IET Signal Processing最新文献

Sensing, Computing and Communication Integration (SC2) is widely believed as a new enabling technology. A non-negative tensor sparse factorisation (NTSF) algorithm based on tensor analysis is proposed for sensing and classification of Small Geological Structure in coal mines. Utilising this method, advanced detection of geological anomalies hidden in coal seams was achieved. The morphological properties of geological anomalies in coal seams and the propagation characteristics of guided waves were first thoroughly studied. A three-dimensional (3D) medium geometry model was developed for a complicated coal seam with Goaf, collapse column, scouring zone, and tiny fault based on COMSOL Multiphysics. On this model, the third-order tensors data was constructed. Then, the TUCKER-based NTSF algorithm was employed for feature extraction and classification. To achieve multi-dimensional feature, the two-dimensional data in the form of a matrix is collected, and a multiplicative update method is introduced to update the algorithm iteratively. Finally, the Support Vector Machine (SVM) multi-classifier with Gaussian radial basis kernel function is selected for classification of Small Geological Structure. The experimental results show that the classification accuracy based on the NTSF and SVM is as high as 97.33%, which demonstrates that the proposed algorithm is suitable for Sensing and Classification of Small Geological Structure in coal mines.

In this study, a dual-function radar-communications (DFRC) system based on the circulating code array is presented to address the contradiction between radar and communications system in beam scanning and beam coverage. Processed orthogonal frequency-division multiplexing (OFDM) signal is transmitted by the circulating code array as the base signal to improve the data rate. Following the spatial angle of the communication receiver, the communication symbols are modulated to part of OFDM signal subcarriers occupying a specific frequency band. A significant property of the circulating code array, which provides a relationship between the baseband frequency of the base signal and the spatial angles, implements a basis for safe telecommunication transmission towards the cooperative receiver and demodulation. Moreover, the circulating code array transmits the same signal and introduces the same time interval between adjacent array elements. Therefore, the complex problems of multi-dimensional orthogonal signal design in the traditional multiple-input-multiple-output-based DFRC system design are transformed into a simple base signal design. Finally, an omnidirectional coverage pattern is obtained. Thus, whether the communication receiver is in the mainlobe or the sidelobe of the radar beam, the communication connection can be established between the designed DFRC system and the communication users. The performance of the described DFRC system is verified through theoretical analysis and simulations.

Human non-speech sounds occur during expressions in a real-life environment. Realising a person's incapability to prompt confident expressions by non-speech sounds may assist in identifying premature disorder in medical applications. A novel dataset named Nonspeech7k is introduced that contains a diverse set of human non-speech sounds, such as the sounds of breathing, coughing, crying, laughing, screaming, sneezing, and yawning. The authors then conduct a variety of classification experiments with end-to-end deep convolutional neural networks (CNN) to show the performance of the dataset. First, a set of typical deep classifiers are used to verify the reliability and validity of Nonspeech7k. Involved CNN models include 1D-2D deep CNN EnvNet, deep stack CNN M11, deep stack CNN M18, intense residual block CNN ResNet34, modified M11 named M12, and the authors’ baseline model. Among these, M12 achieves the highest accuracy of 79%. Second, to verify the heterogeneity of Nonspeech7k with respect to two typical datasets, FSD50K and VocalSound, the authors design a series of experiments to analyse the classification performance of deep neural network classifier M12 by using FSD50K, FSD50K + Nonspeech7k, VocalSound, VocalSound + Nonspeech7k as training data, respectively. Experimental results show that the classifier trained with existing datasets mixed with Nonspeech7k achieves the highest accuracy improvement of 15.7% compared to that without Nonspeech7k mixed. Nonspeech7k is 100% annotated, completely checked, and free of noise. It is available at https://doi.org/10.5281/zenodo.6967442.

Electrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal is a weak bioelectrical signal and is easily disturbed by baseline wander, powerline interference, and muscle artefacts, which make detection of heart diseases more difficult. Therefore, it is very important to denoise the contaminated ECG signal in practical application. In this article, an effective ECG segments denoising method combining the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) is designed. The ECG signal is decomposed using the EEMD for the first time, and then the highest frequency component is decomposed by the EMD for the second time, and the high frequency components obtained from the second time are decomposed and reconstructed by the WP for the third time. Finally, the processed signal components are fused to obtain the denoised ECG signal. Furthermore, the signal-to-noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalised cross correlation coefficient (R) are used to evaluate the noise reduction algorithm. The mean SNR, MSE, RMSE, and R are 5.7427, 0.0071, 0.0551, and 0.9050 in the China Physiological Signal Challenge 2018 dataset. Compared with others denoising methods, the experimental results not only exhibit that the SNR of the ECG signal is effectively improved, but also show that the details of the ECG signal are fully retained, laying a solid foundation for the automatic detection of ECG segments.

Direction of arrival (DOA) and time difference of arrival (TDOA) hybrid localisation is an effective localisation technique. Station position errors affect localisation performance. Owing to the highly non-linear nature of the problem, there are few methods of DOA/TDOA hybrid localisation in the presence of station position errors. Hence, an iterative constrained weighted least squares (ICWLS) algorithm is proposed to estimate locations of multiple targets and stations for DOA/TDOA hybrid localisation with station position errors. To ensure convergence to the global optimal solution, non-convex equality constraints are approximated to linear constraints during each iteration. The weighted averaging strategy using the results of the previous iteration is used to reduce the number of iterations. Theoretical analysis and simulation results show that the ICWLS can reach the Cramér–Rao lower bound. Additionally, the performance of multiple targets is better than that of a single target. The simulation results show that the ICWLS algorithm has higher accuracy than other methods and higher localisation accuracy can be maintained when the observation stations are under an ill-conditioned geometry.

One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision-making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R²), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R² varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.