Signal Values Research Articles

Effect of simultaneous multislice imaging, slice properties, and repetition time on the measured magnetic resonance biexponential intravoxel incoherent motion in the liver.

This study aims to investigate the previously reported dependency of intravoxel incoherent motion (IVIM) parameters on simultaneous multislice (SMS) acquisition and repetition time (TR). This includes the influence of slice thickness, slice gaps, and slice order on measured IVIM parameters. Diffusion-weighted imaging (DWI) of the liver was performed on 10 healthy volunteers (aged 20-30 years) at 3T with a slice thickness of 5 mm, a slice gap of 5 mm, and a linear slice order. Diffusion-weighted images were acquired with 19 b-values (0-800 s/mm2) using both conventional slice excitation with an acceleration factor of one (AF1) and SMS excitation with an acceleration factor of three (AF3). Each of these measurements were carried out with two repetition times (TRs)- 1,300 ms (prefix s) and 4,500 ms (prefix l)-resulting in four different combinations: sAF1, sAF3, lAF1, and lAF3. Five volunteers underwent additional measurements using a 10 mm slice thickness and with AF1. Median signal values in the liver were used to determine the biexponential IVIM parameters. Statistical significances were assessed using the Kruskal-Wallis test, Wilcoxon signed-rank test, and Student's t-test. In-silico investigations were also used to interpret the data. There were no significant differences between the biexponential IVIM parameters acquired from sAF1, sAF3, lAF1, and lAF3. Median values of the perfusion fraction f were as follows: 29.9% (sAF1), 26.9% (sAF3), 28.1% (lAF1), and 27.5% (lAF3). In the 10 mm-thick slices, f decreased from 31.3% (lAF1) to 27.4% (sAF1) (p = 0.141). The slice excitation mode did not appear to have any significant influence on the biexponential IVIM parameters. However, our simulations, as well as values reported from previous publications, show that slice thickness, slice gaps, and slice order are relevant and should thus be reported in IVIM studies.

Weak beat frequency extraction method for photon Doppler signal with low signal-to-noise ratio

Abstract In the window function spectrum of the low signal-to-noise ratio photon Doppler signal after the short-time Fourier transform, the weak beat frequency cannot be obtained by extracting the maximum amplitude spectrum frequency of each window because the amplitude of the noise spectrum exceeds the weak beat frequency. In this article, the value of the beat frequency is first estimated by the Kalman filter. Then, a multiple analytical bandpass filter is constructed to refine the spectrum of the high noise signal with the value of the beat frequency estimate as the spectrum band center. This technique extracts only the beat frequencies from the narrowband refinement spectrum by removing the interference spectrum band range. We use this technique to process the photon Doppler signals from the tiny high-speed flying fragments explosion experiment. After data processing, the beat frequency value heavily affected by high-frequency noise can reduce the error by up to 64.9%. The beat frequency value of the low noise signal can be accurate to 106 Hz, equivalent to 0.775 m/s after velocity demodulation. This method fully considers the positioning and protection of the beat frequency characteristics. It makes weak beat frequencies more obvious in the refined narrowband spectrum without changing the signal amplitude. This article describes a method for extracting the weak beat frequency of the photon Doppler signal or a signal optimization algorithm for needing high-precision beat frequencies in a test environment.

Quality and Image Processing Analysis versus Radiation Dose in Hip Radiographic Examinations

Aims: Evaluate the quality and processing of images in hip radiographic examinations.
 Study Design: Six combinations of exposure techniques commonly used in clinical practice were used, keeping the voltage (kVp) value constant, varying only the current and time relationship (mA.s).
 Place and Duration of Study: The study was carried out at the Radiodiagnosis Laboratory of the Radiology and Medical Physics graduation courses at the Franciscan University (UFN), as part of the research developed during the Image Processing course in the second semester of 2023.
 Methodology: Image acquisition and radiation dose measurement were performed using clinical radiographic equipment and a model resembling hip anatomy, along with a dosimetric system to quantify radiation dose at the entrance surface (ESAK). The captured images were digitized through a computerized radiography (CR) system, enabling quantitative analysis using histograms and Regions of Interest (ROI) in anatomical structures relevant for diagnosis. Signal and noise values were evaluated, allowing the determination of the signal-to-noise ratio (SNR) and radiographic contrast (CR).
 Results: The results revealed significant differences among the various techniques employed. Some images displayed lower SNR, ranging from 8.14% to 15.60%, yet with higher CR, varying between 5.48% and 22.29%. Remarkably, one technique (70 kVp with 16 mA.s) demonstrated the best cost-benefit relationship, reducing radiation exposure by 48.7% (from 6.32 to 3.24 mGy) with minimal reduction in SNR, less than 10%, and a 5.47% increase in CR.
 Conclusion: These findings highlight the importance for radiology professionals seeking imaging techniques that guarantee high diagnostic quality with minimal patient exposure. Prioritizing safer and more effective radiological practices is essential for delivering quality healthcare services.

