Interference Signal Research Articles

Research on modulation aberration numerical correction method of interference signals for high aspect ratio samples utilizing coherence scanning interferometry

Research on modulation aberration numerical correction method of interference signals for high aspect ratio samples utilizing coherence scanning interferometry

Band Correlation‐Based Multichannel Multiscale Convolution Network for Intelligent Interference Recognition

AbstractIn recent years, with the development and extensive application of wireless communication technology, the communication system should have stronger anti‐Jamming ability. Therefore, interference recognition is particularly important as a prerequisite for anti‐interference. However, the existing traditional and intelligent interference recognition algorithms have problems such as complicated feature extraction and low recognition accuracy under low interference‐to‐noise ratio. In order to solve the above problems, this paper introduces parallel multi‐channel multi‐scale convolution to improve the speed and accuracy of network recognition. In addition, combined with frequency band correlation and long‐short‐term memory network (LSTM), an innovative wireless communication interference identification model based on frequency band correlation is proposed, which uses LSTM to detect the frequency band correlation of interference signals and improve the accuracy of interference identification under low Jamming noise ratio (JNR). Experiments prove that the model proposed in this article has faster recognition speed and better generalization. The introduction of frequency band correlation increases the recognition accuracy to more than 99% with low JNR. Therefore, the model proposed in this paper is an effective and available model in complex electromagnetic environments. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Decision Feedback Equalization-Based Low-Complexity Interference Cancellation and Signal Detection Technique Based for Non-Orthogonal Signaling

FTN signalling is an effective communication method that achieves a high spectral efficiency. However, employing a symbol rate faster than the Nyquist rate disrupts the orthogonality between symbols, leading to unavoidable inter-symbol interference (ISI). To mitigate the effects of ISI, interference cancellation and signal detection processes are essential for FTN receivers. Conventional ISI reduction techniques often utilize trellis-based algorithms. However, as the number of states increases due to additional interference symbols, the complexity of these algorithms grows exponentially. To address this challenge, this paper explores a matrix computation-based interference cancellation technique tailored for FTN communication systems, aiming to significantly reduce the complexity of the ISI mitigation process. To execute ISI cancellation and signal detection more precisely, the proposed technique includes iterative interference cancellation and a signal detection process. When six interference symbols are considered, the complexity of the proposed technique is reduced by 97% compared with that of the conventional Viterbi algorithm. Furthermore, in the case of τ = 0.85, the performance of the proposed technique is about 1 dB better than that of the Viterbi algorithm at BER = 10−4.

COSMIC-2 RFI Prediction Model Based on CNN-BiLSTM-Attention for Interference Detection and Location.

As the application of the Global Navigation Satellite System (GNSS) continues to expand, its stability and safety issues are receiving more and more attention, especially the interference problem. Interference reduces the signal reception quality of ground terminals and may even lead to the paralysis of GNSS function in severe cases. In recent years, Low Earth Orbit (LEO) satellites have been highly emphasized for their unique advantages in GNSS interference detection, and related commercial and academic activities have increased rapidly. In this context, based on the signal-to-noise ratio (SNR) and radio-frequency interference (RFI) measurements data from COSMIC-2 satellites, this paper explores a method of predicting RFI measurements using SNR correlation variations in different GNSS signal channels for application to the detection and localization of civil terrestrial GNSS interference signals. Research shows that the SNR in different GNSS signal channels shows a correlated change under the influence of RFI. To this end, a CNN-BiLSTM-Attention model combining a convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM), and attention mechanism is proposed in this paper, and the model takes the multi-channel SNR time series of the GNSS as the input and outputs the maximum measured value of RFI in the multi-channels. The experimental results show that compared with the traditional band-pass filtering inter-correlation method and other deep learning models, the model in this paper has a root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) of 1.0185, 1.8567, and 0.9693, respectively, in RFI prediction, which demonstrates a higher RFI detection accuracy and a wide range of rough localization capabilities, showing significant competitiveness. Since the correlation changes in the SNR can be processed to decouple the signal strength, this model is also suitable for future GNSS-RO missions (such as COSMIC-1, CHAMP, GRACE, and Spire) for which no RFI measurements have yet been made.

