Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.

An improved centroid extraction algorithm for autonomous star sensor is proposed in this study, which focuses on the improvements of the location accuracy of stars and the speed of the centroid extraction. First, the coarse positioning of stars is carried out to achieve the dispersive regions of the stars quickly. Then the stars pixels are chosen by the automatic seeded region growing algorithm. Subsequently, in order to restrain the interference of noise, the grey values of the stars pixels are modified according to the characteristics of the star energy distribution. Finally, the fine positioning of the star can be achieved using the proposed centroid calculation formula. Experimental results show that the proposed algorithm has high‐positioning accuracy and good noise resistant ability compared with the other two centroid extraction algorithms. Moreover, the computational complexity of the proposed algorithm is lower than that of the other two algorithms.

This study proposes a novel rolling bearing fault diagnosis technique based on a synchrosqueezing wavelet transform (SWT) and a transfer residual convolutional neural network (TRCNN) designed to address the difficulties of feature extraction caused by the non-stationarity of fault signals, as well as the issue of low fault diagnosis accuracy resulting from small sample quantities. This approach transforms the one-dimensional vibration signal into time–frequency diagrams using an SWT based on complex Morlet wavelet basis functions, which redistributes (squeezes) the values of the wavelet coefficients at different localized points in a time–frequency plane to the estimated instantaneous frequencies. This allows the energy to be more fully concentrated in actual corresponding frequency components. This strategy improves both the time–frequency aggregation and the resolution, which better reflects the eigenvalues of non-stationary signals. In this process, transfer learning and a residual structure are used in the training of a convolutional neural network. The resulting time–frequency diagrams, acquired using the steps discussed above, are then input to the TRCNN for diagnosis. A series of validation experiments confirmed that applying the TRCNN structure made it possible to achieve high diagnostic accuracy, even when training the network with only a small number of fault samples, as all 12 fault types from the test dataset were diagnosed correctly. Further simulation experiments demonstrated that our proposed method improved fault diagnosis accuracy compared to that of conventional techniques (with increases of 1.74% over RCNN, 1.28% over TCNN, 1.62% over STFT, 1.73% over WT, 2.83% over PWVD, and 1.39% over STFA-PD). In addition, diagnostic accuracy reached 100% during the application of three-time transfer learning, validating the effectiveness of the proposed method for rolling bearing fault diagnosis.

Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg–Marquardt (LM) method iteratively. Its performance including Cramér–Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances.

The improved dual-layer mushroom (IDLM) and back-to-back E-shaped stubs for mutual coupling reduction between microstrip patch antennas are presented in this paper. The IDLM unit consists of one upper layer complementary split-ring resonator lattice and four lower layer lattices, whose centers are connected to the ground by a pin. The decoupling structure can prevent the surface current from one antenna port to another, so as to improve the isolation between the antennas. The proposed antenna works in the open wireless communication band of 2.45GHz. Using the proposed decoupling structure, a low mutual coupling level ranging from 27 to 40dB is obtained when the center distance of the adjacent patches is 0.5λ₀. The total size of the decoupling antenna is 99×41×2.4mm³ with a frequency range of 2.422.48GHz for S₁₁<10dB. The proposed decoupling structure can also improve the average gain and efficiency of the antenna by 0.1dB and 5, respectively. The antenna is studied from the aspects of isolation, return loss, current and electric field distribution, radiation pattern, and diversity performance. The designed decoupling antenna is fabricated and measured. The pattern, isolation, and return loss of the tested results show good consistence with the simulation results. The diversity gain and envelop correlation coefficient of the diversity performance show that the designed antenna can be used in MIMO or Rx/Tx systems.

In small sample scenarios where rotating machinery signals are heavily obscured by noise, complicating feature extraction and reducing model learn efficiency, this paper proposes an optimized LSTM-BiTCN parallel network for anomalous sound detection of rotating machinery. Firstly, fast Fourier transform is used to extract spectral characteristics, while VMD captures multi-scale temporal characteristics, mitigating key feature loss. Secondly, LSTM networks and BiTCN are utilized to construct and optimize a parallel network model, which simultaneously extracts the temporal and spatial information from the rotating machinery’s sound signals to enhance feature extraction efficiency. Then, a cross-attention mechanism is used to build temporal-spatial feature, enhancing the network model’s focus on important features; finally, the temporal-spatial features are reduced in dimensionality, and the detection results are obtained through the sigmoid function in the output layer. Experiments show that under strong noise and small sample conditions, the proposed method outperforms the DCASE2022 Task2 winning system, with an 16.2% increase in F1-score and a 0.7% boost in AUC. It also surpasses other models, including the pre-trained model and transformer and dynamic graph convolution (Unsuper-TDGCN).