Hongyan Zhang

This study addresses the challenges of passion fruit visual maturity-class detection in complex natural orchard environments, particularly the missed detections of visually challenging fruit instances caused by background interference, occlusion, and illumination variations. To address this issue, an improved detection model, WEC-YOLO11, is proposed based on YOLO11n. The model incorporates the Efficient Channel Attention (ECA) mechanism to enhance discriminative feature representation while suppressing background interference, introduces the Content-Aware ReAssembly of Features (CARAFE) operator for content-aware upsampling, and adopts the WIoUv3 loss function with a dynamic non-monotonic focusing mechanism for bounding box regression. Across three independent runs, WEC-YOLO11 achieved an average mean average precision (mAP)@0.5 of 86.2% and an average recall of 81.1%, with 2.69 M parameters and 6.5 giga floating-point operations per second (GFLOPs). Compared to YOLO11n, the recall of ripe passion fruit (rpf) increased from 76.5% to 79.9% (+3.4 percentage points), while rpf precision showed a slight decrease from 77.6% to 77.1%, indicating a recall-oriented precision–recall trade-off. The overall recall increased from 79.9% to 81.1% (+1.2 percentage points). Under the tested batch-1 RTX 4090 setting, the measured throughput was 91.6 frames per second (FPS). These results indicate a favorable accuracy–complexity trade-off for passion fruit visual maturity-class detection within the evaluated public orchard dataset.

Aiming at the problem of low accuracy of Doppler frequency offset estimation under the low SNR (Signal-to-Noise Ratio) condition of PMF-FFT acquisition algorithm, we propose an improved PMF-FFT acquisition algorithm based on trigonometric polynomial interpolation. Based on the PMF-FFT acquisition algorithm, the improved algorithm uses the differential coherent accumulation algorithm to process the PMF-FFT output results to improve the SNR of the signal, and uses the trigonometric polynomial interpolation algorithm to improve the Doppler frequency shift estimation accuracy. The simulation results show that the improved PMF-FFT acquisition algorithm effectively improve the SNR and accuracy of Doppler frequency offset estimation, without increasing the number of FFT (Fast Fourier Transform) operation points, and is more suitable for low SNR environments, ensuring the effective acquisition of the receiver loop in the low SNR environment, is of great significance to modern satellite communications.

Traditional fundamental frequency extraction algorithms mainly include cepstrum method, short-time autocorrelation method. Some end-point extraction methods can't correctly select the part of effective value due to the particularity of dialect audio and the noise influence. And, some fundamental frequency extraction methods cannot get the data consistent with the trend of tuning values. Against the above, this paper proposes an improved double threshold method for the extraction of the basic frequency according to the characteristics of the vowels of the initials of the one-word tone, which extracts the continuous vowels of the one-word tone, and looks for the basic frequency extraction algorithm that meets the classification of the tones of one-word. Finally, the effective basic frequency data is obtained. This method is more effective than other methods in speech tone recognition. It is of great significance to the study of speech tone, especially the study of dialect tone.