Impact Analysis of Orthogonal Circular-Polarized Interference on GNSS Spatial Anti-Jamming Array

With the continuous advancement of electromagnetic countermeasures, new types of interference signals (e.g., multi-polarization suppression interference) pose a significant threat to conventional Global Navigation Satellite System (GNSS) services, even when the receiver employs a right-handed circularly polarized (RHCP) anti-jamming array. This paper proposes a receiving signal model for orthogonal circularly polarized (OCP) interference signals based on conventional arrays, following an analysis of the non-ideal characteristics of actual arrays. Furthermore, the mechanism by which OCP interference signals affect anti-jamming performance is examined. Power inversion (PI) and linear constrained minimum variance (LCMV) techniques, applied to both uniform linear arrays and central circular arrays, are utilized to verify the impact of these interference signals. Simulation and physical testing demonstrate that OCP interference significantly affects the interference subspace of the conventional RHCP array, potentially leading to a reduction in the anti-jamming performance of the receiver. To effectively suppress multi-polarization interference, anti-jamming GNSS receivers must either ensure the consistency of cross-polarization among the elements of the array or adopt polarization-sensitive arrays.

Deep learning and time series signal processing for bending detection in mining environment using optical fiber sensor

Optical fiber sensors are widely used to monitor environmental disturbances that may trigger or affect mining landslides, a complex phenomenon that involves various factors and can cause severe damage. In this study, we propose a method to detect the curvature of a bent optical fiber, which affects the optical signals traveling through it, using deep learning and time series signal processing. We use a Mach-Zehnder interferometer (MZI) sensor and a single-mode fiber (SMF) cable to capture the interference signal caused by different bending angles. We apply a transformation random convolutional kernel (TRANCE) to reduce the signal and extract its mean and standard deviation as features. We train a dense neural network (DNN) with three hidden layers to classify the bending events into four categories: low, medium, high, and extreme. We evaluate our method on a dataset of 400 samples and achieve an average accuracy of 99% in the confusion matrix and the fifty times tenfold validation experiments. Our method outperforms other methods that use multimode fiber (MMF), speckle images, and convolutional neural networks (CNNs) in terms of accuracy, speed, and generalization. Our method also has the advantages of using SMF, which enables integrated sensing and communication, and using 1D signals, which are easier to process than 2D images. Our method is suitable for high-speed signals containing information in dangerous environments.

Near-infrared spectroscopy during organ donation in brain-dead patients

Near-infrared spectroscopy (NIRS) is essential for monitoring cerebral oxygenation in anaesthesia and critical care, offering non-invasive insights into regional cerebral oxygen saturation (rSO2). However, extracerebral contamination can compromise NIRS accuracy. The O3™ regional oximetry system, with its advanced multi-wavelength technology, may contribute by providing more accurate measurements. In a study involving brain-dead organ donors, near-normal rSO2 values were initially observed despite cerebral anoxia, suggesting extracerebral signal interference. Following aortic clamping and cessation of circulation, rSO2 levels dropped but remained above zero, indicating potential limitations in accurately detecting severe hypoxia. This case study demonstrated the effectiveness of the O3™ system in monitoring rSO2 in such critical situations, uniquely supported by the cerebral haemoglobin index, oxygenated haemoglobin index, and deoxygenated haemoglobin index. The findings emphasise the importance of understanding oxygen transport mechanisms and haemoglobin affinity to accurately interpret NIRS data, especially in brain-dead patients and organ donation procedures.

