Emitter Signals Research Articles

‘Forbidden’ States Boost Fluorescence Resonance Energy Transfer (FRET) Studies of Membrane Receptors in Live Cells

For probing biomolecular interactions or conformational changes in a live-cell setting the distance depending Fluorescence resonance energy transfer (FRET) is often the method of choice. The G-protein-coupled receptor (GPCR) superfamily mediates cellular responses and communication across cellular membranes, and is the largest known class of molecular targets with proven therapeutic value. On the one hand, real-time monitoring of membrane receptors and their interactions is vital to understand how they act in concert. On the other hand, the design of GPCR FRET probes is crucial to ensure unhampered functionality and binding kinetics of the molecular complex. Thus, such FRET probes usually require labeling compromises concerning donor-acceptor positions, distances and orientations, which limit the detectability of emitter signals in general, and the dynamic range of the FRET changes in particular. Here, we present an approach to optimize the energy transfer without changing the design of the FRET probe. We show that gold coated glass cover slips allow reinforcing the otherwise forbidden donor-acceptor energy transfer by virtual optimization of the dipole orientation. First, we confirm the resulting enhanced FRET on our nano-coatings for the inactive M1 muscarinic acetylcholine receptors (mAChRs) labeled with a CFP-FlAsH pair in classical bleaching experiments. Second, we demonstrate the advantage of this enhanced FRET technique for ligand binding studies in live cells by the increased dynamic FRET response between the inactive and active state of the M1 mAChR receptor. We believe that our techniques has particular potential for pharmaceutical drug screening to boost non-ideal FRET probes, and thus amplify signal responses without interfering with the well-characterized molecular assay.

Radar Emitter Recognition based on Transfer Learning

Radar emitter identification based on machine learning technology at present mostly assumes that the test set is identically distributed with and the training set, which causes the classification effect is not well when the database samples and the true distribution of the signals are biased. Thus, the theory of transfer learning is introduced into the identification system, and a radar emitter signal identification method based on structural discovery and re-balancing is proposed. By means of database data and target data clustering analysis and re-sampling, correct the distribution and put the new data to the Support Vector Machine (SVM) for training and identifying reconnaissance samples. The simulation results show that the classification performance of the Support Vector Machine model in the new training sample set has been greatly improved.

Body Size Predicts Echolocation Call Peak Frequency Better than Gape Height in Vespertilionid Bats

In most vocalizing vertebrates, lighter animals tend to produce acoustic signals of higher frequency than heavier animals. Two hypotheses propose to explain this negative relationship in vespertilionid bats: (i) mass-signal frequency allometry and (ii) emitter-limited (maximum gape) signal directionality. The first hypothesis, that lighter bats with smaller larynges are constrained to calls with higher frequencies, is supported at the species level. The second hypothesis proposes that in open space, small bats use higher frequencies to achieve narrow sonar beams, as beam directionality increases with both emitter size (maximum gape) and signal frequency. This hypothesis is supported within a comparative context but remains untested beyond a few species. We analyzed gape, body mass, and echolocation data under a phylogenetic comparative framework to test these hypotheses, and considered forearm length as both a proxy for wing design and an alternative measure of bat size. Controlling for mass, we found no support for the directionality hypothesis. Body mass and relative forearm length were negatively related to open space echolocation call peak frequency, reflecting species-specific size differences, but also the influence of wing design and preferred foraging habitat on size-independent species-specific differences in echolocation call design.

Robust Radar Emitter Recognition Based on the Three-Dimensional Distribution Feature and Transfer Learning.

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for radar emitter signal recognition. To address this challenge, multi-component radar emitter recognition under a complicated noise environment is studied in this paper. A novel radar emitter recognition approach based on the three-dimensional distribution feature and transfer learning is proposed. The cubic feature for the time-frequency-energy distribution is proposed to describe the intra-pulse modulation information of radar emitters. Furthermore, the feature is reconstructed by using transfer learning in order to obtain the robust feature against signal noise rate (SNR) variation. Last, but not the least, the relevance vector machine is used to classify radar emitter signals. Simulations demonstrate that the approach proposed in this paper has better performances in accuracy and robustness than existing approaches.

