Unknown Signal Research Articles

A Multiantenna Spectrum Sensing Method Based on HFDE‐CNN‐GRU under Non‐Gaussian Noise

In many practical communication environments, traditional feature extraction methods in spectrum sensing fail to fully exploit the information of primary users. Additionally, conventional machine learning methods have weak learning capabilities, making it difficult to maintain efficient and stable spectrum sensing performance in complex noise environments. Furthermore, non‐Gaussian noise can significantly affect the detection performance of spectrum sensing. To address these issues, this paper first proposes a feature extraction method based on Hierarchical Fuzzy Dispersion Entropy (HFDE) to better extract high‐frequency and low‐frequency information from signal samples, providing more comprehensive features for subsequent models to optimize feature extraction effectiveness. Then, a parallel model combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) is constructed to enhance learning ability. While CNN extracts local features, GRU processes temporal relationships, and the features output by both are concatenated to achieve effective feature learning and temporal modeling of primary user signal data represented by HFDE. Finally, using the feature vectors output by the CNN‐GRU model, detection statistics and detection thresholds for spectrum sensing are constructed for online detection. Simulation results validate the effectiveness and robustness of this method in spectrum sensing under non‐Gaussian noise. In the presence of significant non‐Gaussian noise intensity and a signal‐to‐noise ratio of −14 dB, the detection probability can reach 97.1%. Additionally, for the detection of unknown signals, the model can still maintain a detection probability of over 90%.

Plant elicitor peptide induces endocytosis of plasma membrane proteins in Arabidopsis.

In plants, the regulation of plasma membrane (PM) dynamics through endocytosis plays a crucial role in responding to external environmental cues and defending against pathogens. The Arabidopsis plant elicitor peptides (Peps), originating from precursor proteins called PROPEPs, have been implicated in various aspects of plant immunity. This study delves into the signaling pathway of Peps, particularly Pep1, and its effect on PM protein internalization. Using PIN2 and BRI1 as PM markers, we demonstrated that Pep1 stimulates the endocytosis of these PM-localized proteins through clathrin-mediated endocytosis (CME). CLC2 and CLC3, two light chains of clathrin, are vital for Pep1-induced PIN2-GFP and BRI1-GFP internalization.The internalized PIN2 and BRI1 are subsequently transported to the vacuole via the trans-Golgi network/early endosome (TGN/EE) and prevacuolar compartment (PVC) pathways. Intriguingly, salicylic acid (SA) negatively regulates the effect of Pep1 on PM endocytosis. This study sheds light on a previously unknown signaling pathway by which danger peptides like Pep1 influence PM dynamics, contributing to a deeper understanding of the function of plant elicitor peptide.

The Effect of Cyclic Pressure on Integrin β1-Hippo/YAP Signaling Pathway

The manipulation of regulating tendons could delay the degeneration of chondrocytes and that mechanical stimulation to chondrocytes was partly mediated by ITG-β1 involved in the classical Hippo/YAP signaling pathway, and there were other unknown signaling pathways involved in the regulation of YAP protein by ITG-β1.

Magnetically Induced Grid Structure for Enhancing the Performance of a Dual-Mode Flexible Sensor with Tactile/Touchless Perception.

As an advanced sensing technology, dual-mode flexible sensing, integrating both tactile and touchless perception, propels numerous intelligent devices toward a more practical and efficient direction. The ability to incorporate multiple sensing modes and accurately distinguish them in real time has become crucial for technological advancements. Here, we proposed a dual-mode sensing system (B-MIGS) consisting of a dual-layer sensing device with a magnetically induced grid structure and a testing device. The system was capable of utilizing mechanical pressure to perceive tactile stimulation and magnetic sensing to simultaneously transduce touchless stimulation simultaneously. By leveraging the triboelectric effect, the decoupling of tactile and touchless signals in the presence of unknown signal sources was achieved. Additionally, the sensing characteristics of the B-MIGS were optimized by varying the curing magnetic induction intensity and magnetic particle concentration. The influence of the temperature and humidity on the sensing signals was also discussed. Finally, the practical value of the B-MIGS as a dual-mode monitoring system was demonstrated on soft petals and sensor arrays, along with exploration of its potential application in underwater environments.

Dynamic Event-Triggered Formation Control for Heterogeneous Multiagent Systems With Nonautonomous Leader Agent.

