Interference Signal Research Articles

Integrated indoor positioning methods to optimize computations and prediction accuracy enhancement

AbstractIndoor GPS location estimation encounters accuracy challenges from intricate building structures and diverse signal interferences. Trilateration methods utilising APs are typically employed to estimate indoor locations. Nevertheless, estimation errors from multipath effects and high power consumption of sensors employed in location estimation curtail battery life. To address this issue, research into location estimation methods utilising machine learning has been conducted. However, challenges involving the selection of the optimal access point locations and obtaining dense RSSI data have been noted. In this article presents a solution based on sparse radio maps for decreasing the expenses of collecting RSSI data while simultaneously enhancing indoor location accuracy through the integration of image data. The proposed approach integrates matrix‐based RSSI indoor positioning (M‐RIP) for initial location estimation and feature‐based image indoor positioning (F‐IIP) for position determination via image feature matching. Furthermore, extended area‐based post‐processing (EA‐PP) is employed to augment M‐RIP's precision and minimize image matching computation in F‐IIP, improving overall performance. This article utilizes actual building data to validate the precision of the position estimation and efficiency of computation reduction using the proposed method.

Modifikasi Protokol Routing DSDV Menggunakan Algoritma Dynamic-power transmission untuk Mengurangi Interferensi Sinyal dalam Pengiriman Data Berdasarkan Tingkat Kepadatan Node di Jaringan MANET

Mobile Ad-Hoc Network (MANET) is a decentralized collection of nodes that exchange information temporarily via wireless transmission. When nodes communicate, all nodes in the communication range will be affected by signal interference from other nodes. The problem that arises due to this signal interference is that it will affect or interfere with the signal transmitted to the receiver, resulting in a decrease in network performance which will impact the quality of service (QoS). To overcome the problem of signal interference, use interference management. This study applies the Dynamic-Power Transmission (DPT) algorithm to the Destination Sequence Distance Vector (DSDV) routing protocol framework. DPT-DSDV will adjust the communication range automatically based on the node density level. When a node sends data packets to neighbouring nodes, the communication range will be adjusted automatically based on the selected level. The performance of DPT-DSDV will be compared with standard DSDV and standard AODV by looking at performance based on three parameters, namely throughput, packet delivery ratio (PDR), and end-to-end delay. Based on the results of research trials, applying the dynamic-power transmission algorithm can improve the performance of the standard DSDV routing protocol. Throughput results on DPT-DSDV increased by 0.90%. The packet delivery ratio (PDR) increased by 0.50%, and end-to-end delay decreased by 2.80%.
 Key words: MANET, Interference, Quality of Service, Routing, DSDV, Dynamic-Power Transmission.

Relay‐Assisted Aerial Communication over a Single‐Shadowed Communication Channel

This paper discusses amplify‐and‐forward relay‐assisted air‐to‐ground unmanned aerial vehicle communication over a recently proposed single‐shadowing‐based composite fading scenario. Included are the results derived from the Fluctuating Beckmann faded co‐channel interference at the receiver node and relay. Maximal ratio combining based diversity scheme is considered. The power splitting at the receiver for energy harvesting has been assimilated. The utilization of characteristic function leads to the derivation of outage, success probabilities, and capacity with outage expressions. These expressions represent the impact of path‐loss, aerial vehicle‐relay, relay‐receiver distances, and channel characteristics of desired and interference signals. Utilizing these expressions, numerical outcomes are presented and evaluated.

A miniaturized ultra-wideband filter with high rejection and selectivity based on dual-notch bands.

A novel compact and highly selective Ultra Wide Band (UWB) filter is proposed using multimode resonator (MMR) technology. To begin with, the filter's ultra-wideband performance is achieved by coupling a stepped-triangular multimode resonator with input-output feedlines. Furthermore, dual-notch band characteristics are realized at 6.80 GHz and 9.82 GHz, employing asymmetric coupled lines and the split ring resonator (SRR) methods. Eventually, by using a Defected Ground Structure (DGS), the filter's correct transmission zero is deepened, further enhancing the out-of-band suppression performance at higher frequencies. The measured results are in excellent agreement with the experimental results, and the filter has a passband range of 3.52-11.68 GHz, a center frequency of 7.59 GHz, an insertion loss of just 0.61 dB, and a return loss of more than 18 dB. The transmission zeros have a rejection capability of more than 47 dB attenuation, and the rectangular coefficient of the filter is 1.34, which is outstanding for filtering out the interference signals in the parasitic passband with superior selectivity. The overall structure is compact, and the size is just 0.41λg×0.20λg. The filter can be used for UWB system filtering and also to avoid interference from some Wireless Local Area Network (WLAN) IEEE 802.11 series and x-band satellite link frequency bands.