Computational Feasibility Study of a Torsional Wave Transducer for Tissue Stiffness Monitoring

A torsional piezoelectric ultrasonic transducer design is proposed to measure shear moduli in soft tissue with direct access availability, using shear wave elastography technique. The measurement of shear moduli of tissues is a challenging problem, mainly derived from a) the difficulty of isolating a pure shear wave, given the interference of multiple waves of different types (P, S, even guided) emitted by the transducers and reflected in geometric boundaries, and b) the highly attenuating nature of soft tissular materials. An immediate application, overcoming these drawbacks, is the measurement of changes in cervix stiffness to estimate the gestational age at delivery. The design has been optimized using a finite element model (FEM) and a semi-analytical estimator of the probability of detection (POD) to determine a suitable geometry, materials and generated waves. The technique is based on the time of flight measurement between emitter and receiver, to infer shear wave velocity. Current research is centered in prototype testing and validation. The geometric optimization of the transducer was able to annihilate the compressional wave emission, generating a quite pure shear torsional wave. Currently, mechanical and electromagnetic coupling between emitter and receiver signals are being the research focus. Conclusions: the design overcomes the main described problems. The almost pure shear torsional wave along with the short time of flight avoids the possibility of multiple wave interference. This short propagation distance reduce the effect of attenuation, and allow the emission of very low energies assuring a good biological security for human use. Keywords—Cervix ripening, preterm birth, shear modulus, shear wave elastography, soft tissue, torsional wave.

A three-way incremental-learning algorithm for radar emitter identification

Radar emitter identification has been recognized as an indispensable task for electronic intelligence system. With the increasingly accumulated radar emitter intelligence and information, one key issue is to rebuild the radar emitter classifier efficiently with the newly-arrived information. Although existing incremental learning algorithms are superior in saving significant computational cost by incremental learning on continuously increasing training samples, they are not adaptable enough yet when emitter types, features and samples are increasing dramatically. For instance, the intra-pulse characters of emitter signals could be further extracted and thus expand the feature dimension. The same goes for the radar emitter type dimension when samples from new radar emitter types are gathered. In addition, existing incremental classifiers are still problematic in terms of computational cost, sensitivity to data input order, and difficulty in multiemitter type identification. To address the above problems, we bring forward a three-way incremental learning algorithm (TILA) for radar emitter identification which is adaptable for the increase in emitter features, types and samples.

Recognition of radar emitter signals based on SVD and AF main ridge slice

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

Emitter Target Tracking by Tracklet Association Using Affinity Propagation

Due to electromagnetic silence, passive tracking systems for emitter targets usually produce track segments (i.e., tracklets) rather than an entire trajectory of the target. Therefore, a multistage method for emitter target tracking is proposed in this paper. In the stage of tracklet generation, the Gaussian mixture-probability hypothesis density tracker with adaptive estimation of target birth intensity is applied to generate reliable tracklets of the emitter targets. After that, in the stage of tracklet association, the multipoint motion information and emitter signal information are integrated to compute the similarities between the tracklets. The affinity propagation algorithm, which does not impose the constraint of one-to-one correspondence, is then used to cluster the tracklets. In the stage of association refining, the clustering result is adjusted to refine the final trajectories according to the spatial-temporal constraint of the tracklets. The simulation results show that the proposed method is robust and performs well.

Time Frequency Single Source Point Detection Based Method for Radar Signal Sorting

Common sorting method have low sorting rates and is sensitive to the Signal-to-Noise Ratio(SNR), detection of time frequency single source point is applied to sort unknown complicated radar signal, high sorting accuracy can be got. The single source points of each radar source signal is detected, then the mixing vector in the corresponding single source point set was estimated by Singular Value Decomposition (SVD), the mixed matrix is estimated simultaneously by cluster validation technique, based on k-means clustering algorithm. Finally, Pulse Description Words (PDW) of each radar signal can be worked out. Experiment results demonstrated that the radar emitter signals extracted by this method showed good performance of noise-resistance and clustering at large-scale SNR. Radar recommaissance is a key part of electromagnetic countermeasure. The mixed signal is sorted by reconnaissance system and the pulse description words of each signal pulse can be calculated to locate radiation sources or assess the threat of each source. In (1), Blind Signal Separation (BSS) was applied in radar signal sorting, a method based on fourth-order cumulant was proposed to accomplish signal sorting and achieved good results, but the number of source signals had been already known in that simulation experiment, the circumstances of unknown signal numbers was not mentioned in that paper. In (2), another blind separation method based on fixed point independent component analysis algorithm was proposed to solve the problem of overdetermined radar signal sorting, but the method can not accomplish underdetermined radar signal sorting. In order to solve the problem of unknown signal numbers in radar signal sorting by BSS, too many methods have been presented. In (3), a robust algorithm under the condition of unknown signal source number was proposed. In that algorithm, the data received by the array was pretreated via the projection transformation to inhibit model errors and reduce data dimension,thus improving the robustness and lightening the computation load. Then, the number of signal could be estimated according to the transformed m-Capon spatial spectrum function. In (4), the author took advantage of the straight line clustering of the sparse source signals, standardized the aliasing signals, then the aliasing signals were formed spherical cluster, thus the linear cluster was turned into density cluster. And the clustering center was searched and obtained by using the ant clustering algorithm, the aliasing matrix can be accurately evaluated. In a word, most methods require clustering to avoid the problem of unknown signal numbers, thus a good clustering algorithm is the key to solve this problem. The problem of underdetermined signal sorting can be settled by two methods: 1. Using the sparsity of source signals; 2. 'Two-step approach'. Both of them work under the condition of sparse signal. In the

