Signal Analysis Research Articles

Phase signal analysis for high-sensitive temperature fiber-optic external Fabry-Perot-cavity sensor

We experimentally demonstrate a highly temperature-sensitive external Fabry-Perot cavity. The interferometric structure is composed of an air-microcavity; its fabrication uses a microcapillary and UV polymer. A temperature sensitivity close to 5.7 nm/ºC is achieved with suitable linearity (0.9896) and minimal hysteresis; a phase analysis technique is proposed and applied to overcome the trade-off between sensitivity and range of operation. This technique provides a competitive sensitivity (0.84 rad/ºC), good linearity (0.9934), and a range of operation from 25 °C to 41 °C.

Variational Autoencoder feature clustering for tissue classification in robotic palpation

Abstract Minimally Invasive Robotic Surgery (MIRS) has emerged as a transformative approach in surgical practice, offering reduced patient trauma and enhanced precision. However, challenges persist, including the loss of tactile feedback for surgeons. This study explores the application of machine learning algorithms, specifically variational autoencoders, in vibro-acoustic (VA) signal analysis to address this issue. Our comparative analysis evaluates the potential of supervised learning in surgical data analysis, contributing to advancements in surgical technology. Despite achieving an accuracy of 81%, our results indicate opportunities for further refinement, considering the superior accuracies reported in previous studies. This research underscores the importance of innovative approaches in medical data analysis for optimizing patient care in minimally invasive surgery.

Small signal analysis for the characterization of organic electrochemical transistors

A method for the characterization of organic electrochemical transistors (OECTs) based on small signal analysis is presented that allows to determine the electronic mobility as a function of continuous gate potential using a standard two-channel AC potentiostat. Vector analysis in the frequency domain allows to exclude parasitic components in both ionic and electronic conduction regardless of film thickness, thus resulting in a standard deviation as low as 4%. Besides the electronic mobility, small signal analysis of OECTs also provides information about a wide range of other parameters including the conductance, transconductance, conductivity and volumetric capacitance through a single measurement. General applicability of small signal analysis is demonstrated by characterizing devices based on n-type, p-type, and ambipolar materials operating in accumulation or depletion modes. Accurate benchmarking of organic mixed ionic-electronic conductors through small signal analysis can be anticipated to guide both materials development and the design of bioelectronic devices.

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

Calcareous Nannofossil variability controlled by Milankovitch and sub-Milankovitch periodicity in the Monte San Nicola section (Gelasian GSSP / MIS 100–104)

The Quaternary marks the beginning of the ice ages, with the establishment of a stable Northern Hemisphere ice sheet. The Monte San Nicola section, southern Sicily (Italy) is the Global Boundary Stratotype Section and Point of the Gelasian Stage of the Lower Quaternary Subseries and is attracting new attention for providing valuable information on paleoclimate evolution.Here we present a paleoenvironmental reconstruction based on new data from calcareous nannoplankton, the phytoplankton organisms that are sensitive to sea surface changes and water column dynamics. We adopt statistical and signal analysis to support our paleoenvironmental model. The most evident paleoenvironmental signal throughout the investigated interval is the contrast between the abundance patterns of placoliths and Florisphaera profunda, the former pointing to surface productivity (water column mixing, shallow nutricline), the latter to the establishment of a deep nutricline. The observed nutricline depth shift occurred with a regular precessional pace, following Northern Hemisphere summer insolation and, likely, North African monsoon activity. A significant periodicity of 8 kyr, in tune with late Quaternary Heinrich events, is also observed in nannoplankton taxa, supporting previous findings on the existence of suborbital climatic variability even at the Pliocene-Pleistocene transition.

Motor fault diagnosis based on multisensor-driven visual information fusion

To need of accurate motor fault diagnosis in industrial system, we propose a fault diagnosis framework that utilizes motor current and electromagnetic signals, combining them with a self-attention-enhanced capsule network for enhanced signal analysis and accuracy. Firstly, the original signal extracted by multiple sensors is constructed into a symmetric point mode (SDP) image, and the visual fault information of different sensors and fusion signals of different motion health states are obtained by the proposed multi-channel image fusion method. Then, the capsule network, combined with self-attention, extracts spatial features from the high-dimensional tensor of the multi-channel fused image for adaptive recognition and extraction. Subsequently, advanced feature vector information is obtained through softmax for diagnosis. Diagnosis results of several datasets indicate that the developed diagnosis framework with compressed image information can availably identify 8 kinds of motor fault states under various loads, and the fault diagnosis rate is as high as 99.95 %, it is helpful for low cost and high-speed diagnosis of motors. In addition, by learning multiple sensor signals in the same state, it obtains stronger robustness and effectiveness than a single signal model.

