Modification of backpropagation networks for complex-valued signal processing in frequency domain

An experimental study of mobile teletext reception is described. Teletext in Japan is a broadband signal with 5.73 Mb/s; therefore, multipath interference is a serious problem in mobile reception, and it is impossible to obtain the teletext correctly by conventional methods. A multipath reduction technique using signal processing in the frequency domain is developed. In this technique, an original chirp signal is used for reference to identify radio propagation paths. In field trials, the delay profile is precisely obtained, and the bit error rate can be reduced. the error correction capability is also tested in a computer simulation. The proposed method was found to improve the error rate when the signal-to-noise ratio was greater than 20 dB. >

Bilateral collicular interaction: Modulation of auditory signal processing in frequency domain

The Fast Discrete Fourier Transform is commonly analysis method for speech signal processing in frequency domain. It has the problem of adjusting window size for desired resolution. But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities. This paper performs global processing by combining Fractional Fourier Transform and wavelet multi-scale local elimination on the speech signal and enhancement experiment with RNN neural network. The experimental results show that the speech signal is processed both locally by wavelet and globally by Fractional Fourier Transform. The combination of the two allows the speech signal to take into account both local and global information in the time and frequency domain.

Simple cells Gabor wavelet transform and human visual system in the visual stimulus response very similar. It has the good characteristics of the local space in the extraction of target and frequency domain information. Although the Gabor wavelet does not of itself constitute orthogonal basis, but in the specific parameters can form a tight frame. Gabor wavelet is sensitive to the image edge, can provide good direction and scale selection characteristics, but also insensitive to illumination changes, can provide the illumination change good adaptability. These features make Gabor wavelet is widely used in visual information understanding. The two-dimensional Gabor wavelet transform is an important tool for signal analysis and processing in frequency domain in, the coefficient of wavelet transform with the visual characteristics and good biology background, so it is widely used in image processing, pattern recognition and other fields.

The authors derive efficient parallel algorithms on a VLSI array for several signal and image processing tasks. The parallel architecture has n/sup 2/ memory modules storing the input data which is accessed by n PEs. They consider several signal processing tasks such as k selection, median filtering, labeling 0/1 image, etc. For these problems, linear speedup is obtained, compared to a single-processor system. For the k selection problem they also provide an efficient parallel solution using log transformation on the data. The expected time of this method is O(n) with a small constant factor. The proposed array is suitable for general-purpose signal processing and can be implemented in VLSI using a limited chip set.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In our study, we use six nonlinear indexes and two linear ones, which are Complexity, Lyapunov Exponent, Predictability, Correlation Dimension, Pioncare Scattergraph, Approximate Entropy; Time and Frequency Domains, respectively, as input parameters of neural networks, i.e. Back-Propagation (BP) networks with only one hidden layer. According to all eight indexes of the subjects given by WuHan TongJi Hospital, we not only introduce a BP network, but improve the algorithm to avoid its proper weakness as well. Neural networks are always regarded as an accurate way to analyze and study the structure of a system. The simulation results reinforce the fact that it is more effective to clinically diagnose cardiovascular diseases by combining the eight indexes with the optimized algorithm in BP networks.

This paper presents real-time musical applications using the IRCAM signal processing workstation which make use of FFT/IFFT-based resynthesis for timbral transformation in a musical context. An intuitive and straightforward user interface, intended for use by musicians, has been developed by the authors in the Max programming environment. Techniques for high quality time-stretching, filtering, cross-synthesis, dynamic range processing, and spectrum shaping are presented along with dynamic control structures that allow for both fine timbral modification and control of complex sound transformations using few parameters.

Muscle behaviour during the delivery of neuromuscular electrical stimulation (NMES) is poorly understood, and the use of a novel signal processing and machine learning approach can automate the detection of pain during therapy delivery. Surface electromyography (sEMG) and mechanomyography (MMG) signals represent the electric current generated in muscles and are correlated with neuromuscular activity. Rectus femoris, vastus lateralis, and tibialis anterior muscle groups were stimulated in a self-administered, virtual setting using custom hardware on 12 healthy participants. Subject-reported Verbal Rating Score pain labels were collected during signal acquisition. sEMG and MMG signals were preprocessed, manually and auto-feature extracted, and trained. Noise reduction, time and frequency domain signal processing, and envelope detection techniques were explored. Machine learning architectures were trained using a Leave-One-Subject-Out cross-validation approach to determine the optimal proposed learning architecture: a regularized Long-Term Short Memory neural network with an F1 score of 84.96% over 10 training epochs. This novel pipeline demonstrates the feasibility of using the sEMG and MMG biosignals to predict pain labels. The results show promise in automating NMES delivery to combat muscle atrophy and can be extended to non-healthy subjects in the future.&lt;p&gt;&lt;/p&gt;

