Adaptive digital shaping of nuclear pulse based on real-time tracking of system transfer function and xLSTM.

In the design of filter shaping circuits for nuclear pulse signals, inverting filter shaping circuits perform better than non-inverting filter shaping circuits. Because these circuits facilitate changing the phase of a pulse signal, they are widely used in processing nuclear pulse signals. In this study, the transfer functions of four types of inverting filter shaping circuits, namely the common inverting filter shaping, improved inverting filter shaping, multiple feedback low-pass filter shaping, and third-order multiple feedback low-pass filter shaping, in the Laplacian domain, are derived. We establish the numerical recursive function models and digitalize the four circuits, obtain the transfer functions in the Z domain, and analyze the filter performance and amplitude–frequency response characteristics in the frequency domain. Based on the actual nuclear pulse signal of the Si-PIN detector, we realize four types of inverting digital shaping. The results show that under the same shaping parameters, the common inverting digital shaping has better amplitude extraction characteristics, the third-order multiple feedback low-pass digital shaping has better noise suppression performance, and the multiple feedback digital shaping takes into account both pulse amplitude extraction and noise suppression performance.

Design and characterization of third-order Sallen–Key digital filter in nuclear signal processing

A long short-term memory (LSTM) neural network has excellent learning ability applicable to time series of nuclear pulse signals. It can accurately estimate parameters associated with amplitude, time, and so on, in digitally shaped nuclear pulse signals—especially signals from overlapping pulses. By learning the mapping relationship between Gaussian overlapping pulses after digital shaping and exponential pulses before shaping, the shaping parameters of the overlapping exponential nuclear pulses can be estimated using the LSTM model. Firstly, the Gaussian overlapping nuclear pulse (ONP) parameters which need to be estimated received Gaussian digital shaping treatment, after superposition by multiple exponential nuclear pulses. Secondly, a dataset containing multiple samples was produced, each containing a sequence of sample values from Gaussian ONP, after digital shaping, and a set of shaping parameters from exponential pulses before digital shaping. Thirdly, the Training Set in the dataset was used to train the LSTM model. From these datasets, the values sampled from the Gaussian ONP were used as the input data for the LSTM model, and the pulse parameters estimated by the current LSTM model were calculated by forward propagation. Next, the loss function was used to calculate the loss value between the network-estimated pulse parameters and the actual pulse parameters. Then, a gradient-based optimization algorithm was applied, to feedback the loss value and the gradient of the loss function to the neural network, to update the weight of the LSTM model, thereby achieving the purpose of training the network. Finally, the sampled value of the Gaussian ONP for which the shaping parameters needed to be estimated was used as the input data for the LSTM model. After this, the LSTM model produced the required nuclear pulse parameter set. In summary, experimental results showed that the proposed method overcame the defect of local convergence encountered in traditional methods and could accurately extract parameters from multiple, severely overlapping Gaussian pulses, to achieve optimal estimation of nuclear pulse parameters in the global sense. These results support the conclusion that this is a good method for estimating nuclear pulse parameters.

The optical transfer function (OTF) can be used to give the first order performance characteristics of space telescope systems as a function of cost. Using the effective transfer functions of the optical system, instruments, sensors and communication system, a total system transfer function can be defined. Utilizing the properties of the transfer function, specific quality criteria are applied that are measures of certain astro-nomical objectives; such as imaging, photometry, spectroscopy, etc. The specific criteria are then related to a dominant independent variable which is a measure of the scientific objectives such as spatial resolution, at a given contrast level, for imaging and the amount of energy passing through a slit for spectroscopy. These measures can be related to a set of basic astronomical observations that are to be made by that particular telescope. Since nearly every sub-system has a cost-accuracy relationship, we can evaluate changes in subsystems to their corresponding impact on astronomical observations through changes in the transfer function of the system and the quality criteria.

