Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

About theory of extended interaction klystron and drift space in the form of medium with complex permittivity

Purpose of this work is to construct a theory of extended interaction klystron with ordinary distributed resonators, but with a drift space in the form of medium with complex permittivity. Methods. For this, a hybrid of extended interaction klystron and an amplifier with a complex permittivity is considered in the framework of the weak signal approximation. Two types of configurations of a extended interaction klystron were considered: with two and three distributed resonators. For a two-resonator klystron with distributed interaction, two cases are considered: without reflections from the ends of distributed resonators and the case when the input binder is fully matched to the external transmission line, and for the second distributed resonator, the so-called condition of critical coupling of the “hot” resonator with the transmission line is satisfied. For a three-resonator klystron with distributed interaction, the case is considered without reflections from the ends of distributed resonators. Results and conclusion. According to the results of the developed theory of a weak signal in a extended interaction klystron with ordinary distributed resonators and a drift space with a complex dielectric constant, by choosing the parameters, it is possible to achieve a greater gain at a length that is shorter than in a conventional extended interaction klystron, all other things being equal. In addition, the presence of an intermediate distributed resonator makes it possible to increase the gain while maintaining the full length of the device.

End-to-End Monaural Speech Separation with a Deep Complex U-Shaped Network

Conventional time–frequency (TF) domain source separation methods mainly focus on predicting TF-masks or speech spectrums, where complex ideal ratio mask (cIRM) is an effective target for speech enhancement and separation. However, some recent studies employ a real-valued network, such as a general convolutional neural network (CNN) and a recurrent neural network (RNN), to predict a complex-valued mask or a spectrogram target, leading to the unbalanced training results of real and imaginary parts. In this paper, to estimate the complex-valued target more accurately, a novel U-shaped complex network for the complex signal approximation (uCSA) method is proposed. The uCSA is an adaptive front-end time-domain separation method, which tackles the monaural source separation problem in three ways. First, we design and implement a complex U-shaped network architecture comprising well-defined complex-valued encoder and decoder blocks, as well as complex-valued bidirectional Long Short-Term Memory (BLSTM) layers, to process complex-valued operations. Second, the cIRM is the training target of our uCSA method, optimized by signal approximation (SA), which takes advantage of both real and imaginary components of the complex-valued spectrum. Third, we re-formulate STFT and inverse STFT into derivable formats, and the model is trained with the scale-invariant source-to-noise ratio (SI-SNR) loss, achieving end-to-end training of the speech source separation task. Moreover, the proposed uCSA models are evaluated on the WSJ0-2mix datasets, which is a valid corpus commonly used by many supervised speech separation methods. Extensive experimental results indicate that our proposed method obtains state-of-the-art performance on the basis of the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) metrics.

New Orthogonal Transforms for Signal and Image Processing

In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh–Hadamard Transforms. The simplicity of calculating the forward and inverse transforms is one of the important features of the presented approach. The conditions for creating symmetric, orthogonal matrices are defined. It is shown that for the selection of the elements of an orthogonal matrix that meets the given conditions, it is necessary to select only a limited number of elements. The general form of the orthogonal, symmetric matrix having an exponential form is also presented. Orthogonal basis functions based on the created matrices can be used for orthogonal expansion leading to signal approximation. An exponential form of orthogonal, sparse matrices with variable parameters is also created. Various versions of orthogonal transforms related to the created full and sparse matrices are proposed. Fast computation of the presented transforms in comparison to fast algorithms of selected orthogonal transforms is discussed. Possible applications for signal approximation and examples of image spectrum in the considered transform domains are presented.

Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

Radiation instability of a relativistic electron beam moving in a split resonator

In this paper, we considered the radiation instability in a split asymmetric resonator for the relativistic case assuming the space charge of the beam. In the small-signal approximation, expressions for the energy loss by a particle passing through the resonator and for the beam current modulation are obtained. Based on analytical and numerical calculations, it is shown that the symmetric configuration provides the highest growth rate of instability. It is found that with the increase of the initial electron energy, the modulation of the beam current as well as the efficiency of the energy transfer from particles to the electromagnetic field decrease. The increase of the beam density has a positive effect on the radiation instability. The results obtained have to be taken into account when developing generators of electromagnetic radiation or a system for modulating the beam current based on a split resonator.

Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer

In this work, a hybrid procedure for bearing fault identification using a machine learning and adaptive cascade observer is explained. To design an adaptive cascade observer, the normal signal approximation is the first step. Therefore, the fuzzy orthonormal regressive (FOR) technique was developed to approximate the acoustic emission (AE) and vibration (non-stationary and nonlinear) bearing signals in normal conditions. After approximating the normal signal of bearing using the FOR technique, the adaptive cascade observer is modeled in four steps. First, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) is developed. In the second step, to increase the power of uncertaintie rejection (robustness) of the FOR-PIO, the structure procedure is used serially. Next, the fuzzy like observer is selected to increase the accuracy of FOR structure PI observer (FOR-SPIO). Moreover, the adaptive technique is used to develop the reliability of the cascade (fuzzy-structure PI) observer. Additionally to fault identification, the machine-learning algorithm using a support vector machine (SVM) is recommended. The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Based on the results, the average vibration and AE fault diagnosis using the adaptive cascade observer with the SVM fault identifier are 97.8% and 97.65%, respectively.