An Improved Lightweight YOLO Algorithm for Recognition of GPS Interference Signals in Civil Aviation

Considering several sources that cause global position system (GPS) interference in civil aviation and the challenges faced by interference recognition algorithms in terms of efficiency and accuracy, we propose an improved You Only Look Once (YOLO)v7‐CHS algorithm (YOLOv7‐CHS) and investigate its effectiveness in identifying GPS signals and different types of interference signals. First, continuous wavelet transform (CWT) is introduced as a method for processing and analyzing signals in the time–frequency (TF) domain to effectively obtain their temporal and spectral characteristic information. Second, the ConvNeXt structure is integrated into the YOLOv7 backbone network to create a ConvNeXtBlock (CNeB) module to enhance the classification and recognition accuracy of interference signals. Additionally, an attention mechanism is introduced to further improve model recognition accuracy. To effectively improve the capability of signal feature extraction and mitigate the impact of background noise on TF feature suppression, we have integrated the efficient channel attention (ECA) channel attention module with the convolutional block attention module (CBAM) spatial attention module, thereby proposing a hybrid CBAM and ECA (HCE) attention module. Last, to address issues arising from accidental deletion of detection frames and multipath interference negatively affecting model recognition performance, we have employed the soft nonmaximum suppression (Soft‐NMS) algorithm while selecting an optimal loss function through comparative analysis. The comparative evaluation experimental results under different circumstances show that YOLOv7‐CHS achieves recognition accuracies of 98.0% and 99.6% for various types of signals, respectively. These values represent an increase of 1.7% and 1%, respectively, compared to YOLOv7. Moreover, in terms of lightweight indicators, YOLOv7‐CHS exhibits a significant improvement in performance: the frames per second (FPS) is increased by 75.1, the number of parameters (Params) was reduced by 4.75 M, and giga floating point operations per second (GFLOPs) were reduced by 65.9 G while effectively enhancing recognition capabilities. The proposed YOLOv7‐CHS not only improves signal recognition accuracy but also reduces model Params and computational complexity, achieving a lightweight model with promising application prospects in the rapid detection and recognition of GPS interference sources in civil aviation.

DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis.

Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varying resolutions, effective regions, and interference signals, making the distribution of the data highly variable and negatively impacting the performance of deep learning models. Additionally, manual labeling of images is time-consuming and requires significant effort, leading to the desire to effectively utilize a large amount of unlabeled data for network training. To address these challenges, this study proposes the Dual Self-supervised Multi-Operator Transformation Network (DSMT-Net) for multi-source EUS diagnosis. The DSMT-Net includes a multi-operator transformation approach to standardize the extraction of regions of interest in EUS images and eliminate irrelevant pixels. Furthermore, a transformer-based dual self-supervised network is designed to integrate unlabeled EUS images for pre-training the representation model, which can be transferred to supervised tasks such as classification, detection, and segmentation. A large-scale EUS-based pancreas image dataset (LEPset) has been collected, including 3,500 pathologically proven labeled EUS images (from pancreatic and non-pancreatic cancers) and 8,000 unlabeled EUS images for model development. The self-supervised method has also been applied to breast cancer diagnosis and was compared to state-of-the-art deep learning models on both datasets. The results demonstrate that the DSMT-Net significantly improves the accuracy of pancreatic and breast cancer diagnosis.

The ethical significance of user-control in AI-driven speech-BCIs: a narrative review.

AI-driven brain-computed interfaces aimed at restoring speech for individuals living with locked-in-syndrome are paired with ethical implications for user's autonomy, privacy and responsibility. Embedding options for sufficient levels of user-control in speech-BCI design has been proposed to mitigate these ethical challenges. However, how user-control in speech-BCIs is conceptualized and how it relates to these ethical challenges is underdetermined. In this narrative literature review, we aim to clarify and explicate the notion of user-control in speech-BCIs, to better understand in what way user-control could operationalize user's autonomy, privacy and responsibility and explore how such suggestions for increasing user-control can be translated to recommendations for the design or use of speech-BCIs. First, we identified types of user control, including executory control that can protect voluntariness of speech, and guidance control that can contribute to semantic accuracy. Second, we identified potential causes for a loss of user-control, including contributions of predictive language models, a lack of ability for neural control, or signal interference and external control. Such a loss of user control may have implications for semantic accuracy and mental privacy. Third we explored ways to design for user-control. While embedding initiation signals for users may increase executory control, they may conflict with other aims such as speed and continuity of speech. Design mechanisms for guidance control remain largely conceptual, similar trade-offs in design may be expected. We argue that preceding these trade-offs, the overarching aim of speech-BCIs needs to be defined, requiring input from current and potential users. Additionally, conceptual clarification of user-control and other (ethical) concepts in this debate has practical relevance for BCI researchers. For instance, different concepts of inner speech may have distinct ethical implications. Increased clarity of such concepts can improve anticipation of ethical implications of speech-BCIs and may help to steer design decisions.