Transfer functions of solar heating systems for dynamic analysis and control design

Three-dimensional (3D) transfer functions build the basis for a comprehensive characterization of optical imaging systems in the spatial frequency domain. Utilizing the projection-slice theorem, the 2D modulation transfer function of an incoherent imaging system can be derived from a 3D transfer function by integration with respect to the axial spatial frequency. For a diffraction limited microscope with homogeneous incoherent pupil illumination, the modulation transfer function equals the 2D autocorrelation function of a circular disc. However, until now to the best of our knowledge no 3D transfer function has been published, which exactly leads to the 2D modulation transfer function of a diffraction limited microscope in reflection mode. In this article, we derive a formula, which after integration with respect to the axial spatial frequency coordinate perfectly fits to the diffraction limited 2D modulation transfer function. The inverse three-dimensional Fourier transform of the 3D transfer function results in a complex-valued 3D point spread function, from which the depth of field, the lateral resolution and, in addition, the corresponding 3D point spread function of both, a conventional and an interference microscope, can beobtained.

Purpose Single-period segmentation is one of the important steps in time-domain analysis of pulse signals, which is the basis of time-domain feature extraction. The existing single-period segmentation methods have the disadvantages of generalization, reliability, and robustness. Method This paper proposed a period segmentation method of pulse signals based on long short-term memory (LSTM) network. The preprocessing was performed to remove noises and baseline drift of pulse signals. Thus, LabelMe was used to label each period of the pulse signals into two parts according to the location of the starting point of main wave and the dicrotic notch, and the dataset of the pulse signal period segmentation was established. Consequently, the labeled dataset was input into the LSTM for training and testing, and the results were compared with sum slope function method. Result The remarkable result with the whole period segmentation accuracy of 92.8% was achieved for the segmentation of seven types of pulse signals. And the segmentation accuracies of the systolic phase, diastolic phase, and whole period using this method were higher than those of the sum slope function method. Conclusion LSTM-based pulse signal segmentation method can achieve outstanding, robust, and reliable segmentation effects of the systolic phase, diastolic phase, and whole period of pulse signals. The research provides a new idea and method for the segmentation of pulse signals.

Concerns the problem of pole-zero representation of linear time-invariant generalized state space or descriptor systems described by Edx(t)/dt=Ax(t)+bu(t), y(t)=cx(t)+du(t) where x(t)/spl isin/R/sup n/, u(t), y(t)/spl isin/R and det(/spl lambda/E-A)/spl ne/0, i.e., the pencil (/spl lambda/E-A) is regular. The transfer function of this system is G(X)=c(/spl lambda/E-A)/sub -/1b+d. If the descriptor matrix E has full rank, the system is nonsingular, otherwise it is singular. If the system is nonsingular, theoretically, we can obtain an equivalent state space realization by premultiplying the state equation by E-1. Once we have this 4-tuple, we can easily obtain its pole-zero representation. However, obtaining this by first computing the transfer function can be numerically quite sensitive. A small perturbation in coefficient of the transfer function can lead to significant loss of accuracy in numerical computation of poles and/or zeros. A pole-zero representation algorithm was proposed by Varga (1989). The present approach has several features that make it more efficient and reliable. >

A general treatment of the transfer function (TF) of spectroscopic systems is presented. The TF is expressed as a function of the delay time but not of the spatial frequency. Second, a new method for measuring the TF of spectroscopic systems with a sinusoidally modulated spectrum (SMS) is proposed, and the TF of a prism spectroscope is measured to verify the effectiveness of the method. As this new method is similar to methods of measuring the TF of ordinary imaging systems by using a spatial sine grating, the TF of spectroscopic systems is obtained from measurements of the contrast in the spectrogram of the SMS. This SMS is produced by means of a Michelson interferometer with white light. The period of the SMS can be varied by changing the path difference between two reflecting mirrors of the interferometer.

According to the wavefront filtering idea of wavelet optics, the transfer function of an optical system is described with a wavelet scale function. In the transfer function described with a wavelet scale function, different scale parameters a,c and shift parameters b,d correspond to different subtransfer functions, which correspond to different situations of the optical system. According to the request of the optical system, by adjusting all these scale parameters, not only can we obtain the optical images under different conditions, but we can also obtain the singular points under this scale parameter; hence a more ideal output can be obtained by such processing. The transfer function described with a wavelet scale function can be adjusted according to the request of the optical system, which makes the described transfer function self-adjustable. According to all types of disturbing effects to the system, by adjusting the scale and shift parameters, the practical form of the transfer function of an optical system can be confirmed, which satisfies the request of the self-adjustability of the optical imaging system. The result of our analysis shows that describing the transfer function of an optical system with a wavelet scale function is not only feasible but also satisfies the request of the self-adjustability of the optical imaging system, and different optical systems can be described by different wavelet scale parameters. This work breaks from the formal additional describing mode of the transfer function of an optical system and makes description of the transfer function of an optical system convenient.