Kulturelle Schlüsselwörter und ihre Ausdrucksvarianten im korpusgestützten Sprach- und Kulturvergleich

By analysing large and very large corpora, it is possible to capture fine-grained subsections of semantic networks. This more precise capture on the basis of more meaningful datasets makes it possible to pursue various research questions that could previously have been considered but not investigated due to a lack of quantitative research options. From a discourse-analytical perspective, but also with regard to the use of keywords in pedagogical practices in museum contexts (see also Milanese 2014), this article focuses on the different meanings that the various variants of keywords possess and which, as will be shown on the basis of selected examples, are coupled with a different signal value that becomes apparent in intra- and interlingual comparison. Of particular interest is the divergence of communicative traditions. In addition to a highly specialised corpus of specialist texts in of Germanand Italian on Renaissance works, the currently available German and Italian TenTen-corpora (itTenTen20, deTenTen20) are used as a data basis.

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

Impact of Graduating with Honours on Entry Wages of Economics Majors*

Employers use various proxies to predict the future labour productivity levels of the job applicants. Success in school, especially in high‐level coursework, is among the most widely used proxies to screen entry‐level candidates. We estimate the causal effect of graduating with honours (i.e. with a grade point average of 3.00 and above out of 4.00) on the starting wages of economics majors in Türkiye. Using comprehensive micro data on all economics majors between 2014 and 2018, matched with administrative records about their first jobs, we implement a regression discontinuity analysis to investigate whether there is any statistically significant jump in the starting wages at the honours‐degree cutoff. We find that graduating with honours increases the wages of males, while there is no impact on females. We further document that the impact on males is almost entirely driven by the graduates of non‐elite universities. In particular, graduating with an honours degree increases the entry wages of males from non‐elite universities by about 4%, on average. We provide an explanation for these patterns using the theory of statistical discrimination. We discuss the potential reasons behind the heterogeneous signal value of graduating with honours between males vs. females and elite versus non‐elite university graduates.

The Construction of Teaching Effect Indicator Management System for Wushu Courses in Colleges and Universities Oriented by “Internet+Education”

Abstract In this paper, the simpler wavelet transform method is used to remove the Gaussian noise contained in the image, reconstruct the approximation signal and the final processed detail signal, and obtain the estimated value of the noise signal to complete the image preprocessing. In the process of human static feature extraction, due to the structural limitations of the human body shoulder joint activity angle is low. Therefore, the local spherical coordinates are used to represent the joint angle characteristics of the human body in the static posture and, through the completion of the calculation of the distribution function of the action features of the target, to be tested to realize the recognition of the martial arts action. After completing the judgment matrix and consistency test, we finally establish the teaching effect index system for college wushu courses and analyze the teaching of ‘Internet Education’ wushu courses. The results show that the accuracy of the results of the four algorithms test dataset is stably maintained between 0.8 and 0.9, DNN algorithm, CNN algorithm, RNN algorithm, this paper’s algorithm loss average value of 1.34, 1.58, 1.75, 1.23, respectively, the combined accuracy and loss value, this paper’s algorithm compared to DNN algorithm, CNN algorithm, RNN algorithm performs better. Namely, the algorithm in this paper can accomplish action recognition with high accuracy. In the analysis of the teaching effect index evaluation of wushu course, the experts think that the matter dimension wushu course design index weight (0.800) > wushu teaching system (0.200), and wushu lecturers should design the course key points before the class. In this study, through the digital construction of teaching wushu courses in colleges and universities, we can achieve long-term development.