The part and the whole: how single nodes contribute to large-scale phase-locking in functional EEG networks

ObjectiveThe application of signal analysis techniques to electroencephalographic (EEG) recordings from epilepsy patients shows that epilepsy involves not only altered neuronal synchronization but also the reorganization of functional EEG networks. This study aims to assess the large-scale phase-locking of such functional networks and how individual network nodes contribute to this collective dynamics. MethodsWe analyze the EEG recorded before, during and after seizures from sixteen patients with pharmacoresistant focal-onset epilepsy. The data is filtered to low (4–30 Hz) and high (80–150 Hz) frequencies. We define the multivariate phase-locking measure and the univariate phase-locking contribution measure. Surrogate signals are used to estimate baseline results expected under the null hypothesis that the EEG is a correlated linear stochastic process. ResultsOn average, nodes from inside and outside the seizure onset zone (SOZ) increase and decrease, respectively, the large-scale phase-locking. This difference becomes most evident in a joint analysis of low and high frequencies. ConclusionsNodes inside and outside the SOZ play opposite roles for the large-scale phase-locking in functional EEG network in epilepsy patients. SignificanceThe application of the phase-locking contribution measure to EEG recordings from epilepsy patients can potentially help in localizing the SOZ.

Composite fault diagnosis of gearbox based on deep graph residual convolutional network

In gearbox systems, a composite fault diagnosis resulting from mutual interference among different components poses a significant challenge. The traditional composite fault diagnosis methods based on conventional signal analyses and feature extractions often suffer from low sensitivity to fault characteristics and difficulty in effectively identifying composite faults. On the other hand, composite fault diagnosis research via deep learning and data-driven approaches typically faces issues such as incomplete training datasets and insufficient exploration of feature correlation information, leading to an underutilization of the fault information. Therefore, this paper proposes a deep graph residual convolutional neural network (DGRCN) based on feature correlation mining for composite fault diagnosis in gearboxes. First, Pearson correlation coefficients are utilized to explore the relationships among features in the traditional feature set, transforming these relationships into a graph-structured feature set. Next, a deep graph residual convolutional network is constructed by integrating deep graph structures into a residual framework. This network globally extracts composite fault subgraph features and explores local feature correlations. Finally, the model is trained via various composite fault datasets under complex working conditions, achieving the diagnosis and identification of composite faults under the constraint of limited samples. The experimental results demonstrate that the proposed method significantly improves composite fault diagnosis accuracy, outperforming commonly used methods in this field.

Validation of a PGNAA sensor model for bulk material sorting

Global mineral demand is forecast to increase significantly to achieve the transition to renewable energy. Greater volumes of ore of lower grade will have to be mined to meet demand. Techniques to process large volumes of low-grade ore efficiently are being investigated to reduce the cost and impact of mining. One technique is to use sensor information to sort mined material, allowing waste to be discarded early in mineral processing. Prompt gamma neutron activation analysis (PGNAA) is a sensing technique that can provide information on the multi-elemental composition of a bulk sample which can be used for bulk material sorting. This paper presents the development of a Monte Carlo simulation model of a PGNAA sensor for bulk sorting using the Geant4 toolkit. The GEOSCAN sensor (Scantech Australia) was used as a case-study to demonstrate the application of the model. The sensor responses for a range of pure mineral samples (Fe2O3, SiO2, S, Na2CO3 and MnO2) were measured to validate the developed model. The sensitivity of the simulation results to the hadronic and electromagnetic physics models used was tested. It was determined that the PGNAA sensor model can reproduce measurements obtained from the GEOSCAN sensor. In particular, the model can provide a good reproduction of the overall spectral shape and the locations of distinct characteristic peaks. The differences between simulated and experimental results are within 30% on average. It was found that the Geant4 HP neutron model best reproduces the activation peaks observed in experimental measurements. Additionally, the PGNAA spectrum was found to be insensitive to the choice of electromagnetic model for the photon interactions. The validated sensor model provides a useful tool for investigating PGNAA sensor applications including a bulk sorting strategy for new materials, sensor calibration, improvements in signal analysis and optimised sensor design.