The Long Short-Term Memory neural network (LSTM) has excellent learning ability for the time series of the nuclear pulse signal. It can accurately estimate the parameters (such as amplitude, time constant, etc.) of the digitally shaped nuclear pulse signal (especially the overlapping pulse signal). However, due to the large number of pulse sequences, the direct use of these sequences as samples to train the LSTM increases the complexity of the network, resulting in a lower training efficiency of the model. The convolution neural network (CNN) can effectively extract the sequence samples by using its unique convolution kernel structure, thus greatly reducing the number of sequence samples. Therefore, the CNN-LSTM deep neural network is used to estimate the parameters of overlapping pulse signals after digital trapezoidal shaping of exponential signals. Firstly, the estimation of the trapezoidal overlapping nuclear pulse is considered to be obtained after the superposition of multiple exponential nuclear pulses followed by trapezoidal shaping. Then, a data set containing multiple samples is set up; each sample is composed of the sequence of sampling values of the trapezoidal overlapping nuclear pulse and the set of shaping parameters of the exponential pulse before digital shaping. Secondly, the CNN is used to extract the abstract features of the training set in these samples, and then these abstract features are applied to the training of the LSTM model. In the training process, the pulse parameter set estimated by the present neural network is calculated by forward propagation. Thirdly, the loss function is used to calculate the loss value between the estimated pulse parameter set and the actual pulse parameter set. Finally, a gradient-based optimization algorithm is applied to update the weight by getting back the loss value together with the gradient of the loss function to the network, so as to realize the purpose of training the network. After model training was completed, the sampled values of the trapezoidal overlapping nuclear pulse were used as input to the CNN-LSTM model to obtain the required parameter set from the output of the CNN-LSTM model. The experimental results show that this method can effectively overcome the shortcomings of local convergence of traditional methods and greatly save the time of model training. At the same time, it can accurately estimate multiple trapezoidal overlapping pulses due to the wide width of the flat top, thus realizing the optimal estimation of nuclear pulse parameters in a global sense, which is a good pulse parameter estimation method.

This paper addresses fully-online always-adaptation of a transfer function for robot audition systems based on microphone array processing. The transfer function represents signal propagation characteristics between a microphone and a sound source, which provides essential information for real-world scene analysis, such as sound source localization and separation for robots. Although it is commonly defined as a stationary function, it should be considered together with room acoustics and their environmental changes for practical use, that is, it should be defined as a dynamically-changing function. To fulfill this requirement, we propose a fully-online always-adaptation method for a transfer function, by continuously estimating the transfer function from the observed signals in a passive manner, while performing sound source localization and separation. The proposed method was implemented on open source robot audition software HARK as modules which works online. These modules are applied to sound source localization and separation which are primary functions in robot audition. Experimental results showed that the proposed method successfully adapted to an office environment and improved the performance of sound source localization and separation at a close level to the transfer function recorded in the room.

Nonlinear analysis of spatial vision using first-and-second-order volterra transfer functions measurement

Wind power forecast based on improved Long Short Term Memory network

In EMC studies one often wishes to determine the effect of a pulsed RF signal on a receiver which is tuned off the signal center frequency. Measurements of pulsed spectra are made by means of spectrum analyzers and field intensity meters. However, there exists some confusion about the shape of the resultant pulse at the output of the IF amplifier of a receiver which is off-tuned from the pulsed signal. In order to properly interpret the effects of pulsed signals on receivers and to interpret the readings of spectrum analyzers and field intensity meters, it is necessary to determine the envelope of the waveform at the input to the detector. These waveforms can be calculated by convolving the input time waveform of the receiving device with the time domain transfer function of the receiver up to the detector. However, it is extremely difficult to obtain the mathematical form of the time domain transfer function of a receiver. However, use is made of the relationship that the LaPlace or Fourier transform of convolution is equal to the frequency domain multiplication of signal and transfer function. A computer program for obtaining the Fourier transform of most time waveforms including RF pulses has been written. The program integrates the time waveform by straight line segments between the sampled points on the time waveform. The frequency domain transfer function of cascaded tuned amplifiers is used to simulate the receiver.