Feedthrough effect in MEMS gyroscopes and fully differential feedthrough cancellation method.

In the operation of MEMS gyroscopes, the feedthrough signal is inevitably introduced through the feedthrough capacitance and significantly affects the performance of MEMS gyroscopes. The common feedthrough cancellation methods highly depend on the microfabrication process and the structural symmetry of MEMS gyroscopes, which cannot effectively eliminate the feedthrough signal. In order to solve this problem, a new feedthrough cancellation method based on a fully differential configuration is proposed in this paper. The influence of the feedthrough effect is analyzed by establishing an equivalent circuit model including the feedthrough capacitance and described by the admittance diagram. Different drive and detection configurations are compared for the problem of feedthrough mismatch on different paths. Based on the theoretical analysis, a fully differential feedthrough cancellation scheme combined with two inverse feedthrough cancellation circuits is proposed. The deviation of feedthrough signal caused by the asymmetry of two differential paths can be adjusted by the two inverse feedthrough cancellation circuits. The fully differential configuration can further increase the drive force and suppress the common mode errors. The experimental results indicate that the feedthrough signal is suppressed in a large degree by the designed feedthrough cancellation method. The feedthrough level is reduced by 50.53 dB, and the amplitude of the effective signal increases from 4.10 to 9.46 dB. The signal-to-noise ratio has an improvement of 212.48% than that before feedthrough cancellation. The proposed feedthrough cancellation method can significantly reduce the interference of feedthrough signals on the effective signal, effectively improving the signal quality of MEMS gyroscopes.

VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution.

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized L2p -norm ( ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git.

A Secure, Flexible, and PPG-Based Biometric Scheme for Healthy IoT Using Homomorphic Random Forest

Advances in the Internet of Things such as biosensing and camera-capturing technologies have also resulted in biometric-based authentication approaches becoming more viable. Photoplethysmography (PPG), for example, can be leveraged to provide better biometric features for continuous authentication, in comparison to other biometrics such as fingerprint. However, the fewer morphological features in PPG signals can complicate the accurate authentication of PPG signals. Furthermore, one has to also consider transmission security and PPG template storage security. These two considerations are typically not taken into consideration in most existing PPG-based authentication schemes. Therefore, we design a secure PPG-based biometric system to achieve accurate authentication with biometric privacy protection. In our design, Homomorphic Random Forest is adopted to classify the homomorphically encrypted biometric features, thus protecting the user’s PPG biometrics from being compromised in authentication. Furthermore, beat qualification screening is set up to avoid the interference of unqualified signals, and 19 features with the least redundancy from the 541 features extracted are selected as the biometric features of the user. Doing so allows us to ensure the accuracy of authentication, as demonstrated in our evaluations using five PPG databases collected by three different collection methods (contact, remote, and monitor). In addition, our experiments adopt PPG signals acquired from remote cameras, which are not considered in other PPG-based biometric systems. The experimental results show that the average accuracy of our biometric system is 96.4%, the F1 score is 96.1%, the EER is 2.14%, and the authentication time is about 0.5s.

Time-resolved subcycle and intercycle interference influenced momentum shift in nonadiabatic tunnelling ionization

Momentum shift is an important sign of nonadiabatic tunnelling ionization process. To investigate the mechanism of momentum shift, we use the strong-field approximation theory to track the formation of ionization momentum spectra of hydrogen atom under the action of different laser pulses in the time domain. By observing the ionization momentum spectra of different structures over time, we find that the momentum shift is formed by the continuous interference and evolution of ionization signals. Meanwhile, we further analyze how subcycle and intercycle interference influence the formation of momentum shift. Before the duration is long enough for intercycle interference to emerge, momentum shift grows smoothly. Our findings reveal different intrinsic mechanisms for the formation of momentum shift in many-cycle and few-cycle laser pulses, showing that subcycle interference plays a dominant role in the former while intercycle interference is critical in the latter. This work lays the foundation for a deeper understanding of the nonadiabatic tunnelling process and makes the regulation of momentum shift possible.