Understanding Wireless Debugging with Sniffers in Smart Home Environments

Wireless communication is becoming increasingly prevalent in smart home environments; the need for effective debugging methodologies has become paramount for system reliability and performance optimization. This comprehensive article analysis explores the implementation of wireless sniffers across multiple protocols, including Wi-Fi, Bluetooth, and Zigbee, with particular emphasis on their application in contemporary IoT devices. Through a detailed examination of chipset capabilities from major manufacturers, including Qualcomm Atheros, Texas Instruments, and Nordic Semiconductor, this article presents a structured approach to wireless debugging that encompasses packet capture mechanisms, signal analysis, and protocol verification. The investigation demonstrates how modern sniffer technologies can effectively diagnose common issues such as packet loss, signal interference, and protocol misconfigurations in smart home devices, including voice assistants, security systems, and environmental controls. By synthesizing practical implementation strategies with technical analysis, this article provides developers and system architects with actionable methodologies for improving wireless system reliability while highlighting the critical role of protocol analysis in ensuring robust IoT deployments.

Improving signal-to-noise ratio of Raman measurements based on ensemble learning approach.

Raman spectroscopy is an extensively explored vibrational spectroscopic technique to analyzethebiochemical composition and molecular structure of samples, which is often assumed to be non-destructive when carefully using proper laser power and exposure time. However, the inherentlyweak Raman signal and concurrent fluorescence interference often lead to Raman measurements withalow signal-to-noise ratio (SNR), especially for biological samples. Great efforts have been made to develop experimental approaches and/or numerical algorithms to improvetheSNR. In this study, we proposed an ensemble learning approach to recover and denoise Raman measurements withalow SNR. The proposed ensemble learning approach was evaluated on 986 pairs of Raman measurements, each pair of which consists of a low SNR Raman spectrum and a high SNR reference Raman spectrum from the exact same fungal sample but uses 200 timestheintegration time. Compared with conventional methods, the Raman measurementsrecoveredby the proposed ensemble learning approach are more identical to high SNR reference Raman measurements, with anaverage RMSE and MAE of only 1.337 × 10-2 and 1.066 × 10-2, respectively; thus, the proposed ensemble learning approach is expected to be a powerful tool for numerically improving the SNR of Raman measurements and further benefits rapid Raman acquisition from biological samples.

Vulnerability of Wi-Fi wireless network to signal interference

Wi-Fi networks are often susceptible to signal interference that can disrupt services, intercept sensitive data, and allow unauthorized access. Exploiting this vulnerability, attackers can launch Intentional Electromagnetic Interference (IEMI) attacks or protocol-based attacks. IEMI attacks include, but are not limited to, jamming, which involves the introduction of interference signals in the frequency band used by a given Wi-Fi network, thus disrupting its proper operation. The article presents research on different types of IEMI attacks, aimed at illustrating this type of threat to video transmission from Wi-Fi cameras and discusses methods of protection against this type of attack. The paper examines the methods of jamming the wireless network band, which are used on the distribution methods and the Sweeping Jammer methods that cyclically disrupt all available devices. Based on experiments, it has been confirmed that SOHO devices are highly susceptible to this type of simple attacks and implementing a method to minimize the effects of jamming yields poor results. The simplicity of this type of attack and the high availability of software and hardware tools on the market make the threat highly popular and still dangerous.

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Ungated, plug-and-play preclinical cardiac CEST-MRI using radial FLASH with segmented saturation.