In this article, the time-varying output formation issue of heterogeneous multiagent systems is investigated by the event-triggered control scheme. Only the outputs of all agents, including leader agent and follower agents, are measurable. The leader agent contains an unknown input signal to generate flexible reference trajectory. Also, only a subset of follower agents have the direct access to the leader agent. First, for each follower, the leader-state compensator is designed to estimate the state of leader. Two kinds of dynamic event-triggered (DET) mechanisms, i.e., node- and edge-based event-triggered schemes, can be equipped on the compensator to save the communication bandwidth of leader-follower and follower-follower interactions, respectively. Then, the distributed formation controller is built to drive each follower achieving formation tracking. The presented control protocol consisting of the DET state compensator and formation controller is fully distributed, which is independent of the global information of communication topology, such as the eigenvalues of Laplacian matrix of communication topology and amount of whole agents. Finally, the numerical experiments and comparison experiments are exhibited to verify the effectiveness of the presented control protocol.

Parameter identification of the linear discrete-time stochastic systems with unknown exogenous inputs

The paper addresses a parameter identification problem for linear discrete-time stochastic systems with unknown exogenous inputs. Such systems are considered when solving practical problems related to the measurements processing in the case when it is impossible to do any assumptions about the evolution of unknown input signal or its statistical characteristics that can change over time. We consider a class of discrete time linear stochastic systems with unknown exogenous inputs, where an additional source of a priori uncertainty of the system model is introduced, namely, the unknown parameter, on the elements of which the system model matrices can depend. This formulation of the parameter identification problem under the conditions of unknown inputs and the presence of random noises describes a high degree of uncertainty of a discrete time linear stochastic system. We propose a novel solution to this problem based on the construction of a new instrumental identification criterion. Minimization of this criterion allows for evaluating the unknown system model parameters simultaneously with the estimating of the state vector and unknown exogenous inputs of the system. Numerical experiments confirm the validity and efficiency of the proposed parameter identification method.

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied. Therefore, in this paper, we propose a novel multi-modal marginal prototype framework for radio frequency (RF) signals (MMPRF) to improve AMOSR performance. First, MMPRF addresses the problem of simultaneous recognition of closed and open sets by partitioning the feature space in the way of one versus other and marginal restrictions. Second, we exploit the wireless signal domain knowledge to extract a series of signal-related features to enhance the AMOSR capability. In addition, we propose a GAN-based unknown sample generation strategy to allow the model to understand the unknown world. Finally, we conduct extensive experiments on several publicly available radio modulation data, and experimental results show that our proposed MMPRF outperforms the state-of-the-art AMOSR methods.

Research on filtering and classification method for white-feather broiler sound signals based on sparse representation

Aiming at the problems in the existing white-feather broiler sound signal classification research, such as not considering the information and requirements in the real engineering application scenarios, not finding new applicable sound features, not providing the basis for the selection of classification learners, and the problems in the existing white-feather broiler sound signal filtering research, such as not carrying out in-depth research, not considering the signal characteristics, not analyzing the filtering effects in detail. Therefore, in this paper, the authors cleverly combined four methods of signal filtering, signal classification, machine learning, and sparse representation to propose a filtering and classification method for white-feather broiler sound signals based on sparse representation. Specifically, in terms of signal filtering, the signal characteristics of the white-feather broiler sound signal was carefully analyzed, a combination of noise reduction processing and signal reconstruction was newly proposed, and three classic and effective approximation algorithms were adopted, then the optimal filtering method of “using OMPA for noise reduction processing, and using 50-atom OMPA for signal reconstruction” was obtained. In terms of signal classification, in addition to the time-frequency domain and Mel-Frequency Cepstral Coefficients (abbr. MFCCs), time-frequency parameter features from sparse representations were newly extracted, and a total of 60-dimensional sound features were calculated on the frame signals, thus the data set was created. Feature engineering was performed on the data set, finally, the high-quality data set was obtained. Several common machine learning classification algorithms were used to train classification learners on the data set, and random forest with better performance was selected for parameter optimization. The parameter-optimized classification learner achieved the highest average prediction accuracy of 94.09%. Majority voting was newly proposed to process the prediction results to get the required signal classification result. On this basis, a new definition of classification accuracy suitable for white-feather broiler sound signals was newly proposed. Test results in real engineering application scenarios show that, the classification accuracy achieved by the proposed method on unknown white-feather broiler sound signals is about 88.89%. Compared with the existing research, the optimal filtering method considers the signal characteristics for the first time and achieves good filtering effects. The trained classification learner obtains excellent prediction effects and achieves good classification effects on several real sound types. This study is an important supplement to existing research on animal sound signal processing and classification, and provides important reference for the subsequent white-feather broiler health monitoring research.