Rapid sexual signal diversification is facilitated by permissive females

The initial process by which novel sexual signals evolve remains unclear, because rare new variants are susceptible to loss by drift or counterselection imposed by prevailing female preferences.1,2,3,4 We describe the diversification of an acoustic male courtship signal in Hawaiian populations of the field cricket Teleogryllus oceanicus, which was brought about by the evolution of a brachypterous wing morph ("small-wing") only 6 years ago.5 Small-wing has a genetic basis and causes silence or reduced-amplitude signaling by miniaturizing male forewings, conferring protection against an eavesdropping parasitoid, Ormia ochracea.5 We found that wing reduction notably increases the fundamental frequency of courtship song from an average of 5.1 kHz to 6.4 kHz. It also de-canalizes male song, broadening the range of peak signal frequencies well outside normal song character space. As courtship song prompts female mounting and is sexually selected,6,7,8,9 we evaluated two scenarios to test the fate of these new signal values. Females might show reduced acceptance of small-wing males, imposing counterselection via prevailing preferences. Alternatively, females might accept small-wing males as readily as long-wing males if their window of preference is sufficiently wide. Our results support the latter. Females preferred males who produced some signal over none, but they mounted sound-producing small-wing males as often as sound-producing long-wing males. Indiscriminate mating can facilitate the persistence of rare, novel signal values. If female permissiveness is a general characteristic of the earliest stages of sexual signal evolution, then taxa with low female mate acceptance thresholds should be more prone to diversification via sexual selection.

On the optimization of 3D inflow-based vascular-space-occupancy (iVASO) MRI for the quantification of arterial cerebral blood volume (CBVa).

The inflow-based vascular-space-occupancy (iVASO) MRI was originally developed in a single-slice mode to measure arterial cerebral blood volume (CBVa). When vascular crushers are applied in iVASO, the signals can be sensitized predominantly to small pial arteries and arterioles. The purpose of this study is to perform a systematic optimization and evaluation of a 3D iVASO sequence on both 3 T and 7 T for the quantification of CBVa values in the human brain. Three sets of experiments were performed in three separate cohorts. (1) 3D iVASO MRI protocols were compared to single-slice iVASO, and the reproducibility of whole-brain 3D iVASO MRI was evaluated. (2) The effects from different vascular crushers in iVASO were assessed. (3) 3D iVASO MRI results were evaluated in arterial and venous blood vessels identified using ultrasmall-superparamagnetic-iron-oxides-enhanced MRI to validate its arterial origin. 3D iVASO scans showed signal-to-noise ratio (SNR) and CBVa measures consistent with single-slice iVASO with reasonable intrasubject reproducibility. Among the iVASO scans performed with different vascular crushers, the whole-brain 3D iVASO scan with a motion-sensitized-driven-equilibrium preparation with two binomial refocusing pulses and an effective TE of 50 ms showed the best suppression of macrovascular signals, with a relatively low specific absorption rate. When no vascular crusher was applied, the CBVa maps from 3D iVASO scans showed large CBVa values in arterial vessels but well-suppressed signals in venous vessels. A whole-brain 3D iVASO MRI scan was optimized for CBVa measurement in the human brain. When only microvascular signals are desired, a motion-sensitized-driven-equilibrium-based vascular crusher with binomial refocusing pulses can be applied in 3D iVASO.

Sensitive SERS aptasensor for histamine detection based on Au/Ag nanorods and IRMOF-3@Au based flexible PDMS membrane