Electrocardiography (ECG) and respiratory-gated preclinical cardiac CEST-MRI acquisitions are difficult because of variable saturation recovery with T1, RF interference in the ECG signal, and offset-to-offset variation in Z-magnetization and cardiac phase introduced by changes in cardiac frequency and trigger delays. The proposed method consists of segmented saturation modules with radial FLASH readouts and golden angle progression. The segmented saturation blocks drive the system to steady-state, and because center k-space is sampled repeatedly, steady-state saturation dominates contrast during gridding and reconstruction. Ten complete Z-spectra were acquired in healthy mice using both ECG and respiratory-gated and ungated methods. Z-spectra were also acquired at multiple saturation B1 values to optimize for amide and Cr contrasts. There was no significant difference between CEST contrasts (amide, Cr, magnetization transfer) calculated from images acquired using ECG and respiratory-gated and ungated methods (p = 0.27, 0.11, 0.47). A saturation power of 1.8μT provides optimal contrast amplitudes for both amide and total Cr contrast without significantly complicating CEST contrast quantification because of water direct saturation, magnetization transfer, and RF spillover between amide and Cr pools. Further, variability in CEST contrast measurements was significantly reduced using the ungated radial FLASH acquisition (p = 0.002, 0.006 for amide and Cr, respectively). This method enables CEST mapping in the murine myocardium without the need for cardiac or respiratory gating. Quantitative CEST contrasts are consistent with those obtained using gated sequences, and per-contrast variance is significantly reduced. This approach makes preclinical cardiac CEST-MRI easily accessible, even for investigators without prior experience in cardiac imaging.

Regulating Electron Acceptor Unit to Construct Conjugated Polymer Probe for Raman Imaging of Tumor and Sentinel Lymph Nodes.

Donor-acceptor (D-A) conjugated polymers have large Stokes shifts, high photostability, and good biocompatibility and thus are ideal for use as Raman probes in vivo. However, few D-A conjugated polymers are used as Raman probes for Raman imaging due to their weak Raman signal intensity and intrinsic fluorescence interference. Here, we developed a D-A conjugated polymer probe (CDT-TT) containing alternating cyclopentadithiophene-thienothiophene units for Raman imaging of tumor and sentinel lymph nodes (SLNs). The CDT-TT shows a strong Raman signal at 1389 cm-1 without fluorescence and lipid background signal interference under 785 nm near-infrared light. Moreover, the CDT-TT loaded nanoparticles realized the accurate imaging of tumor cells and tumor tissues. In addition, a high-resolution margin imaging of in situ SLNs was acquired. Taken together, the established method is effective for accurate Raman detection of tumors and SLNs, which may shed new light on the development of D-A polymer Raman probes for clinical imaging.

Penerapan Komunikasi ESP-NOW dengan Topologi Jaringan Mesh Sebagai Bentuk Interoperability dalam Konsep Network Centric Warfare

Network-centric warfare (NCW) is a concept that prioritizes integration and interconnection between combat elements to improve operational effectiveness. One approach to achieve high interoperability is ESP-NOW communication with mesh network topology. ESP-NOW technology enables fast and efficient wireless communication between devices without Wi-Fi infrastructure or routers. This research implements a mesh network topology using ESP32 modules that support ESP-NOW, where each node can function as a repeater to facilitate communication between devices beyond direct range. Reliability and network coverage testing was conducted by measuring latency and throughput parameters, which showed an average latency of 15 ms and a throughput of up to 2 Mbps, sufficient to support tactical communications in real-time. Although the performance is satisfactory, the obstacles encountered include limited coverage in dense environments and signal interference, which affects the stability of the connection. The study illustrates how the integration of ESP-NOW with mesh improves interoperability within NCW, allowing military units to share real-time data, such as position coordination, situational information, and operational commands. Thus, this implementation has great potential in future military operations, strengthening the integration of combat units and situational awareness on the battlefield.

Assessing the impact of overhead agrivoltaic systems on GNSS signal performance for precision agriculture