Artificial synaptic transistors represent promising building blocks for time- and frequency-domain signal processing. However, most electrolyte-gated devices are constrained by fixed synaptic plasticity and frequency responses, limiting their functional versatility. Herein, we demonstrate a dual-gate MoS2 synaptic transistor, with the second gate serving as an independent control terminal to achieve gate-tunable frequency-selective characteristics. The device exhibits essential synaptic functionalities, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and spike-frequency-dependent plasticity (SFDP). It achieves a millivolt-level gate sensitivity (down to 5 mV) and an ultralow energy consumption of ∼187 fJ per synaptic event. Notably, the frequency-dependent response of the transistor can be dynamically modulated via the dual-gate configuration, where the second gate enables the cutoff frequency (fc) to be tuned from 10.79 to 12.8 Hz, providing a tunable window for high-pass signal processing. Image-processing simulations confirm that tuning the cutoff frequency allows selective enhancement of high-frequency features (e.g., edges and textures). Collectively, these findings illustrate that a dual-gate MoS2 synaptic transistor can integrate short-term plasticity, tunable frequency-selective processing, and low energy consumption into a compact platform, holding significant promise for advanced neuromorphic signal processing.

It is of clinical interest to estimate pure-tone thresholds from potentially available objective measures, such as stimulus-frequency otoacoustic emissions (SFOAEs). SFOAEs can determine hearing status (normal hearing vs. hearing loss), but few studies have explored their further potential in predicting audiometric thresholds. The current study investigates the ability of SFOAEs to predict hearing thresholds at octave frequencies from 0.5 to 8 kHz. SFOAE input/output functions and pure-tone thresholds were measured from 230 ears with normal hearing and 737 ears with sensorineural hearing loss. Two methods were used to predict hearing thresholds. Method 1 is a linear regression model; Method 2 proposed in this study is a back propagation (BP) network predictor built on the bases of a BP neural network and principal component analysis. In addition, a BP network classifier was built to identify hearing status. Both Methods 1 and 2 were able to predict hearing thresholds from 0.5 to 8 kHz, but Method 2 achieved better performance than Method 1. The BP network classifiers achieved excellent performance in determining the presence or absence of hearing loss at all test frequencies. The results show that SFOAEs are not only able to identify hearing status with great accuracy at all test frequencies but, more importantly, can predict hearing thresholds at octave frequencies from 0.5 to 8 kHz, with best performance at 0.5 to 4 kHz. The BP network predictor is a potential tool for quantitatively predicting hearing thresholds, at least at 0.5 to 4 kHz.

The frequency-domain representation of images is presented in the last chapter. Depending on the problem, either the spatial-domain or frequency-domain processing is advantageous. The interpretation of operations on images is often easier in the frequency domain. For longer filter lengths, frequency-domain processing provides faster processing. Linear filtering operations using a variety of filters are described in the frequency domain.

The massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to −10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.

We describe the main properties of the EQUATOR airborne electromagnetic system and how the system is employed to process data in both time- and frequency domains. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We explain the difference between the two methods on some numerical and field examples. Also, we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 200 m of the Earth. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface.

Representing data residing on a graph as a linear combination of building block signals can enable efficient and insightful visual or statistical analysis of the data, and such representations prove useful as regularizers in signal processing and machine learning tasks. Designing collections of building block signals -- or more formally, dictionaries of atoms -- that specifically account for the underlying graph structure as well as any available representative training signals has been an active area of research over the last decade. In this article, we survey a particular class of dictionaries called localized spectral graph filter frames, whose atoms are created by localizing spectral patterns to different regions of the graph. After showing how this class encompasses a variety of approaches from spectral graph wavelets to graph filter banks, we focus on the two main questions of how to design the spectral filters and how to select the center vertices to which the patterns are localized. Throughout, we emphasize computationally efficient methods that ensure the resulting transforms and their inverses can be applied to data residing on large, sparse graphs. We demonstrate how this class of transform methods can be used in signal processing tasks such as denoising and non-linear approximation, and provide code for readers to experiment with these methods in new application domains.

The conventional signal processing and beam-forming tasks that must be performed in a sonar system are traditionally accomplished by electronic analog and digital means. Conceptually, these signal-processing tasks can be very conveniently accomplished with optical systems, although the required accompanying optical input recording process is usually a practical difficulty. From another point of view, the process of acoustical holography, with optical reconstruction, can be used as a sonar device, i.e., to see objects in water. These two conceptual approaches to sonar have much in common, and are both subject to several fundamental limitations. An important difference between them is the pulse echo-timing ability of conventional sonar, where range information is implied by echo delay, versus the CW nature of a holographic system, where range is obtained by parallactic or focusing effects. Hybrid systems are possible which incorporate the attributes of both approaches.