BackgroundHistamine is a kind of biogenic amine with strong toxicity and potential carcinogenicity. Many traditional methods of detecting histamine have the disadvantages of cumbersome detection steps, expensive equipment, and high professional requirements for staff. In contrast, SERS has become the preferred method for quantitative analysis of histamine because of rich fingerprint information, rapidity and economy. However, most of SERS substrates still have technical problems, such as poor stability, low sample collection rate, and detection efficiency. Therefore, there is a great need for new strategies to develop high-performance SERS substrates based sensors. ResultsIn our study, a sensitive SERS aptasensor for the detection of histamine was synthesized. The assembly was formed between IRMOF-3@Au/PDMS (flexible SERS substrate) and AuNR-DTNB@Ag-HA apt (Raman signal probe with both the target capture ability) via π-π stacking interaction from HA aptamer and IRMOF-3. Consequently, the SERS signal of the assembly derived from DTNB reached highest due to the synergistic enhancement effect by AuNR@Ag and IRMOF-3@Au. Meanwhile, HA aptamer can specifically capture histamine, therefore histamine addition competitively bound to the probe, leading to a corresponding decrease in the DTNB signal value on the SERS substrate. The SERS intensity at 1331 cm−1 presented a good linear relationship towards the logarithmic value of histamine concentrations ranging from 0.0001 mg/L to 400 mg/L (R2 = 0.990) with the LOD of 3.6 × 10−5 mg/L. Furthermore, the application in wine samples demonstrated the accuracy and applicability of the developed sensor. SignificanceThis method effectively improves substrate stability, detection sensitivity and signal response immediacy to amplify the SERS sensor, thus satisfying the histamine detection requirements of various systems. According to this aptasensor design, our strategy can be extended to create other MOF-based SERS substrates for accurately detecting relative targets to ensure food safety.

Asynchronous event‐triggered adaptive robust switching filtering for nonlinear industrial cyber physical systems under data injection attacks

AbstractBy fully taking the effects of data injection attacks and nonlinear disturbances into consideration, based on dynamic asynchronous event‐triggered approach, an adaptive robust H‐Infinity switching filtering (ARSF) method is proposed for the stability of industrial cyber physical systems. An adaptive feedback controller utilizing anticipated states and error‐dependent parameters can proficiently counteract the deleterious impacts of data injection attacks originating from the measurement channel. Furthermore, a filter, which includes the value of the measurement signal, is designed to monitor the system's state changes in real‐time and ensure the stable operation of the system under attacks. Based on the design of an adaptive feedback controller and a filter eliminating the effects of data injection attacks originating from the measurement channel, a switching scheme is further proposed for compensating the effect of data injection attacks in the control channel. Finally, simulation tests are conducted by using MATLAB to control the robustness of a practical quadrotor UAV system's angles by adopting ARSF, whose results verify that the correctness and effectiveness of the proposed ARSF.

Application of Fast Fourier Transform to the Speech Signals Scrambling

The article is devoted to the closure of speech signals by implementing a fast Fourier transform in a bandpass scrambler. The most radical measure to prevent the interception of speech signals is the use of cryptographic security methods. The first technical security systems began to be developed immediately after the invention of the telephone. The main goal when developing speech transmission systems is to preserve those characteristics that are most important for the listener to perceive. Communication security in the transmission of voice messages is based on the use of a large number of different methods of blocking signals that change the characteristics of speech in such a way that it becomes illegible and unrecognizable to attackers who listen to or intercept the closed voice message. The choice of closure methods depends on the specific application and the technical characteristics of the transmission channel. That is, technical channels of information leakage are a source of information for technical intelligence, which extracts information using technical means. Therefore, the problem of protecting information from technical intelligence is of particular relevance. The object of the study is the transmission of voice messages. The subject of the research is real-time speech closing systems. The purpose of the paper is to use fast Fourier transform to close speech signals in a bandpass scrambler. To do this, it is necessary to assess the capabilities of technical intelligence to intercept information of limited distribution; analyze types of speech signal closing systems.
 The paper presents a block diagram of a combined scrambler, an algorithm for rearranging input signal values using the fast Fourier transform, and hashing functions that are used to obtain a speech signal from a combined scrambler. It has been determined that "through-window" scramblers are the most reliable, and for greater levels of closure, combination scramblers should be used. The task set for further development of the topic is the development of a software application for the operation of streaming data encryption methods in real time.