Agrivoltaic systems, which integrate solar energy production with agricultural practices, present a sustainable solution to optimize land use. However, specific configurations, such as overhead solar panels, can obstruct Global Navigation Satellite System (GNSS) signals, which are vital for precision agriculture technologies. This study investigates the impact of an agrivoltaic system on GNSS signal performance compared to a conventional orchard in Kressbronn am Bodensee, Germany, using data from multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou). Key metrics such as carrier-to-noise density ratio (C/N₀), positional accuracy, time to first fix (TTFF), and dilution of precision (DOP) were analyzed. The results showed significant signal degradation under the overhead agrivoltaic system, particularly in C/N₀ and positional accuracy. While there were no significant differences in DOP metrics or TTFF, the average C/N₀ decreased from 30.62 dB-Hz in the conventional orchard to 26.92 dB-Hz in the agrivoltaic system. Moreover, the average of satellites with C/N₀ values below 24 dB-Hz was notably higher in the agrivoltaic area, indicating critical signal degradation. In addition, relying on a single constellation, like GPS, may not provide enough satellites for reliable GNSS performance.Despite the lower signal quality, the average number of satellites with C/N₀ above 24 dB-Hz remained over 22 in both areas. Combined with slight or no significant differences in other metrics compared to the conventional orchard, this suggests GNSS-based systems can support precision agriculture tasks within the agrivoltaic environment. The study highlights the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in agrivoltaics management, and suggests alternative solutions, such as Real-Time Kinematic (RTK) correction or Simultaneous Localization and Mapping (SLAM) techniques, to enhance performance. These findings are critical as agrivoltaic adoption grows, requiring a balance between solar energy production and the operational needs of precision agriculture.

Metal-Organic Framework (MOF)-Based Sensors for Mercury (Hg) Detection: Design Strategies and Recent Progress.

Monitoring mercury (Hg) is critical for environmental and public health. Metal-organic framework (MOF)-based sensors demonstrate the advantage of high sensitivity and rapid response. We summarize the advances of MOF sensors for Hg2+ detection from the perspective of MOF type and role in the sensors. First, we introduce three MOFs used in Hg sensors-UIO, ZIF, and MIL-that have demonstrated superior performance. Then, we discuss the specifics of MOF-based sensors for Hg2+ detection in terms of the recognition and signal elements. Currently, the recognition elements include T-rich aptamers, noble metal nanoparticles, central metal ions, and organic functional groups inherent to MOFs. Sensors with fluorescence and colorimetric signals are the two main types of optical MOF sensors used for Hg detection. Electrochemical sensors have also been fabricated, but these are less frequently reported, potentially due to the limited conductivity and cycling stability of MOFs. Notably, dual-signal sensors mitigate background signals interference and enhance the accuracy of Hg2+ detection. Furthermore, to facilitate portability and user-friendliness, portable devices such as microfluidics, paper-based devices, and smartphones have been developed for Hg2+ detection, showcasing potential applications. We also address the challenges related to MOF-based sensors for Hg2+ and future outlook.

SINGLE-CYCLE PULSE WAVEFORM RECOGNITION METHOD BASED ON SVM

The single-cycle pulse waves’ morphological characteristics can reflect rich physiological and pathological information about the human cardiovascular system, playing an important role in continuous blood pressure monitoring and cardiovascular disease risk warning. Pulse signals belong to weak physiological signals, and the acquisition process is easily affected by factors such as adjacent channel interference and motion artifacts, leading to abnormal cycles and affecting subsequent feature extraction. Therefore, based on support vector machines (SVM), a single-cycle pulse waveform recognition algorithm is proposed. Using a multi-channel pulse wave acquisition device to collect radial artery pulse signals from 150 subjects. Preprocess the signal to obtain a single-cycle pulse waveform. A total of 54 frequency domain and time-frequency domain feature parameters were extracted by using Fourier transform and wavelet transform to extract the frequency domain and time-frequency domain features. Use the principal components analysis (PCA) method to reduce parameter dimensions. Using the SVM for model training to achieve the classification of single-cycle effective pulse signals, pseudo noise and interference signals. The results show that the algorithm’s classification accuracy is 97.42%, indicating that the algorithm can effectively identify pulse signals, pseudo noise and interference signals, laying the foundation for modeling and suppressing interference signals in the next step.