Neuron circuit made of a single locally-active memristor

Neuromorphic computing, inspired by the human brain’s architecture, is expected to break the physical limits of transistors and von Neumann bottleneck. The multiple internal state variables of higher-order memristors (second-order or above) possess dynamic complexity and adaptability, enabling them to mimick the characteristics of biological neurons, which are very important building blocks for neuromorphic computing. This paper presents a simple neuron circuit containing a single second-order current-controlled locally-active memristor (LAM). The pinched hysteresis loop and DC V–I curve of the proposed second-order LAM show good odd symmetry. Applying small signal analysis method, we obtain the small-signal equivalent circuit of the neuron circuit, showing a [Formula: see text] parallel structure and an edge of chaos kernel in its locally-active domain. Also, we draw a parameter classification of the neuron circuit, showing four symmetrical edge of chaos domains, which plays an important role in biphasic action potentials. Finally, we demonstrate that the simple neuron circuit can produce monophasic action potentials, biphasic action potentials and co-existing neuromorphic phenomena via subcritical Hopf bifurcation with different input, verifying the simple circuit is suitable as artificial neurons.

Intrinsic mode ensembled statistical cepstral coefficients for feature extraction of ship-radiated noise

Effective analysis of ship underwater acoustic signals requires accurately capturing and distinguishing subtle differences between various types of signal features. This paper introduces a multi-objective feature extraction method based on intrinsic mode decomposition and statistical parameterized cepstral coefficients, aimed at identifying different ship signals. Firstly, the original sample signals are preprocessed and converted into multiple frame signals. Each acoustic signal frame is then decomposed into intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Cepstral coefficients are extracted from each IMF, and the statistical parameter features of each IMF are integrated to enhance the differentiation of various types of ship-radiated noise. These features also form unique “fingerprints” for each ship type, facilitating identity accurate authentication. The performance of the proposed method is evaluated using both K-nearest neighbors (KNN) and support vector machine (SVM) classification models. Experimental results demonstrate that the synergy between the proposed method and SVM significantly outperforms KNN, effectively distinguishing between 12 types of signals, including 11 ship-radiated signals and background noise, achieving an accuracy rate exceeding 89% across 1000 random tests. This method significantly increases the number of classifiable ship targets, demonstrating its considerable potential in distinguishing various underwater acoustic signals.

Resistance to high-fat diet-induced weight gain in transgenic mice overexpressing human wild-type α-synuclein: A model for metabolic dysfunction in Parkinson's disease.

Unintentional weight loss, primarily due to the loss of fat mass rather than muscle mass, is common among patients with Parkinson's disease (PD) and is associated with poor quality of life and accelerated disease progression. Since transgenic mice overexpressing human wild-type α-synuclein (α-Syn mice) are modestly leaner than control mice, and since diabetes, a metabolic disorder, is a major risk factor for PD, we reasoned that high-fat diet-induced diabetes/metabolic dysregulation in α-Syn mice may serve as a robust tool for exploring how early α-synuclein pathology contributes to metabolic dysregulation, leading to weight loss in PD. Thus, α-Syn and age-matched controls were fed a high-fat diet (HFD) chow (60% fat calories) ad libitum for four months. Compared with controls on HFD (control-HFD), α-Syn mice on HFD (α-Syn-HFD) were dramatically leaner. The resistance to gaining weight in α-Syn-HFD mice was accompanied by improved glucose tolerance, a dramatic decrease in fat mass, and an increase in energy expenditure. Despite this leaner phenotype and better glucose tolerance, the mortality was much higher in male α-Syn-HFD mice than in all controls, but was unaffected in females, suggesting protective effects of female sex hormones, as well as lower α-synuclein levels. Immunoblot analysis of insulin signaling in the olfactory bulb, the proposed initial seeding site of α-synuclein pathology, revealed a decrease of IGF-IRβ, p GSK, and p mTOR in α-Syn-HFD mice. Since GSK-3β and mTOR regulate synaptic plasticity, we assessed levels of PSD-95 and synaptophysin in the olfactory bulb. As anticipated, we observed a significant decrease in the levels of PSD-95, along with a potentially compensatory increase in synaptophysin levels. Our results show that α-Syn mice, when challenged with diet-induced diabetes/metabolic dysregulation, clearly reveal a profile of robust metabolic dysfunction, thus providing a sensitive tool for assessing the underlying mechanism of metabolic dysfunction and its impact on weight loss and disease progression in PD. We propose a role for olfactory dysfunction in PD-related unintentional weight loss and suggest that strategies aimed at increasing body weight/BMI will improve the quality of life and prognosis for people living with PD.

