Frequency Domain Transformation Research Articles

A 34.7 µW Speech Keyword Spotting IC Based on Subband Energy Feature Extraction

In the era of the Internet of Things (IoT), voice control has enhanced human–machine interaction and the accuracy of keyword spotting (KWS) algorithms has reached 97%; however, the high power consumption of KWS algorithms caused by their huge computing and storage requirements has limited their application in Artificial Intelligence of Things (AIoT) devices. In this study, voice features are extracted by utilizing the fast discrete cosine transform (FDCT) for frequency-domain transformation and to shorten the process of calculating the logarithmic spectrum and cepstrum. The designed KWS system is a two-stage wake-up system, with a sound detection (SD) awakening KWS. The inference process of the KWS network is achieved using time-division computation, reducing the KWS clock to an ultra-low frequency of 24 kHz.At the same time, the implementation of a depthwise separable convolution neural network (DSCNN) greatly reduces the parameter quantity and computation. Under the GSMC 0.11 µm technology, post-layout simulation results show that the total synthesized area of the entire system circuit is 0.58 mm2, the power consumption is 34.7 µW, and the F1-score of the KWS is 0.89 with 10 dB noise, which makes it suitable as a KWS system in AIoT devices.

Evaluations of Clarinet Sound Quality and Register Characteristics

The clarinet is a commonly used woodwind instrument that has a unique sound quality, making it a prominent instrument in solo performances and orchestral music. Therefore, sound quality plays an important role in the perception of clarinet sound. This study provides a review of the relevant research in the field of clarinet sound analysis and synthesis, and identifies many valuable references from previous studies. In studying the noise component of clarinet sound, this study recorded audio samples of E3, E4, and E5 and preprocessed them by slicing and performing frequency domain transformations. Using 13 representative spectral features, this paper extracted features from the spectral signals and constructed a classification model of sound quality based on the SVM algorithm of machine learning. Finally, this study analyzed the shortcomings of the SVM algorithm in high-pitch analysis and proposed future research directions of combining the SVM algorithm with different sound characteristics of clarinet registers and spectral changes throughout the entire process of playing. These results shed light on guiding further exploration of the timbre of the clarinet.

TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields

Voxel grid representation of 3D scene properties has been widely used to improve the training or rendering speed of the Neural Radiance Fields (NeRF) while at the same time achieving high synthesis quality. However, these methods accelerate the original NeRF at the expense of extra storage demand, which hinders their applications in many scenarios. To solve this limitation, we present TinyNeRF, a three-stage pipeline: frequency domain transformation, pruning and quantization that work together to reduce the storage demand of the voxel grids with little to no effects on their speed and synthesis quality. Based on the prior knowledge of visual signals sparsity in the frequency domain, we convert the original voxel grids in the frequency domain via block-wise discrete cosine transformation (DCT). Next, we apply pruning and quantization to enforce the DCT coefficients to be sparse and low-bit. Our method can be optimized from scratch in an end-to-end manner, and can typically compress the original models by 2 orders of magnitude with minimal sacrifice on speed and synthesis quality.

Research on Abnormal Feature Extraction and Early Fault Alarm Method of Rolling Bearing's Based on CDAE and KLD

A rolling bearing is an important part of rotating machinery, and it is widely used in the petrochemical industry, aerospace industry and other industries. Hence, it is of great significance to carry out condition monitoring and fault alarms for rolling bearings. Aiming at the problem of the rolling bearing fault, a method of an improved deep convolutional denoising auto encoder abnormal feature extraction and the Kullback-Leibler divergence threshold alarm is proposed. The experiment verification is carried out on the rotor bearing experiment platform. The experiment results show that the proposed method has good denoising performance and micro fault feature extraction ability under the condition of no fault data training and no frequency domain transformation. High accuracy, good efficiency and strong robustness of the proposed method for an early fault alarm are demonstrated by the experiment as well.