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis.

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

Спосіб декореляції сигналів пасивних завад в системі селекції рухомих цілей малої швидкості

Спосіб декореляції сигналів пасивних завад в системі селекції рухомих цілей малої швидкості

Notice of Removal: Removal of ocular artifacts using ICA and adaptive filter for motor imagery-based BCI

Preprocessing is the first step of Brain Computer Interface U+0028 BCI U+0029 based on Electroencephalogram U+0028 EEG U+0029, the most important aspect in this step is to remove Ocular Artifacts U+0028 OAs U+0029 included in EEG signals. OAs are the main interference signals to the EEG signals. In this paper, a new method called ICA-AF is proposed. This method using Independent Component Analysis U+0028 ICA U+0029 and Adaptive Filter U+0028 AF U+0029 can remove OAs from the EEG signals without measuring Electrooculogram U+0028 EOG U+0029. The method firstly decomposes the multi-channel EEG signals into the statistically independent components by ICA, then extracts the exact OAs that do not include the brain activity information in the OA-related independent components, and converts the exact OAs to the OAs at each electrode using inverse ICA U+0028 iICA U+0029. An adaptive filter uses this OA and the EEG signal in each channel as reference and input signal, respectively. Compared to ICA and ICA-RLS U+0028 Independent Component Analysis-Robust Least Square U+0029 methods, the proposed method can remove most of exact OAs and preserve as much brain activity information as possible. In addition, the average classification accuracy of the new method is 19 U+0025 and 8 U+0025 higher than the previous two methods, in the ratio of the average classification accuracy of the raw EEG, respectively. Besides, the new method is 1.45 times faster than ICA-RLS method, with respect to the execution time of ICA method.

ШЛЯХИ ЗМЕНШЕННЯ ПОХИБОК ВИМІРЮВАННЯ ДІЕЛЕКТРИЧНОЇ ПРОНИКНОСТІ СЛАБОПОГЛИНАЮЧИХ ДІЕЛЕКТРИКІВ У МІЛІМЕТРОВОМУ ТА СУБМІЛІМЕТРОВОМУ (ТЕРАГЕРЦОВОМУ) ДІАПАЗОНАХ ДОВЖИН ХВИЛЬ МЕТОДОМ ПОВЕРХНЕВОГО ПЛАЗМОННОГО РЕЗОНАНСУ

Subject and Purpose. The sources of errors are identified that may arise in the course of terahertz-range measurements of the dielec- tric constant of weakly absorbent dielectrics, if performed within the surface plasmon resonance (SPR) technique. Possible ways are analyzed for reducing or fully eliminating such errors. Methods and Methodology. Specific details of applying the SPR method for dielectric constant measurements have been analyzed, with the aim of identifying the major factors that particularly affect the measurement accuracy. Results. It has been noted that in order to reduce the level of backlight interference (which may lead to blinding the receiver), it is expedient to make surface resonance records via frequency scanning. In that case the impact of the interference signal nonstationa- rity arising from the partial conversion of the surface wave energy into that of the volume wave, which occurs at the grating edges, is markedly lower than in the case of angular scanning. A mathematical expression has been derived which suggests a relation between scanning step sizes in angle and in frequency (for the angular and frequency scanning, respectively). As has been shown, a better mea- surement accuracy is achievable if the SPR is recorded as a function of frequency. Indeed, the frequency can be varied, with the use of familiar technologies, in steps of a much smaller size than such adopted for angular scanning. Errors in the above measurements can also arise if the resonance is excited on a grating whose Fourier spectrum contains many high-frequency components which carry a noticeable portion of the diffracted radiation energy. These energy losses can be greatly reduced if the SPR is excited on a grating whose profile involves the lowest number of spatial Fourier harmonics. Conclusions. The method suggested allows a significant reduction in the level of errors of the dielectric constant measurements in weakly absorbing dielectrics if the surface plasmon resonance effects are registered in dependence on the incident frequency, while the SPR is excited at a diffraction grating whose troughs-and-peaks profile is close to harmonical.