Research on magnetic memory inspection signal characteristics of multi-parameter coupling pipeline welds

Magnetic memory inspection technology is an effective method for detecting early damage in ferromagnetic materials. However, the commonly used force-magnetic coupling models are insufficient for analyzing the distribution characteristics of magnetic signals in ferromagnetic material welds. This study first analyzes the influence of metal phase transformation and residual stress on the magnetic hysteresis characteristics of materials. Based on the principle of magnetic hysteresis characteristics, the magnetic charge density under the combined action of stress and phase transformation is determined, and a magnetic memory analysis model is established for multi-parameter coupling. The study quantitatively calculates the effects of phase transformation volume fraction, residual stress, the lift-off effect, and cracks on the magnetic signal features at welds. The research results show that the magnetic memory signals at welds exhibit extreme values in the tangential component and sinusoidal fluctuations with zero-crossings in the normal component. The peak-to-peak values of both the normal and tangential components of the magnetic signal increase with an increase in metal phase transformation and residual stress, while they decrease with the increase of the lift-off effect. When cracks appear in the weld, the normal component exhibits a double-peak and valley structure, with the inner side representing crack features and the outer side representing weld features. The tangential component shows a maximum value at the center of the crack. Based on the peak and valley values of the magnetic signal, a damage grade index F is determined. The damage index F can characterize the level of weld damage, decreasing approximately linearly with increasing stress.

Investigating the prognostic value of mTORC1 signaling in bladder cancer via bioinformatics evaluation

Bladder cancer, a prevalent and heterogeneous malignancy, necessitates the discovery of pertinent biomarkers to enable personalized treatment. The mammalian target of rapamycin complex 1 (mTORC1), a pivotal regulator of cellular growth, metabolism, and immune response, exhibits activation in a subset of bladder cancer tumors. In this study, we explore the prognostic significance of mTORC1 signaling in bladder cancer through the utilization of bioinformatics analysis. Our investigation incorporates transcriptomic, somatic mutation, and clinical data, examining the mTORC1 score of each sample, as well as the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. Our findings reveal that elevated mTORC1 levels serve as an adverse prognostic indicator for bladder cancer patients, exhibiting a significant association with Basal-type bladder cancer. Patients with heightened mTORC1 activation display heightened levels of pro-carcinogenic metabolism. Additionally, these individuals demonstrate enhanced response to immunotherapy. Finally, we develop an mTORC1-related signature capable of predicting the prognosis of bladder cancer patients.The signature offers novel mTORC1-related biomarkers and provides fresh insights into the involvement of mTORC1 in the pathogenesis of bladder cancer.

Diagnostic value of adenohypophyseal MRI features in female children with precocious puberty

To evaluate the diagnostic value of adenohypophyseal magnetic resonance imaging (MRI) features for precocious puberty (PP) in female children and also to establish a non-invasive diagnostic approach in clinics. A total of 126 female children (37, 57, and 32 female children clinically diagnosed with central PP [CPP], incomplete PP [IPP], and controls, respectively) were enrolled in this study. Data were collected and analysed using analysis of variance. Pearson correlation and stepwise multivariate linear regression analysis were used to examine the association and build prediction models. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy. The values of adenohypophysis volume (aPV), adenohypophysis height (aPH), and signal-intensity ratio (SIR), height, weight, and seven laboratory testing characteristics were correlated closely with the activation status of the hypothalamic-pituitary-gonad axis in the different groups (all p<0.05). Model 1 including aPV, weight, and aPH and Model 2 including SIR, aPV, and height were built to obtain predicted luteinising hormone (LH; R2=0.271) and LH/follicle stimulating hormone (FSH; R2=0.311). ROC analysis showed the predicted LH, predicted LH/FSH, and aPV were the top 3 best predictors in distinguishing CPP from controls (AUC=0.969, 0.949, and 0.938) while predicted LH/FSH was the best predictor in distinguishing CPP from IPP and controls (AUC=0.829 and 0.828). The adenohypophysis volume itself and the prediction models including main adenohypophyseal MRI features increased diagnostic efficiency for PP and offered a non-invasive and credible diagnostic method.

Signals from CSR competition: The influence of relative CSR performance on analysts’ recommendations

Due to the institutionalization of corporate social responsibility (CSR) and its integration into firm strategy, firms are engaged in fierce competition, which has gained stakeholders' attention. As intermediary stakeholders, security analysts screen information on firms' CSR activities to make more accurate investment recommendations. Integrating signaling through CSR competition and screening theory, we develop a framework wherein firms' relative CSR performance and improvement across two years are viewed as complementary signals reflecting their ability and intent to engage in CSR and affect analysts' recommendations. Using a panel of Chinese listed firms from 2011 to 2019 (n = 15,735 firm-year observations), we find that analysts respond positively to firms' relative CSR performance. Further analyses show that firms' CSR performance improvement has a decreasingly positive effect on analysts’ recommendations, and this effect is more pronounced for firms with higher relative CSR performance. Our study contributes to the literature on CSR and screening theory by highlighting the value of comparative CSR signals and generates practical implications for participants in CSR competitions.