Machine tool spindle vibration monitoring system based on Bluetooth wireless network and LabVIEW

The spindle system is important in the machine tool. The vibration signal generated by the spindle system in the machine tool has a great influence on the machining accuracy of the product and the abrasion and lifetime of the tool. Therefore, it is necessary to monitor the vibration signal in the machining process of the machine tool spindle in real time. The vibration sensor is used to monitor the vibration signal of the machine tool spindle in this paper. The data collected by the sensors are transmitted to the Bluetooth module by the serial port for wireless communication. The vibration signal and its frequency domain transformation of the spindle are received by LabVIEW software platform, and the original waveform is displayed, the threshold alarm and the storage of data are realized. The experimental results show that the designed system can realize the online monitoring of the vibration state of the machine tool spindle effectively.

Implementation and analysis of symmetrical signals in the sequency domain

Every transform has unique attributes and traits that are crucial to reducing computing costs and offering simple solutions. Many different frequency domain transformations, for instance, have properties that can be used in a variety of signal processing applications and analyses. Some of the Complex Hadamard Transform's variants' sequencies can be compared to those of the Discrete Fourier Transforms. It is proven the characteristics of the Conjugate Symmetric Sequency-Ordered Complex Hadamard Transform symmetry. These qualities are crucial for signal analysis and image processing. Due to duplicate spectra across the origin, it reduces computational complexity, makes an analytical analysis for symmetric signals simpler, and needs less storage. Its analysis shows that the Discrete Fourier Transform and this Complex Hadamard Transform version exhibit similar symmetry tendencies. By using elementary signals in the time domain to connect the positive and negative sequencies with their associated phasor conceptions, sequency domain spectra are used to highlight these properties. As a result of image representation, relative spectra are represented in related domains. Transform can be used to extract and analyze various aspects from a wide range of medical images.

Zero-Watermarking for Vector Maps Combining Spatial and Frequency Domain Based on Constrained Delaunay Triangulation Network and Discrete Fourier Transform.

With its lossless properties, zero-watermarking has attracted a lot of attention in the field of copyright protection for vector maps. However, the common zero-watermarking algorithm puts too much emphasis on mining for global features, making it vulnerable to cropping attacks, and the robustness is not comprehensive enough. This study provides a vector map zero-watermarking scheme that utilizes spatial statistical information and frequency domain transformation methods in an effort to solve the aforementioned issue. In order to make the scheme more resistant to cropping and compression, it is constructed on the basis of feature point extraction and point constraint blocking of the original vector map. Within each sub-block, feature points are used to build constraint Delaunay triangulation networks (CDTN), and the angular values within the triangle networks are then extracted as spatial statistics. The angle value sequence is further transformed by discrete Fourier transform (DFT), and the binarized phase sequence is used as the final feature information to build a zero watermark by executing an exclusive disjunction operation with the encrypted copyright watermark image, both of which contribute to the scheme's robustness and security. The results of the attack experiments show that the proposed vector map zero-watermarking can restore identifiable copyright images under common geometric attacks, cropping attacks, and coordinate system transformations, demonstrating a high level of robustness. The theoretical basis for the robustness of this watermarking scheme is the stability of CDTN and the geometric invariance of DFT coefficients, and both theory and experiment validate the method's validity.

Real-time cable force identification based on block recursive Capon spectral estimation method

Cables are critical force-bearing components of cable-supported structures that greatly influence structure security. The changes in cable forces can reflect the cable service state and provide an important basis for structural health assessment. This paper proposes a method for real-time cable force identification (CFI) to tracking its varying rules. First, a recursive Capon method based on block updating is derived to estimate the amplitude spectrum at each observation moment. Second, determine the natural frequency (and corresponding mode order) in the transformed frequency domain and calculate the cable tension according to related formulas. Proper algorithm optimization and parameter selection are studied to ensure that the calculation cost meets the real-time requirement of CFI. The proposed method is applied to an experimental cable and the cables of a half-through arch bridge for validation.