Is it possible to use the male calling signal analysis for species diagnostics in Dictyophara Germar, 1833 (Hemiptera: Fulgoroidea: Dictyopharidae)?

Is it possible to use the male calling signal analysis for species diagnostics in Dictyophara Germar, 1833 (Hemiptera: Fulgoroidea: Dictyopharidae)?

Instability monitoring of molten pool in pure copper laser welding based on a multi-scale cascade model and spatial optical signals

During laser welding of pure copper, instabilities such as violent molten pool oscillation, large spatters, and melt ejection severely damage weld quality, which are caused by copper’s high reflectivity on commonly used infrared laser. The seriously unstable molten pool and keyhole and complicated laser-material interactions result in complex process signal emission waveforms, adding to the difficulties in process stability monitoring tasks. In this work, to break down different contents in the complex signals and deeply analyzing signal-process relation, combinative spatial optical sensor system was designed, and time-frequency signal analysis in multi-scale windows was performed. It was found that the infrared radiation at the front and end side of molten pool indicates the oscillation behavior of liquid metal surface, and the signal fluctuation patterns of visible radiation from different height of metal vapor varied when meeting severe instability like melt ejections. Signal features were extracted based on the understanding of process mechanism and signal behaviors. A cascade model combining Artificial Neural Network (ANN) and Support Vector Machine (SVM) was introduced to predict weld seam quality, where the ANN model focused on short-time stability status perception and the SVM model was used to decide macroscopic seam formation defects based on combining outputs of the ANN model in a long-term sampling window. Application results showed that the recognition accuracy of pit was 100 % and the accuracy of uneven toe reached 86.3 %. The multi-source signals of unstable molten pool recognized by the cascade model were summarized. The evolution process of copper molten pool ejection was revealed.

Affine phase retrieval of quaternion signals

Quaternion algebra H is an extension of the complex number field, which is a noncommutative associative algebra. In recent years, quaternionic Fourier analysis has interested some mathematicians due to its applications in signal analysis and image processing. This paper addresses quaternionic affine phase retrieval (QAPR) in quaternion Euclidean spaces H M , which aims to exactly recover a signal in H M from the magnitudes of its affine measurements. We introduce the concepts of QAPR and phaselift operator in H M . Then, we characterize QAPR in terms of the real Jacobian matrix, prove that 5M is the minimal measurement number for QAPR in H M , study the stability of QAPR-sequences, and use the phaselift techniques to give some sufficient conditions on QAPR for H M which provide us with a method to construct QAPR-sequences.

GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to the complex task of identifying and categorizing various waveforms and morphologies within the data. Additionally, ECG datasets often suffer from a significant class imbalance issue, which can lead to inaccuracies in detecting minority class samples. To address these challenges and enhance the effectiveness and efficiency of cardiac arrhythmia detection from imbalanced ECG datasets, this study proposes a novel approach. This research leverages the MIT-BIH arrhythmia dataset, encompassing a total of 109,446 ECG beats distributed across five classes following the Association for the Advancement of Medical Instrumentation (AAMI) standard. Given the dataset’s inherent class imbalance, a 1D generative adversarial network (GAN) model is introduced, incorporating the Bi-LSTM model to synthetically generate the two minority signal classes, which represent a mere 0.73% fusion (F) and 2.54% supraventricular (S) of the data. The generated signals are rigorously evaluated for similarity to real ECG data using three key metrics: mean squared error (MSE), structural similarity index (SSIM), and Pearson correlation coefficient (r). In addition to addressing data imbalance, the work presents three deep learning models tailored for ECG classification: SkipCNN (a convolutional neural network with skip connections), SkipCNN+LSTM, and SkipCNN+LSTM+Attention mechanisms. To further enhance efficiency and accuracy, the test dataset is rigorously assessed using an ensemble model, which consistently outperforms the individual models. The performance evaluation employs standard metrics such as precision, recall, and F1-score, along with their average, macro average, and weighted average counterparts. Notably, the SkipCNN+LSTM model emerges as the most promising, achieving remarkable precision, recall, and F1-scores of 99.3%, which were further elevated to an impressive 99.60% through ensemble techniques. Consequently, with this innovative combination of data balancing techniques, the GAN-SkipNet model not only resolves the challenges posed by imbalanced data but also provides a robust and reliable solution for cardiac arrhythmia detection. This model stands poised for clinical applications, offering the potential to be deployed in hospitals for real-time cardiac arrhythmia detection, thereby benefiting patients and healthcare practitioners alike.