УСУНЕННЯ ЗАВАД У ДИНАМІЧНИХ СПЕКТРАХ ЗА НАЯВНОСТІ ПОТУЖНОГО СИГНАЛУ. Частина 2. ВУЗЬКОСМУГОВІ ЗАВАДИ УМОВНО-СТАЦІОНАРНОГО ХАРАКТЕРУ

Subject and Purpose. A new method for detecting narrow band interferences is discussed, with the use of an example record of Jovi- an Io-C decametric radio storm as obtained with the UTR-2 array on April 10, 2020. We aimed at developing an efficient and simple algorithm based on a detailed analysis of the effect the radio interference environment and the frequency response of the telescope may have on the efficiency of the interference mitigation procedure. Methods and Methodology. The ‘orthogonal detection’ method proposed for identifying linear interference patterns in dynamic spectra of powerful radio sources has been adapted for application to narrow band interferences of various spectral widths and variable brightness, which often happen to be located close to (or intersect with) the signal of interest. In order to minimize the impact of the telescope’s frequency response, a discretized frequency-scanning technique is used, which permits a steady approximation of the averaged spectrum by low order polynomials, as well as removal of the frequency trend which hinders distinguishing between the low-level interference signals and those coming from the source. Results. An efficient approach to the problem of detecting long lasting, narrowband interferences in dynamical spectra (con- ditionally stationary interferences) is proposed. The algorithm has been tested on the example of a powerful storm of Jovian radio emission. The proposed technique can be especially useful in situations where the signal of interest overlaps with the interference in the time-frequency domain. Conclusions. The new approach to the problem of interference mitigation, based on the development of combined algorithms of signal separation in the time-frequency domain, has been shown to offer an effective method of signal processing. In contrast to the previously used methods based on calculation of statistical moments for amplitudes, the new approach allows avoiding use of the same statistical indicators for both interference detection and subsequent steps of data analysis intended for building physical models and interpreting the observational data.

Automatic Modulation Recognition of Unknown Interference Signals Based on Graph Model

Automatic Modulation Recognition of Unknown Interference Signals Based on Graph Model

The technological development of infrared sensors in substations and the key determining factors affecting the accuracy of measurement data

An overview of the developmental history, characteristics, and applications of infrared sensors are firstly provided in the paper. Subsequently, it delves into the sensing mechanisms of commonly employed infrared sensors in substations, namely the thermistor sensors, pyroelectric sensors, and photoconductive sensors. Thermistor infrared sensors gauge infrared radiation by tracking changes in material resistance with temperature. Pyroelectric sensors detect thermal changes in objects based on the thermal effects induced by infrared radiation. Photoconductive-type sensors leverage the photoconductive effect of semiconductors to convert optical signals into electrical signals, adapting to various light conditions. In the process of measurements, variables like temperature, distance between the target object and the infrared sensor, and electromagnetic interference impact accuracy. In extreme temperatures, the cooling precision of infrared thermal imaging systems may decline, affecting measurement accuracy. The distance between the target and sensor also influences results due to energy attenuation in the infrared radiation path through the atmosphere. Faraday’s law of electromagnetic induction warns of potential electromagnetic interference signals in substations, causing device breakdowns, output image halo, or increased internal noise. Finally, the paper envisions the future development of infrared sensors.

ВИБІР НЕЙРОМЕРЕЖЕВОГО КЛАСИФІКАТОРА ДЛЯ ВИРІШЕННЯ ЗАВДАННЯ АНАЛІЗУ СИГНАЛІВ РЕБ ТА ШТУЧНИХ ЗАВАД

At present, the task of ensuring high-quality control of unmanned aerial vehicles and the transmission of reliable information in a complex interference environment is relevant. This task becomes especially important when intentional interference is used in the form of countermeasures. Various methods are used to detect and localize powerful sources of parasitic signals, including: time domain methods: signal spectrum analysis, correlation function analysis, phase shift analysis; frequency domain methods: signal amplitude response analysis, signal phase response analysis, power spectral density analysis; spatial domain methods: antenna directivity analysis, signal strength analysis at different points in space. Analyzing signals affected by EW and artificial interference is a challenging task. Traditional signal analysis methods are often ineffective for this task because they cannot take into account the complex characteristics of EW and artificial interference. Neural network classifiers are a promising tool for analyzing signals with EW and artificial interference. Neural networks can learn from large datasets and detect complex patterns that cannot be detected by traditional methods. This article describes the process of selecting a neural network classifier for analyzing EW and artificial interference signals. Various factors that should be considered when choosing a neural network architecture, training algorithm, and network parameters are discussed). The paper presents an analytical review of neural network classifiers, their varieties and features, and selects the optimal one for solving the task.

Phase extraction method for nonlinear interference signals based on a 90° hybrid coupler in frequency scanning interferometry

Phase extraction method for nonlinear interference signals based on a 90° hybrid coupler in frequency scanning interferometry