, especially for patients with breast cancer at early stage (AUC=0.9229). ConclusionConsequently, this study proposes a promising strategy for accurately identifying and analyzing specific EV subgroups through the co-detection of proteins and mRNA at the single EV level, holding significant potential for future clinical applications.

Detection of contractility changes in the heart from arterial blood pressure data using symmetric Projection Attractor Reconstruction

The potential for unintended drug induced changes in cardiac contractility is a major concern in medicines development. Whilst direct left ventricular pressure (LVP) measurement is the gold standard for measuring cardiac contractility in vivo, it is resource intensive and poses a welfare burden on research animals. In contrast, arterial blood pressure (BP) measurement has fewer challenges. Symmetric Projection Attractor Reconstruction (SPAR) is a signal processing technique which transforms physiological time-series signals into a corresponding visual image (‘attractor’), amplifying morphology changes within physiological waveforms. It was hypothesized that SPAR analysis of BP signals would provide a surrogate measure of cardiac contractility by specifically amplifying the maximum slope of the systolic upstroke. BP (abdominal aorta) signals obtained from beagle dogs, treated with positive and negative inotropes, were retrospectively analysed to identify signal features that correlated with the maximum upslope of the LVP signal from simultaneously acquired LVP recordings. SPAR transformation of BP signals quantified drug induced changes in the maximum slope of the systolic upstroke. We identified key SPAR metrics that provided >0.8 correlation with the LVP maximum upslope, outperforming the BP systolic upstroke alone. This was observed for all 4 different drugs, doses and time points evaluated across studies. Thus, we conclude that the SPAR measures derived from the BP signal could be used as a first pass in vivo screen to flag any risk of drug induced changes in cardiac contractility during the conduct of non-clinical medicines development, potentially reducing or replacing the need to perform direct left ventricular measurements.

Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex

How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.Graphical

Intelligent fault diagnosis of rolling bearings in strongly noisy environments using graph convolutional networks

SummaryRolling bearings often function under complex and non‐stationary conditions, where significant noise interference complicates fault diagnosis by obscuring fault characteristics. This paper presents an innovative fault diagnosis technique using graph convolutional networks (GCN) to address these challenges. Vibration signals are first transformed into the frequency domain through fast Fourier transform (FFT), creating a detailed graph where nodes and edges encapsulate fault signals. The GCN method then extracts complex node features from this graph, enabling a classifier, comprising a fully connected layer and Softmax function, to accurately identify fault types. Experimental results demonstrate the superior performance of the proposed GCN‐based fault diagnosis method, achieving an accuracy of 99.79%. This significantly surpasses traditional machine learning methods (85.4%), deep learning models (92.3%), and other graph neural network approaches (94.1%). Notably, the method shows exceptional resilience to noise, maintaining high accuracy even with 20% added noise, underscoring its robustness for practical industrial applications. The transformation of vibration signals into the frequency domain using FFT, followed by constructing a detailed graph structure, enables the GCN to effectively capture and represent intricate fault characteristics, thus enhancing accurate fault classification. These findings highlight the method's practical applicability and potential for deployment in advanced industrial settings characterized by high noise levels and complexity.

Регистрация и локализация гидроакустического источника береговым измерительным комплексом

The actual problem of the presented research is to study the peculiarities of acoustic signal transformation at the geosphere boundary in the “water-land” transition zone. The signal energy of a moving hydroacoustic radiation source is transformed in the coastal zone into seismoacoustic oscillations recorded by a coastal two-coordinate laser deformograph. The spatial position of the measuring components of the laser deformograph makes it possible to localize the displacement of an object in the nearby water area based on the study of variations in the amplitude of the recorded signal. The methodology of the experiment based on the movement of the transmitter with constant speed and distance from the shore measurement system is described. As a result of the experiment, a stable registration of the seismoacoustic signal was obtained along the entire trajectory of the transmitter with a decrease in the amplitude of the received signal when it is at an angle close to 45 degrees. According to the results of the analysis of variations in the amplitude of the registered signal on differently directed components of the laser deformograph, the results of direction finding of the source of low-frequency hydroacoustic radiation were obtained, with the error in determining the direction to the hydroacoustic source ranging from 0.2 % to 10.5 %.

Solid–liquid–gas-like phase transition in electric field driven dense granular media

The polarization induced by an external electric field significantly influences the contact charge of particles. It can easily cause solid–liquid–gas-like phase transitions in dense granular media, which are the key factors leading to environmental and safety disasters such as sandstorms, volcanic eruptions, and spacecraft emergencies. In this letter, our investigation focused on a densely packed granular system with different thicknesses subjected to bottom excitation and an external electric field. Using the discrete element method (DEM), we proposed conditions for the first- and second-order phase transitions and revealed the mechanism of electrical signal transformation. We presented the physical characteristics of charge and polarization diffusion coefficients for charged particles, and introduced a theoretical Turing model to predict critical phase transitions. Additionally, this phase transition model was extended to the application of electrostatic dust removal, quantitatively predicting the relationship between dust removal efficiency, particle layer thickness, and electric field intensity.

Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex.

How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.

A cellulose nanocrystal-based dual response of photonic colors and fluorescence for sensitive benzene gas detection

Benzene, as a common volatile organic compound, represents serious risk to human health and environment even at low level concentration. There is an urgent concern on visualized, sensitive and real time detection of benzene gases. Herein, by doping Fe3+ and graphene quantum dots (GQDs), a cellulose nanocrystal (CNC) chiral nematic film was designed with dual response of photonic colors and fluorescence to benzene gas. The chiral nematic CNC/Fe/GQDs film could respond to benzene gas changes by reversible motion. Moreover, chiral nematic film also displays reversible responsive to humidity changes. The resulting CNC/Fe/GQDs chiral nematic film showed excellent response performance at benzene gas concentrations of 0–250 mg/m3. The maximal reflection wavelength film red shifted from 576 to 625 nm. Furthermore, structural color of CNC/Fe/GQDs chiral nematic film change at 44 %, 54 %, 76 %, 87 %, and 99 % relative humidity. Interestingly, due to the stability of GQDs to water molecules, CNC/Fe/GQDs chiral nematic film exhibit fluorescence response to benzene gas even in high humidity (RH = 99 %) environment. Besides, we further developed a smartphone-based response network system for quantitively determinization and signal transformation. This work provides a promising routine to realize a new benzene gas response regime and promotes the development of real-time benzene gas detection.

Equivalent network modeling of eddy-current transfer functions

The use of eddy-current techniques as a measurement method for material characterization instead of simple fault detection requires transformation of the electrical response signals to quantifiable physical parameters that correlate with material properties. Apart from calibration-curve methods, exact approaches that correctly represent all interactions between excitation field and response field are computationally intensive and not well suited to real-time application. In this work, an equivalent circuit network model is constructed and its analytical equation for the voltage transfer function is derived. This model is then implemented in a soft sensor system. Finally, measurement results from various materials with different electromagnetic properties are presented.

Improved performance of a fiber-optic hydrogen sensor based on a controllable optical heating technology.

A novel, to the best of our knowledge, and compact fiber-optic hydrogen sensor based on light intensity demodulation and controllable optical heating technology is proposed and experimentally investigated. This system employs three photodetectors for optic signal transformation. The first PD is used to receive a little fraction of the amplified spontaneous emission (ASE) for calibration, and the second PD is utilized to detect optic signal reflected by a single mode fiber deposited with WO3-Pd2Pt-Pt composite film. The last PD is utilized to receive the optical power reflected by the short fiber Bragg grating (SFBG) with a central wavelength located in a steep wavelength range (the intensity decreases approximately linearly with the increase of the wavelength) of the ASE light source. A 980 nm laser and proportion integration differentiation (PID) controller were employed to ensure the hydrogen sensitive film working at an operating temperature of 60°C. This sensing system can display a quick response time of 0.4 s toward 10,000 ppm hydrogen in air. In addition, the detection limit of 5 ppm in air can be achieved with this sensing system. The stability of this sensor can be greatly enhanced with a controllable optical heating system, which can greatly promote its potential application in various fields.

An Interference Mitigation Method for FMCW Radar Based on Time-Frequency Distribution and Dual-Domain Fusion Filtering.

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time-frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time-frequency domain, thus converting the interference mitigation to the task of time-frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time-frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

A Novel Energy-Efficient Coding Based on Coordinated Group Signal Transformation for Image Compression in Energy-Starved Systems

This paper introduces a new method for compressing images in energy-starved systems, like satellites, unmanned aerial vehicles, and Internet of Things nodes, which is based on coordinated group signal transformation (CGST). The transformation algorithm is a type of difference coding and may be classified as a non-transform-based image-compression method. CGST simplifies the difference signal conversion scheme using a single group codec for all signals. It considers color channels as correlated signals of a multi-channel communication system. The performance of CGST was evaluated using a dataset of 128 × 128 pixel images from satellite remote sensing systems. To adapt CGST to image compression, some modifications were introduced to the algorithm, such as fixing the procedure of the difference signals calculation to prevent any “zeroing” of brightness and supplementing the group codec with a neural network to improve the quality of restored images. The following types of neural networks were considered: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, fully connected neural networks are recommended if the goal is to minimize processing delay time. These networks have a response time of 13 ms. Conversely, suppose the priority is to improve quality in cases where delays are not critical. In that case, convolution neural networks in the Fourier space should be used, providing an image compression ratio of 4.8 with better minimum square error and Mikowsky norm values than JPEG with the same compression ratio.