The bacterial tubulin homolog FtsZ generates electrical oscillations

FtsZ, a major cytoskeletal protein in all bacteria and archaea, forms a ring that directs cytokinesis. Bacterial FtsZ is considered the ancestral homolog of the eukaryotic microtubule (MT)-forming tubulins, sharing GTPase activity and the ability to assemble into protofilaments, rings, and sheets, but not MTs. Previous studies from our laboratory demonstrated that structures of isolated brain MTs spontaneously generate electrical oscillations and bursts of electrical activity similar to action potentials. No information about whether the prokaryotic tubulins may share similar properties is available. Here, we obtained by ammonium sulfate precipitation an enriched protein fraction of the endogenous FtsZ from wild-type Escherichia coli ATCC 25922 without any transfection or overexpression of the protein. As revealed by electron microscopy, FtsZ was detected by dot blot analysis and immunofluorescence that assembled into filaments and sheets in a polymerization buffer. We used the patch-clamp technique to explore the electrical properties of sheets of FtsZ and bacterial cells. Electrical recordings at various holding potentials ranging from ±200 mV showed a complex oscillatory behavior, with several peak frequencies between 12 and 110 Hz in the power spectra and a linear mean current response. To confirm the oscillatory electrical behavior of FtsZ we also conducted experiments with commercial recombinant FtsZ, with similar results. We also detected, by local field potentials, similar electrical oscillations in K+-depolarized pellets of E. coli cultures. FtsZ oscillations had a wider range of frequency peaks than MT sheets from eukaryotic origin. The findings indicate that the bacterial cytoskeleton generates electrical oscillators that may play a relevant role in cell division and unknown signaling mechanisms in bacterial populations.

Seismic signal estimation in a building-like structure using an adaptive observer

The article focuses on the estimation of a seismic signal in a building-like structure using an adaptive observer. This algorithm allows to estimate to the behavior of the structure based on the accelation measurements, achieving the estimation of uncertain signal. Thus, an adaptive observer is proposed, which estimates both the state and unknown input. The approach is validated by simulating a two-story system using real data from a real earthquake. The results demonstrate the effectiveness of the observer in estimating the displacements in the structure and the unknown seismic signal.

A Specific Emitter Identification System Design for Crossing Signal Modes in the Air Traffic Control Radar Beacon System and Wireless Devices.

To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term 'modal' refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.

Anti-disturbance observer-based proportional-retarded control design for polytopic uncertain fractional-order systems

This study deals with the stabilisation problem of fractional-order time-delay systems with polytopic uncertainties and multiple disturbances with the use of Lyapunov stability theory. Notably, the multiple disturbances comprising both the known and unknown signals, are respectively characterised by the norm-bounded and exogenous system which signifies the harmonic signals with modelling perturbations. The control protocol is configured by the integration of proportional-retarded controller with gain perturbations and the output of an anti-disturbance observer. Precisely, the disturbance observer is taken into account for attenuating the influence of disturbance signals. Moreover, the implementation of a time-delay term in the feedback loop replicates the dynamic features of a derivative action that relies only on position measurements and its time differencing capabilities make it to be minimally sensitive against noise and provide a smoothing effect. By virtue of these circumstances, the asymptotic stability of the considered system is assured. With the aid of a suitable Lyapunov function, a set of robust order-dependent sufficient conditions is derived and then solved through the MATLAB LMI toolbox. As a result, the controller gain matrices can be computed and two numerical examples, including an electrical circuit model with simulation results are presented to certify the inherent potential of the theoretical outcomes.

Optimal mass variables for semivisible jets

Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets. Classical mass reconstruction techniques may not be optimal for these unusual topologies, in which the missing transverse momentum comes from massive particles and has a nontrivial relationship to the visible jet momentum. We apply the artificial event variable network, a semisupervised, interpretable machine learning technique that uses an information bottleneck, to derive superior mass reconstruction functions for several cases of resonant semivisible jet production. We demonstrate that the technique can extrapolate to unknown signal model parameter values. We further demonstrate the viability of conducting an actual search for new physics using this method, by applying the learned functions to standard model background events from quantum chromodynamics.