Development of a Machine stereo vision-based autonomous navigation system for orchard speed sprayers

In orchards, radio frequency scintillation caused by high vegetation density can hinder the effectiveness of machinery auto guidance based on the Global Navigation Satellite System (GNSS). Signal interference not only leads to poor quality of machine operation but can also pose operational risks. In this work, we propose an alternative trajectory positioning system to supplement traditional GNSS-based navigation for machinery used in orchards. The aim of this research was to develop a deep learning machine stereo vision guidance system onboard an orchard speed sprayer. The developed system combines a collision avoidance methodology along with deep learning-driven machine vision for interrow positioning and a dead reckoning set of rules for alternating U-turns. The developed methodology was tested in 4 rows of an artificial orchard. The results show that it is possible for the embedded EfficientDet target detection algorithm to guide the equipment at 1, 1.5 and 2 km × h−1 with minimum average root-mean-square errors (RMSEs) of 0.24 m, 0.20 m, and 0.31 m, respectively. When the system navigation was performed using YOLOv7, the minimum average RMSEs for each row were 0.40 m, 0.48 m, and 0.43 m, respectively, for the abovementioned speeds. The U-turn by dead reckoning showed minimum average RMSE values of 0.56 m, 0.22 m, and 0.35 m for row navigation based on EfficientDet at 1, 1.5 and 2 km × h−1, respectively. For YOLOv7-based navigation in rows, the minimum average RMSE values at these speeds were 0.35 m, 0.81 m, and 0.44 m, respectively. This study contributes to the field by proposing an alternative navigation system for orchard machines that operates without the limitations associated with GNSS. In addition, our proposed guidance methodology introduces an RGB-D collision avoidance system with a demonstrated safety capacity for navigation under real scenario conditions.

Research on Adjustable Cold View Field Diaphragm in Optical System with Linear Array Detector in AIMS

Abstract The infrared Fourier transform spectrometer needs a cold view field diaphragm to reduce stray radiation. For an infrared spectrometer with linear array detectors, the part of the view field diaphragm where the light passes can be regarded as a slit. If the infrared detectors are in small size, the width of the slit is also small, this will cause serious diffraction phenomena. If the width of the view field diaphragm and the optical system are designed by geometric optics theory, the diffraction light cannot be fully received by the detectors, this will cause energy loss. Expanding the width of the view field diaphragm will introduce stray radiation. Meanwhile, spectrometer follow-up optics should be set in cold environments to reduce the infrared background of the instrument. Optical materials have different thermal characteristics, the optical and mechanical structures will deform at low-temperature, and the cold view field diaphragm is installed at room temperature, so it is impossible to guarantee that the cold view field diaphragm remains in its design position when working at low-temperature.
This paper solves the above problems by designing an adjustable cold view field diaphragm installed in its cryogenic vacuum chamber. The width and position of the view field diaphragm can be adjusted when working in cold temperatures, without opening the cryogenic vacuum chamber. Contrasting the interference signal obtained by the detectors in the adjustment process, the system can get the most suitable width and position of the cold view field diaphragm. The above works are based on the spectrometer used in the Accurate Infrared Magnetic Field Measurements of the Sun (AIMS).

A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge Distillation.

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce DistilLoc: a cross-knowledge distillation framework that transfers knowledge from an RTT-based teacher model to an RSSI-based student model. By applying a teacher-student architecture, where the RTT model (teacher) trains the RSSI model (student), DistilLoc enhances RSSI-based localization with the accuracy and robustness of RTT without requiring RTT data during deployment. At the core of DistilLoc, the FNet architecture is employed for its computational efficiency and capacity to capture complex relationships among RSSI signals from multiple access points. This enables the student model to learn a robust mapping from RSSI measurements to precise location estimates, reducing computational demands while improving scalability. Evaluation in two cluttered indoor environments of varying sizes using Android devices and Google WiFi access points, DistilLoc achieved sub-meter localization accuracy, with median errors of 0.42 m and 0.32 m, respectively, demonstrating improvements of 267% over conventional RSSI methods and 496% over multilateration-based approaches. These results validate DistilLoc as a scalable, accurate solution for indoor localization, enabling intelligent, resource-efficient urban environments that contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).