Wave propagation in thin pretwisted composite strips with an embedded delamination

In this work, a thin pretwisted and delaminated composite strip is modeled using the variational asymptotic method (VAM). The original 3D problem is reduced to a 2D cross-sectional analysis and a 1D problem along the length of the strip. Damage, in the form of an embedded delamination, is accounted in the strip by following a sublaminate approach. The 1D governing elastodynamic equations of the strip model is solved using the spectral finite element (SFE) method, wherein the structure is modeled as a wave guide. In SFE method, the governing elastodynamic equations are solved in transformed frequency domain to obtain the dynamic stiffness matrix. The guided wave response of the delaminated composite structure for different loads and boundary conditions are subsequently computed. Modal and wave response from SFE using the reduced 1D model are validated by comparing results with those reported in the literature and in some cases with the results obtained from a 3D model using a direct finite element (FE) package. Modal and wave propagation analysis is performed for various dimensions of delamination and composite layup. The analysis provides insight into the dynamic characteristic of partially delaminated strip. The usage of SFE is expected to yield a faster result and lesser computation time than the conventional direct FE method for analyzing and monitoring the delaminated strip exposed to high frequency excitations. Additionally, the capability of the model to investigate the inverse problem of damage detection is demonstrated.

Transformations of borehole magnetic data in the frequency domain and estimation of the total magnetization direction: A case study from the Mengku iron-ore deposit, Northwest China

Borehole magnetic prospecting measures the three components of the magnetic field and is sensitive to the vertical depth of the magnetic source, which plays an important role in deep mineral exploration. Magnetic field transformations in the frequency domain constitute common and important processing for ground and airborne data but are rarely applied to borehole magnetic data. Here, we deduce component transformation and magnetization-direction transformation formulas for borehole data. It is found that the transformation factors differ from those of ground and airborne data. The new formulas depend on the position of the drillholes. The transformed data are then used to estimate the total magnetization direction by computing the correlation coefficient between the transformed components and the amplitude anomaly. The advantages of this method are that the borehole data yield a local and accurate magnetization direction and the total amplitude anomaly is computed directly from the individual anomalies in the observed components. The frequency-domain transformation is tested on the synthetic data, and the estimated directions are consistent with the true values. The processing procedure is applied to borehole data collected at the Mengku iron-ore deposit in Northwest China, where magnetic surveys from 55 boreholes have been acquired. The estimated magnetization directions yield good approximations of the results from the ground magnetic data and the physical property measurements of rock samples.

Method of Series Arc Fault Detection Based on Phase Space Reconstruction and Convolutional Neural Network

Series arc fault detection can improve the safety of low-voltage power systems. The existing arc fault detection is mainly based on various indicators of a frequency domain or time-frequency domain transformation for feature extraction, which is difficult to extract comprehensive arc information, resulting in low detection accuracy. This paper presents a method for extracting comprehensive information, combined with a convolutional neural network to detect arc faults. First, the arc fault experimental platform is developed according to the UL1699 standard, and the current signals of various loads under different operating conditions are collected. Then, the current of a single cycle is embedded by coordinate delay, and the distance matrix is calculated by using 50 vectors reconstructed by a single cycle. Finally, a convolutional neural network classification model is designed, which is used to mine the information in the distance matrix to detect series arc faults. The experimental results show that the average accuracy of the method for arc fault identification of various loads is 99.00% and that the sampling frequency is low. It is suitable for lines with different loads and has certain robustness, so this method has the potential to be implemented on hardware.

Analysis of Seismic Damage Modes of Landslides Containing Tunnels under Horizontal Earthquake Action

With the vigorous development of railway and highway construction, tunnel construction often follows the mountains and rivers, crossing high-intensity landslide areas, and potential earthquakes triggering damage in areas including tunnel landslides have become a hot issue today. Based on this, this paper performs a shaker test on the tunnel-containing landslide body, inputs horizontal seismic waves, and tests the acceleration response data of different bits in the landslide body. By analyzing the temporal and frequency domain transformations of the acceleration response at different positions in the slope body combined with the deformation characteristics of the slope, and then analyzing the seismic response of the slope under the action of the front and back seismic sequences, the results show that (1) the existence of the tunnel has the effect of energy dissipation and vibration reduction, making the energy input at T8, and the measuring point on the upper part of the tunnel at the lowest. (2) When the seismic wave is transmitted, it will cause reflection around the tunnel, forming complex seismic wave field, resulting in the irregular distribution of acceleration amplification coefficient. (3) There is a high correlation between seismic responses of different levels. When the acceleration response of the preseismic response to the landslide-containing tunnel is not considered, the acceleration response of the postseismic response to the slope and the structure will be lower than the real value. (4) The sequence of failure at different locations of landslide mass containing tunnel is found through marginal spectrum analysis. It is concluded that the failure mode of landslide mass with tunnel is extrusion and sliding out of the middle slope. The research results can provide some reference for the reinforcement design of landslide with tunnel in high-intensity area.