유전 알고리즘 기반 레이더 펄스 모호성 해결방법

TDOA(Time Difference of Arrival) 기반 수동형 전자감시장비는 이격 동작하는 다수의 수신기에서 수신되는 전자파 신호의 펄스도착시간인 TOA(Time of Arrival) 정보를 이용하여 신호원의 공간상 위치를 탐지한다. 수신기 간 이격 거리 대비 신호원에서 방사되는 신호의 PRI(Pulse Repetition Interval)가 크지 않을 경우 TDOA 기반 위치탐지를 위한 각 수신기에서의 동일 펄스쌍 선정에 모호성이 발생될 수 있다. 본 논문에서는 이격 동작하는 각 수신기에서 수신한 펄스들과 신호원간의 순시 방향탐지결과인 AOA(Angle of Arrival)를 이용하여 펄스 선택의 모호성을 신호원의 위치와 신호원으로부터 수신한 펄스들과의 TDOA 최적화 문제로 변경하고 이를 유전 알고리즘 기반으로 TDOA 기반 위치탐지 과정에서 발생될 수 있는 동일 펄스 선택 모호성을 최소화할 수 있는 새로운 기법을 제안하며, 다양한 모의실험을 통해 제안된 기법의 성능을 분석하였다.

Vacuum Rabi spectra of a single quantum emitter.

We report the observation of the vacuum Rabi splitting of a single quantum emitter by measuring its direct spontaneous emission into free space. We use a semiconductor quantum dot inside a photonic crystal nanocavity, in conjunction with an appropriate cavity design and filtering with a polarizer and an aperture, enabling the extraction of the inherently weak emitter's signal. The emitter's vacuum Rabi spectra exhibit clear differences from those measured by detecting the cavity photon leakage. Moreover, we observe an asymmetric vacuum Rabi spectrum induced by interference between the emitter and cavity detection channels. Our observations lay the groundwork for accessing various cavity quantum electrodynamics phenomena that manifest themselves only in the emitter's direct spontaneous emission.

Incremental Discriminant Analysis on Interval-Valued Parameters for Emitter Identification

Emitter identification has been widely recognized as one crucial issue for communication, electronic reconnaissance, and radar intelligence analysis. However, the measurements of emitter signal parameters typically take the form of uncertain intervals rather than precise values. In addition, the measurements are generally accumulated dynamically and continuously. As a result, one imminent task has become how to carry out discriminant analysis of interval-valued parameters incrementally for emitter identification. Existing machine learning approaches for interval-valued data analysis are unfit for this purpose as they generally assume a uniform distribution and are usually restricted to static data analysis. To address the above problems, we bring forward an incremental discriminant analysis method on interval-valued parameters (IDAIP) for emitter identification. Extensive experiments on both synthetic and real-life data sets have validated the efficiency and effectiveness of our method.

TDOA 방위탐지방식의 위치표정 정확도

Abstract The technology of direction finding is very important to make high position fixing accuracy. TDOA(time difference of arrival) direction finding technology is a high accuracy technology and is used in RF system from 1990. The principle of TDOA is to receive an emitter signal with two antennas, measure the time difference of received signal and then convert the time differences to azimuth angle. For high DF(direction finding) accuracy long basis line and high SNR at receiving system are needed. The DF accuracy and position fixing accuracy are simulated with different SNRs and antenna base lines. We obtain the DF accuracy of 0.51 o at 0 o incident azimuth angle in case of 50m base line and 40dB SNR. Key Words : Position Fixing, SNR, Direction Finding, TDOA, CEP, Position Error Received 27 September 2014, Revised 27 October 2014Accepted 20 November 2014Corresponding Author: Gyoo-Soo Chae(Baekseok University) Email: gschae@bu.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

Radar emitter signal recognition based on multi-scale wavelet entropy and feature weighting

In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio (SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than -4 dB, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.

