8 results about "Capture effect" patented technology

Systems and methods for compensating a transmit signal for charge trapping effects of a power amplifier are provided. In certain embodiments, a non-linear filter is trained based on time aligning a first set of observations taken from digital transmit data prior to conversion to a radio frequency transmit signal, and a second set of observations taken from an output of a power amplifier that amplifies the radio frequency transmit signal. In certain implementations, the first set of observations and the second set of observations are obtained without decimation. Rather, decimation is provided after timing alignment. By implementing the DPD system in this manner, signal data is not lost by decimation and more accurate timing alignment between the sets of observations is achieved.

The invention relates to an apparatus (Rx) for increasing an integrity of signals (S) in a signaling network, each signal comprising a sequence of one or more signal elements. The apparatus comprises an identification unit configured to identify any injection (I) of signal elements into a received signal based on at least one of a) a signal element magnitude variation, b) an inversion of a permutation operation applied to the received signal upon transmission, c) a signal fingerprint, d) a location of the transmitter, and e) an AI / ML, Artificial Intelligence / Machine Learning, model. This allows for a reliable identification of injections of signal elements that are due to the capture effect. In particular, not only overshadow attacks, but also undershadow attacks or other injection attacks can be identified. By processing the received signals based on any identified injections of signal elements, the integrity of signals in the network can be increased.

The invention relates to a concrete filled steel tube defect detection method and system based on an audio signal of a time-frequency domain, and the method comprises the steps: collecting audio time sequence data of the surface of concrete filled steel tube through an acoustic sensor array, and then extracting a time-domain fusion audio feature and a frequency-domain fusion audio feature from the audio time sequence data of the concrete filled steel tube; based on a time domain feature processing model, carrying out dual feature analysis of different domains on the time domain fusion audio features and a frequency domain feature processing model, and capturing more defect features in audio feature maps of different domains; the feature processing model is composed of a plurality of cascaded convolutional long and short-term memory networks, the features of the audio signals can be extracted layer by layer, and the robustness of long sequence signals is improved; the time-frequency domain features extracted by the feature processing model are fused based on the attention module, the feature attention is enhanced, the feature expression ability is improved, the defect feature capture effect is improved, and the defect detection accuracy is improved.

The invention discloses a method for realizing signal acquisition and self-adaptive tracking based on a phased array, which is particularly suitable for a communication system, realizes accurate acquisition and real-time tracking of signals through wide and narrow beam conversion, and mainly comprises three parts of wide beam scanning, wide and narrow beam accurate acquisition and self-adaptive tracking. The phased-array antenna firstly performs preliminary signal scanning in a wide-beam mode to quickly cover a large-range area so as to capture potential target signals, and once the target signals are captured, the phased-array antenna is switched to a narrow-beam mode so as to improve the signal capture precision and the signal-to-noise ratio. Meanwhile, in the capturing process, the beam direction and shape of the phased-array antenna are adjusted in real time, target signals are continuously tracked, and the capturing effect is optimized. According to the invention, the practical problems of low signal acquisition precision, poor tracking real-time performance, easy interruption of a communication link and the like in a complex communication scene are solved.

This invention relates to a multi-functional gimbal stabilizer and its control method, designed to address the technical problems of existing similar products having relatively simple structure and function, large size, inconvenient control methods, and poor automatic mode switching and capture effect. The gimbal stabilizer includes a wireless remote control. It is a single-axis stabilizer, with a motor mount and motor at the bottom of its base. The key feature is that the motor shaft has screw holes at its bottom, protruding from the motor mount, and the motor mount and motor are integrally rotated. A handle holder is located at one end of the handle frame, with one bottom center of the handle holder connected to the handle frame, and a handle screw at the top center of the other end of the handle holder. The stabilizer operates when the gyroscope detects that the PCB board is within a 45-degree horizontal angle range or when the button is switched to follow-up gimbal mode; or when the gyroscope detects that the PCB board is outside a 45-degree horizontal angle range or when the button is switched to stabilizer mode.

The present application discloses a capture method, system and storage medium for CDMA signals, and relates to the field of signal processing technology. The above method includes: respectively delaying the conjugate multiplication to process the signal to be processed and the local code to obtain the conjugated signal and the conjugated local code; based on the sliding window algorithm, coherently integrating the signal segment of the conjugated signal selected by the integration window and the conjugated local code in the conjugated signal to obtain multiple integration results; determining the peak-to-average ratio of the corresponding signal segments of the multiple integration results in the signal to be processed, and when the peak-to-average ratio is greater than the first preset peak-to-average ratio threshold, selecting the position of the maximum integration result in the signal to be processed from the multiple integration results as the secondary processing data position; traversing and searching the secondary processing data segment according to the preset frequency search window to capture the peak signal of the secondary processing data segment. The above method can reduce the resource consumption of low-orbit satellite CDMA signal capture while ensuring the capture effect.

This application relates to a method and system for detecting defects in steel-concrete composite pipes based on time-frequency domain audio signals. The method acquires time-series audio data from the surface of the steel-concrete composite pipe using an acoustic sensor array. Then, it extracts time-domain fused audio features and frequency-domain fused audio features from the audio time-series data. Based on a time-domain feature processing model, it performs dual feature analysis on the time-domain fused audio features, and also on the frequency-domain fused audio features using a frequency-domain feature processing model, capturing more defect features from the audio feature maps in different domains. Furthermore, the feature processing model is composed of multiple cascaded convolutional long short-term memory networks, enabling layer-by-layer feature extraction of audio signals and improving robustness to long-sequence signals. The time-frequency domain features extracted by the feature processing model are fused based on an attention module, and feature attention enhancement is applied to improve the expressive power of the features, enhance the defect feature capture effect, and improve the accuracy of defect detection.

A system and method for detecting and mitigating jamming attacks in low-power wide-area networks (LPWAN) uses machine learning techniques. The system collects network performance data including packet loss ratios and received signal strength indicators from LoRaWAN sensor nodes. A Long Short-Term Memory (LSTM) neural network analyzes temporal patterns in the data to distinguish malicious jamming from normal network congestion. Upon detecting jamming conditions, the system implements automated mitigation through a cloud-based framework that coordinates random channel selection and network reconfiguration. The system leverages LoRaWAN's inherent “Capture Effect” characteristics while providing additional protection through dynamic frequency adjustment. Performance metrics demonstrate 98% accuracy in jamming detection with a 0.5% false positive rate. The architecture enables continuous monitoring and automated response to maintain network integrity while preserving the power efficiency benefits of LPWAN systems.