RECOGNITION OF REFERENCE SIGNALS AND DETERMINATION OF THEIR WEIGHTING COEFFICIENTS IF AN ADDITIVE INTERFERENCE PRESENTS

Context. The subject matter of the article is the recognition of a reference signal in the presence of additive interference.
 Objective. The recognition of the reference signal by the obtained value of its weighting factor in conditions where additive interference is imposed on the spectrum of the reference signal at unknown random frequencies. The task is the development of a method for recognizing a reference signal for the case when the interference consists of an unknown periodic signal that can be represented by a finite sum of basis functions. In addition, interference may also include deterministic signals from a given set with unknown weighting coefficients, which are simultaneously transmitted over the communication channel with the reference signal.
 Method. The method of approximating the unknown periodic component of the interference by the sum of basis functions is used. The current number of values of the signal that enters the recognition system depends on the number of basis functions. This signal is the sum of the basis functions and the reference signal with unknown weighting coefficients. To obtain the values of these coefficients, the method based on the properties of the disproportion functions is used. The recognition process is reduced to the calculation of the weight coefficient of the reference signal. If it is zero, it indicates that the reference signal is not part of the signal being analyzed. The recognition system is multi-level. The number of levels depends on the number of basis functions.
 Results. The obtained results show that, provided that the reference signal differs by at least one component from the given set of basis functions, the recognition is successful. The given examples show that the system recognizes the reference signal even in conditions where the weighting coefficient of the interference is almost 1000 times greater than the coefficient for the reference signal. The recognition system also works successfully in conditions where the interference includes the sum of deterministic signals from a given set, which are simultaneously transmitted over the communication channel.
 Conclusions. The scientific novelty of the obtained results is that a method for recognizing the reference signal has been developed in conditions where only an upper estimate of its maximum frequency is known for the periodic component of the interference. Also, recognition occurs when, in addition to unknown periodic interference, the signals from a given set with unknown weighting coefficients are superimposed on the reference signal. In the process of recognition, in addition to the weighting factor for the reference signal, the factors for the interference components are also obtained.

ECG SIGNAL QUANTITATIVE ANALYSIS BASED ON EXTREMUM ENERGY DECOMPOSITION METHOD

Quantitative analysis of electrocardiogram (ECG) signals plays a pivotal role in objectively and quantitatively assessing cardiac electrical activity. This paper presents an innovative approach for quantitative ECG signal analysis utilizing extremum energy decomposition (EED). The methodology encompasses multiple steps: acquisition of unknown ECG signal under specific time and sampling conditions, denoising of acquired ECG signals, and subsequent decomposition of denoised ECG signals into a set of extremum modal function components alongside a residual. The n extremum modal function components obtained effectively represent different frequency bands. By evaluating these n extremum modal function components, the presence and severity of abnormalities within the ECG signal can be determined. The results showcased the effectiveness of the method in accurately identifying abnormal ECG signals, and the technique demonstrated robustness against noise interference, enhancing its practical utility in clinical and diagnostic settings. This research contributes to the field of ECG analysis by offering a quantitative toolset that enhances the objectivity and accuracy of abnormality assessment in cardiac electrical activity.

Maximum likelihood estimation of the log-concave component in a semi-parametric mixture with a standard normal density

The two-component mixture model with known background density, unknown signal density, and unknown mixing proportion has been studied in many contexts. One such context is multiple testing, where the background and signal densities describe the distribution of the p-values under the null and alternative hypotheses, respectively. In this paper, we consider the log-concave MLE of the signal density using the estimator of Patra & Sen (2016) for the mixing probability. We show that it is consistent and converges at the global rate n−2/5. An EM-algorithm in combination with an active set algorithm implemented in the R-package logcondens was used to compute the log-concave MLE. When one is interested in estimation at a fixed point, a conjecture is made about the limit distribution of our estimator. The performance of our method is assessed through a simulation study.