Characteristic Mean Kurtosis Values in Simple Diffusion Kurtosis Imaging of Dentigerous Cysts.

We evaluated the usefulness of simple diffusion kurtosis (SD) imaging, which was developed to generate diffusion kurtosis images simultaneously with an apparent diffusion coefficient (ADC) map for 27 cystic disease lesions in the head and neck region. The mean kurtosis (MK) and ADC values were calculated for the cystic space. The MK values were dentigerous cyst (DC): 0.74, odontogenic keratocyst (OKC): 0.86, ranula (R): 0.13, and mucous cyst (M): 0, and the ADC values were DC: 1364 × 10-6 mm2/s, OKC: 925 × 10-6 mm2/s, R: 2718 × 10-6 mm2/s, and M: 2686 × 10-6 mm2/s. The MK values of DC and OKC were significantly higher than those of R and M, whereas their ADC values were significantly lower. One reason for the characteristic signal values in diffusion-weighted images of DC may be related to content components such as fibrous tissue and exudate cells. When imaging cystic disease in the head and neck region using SD imaging, the maximum b-value setting at the time of imaging should be limited to approximately 1200 s/mm2 for accurate MK value calculation. This study is the first to show that the MK values of DC are characteristically higher than those of other cysts.

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Acoustic signal detection technology has significant advantages in detecting the leakage and bursting of heat exchange pipes in boilers. To address the current lack of sound signal data for steam medium leakages and the problem of large errors in the complex sound field environment of power plants, we performed an innovative experimental comparative analysis of leakage acoustic signals under a dual medium of compressed air and steam to provide a reference for detecting leakage acoustic signals. During the experiment, the time and frequency domains were analyzed by changing the pressure of the leakage pipeline and aperture of the leakage hole, and the spectrogram and power spectrum of the leakage acoustic signal were obtained using fast Fourier transform and autocorrelation analysis. The results showed that the signal value of the leakage medium increased with increasing pipeline pressure and leakage aperture and that the energy of the steam leakage acoustic signal was greater than that of compressed air under the same pressure and aperture because of its larger specific heat capacity. In addition, the complete empirical mode decomposition of adaptive noise (CEEMDAN) algorithm was introduced into the denoising decomposition of the leakage sound signal in the furnace, and the average error of the time delay value of the leakage acoustic signal calculated using the CEEMDAN algorithm was observed to be within 5%.

Discontinuity-Aware 2D Neural Fields

Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadratic, or cubic Bézier curves, and construct a feature field that is discontinuous across these locations and smooth everywhere else. Finally, we use a shallow multi-layer perceptron to decode the features into the signal value. To construct the feature field, we develop a new data structure based on a curved triangular mesh, with features stored on the vertices and on a subset of the edges that are marked as discontinuous. We show that our method can be used to compress a 100, 000 2 -pixel rendered image into a 25MB file; can be used as a new diffusion-curve solver by combining with Monte-Carlo-based methods or directly supervised by the diffusion-curve energy; or can be used for compressing 2D physics simulation data.

Complementary Ensemble Empirical Mode Decomposition and Maximum Correlated Kurtosis Deconvolution for Wind Turbine Bearing Fault Feature Extraction

Wind turbine bearing fault signals exhibit characteristics such as nonlinearity, non-stationarity, and susceptibility to external noise interference, making it challenging to extract fault features and identify them accurately. In light of these issues, this paper proposes a fault signal feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD) and maximum correlated kurtosis deconvolution (MCKD). CEEMD is utilized to decompose the signals, reducing mode mixing and eliminating residual auxiliary noise in the decomposition process, thereby obtaining linear and stationary signals that enhance the significance of the features. MCKD is employed to increase the kurtosis value of the fault signals, facilitating the extraction of continuous transient impacts from weak fault signals. Finally, a Convolutional Neural Network (CNN) is employed to validate that the method enhances fault characteristics and improves the accuracy of fault signal recognition.