A New Method of Time Domain Coherency for Radar Emitter Signal Sorting

£o Based on judging the change of pulse amplitude of phase detecting, a new method of time domain coherency for the radar emitter sorting is proposed in this paper. We analyzed the theory of the signal coherency recognition method. Then, the mathematic derivation process and specific recognition steps of the new method are detailedly given. But above all, the new method can be applied into the signal sorting of radar emitter. Simulation results also reveal that this new method can effectively sort the radar emitter signals under the low signal-to-noise ratio, and it is a huge help to solve the problem of " leakage-batch ". Results demonstrate that the new method has strong theoretical value.

The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree

The optimal group is an important problem of histogram algorithm, and how to confirm group number has not a quantitative rule. So the concept of the close degree is imported to make the close degree between the upper contour line of histogram and the PDF(probability density function) of parameter as the judging criteria of optimal group. With the unknown of the PDF of parameter, the improved kernel density estimation algorithm can pre-select and estimate the PDF. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. In the parameter analysis of radar emitter signal, the algorithm based on improved kernel density estimation and close degree is used to determine optimal group, and the result indicate that this method is effective and can search the optimal group automatically.

Emitter identification of electronic intelligence system using type-2 fuzzy classifier

Emitter signal recognition is one of the key procedures in signal processing of electronic intelligence (ELINT). In particular, the identification of radar emitters has been important with the advances in radio frequency, electronics and control technologies. Jitter is an unintentional form of modulation that can have a wide variety of sources. Timing-related data errors will occur if jitter is beyond acceptable limits. Designers need a fast and easy way to obtain a complete characterization of clock jitter in the microprocessor controlled. To enhance the ability of specific emitter identification (SEI) to meet the requirement of modern ELINT, a novel identification approach for radar emitter signals based on type-2 fuzzy classifier is presented in this paper. In fuzzy type-2 sets, the uncertainty is represented as an extra dimension. In this article, we show how it is possible to reduce the effect of SEI-induced highly jittered radar emitters in ELINT, with the classifiers of type-1 and type-2 fuzzy logic. This work discusses the impact of unknown jitter sampling on signal estimation. Based on the ELINT feature extraction of radar emitter signals, the type-2 fuzzy classifier is applied to identification of radar emitters effectively. Experiment results shows that the approach can achieve high accurate classification even at higher error deviation level and has good characteristics of identification.

Generalized S-transform and Its Application to Analysis of Complex Emitter Signals

Generalized S-transform and Its Application to Analysis of Complex Emitter Signals

Special issue on multidimensional signal processing applications

This special issue onmultidimensional signal processing applications covers a broad range of methodologies and algorithms for spatially extended configurations of sensors and emitters for various purposes and applications. The spectrum of the typical wavelengths for these applications ranges from very short ones in electron transmission microsopy to visibile light, up to radio frequencies and further to sound waves in the audio range suitable for human perception. In spite of the diversity of the underlying physical principles there are similarities in the arising problems. Consequently also some of the approaches to the processing of the resulting sensor or emitter signals are shared between these applications. One common problem is the detection of drift and translation between images acquired by electron transmission microsopy and conventional cameras. Also the detection of motion in video sequences falls into this category. Another set of problems is concerned with the detection of the direction of a source of either electromagnetic or sound waves using suitable receiver arrays. But the situation may also be turned around by forming directed wave fronts from loudspeaker arrays to produce sound fields with certain spatial requirements. This set of seven papers shows thatmodernmultidimensional signal processing overcomes the traditional separation of image processing and audio processing. The presented methods and algorithms prescind from plain processing of available sensor or emitter signals. Instead the focus lies on physical processes andmathematical foundations underlying all applications. The paper A comparison between minimum variance control and other online compensation methods for specimen drift in transmission electron microscopy by A. Tejada and A.J. den Dekker presents an online method to reduce image blurring by specimen drift dur-

Feature evaluation of radar signal based on aggregation, discreteness and divisibility

Quality of intrapulse feature about radar signals has proved to be a significant foundation to decide whether the signals can be differentiated effectively.For evaluating the quality quantitatively,a method adopting fuzziness and close-degree to evaluate intrapulse feature aggregation and discreteness of signals was proposed in this paper.Space distribution of intrapluse feature about radar signals was analyzed with this method,while intrapulse feature aggregation was evaluated by fuzziness and intrapulse feature discreteness was evaluated by close-degree.And for the overlapping states of feature space distribution,a linear separable measure of intrapluse feature about radar signals was put forward by within-class distance,between-class distance and linear discriminated criterion.The simulation results,based on the experiments of two intrapulse features extracted via time-frequency atom approach about five kinds of radar signal,show that the method and measure proposed in the paper are effective and feasible.It provides a new idea and approach for quantitatively evaluating features of the radar emitter signal.