A survey of artificial intelligence approaches in blind source separation

In various signal processing applications, such as audio signal recovery, the extraction of desired signals from a mixture of other signals is a crucial task. To achieve superior performance and efficiency in separator systems, extensive research has been conducted. Blind source separation emerges as a relevant technique to address the challenge of separating and reconstructing unknown signals when only observations of their mixtures are available to end-users. Blind source separation involves retrieving a set of independent source signals mixed by an unknown and potentially destructive combining system. Notably, the separation process in blind source separation frameworks solely relies on observing the mixed sources without prior knowledge of the mixing algorithm or the source signal characteristics. The significance of blind source separation has garnered substantial attention, and its numerous applications have been demonstrated, which serves as the primary motivation for conducting this comprehensive study. This paper presents a systematic literature survey of blind source separation, encompassing existing methods, approaches, and applications, with a particular focus on artificial intelligence-based frameworks. Through a thorough review and examination, this work sheds light on the diverse techniques utilized in blind source separation and their performance in real-world scenarios. The study identifies research gaps in the current literature, highlighting areas that warrant further investigation and improvement. Moreover, potential avenues for future research are outlined to contribute to the ongoing development of blind source separation techniques.

ERK mediates interferon gamma-induced melanoma cell death

BackgroundInterferon-gamma (IFNγ) exerts potent growth inhibitory effects on a wide range of cancer cells through unknown signaling pathways. We pursued complementary screening approaches to characterize the growth inhibition pathway.MethodsWe performed chemical genomics and whole genome targeting CRISPR/Cas9 screens using patient-derived melanoma lines to uncover essential nodes in the IFNγ-mediated growth inhibition pathway. We used transcriptomic profiling to identify cell death pathways activated upon IFNγ exposure. Live imaging experiments coupled with apoptosis assays confirmed the involvement of these pathways in IFNγ-mediated cell death.ResultsWe show that IFNγ signaling activated ERK. Blocking ERK activation rescued IFNγ-mediated apoptosis in 17 of 23 (~ 74%) cell lines representing BRAF, NRAS, NF1 mutant, and triple wild type subtypes of cutaneous melanoma. ERK signaling induced a stress response, ultimately leading to apoptosis through the activity of DR5 and NOXA proteins.ConclusionsOur results provide a new understanding of the IFNγ growth inhibition pathway, which will be crucial in defining mechanisms of immunotherapy response and resistance.

Genome-wide meta-analysis of 92 cardiometabolic protein serum levels

ObjectivesGlobal cardiometabolic disease prevalence has grown rapidly over the years, making it the leading cause of death worldwide. Proteins are crucial components in biological pathways dysregulated in disease states. Identifying genetic components that influence circulating protein levels may lead to the discovery of biomarkers for early stages of disease or offer opportunities as therapeutic targets. MethodsHere, we carry out a genome-wide association study (GWAS) utilising whole genome sequencing data in 3,005 individuals from the HELIC founder populations cohort, across 92 proteins of cardiometabolic relevance. ResultsWe report 322 protein quantitative trait loci (pQTL) signals across 92 proteins, of which 76 are located in or near the coding gene (cis-pQTL). We link those association signals with changes in protein expression and cardiometabolic disease risk using colocalisation and Mendelian randomisation (MR) analyses. ConclusionsThe majority of previously unknown signals we describe point to proteins or protein interactions involved in inflammation and immune response, providing genetic evidence for the contributing role of inflammation in cardiometabolic disease processes.

Bivariate retrieval from intensity of cross-correlation

Pulse characterization in ultra-fast optics presents a powerful motivation to study phase retrieval problems of high order. Frequency- and time-resolved techniques for pulse characterization both construct measurements that depend on the intensity of the cross-correlation between two unknown signals undergoing known modulations. The problem of recovering these signals has been traditionally studied and solved with alternating minimization, but recently Wirtinger gradient techniques were demonstrated to invert frequency-resolved measurements on a symmetric signal pair (Pinilla et al., 2019). In this paper, we construct a generalized Wirtinger gradient and Hessian to solve a wide breadth of problems including signal recovery from time- and frequency-resolved measurements. We further demonstrate that both measurement paradigms are special cases of low-rank phase retrieval but with a special structure that disrupts spectral initializers. To combat this problem, we present a tensor-based iterative hard thresholding initializer that, when paired with a Wirtinger gradient descent, is capable of recovering unknown signals with fewer measurements than matrix-based alternating minimization or spectral initialization methods. Finally, we employ Wirtinger gradient descent to recover signals from real-world DSCAN (Wilhelm et al., 2021) measurements and compare results with the existing state-of-the-art.