The superheterodyne receiver is a typical device widely used in electronics and information systems. Thus effective performance assessment and prediction for superheterodyne receiver are necessary for its preventative maintenance. A scheme of performance assessment and prediction based on Mahalanobis distance and time sequence analysis is proposed in this paper. First, a state observer based on radial basis function (RBF) neural network is designed to monitor the superheterodyne receiver and generate the estimated output. The residual error can be calculated by the actual and estimated output. Second, time-domain features of the residual error are then extracted; after that, the Mahalanobis distance measurement is utilized to obtain the health confidence value which represents the performance assessment result of the most recent state. Furthermore, an Elman neural network based time sequence analysis approach is adopted to forecast the future performance of the superheterodyne receiver system. The results of simulation experiments demonstrate the robustness and effectiveness of the proposed performance assessment and prediction method.

A superheterodyne receiver is a type of device universally used in a variety of electronics and information systems. Fault detection and diagnosis for superheterodyne receivers are therefore of critical importance, especially in noise environments. A general purpose fault detection and diagnosis scheme based on observers and residual error analysis was proposed in this study. In the scheme, two generalized regression neural networks (GRNNs) are utilized for fault detection, with one as an observer and the other as an adaptive threshold generator; faults are detected by comparing the residual error and the threshold. Then, time and frequency domain features are extracted from the residual error for diagnosis. A probabilistic neural network (PNN) acts as a classifier to realize the fault diagnosis. Finally, to mimic electromagnetic environments with noise interference, simulation model under different fault conditions with noise interferences is established to test the effectiveness and robustness of the proposed fault detection and diagnosis scheme. Results of the simulation experiments proved that the presented method is effective and robust in simulated electromagnetic environments.

This paper presents a novel deep learning-based architecture designed to improve multi-target tracking for UAV fire control systems. The proposed approach integrates a ResNet-50-based Convolutional Neural Network (CNN) for robust feature extraction, a Long Short-Term Memory (LSTM) network for maintaining temporal consistency, and a re-identification module to manage occlusions effectively. These components work together to enhance tracking accuracy, robustness, and real-time performance. The architecture was evaluated on the UAV123 and MOT17 datasets, where it achieved a 15% increase in mean Average Precision (mAP) and a 10% reduction in ID switch rates compared to state-of-the-art methods such as YOLOv4-DeepSORT and Faster R-CNN. The results indicate that the model is highly effective in dynamic, real-world UAV tracking scenarios.

This paper presents a vibration monitoring node integrating the CC2630 wireless communication chip with the ADXL357 MEMS sensor for structural health monitoring (SHM) applications. Leveraging the CC2630’s dual-core architecture, the design simplifies development while ensuring efficient vibration signal acquisition, wireless communication, and low power consumption. The system supports real-time monitoring of structural vibration signatures at critical points, enhancing smart structure technologies and digital engineering approaches. Nodes can be deployed for online data transmission to a host or offline storage on a memory card, offering deployment flexibility. Simulation experiments using a 2kHz sine wave excitation validated the node’s ability to accurately capture high-frequency vibration signals, with spectral analysis confirming alignment between collected and excitation frequencies and a signal-to-noise ratio of 62 dB. This solution addresses the need for intelligent, scalable SHM systems in civil engineering, supporting infrastructure safety, structural dynamics analysis, and disaster prevention through distributed wireless monitoring, paving the way for advanced digital twin implementations.

To solve the problem of displacement survey fleet aircraft pylon and launcher, universal testing equipment is designed. The system is based on industrial computer architecture, the system through the software to control signal analog circuit with accuracy, emission, such as excitation signal, transmission device by signal feedback to complete various suspension test of the internal circuit. It has been proved by use that the test based on IPC is versatile, the operation interface is clear and easy to be used. The use of the test improved the test efficiency and the maintenance reliability, meet the requirement testing of the pylon/launcher.