Real-time musical applications using frequency domain signal processing

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

CHARMM-GUI Membrane Builder for Mixed Bilayers and Its Application to Yeast Membranes

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.

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.<p></p>

It is pointed out that existing backpropagation networks cannot be used for many signal-processing tasks because the signals are often represented with complex values. A complex-valued learning algorithm is derived for a backpropagation network where one or more of the inputs, outputs, and weight factors are complex-valued. The complex valued backpropagation network is tested with exclusive-OR problems for complex inputs, outputs, and weights. The network shows excellent mapping performance for this problem

Evaluation of ultrasonic attenuation in diffuse diseases of spleen and liver

After discussed the difficulty of monitoring transformer fault online, this paper proposes a fault diagnosis scheme based on acoustic wave analysis. The time and frequency domain signal processing is utilized to analyze acoustic signal so that equipments’ running status can be identified and the trend of development be predicted. Firstly, a new scheme of transformer fault diagnosis and trend forecast is designed. Transformer acoustic signal acquisition, noise elimination method in diagnosis system is represented. Secondly, the new algorithm of the signal strangeness detection and trend-based forecasting based on wavelet analysis is put forward. Lastly, the wavelet packet algorithm is utilized to analyze acoustic signal and extract frequency & time-domain features which relative with its development trend, and then the predicting consequence can be use to assess the fault type. The actual measurement test demonstrated that the serial wavelet packet transformation (WPT) algorithms have high availability and feasibility to diagnosis and forecast the transformer faults accuracy.

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.

Dynamic control structures are significant for liquid–liquid extraction combined with heterogeneous azeotropic distillation (LEHAD) and liquid–liquid extraction combined with extractive distillation (LEED) processes, which were proposed in our previous work for the separation of the azeotrope of propylene glycol methyl ether and water. However, there may be complications regarding the dynamic controllability of the two hybrid processes, and this work investigates the dynamic control structures. For the LEHAD process, an improved composition–temperature cascade control structure using the ratio of the reboiler duty to feed flow (QR/F) can realize effective control when feed disturbances are added. For the LEED process, the improved dual temperature control structure with QR/F was able to handle the disturbances well. Moreover, a comparison between the two hybrid processes was made according to the dynamic controllability, and the integral of the squared errors was calculated. The results show that the LEHA...

The proportional-integral-derivative (PID) controller is still the most dominant form of feedback control in use because of its simplicity and efficiency to produce powerful solutions for most cases of industrial processes. However, the PID controller may be inadequate for nonlinear and non-minimum phase systems since the design of the controller is based on constant gain values. On the other hand, many advanced control methods have been proposed to overcome the limitations of the PID controllers. These control methods have highly dynamic structures to minimize the resultant error by optimizing the control signal, but stability is their common problem. This study integrates the classical proportional-derivative (PD) control with the self-tuning adaptive control in order to have a stable and dynamic control structure. The integral part (I) of PID is replaced by explicit self-tuning control to improve the performance of the proposed controller. Some experimental results on a hydraulic robot are presented for validation of the proposed concept of hybrid control.

SUMMARYThis paper addresses the problem of contact force control for multiple contacts distributed over multiple links in a robot. This is of importance when performing complex tasks in unstructured environment, particularly in humanoid robot applications. The proposed multicontact control framework provides a new way of defining the operational space coordinates, which facilitates the specification of multiple contact control. The contact force space on multiple links is constructed as an operational space for the highest priority task. Motion control, given lower priority, can be executed using the rest of degree of freedom within the null-space of the force control. The dynamic control structure, then, provides a means to control each contact force and motion independently. This dynamic decoupling enables each contact force controller to utilize linear control theories. In particular, the contact force controllers adopt full state feedback control and estimation methods to produce robust performance with respect to modeling and parameter uncertainties. The effectiveness of the multiple contact control framework was demonstrated using a PUMA560 manipulator, with multiple contacts on the end-effector and third link. The demonstrated tasks involved controlling each of the contact forces with null-space motion.

The maximum utilization of hydrocracking tail oil becomes increasingly important for petrochemical industry. The aim of this work is to develop optimized distillation processes to achieve various high‐valued qualified oil products from hydrocracking tail oil. Six different oil products is produced and the steady‐state distillation process, which aims to fractionate six qualified narrow distillates is established. The algorithm method incorporating divided‐wall column (DWC) configuration was introduced into the steady‐state design. Compared with traditional separation sequences, the DWC configuration leads to an energy‐saving potential up to 11.17%. Furthermore, effective dynamic control strategies were proposed, demonstrating precise and efficient control performance. In the presence of a 15% feed disturbance, the dynamic control structure is capable of maintaining the product distillation range near the set value. This comprehensive study provides a thorough investigation into the efficient utilization of hydrocracking tail oil, establishing a robust theoretical foundation for its industrial application.

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.

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.