Evaluation of delamination in drilling of SiCp/sisal fiber reinforced epoxy composites by the analysis of acoustic emission and cutting force: Influence of tool condition

The presence of SiC particulate in the newly developed SiCp/sisal fiber/epoxy composites on drilling, influence the wear in the drill bit. This article presents the experimental investigations of the effect of tool condition on delamination during drilling of composite by the time, frequency domain, and discrete wavelet packet transformation (DWPT) of acoustic emission and cutting force measured in process. It may further be helpful for determining and assessing the root cause of the induced delamination damage. The drill bits are made of high-speed steel (HSS), the condition is categorized into three different types namely as received, cryogenically treated (−80°C for 24 h), and cryogenically treated and tempered (−80°C for 24 h and 200°C for 2 h). Study of the frequency domain reveals that the dominant frequency of the acoustic emission (AE) signals falls between 100 and 300 kHz. The AE-specific energies of the decomposed signals are calculated based on DWPT analysis. It is noted from the AE specific energy that shearing occurs during drilling process. The cryogenically treated and tempered HSS drill contributes significantly to the reduction in delamination compared to other tools employed in this investigation.

Frequency Domain Transformations and CNNs to Predict Unlabeled Shark Behavior With GPS Data

This paper provides a comprehensive analysis of frequency domain transformations applied to convolutional neural networks (CNN) to model and predict unlabeled shark behavior in the open ocean with GPS position data. The frequency domain-based CNN networks are compared against the time domain CNN to contrast the two CNN architectures. The shark behavior data were obtained through two datasets where tri-axis accelerometer data were collected from live sharks. The first dataset was from the CSULB Shark Lab and consisted of labeled shark behavior into four shark behavioral categories. The second dataset used in this study was unlabeled and recorded from sharks in the open ocean and had GPS positioning data and depth data points. Findings show that the CNN architecture based on the frequency domain slightly outperforms time-based CNNs for classifying California horn shark behavior. Through spectral density analysis, prominent features are extracted and allow for distinguishing the shark behaviors.

Deep learning in frequency domain for inverse identification of nonhomogeneous material properties

The inverse identification of nonhomogeneous material properties from measured displacement/strain fields, especially when noise exists, is crucial for both engineering and material science. The conventional physics-based solutions either require time-consuming iterative calculations, or are sensitive to noise. While the new machine learning methods either need excess data for high-dimensional matchups, or mainly apply to case-by-case analyses with informed physics. In this paper, to solve the complex matchup between the measured displacement/strain fields and the randomly distributed modulus field rapidly and robustly, a novel method of deep learning in frequency domain is proposed, with discrete cosine transform (DCT) to achieve frequency domain transformation as well as dimensionality reduction and convolutional neural network (CNN) to implement learning in frequency domain. Results show that our method not only has high prediction accuracy on zero-noise samples (with L1-error of 4.249%) but also presents great robustness to noise (with L1-error of 5.085% on large-noise samples). Besides, by our method, only one-time training on a dataset with mixed noise is basically enough to deal with arbitrary levels of noise (with L1-errors below 5.202%), improving the efficiency significantly in practical applications. Moreover, our method can be directly transferred to neighbor sampling spaces with different sampling points, showing a great generalization. The study provides a powerful approach for inverse identification of material properties and promises for wide applications such as real-time elastography and high-throughput non-destructive evaluation techniques.

Selecting an Optimal Faraday Cage To Minimize Noise in Electrochemical Experiments.

The ubiquitous Faraday cage, an experimental component particularly essential for nanoelectrochemical measurements, is responsible for neutralizing noise introduced by electromagnetic interference (EMI). Faraday cage designs abound in the literature, often exhibiting varying thicknesses, mesh sizes, and base materials. The fact that the Faraday cage composition most often goes unreported underscores the fact that many electrochemical researchers assume a 100% EMI reduction for any given design. In this work, this assumption is challenged from a theoretical and empirical perspective by highlighting the physical principles producing the Faraday effect. A brief history of the Faraday cage and a simplified theoretical approach introduce fundamental considerations regarding optimal design properties. In practice, time-domain noise profiles and corresponding Fourier transform frequency domain information for custom-built Faraday cages reveal that maximally conductive cages provide more optimal EMI exclusion.

Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features

Abstract Based on the strong classification feature recognition algorithm, the calculation algorithm of a text semantic similarity is studied with the performance evaluation in this paper. In order to achieve a general algorithm for this function, the semantic function library based on a semantic recognition code as a comparison object is designed. It drives the algorithm modules of two fuzzy neuron deep convolution machine learning, and between these two processes of machine learning, a rigid algorithm based on Fourier transform frequency domain feature is extracted. Finally, a more complex machine learning general algorithm is realized by the use of external data fuzzy algorithm and de-fuzzy algorithm before and after the algorithm module. It is also a technical innovation in this paper. Through the performance evaluation based on the subjective evaluation of volunteers, it is found that the system focuses on the text semantic similarity evaluation of the Chinese language, and achieves a comparison result of 81.78% of the artificial judgment accuracy rate, and only 5.52% of the volunteers believe that the system judgment result is completely different from that of manual judgment.

Erratum to: Recaptured images detection algorithm based on frequency domain transformation

Erratum to: Recaptured images detection algorithm based on frequency domain transformation

Measurement of Orthotropic Material Constants and Discussion on 3D Printing Parameters in Additive Manufacturing

In this study, the orthogonal mechanical properties of additive manufacturing technology were explored. Firstly, six test pieces of different stacking methods were printed with a 3D printer, based on fused deposition modeling. The resonance frequency was measured by a laser Doppler vibrometer as the test piece was struck by a steel ball, which was used to calculate the orthotropic material constants. The accuracy of these orthotropic material constants was then verified using finite element software through a comparison of the experimental results from multiple natural modes. Thus, a set of methods for the measurement and simulation verification of orthotropic material constants were established. Only three specific test specimens are needed to determine the orthotropic material constants using the vibrating sensor technique, instead of a universal testing machine. We also analyzed the influence of different printing parameters, including raster angle and layer height, on the material constants of the test pieces. The results indicate that a raster angle of 0° leads to the highest Young’s modulus, a raster angle of 45° leads to the highest shear modulus G, and a layer height of 0.15 mm leads to the highest material strength. In various stack conditions, the mechanical properties of fuse deposition additive manufacturing can be measured by inversely calculating frequency domain transformation.

A general fault diagnosis framework for rotating machinery and its flexible application example

When dealing with the fault diagnosis of different rotating machines (gear or bearing), different working conditions (such as rotating speed), different signals (acoustic signal or vibration signal), it is usually necessary to establish different models, which is, however, time-consuming and laborious. At the same time, the models have poor generality and portability. In order to solve above problems, a general fault diagnosis framework (GFDF) is proposed in this paper. Firstly, the collected signals, whether vibration signals or acoustic signals, are directly converted into two-dimensional gray images; secondly, the FAST-Enhanced-Unoriented-SIFT (FEUS) feature extraction algorithm proposed in this paper is used to extract feature vectors; then, the feature vectors are encoded by using the bag-of-words (BoW) model to obtain the basic words and codebook vectors; finally, the fault diagnosis is completed by calculating the distance between the description vector of the signal to be diagnosed and the codebook vectors. GFDF’s main feature lies in the evitable frequency domain transformation and noise reduction, which makes GFDF insensitive to signal type and has high diagnostic efficiency. The experimental results show that GFDF has high diagnostic accuracy and stability for acoustic signals and vibration signals of rolling bearing and planetary gear at different rotating speeds, which proves that GFDF has generality and portability and is potential for the application in other scenes. Comparative experiments show that GFDF outperforms the representative traditional classification methods and deep learning models in diagnostic accuracy and stability. In addition, GFDF is applied to the fault diagnosis of the acoustic signals collected in motion to simulate the working state of inspection robots, and the ideal diagnostic result is also achieved. The flexible application example of this framework provides experience for other researchers.