IET Signal Processing最新文献

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.

With the increase of large shopping malls, there are many large parking spaces in complex environments, which increases the difficulty of finding vehicles in such environments. To upgrade the consumer's experience, some car manufacturers have proposed detecting parking space numbers in parking spaces. The detection of parking space number in parking spaces in complex environments has problems such as the diversified background of parking space numbers, tilted direction of parking space numbers, and small parking space number scale. Since no scholar has proposed a high-performance method for such problems, a parking space number detection model based on the multi-branch convolutional attention is presented. Firstly, using ResNet50 as the backbone network, a multi-branch convolutional structure is proposed in the backbone network, which aims to process and fuse the feature map through three parallel branches, and enhance the network to represent ability information by convolutional attention, learn global features to selectively strengthen the features containing helpful information, and improve the ability of the model to detect the parking space number area. Secondly, a high-level feature enhancement unit is designed to adjust the features channel by channel, obtain more spatial correlation, and reduce the loss of information in the process of feature map generation. The data results of the model on the parking space number dataset CCAG show that the precision, recall, and F-measure are 84.8%, 84.6%, and 84.7%, respectively, which has certain advantages for parking space number detection.

Schizophrenia is a disease that affects approximately 1% of the population. Its early accurate diagnosis is of vital importance to apply adequate therapy as soon as possible. We present a Statistical Discriminant Diagnosing (SDD) system that discriminates between healthy controls and subjects and that supports diagnosis by a medical professional. The system works with {feature, electrode} EEG pairs which are selected based on the statistical significance of the p-values computed over the brain P3b wave. A bank of evoked potential pre-processed and filtered EEG signals is recorded during an auditory odd-ball (AOD) task and serves as input to the SDD system. These EEG signals comprise 20 features and 17 electrodes, both in time (t) and frequency (f) domain. The relevance of the Parieto-Temporal region is shown, allowing us to identify highly discriminant {feature, electrode} pairs in the detection of schizophrenia, resulting lower p-values in both Right and Left Hemispheres, as well as in Parieto-Temporal EEG signals. See for instance, the {PSE, P4} pair, with p-value = 0.00003 for (parametric) t Student and p-value = 0.00019 for (nonparametric) U Mann-Whitney tests, both under the 15 Hz cutoff frequency of a low pass EEG preprocessing filter. The relevance of this pair is in agreement with previously published related results. The proposed SDD system may provide the human expert (psychiatrist) with an objective complimentary information to help in the early diagnosis of schizophrenia.

In this paper, a new sparsity-optimised Farrow structure variable fractional delay (SFS-VFD) filter is proposed to address the aperture effect in wideband array. Our method is based on coefficient (anti-)symmetry and optimises the number and orders of its sub-filters, greatly reducing the non-zero coefficients. The established cost function is formulated as a parametric minimisation problem with multiple regularisation constraints, and solved by the modified three-block alternating direction multiplier method (MTB-ADMM), which is improved by introducing core variable correction items to ensure stable and fast convergence. Experimental results show that the SFS-VFD filter reduces the complexity of the system by decreasing the use of multipliers and adders while ensuring high delay accuracy. In wideband array, the SFS-VFD filter effectively corrects the aperture effect and achieves precise beam pointing.

With a focus on integrated sensing, communication, and computation (ISCC) systems, multiple sensor devices collect information of different objects and upload it to data processing servers for fusion. Appearance gaps in composite images caused by distinct capture conditions can degrade the visual quality and affect the accuracy of other image processing and analysis results. The authors propose a fused-image harmonisation method that aims to eliminate appearance gaps among different objects. First, the authors modify a lightweight image harmonisation backbone and combined it with a pretrained segmentation model, in which the extracted semantic features were fed to both the encoder and decoder. Then the authors implement a semantic-related background-to-foreground style transfer by leveraging spatial separation adaptive instance normalisation (SAIN). To better preserve the input semantic information, the authors design a simple and effective semantic-aware adaptive denormalisation (SADE) module. Experimental results demonstrate that the authors’ proposed method achieves competitive performance on the iHarmony4 dataset and benefits from the harmonisation of fused images with incompatible appearance gaps.

The range and azimuth information of a target can be obtained after coherent pulse accumulation of the traditional multiframe stepped-frequency (SF) synthesis wideband echo and spectrum analysis, and high-resolution two-dimensional imaging of the target can be achieved. However, the accumulation of a certain number of pulses requires a long beam dwell time, which cannot meet real-time imaging requirements for high-speed radar moving platforms. To solve the above problems, a scanning imaging mode is proposed by combining forward-looking imaging and scanning imaging, and a target echo signal model with the structure of scanning stepped-frequency is constructed. The SF pulses are grouped and transmitted according to the scanning order, and the echo pulses are sorted and reorganised. After the timing compensation and range Doppler coupling compensation are completed, the target is located and projected. The proposed imaging mode can achieve high-resolution scanning forward-looking imaging and can basically attain an azimuth resolution of approximately 0.1° within the forward-looking scanning range. This imaging mode has higher real-time performance and a larger target imaging range than the traditional methods. Moreover, the simulation results showed good performance via the scanning imaging method.