Integrating Genomics and Transcriptomics to Identify Candidate Genes for Egg Production in Taihe Black-Bone Silky Fowls (Gallus gallus domesticus Brisson).

The Taihe Black-Bone Silky Fowl (Gallus gallus domesticus Brisson) possesses significant value in terms of consumption, medicinal applications, and ornamental appeal, representing a precious genetic resource and traditional Chinese medicinal material. However, considerable variation exists within populations regarding egg-laying performance. This study integrates a whole-genome selection signal analysis (SSA) with a transcriptome analysis to identify genes associated with egg-laying traits in Taihe Black-Bone Silky Fowls. We identified 31 candidate genes under selection from the high-yield chicken (HC) and low-yield chicken (LC) groups. Additionally, through RNA-seq analysis, 257 common differentially expressed genes (DEGs) were identified from four comparative groups. Two overlapping genes-LPL and SETBP1-were found in both the selected gene and DEG lists. These selected genes and DEGs were enriched in pathways related to ovarian development, including the lysosome pathway, the ECM-receptor interaction pathway, the TGF-beta signaling pathway, the Wnt signaling pathway, the PPAR signaling pathway, and the glycerolipid metabolism pathway. These research findings contribute to the breeding of Taihe Black-Bone Silky Fowls with high egg production traits and provide a theoretical foundation for exploring the regulatory mechanisms of avian reproduction.

A phylogenetic approach for identifying new sources of economically important fatty acids in plants and algae

Societal Impact StatementNew sources of essential and other nutritionally and/or pharmacologically important fatty acids for human use can be discovered through bioprospecting. Demonstrating how fatty acids in plants and algae are distributed across a phylogeny is a critical first step in this process. Here, new sources of essential omega‐3 fatty acids that are critically important to human health were identified, such as green and Chromista algae, bryophytes and some angiosperm families. The identification of certain taxa that are high in critical fatty acids is important for developing new sources of these healthful compounds.Summary Essential fatty acids (EFA) including long‐chain omega‐3 and omega‐6 EFA are key nutrients that broadly support the health of individuals and the ecosystems they inhabit. Production of these EFA is critical to global nutritional security, and therefore, it is imperative to assess the distribution patterns of FA in plants to bioprospect new resources that do not threaten biodiversity. We used a meta‐analytic approach (phylogenetic signal analysis), which incorporated a wide variety of taxa to map FA profiles including both presence and abundance of specific FA and EFA onto a plant phylogeny including algae, bryophytes, pteridophytes, gymnosperms and angiosperms. Our phylogenetic signal analysis revealed that presence/absence of most FA of interest were randomly or ubiquitously distributed, while FA content was significantly clustered in specific clades for most key FA (including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and nervonic acid). Both green and Chromista algae displayed relatively high proportions of EPA and DHA, while green algae contained higher proportional amounts of stearidonic acid (SDA). Further, signal analysis revealed high proportional content of gamma‐linoleic acid (GLA) in bryophytes and some angiosperm families, suggesting potential sources for bioprospecting GLA. This analysis provides a crucial foundation for understanding the influence of phylogenetic relationships in the relative proportions of key FA in plants and algae across a diverse phylogeny. Furthermore, in demonstrating how fatty acids in plants and algae are distributed across a phylogeny, we provide a critical first step in bioprospecting for new sources of essential and other nutritionally‐ and/or pharmacologically important fatty acids for human use.

Heterodyne coherent detection of the electric field temporal trace emitted by frequency-modulated comb lasers

Frequency-modulated (FM) combs are produced by mode-locked lasers in which the electric field has a linearly chirped frequency and nearly constant amplitude. This regime of operation occurs naturally in certain laser systems and constitutes a valuable alternative to generate spectra with equidistant modes. Here, we use a low-noise fs-pulse comb as the local oscillator and combine dual comb heterodyne detection with time domain analysis of the multi-heterodyne signal to reveal the temporal trace of both amplitude and phase quadratures of FM comb lasers’ electric field. This technique is applied to both a dense and a harmonic mid-infrared free-running quantum cascade laser frequency comb and shows direct evidence of the FM behavior together with the high degree of coherence of these sources. Our results furnish a deeper insight on the origin of the FM combs and pave the way to further improvement and optimization of these devices.

An Opto-electrophysiology Neural Probe with Photoelectric Artifact-Free for Advanced Single-Neuron Analysis.

Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.