Nonlinear Detection Research Articles

Resolving plasmon-mediated high-order multiphoton excitation pathways in dolmen nanostructures using ultrafast nonlinear optical interferometry.

The multiphoton excitation pathways of plasmonic nanorod assemblies are described. By using dolmen structures formed from the directed assembly of three gold nanorods, plasmon-mediated three-photon excitation is resolved. These high-order multiphoton excitation channels were accessed by resonantly exciting a hybrid mode of the dolmen structure that was resonant with the 800-nm carrier wavelength of an ultrafast laser system. Rotation of the exciting field polarization to a non-resonant configuration did not generate third-order responses. Hence, the multiphoton excitation and resultant non-equilibrium electron distributions were generated by structure- and mode-selective excitation. Correlation between high-order and resonant plasmon excitation was achieved through sub-cycle time-resolved interferometric detection of incoherent nonlinear emission signals. The results illustrate the advantages of nonlinear optical interferometry and Fourier analysis for distinguishing plasmon-mediated processes from those that do not require plasmon excitation.

Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity

Linear and nonlinear signal detection and estimation in high-dimensional nonparametric regression under weak sparsity

Nonlinear Channel Estimation and Signal Detection for Quantized OFDM System

Nonlinear Channel Estimation and Signal Detection for Quantized OFDM System

Geometric Insights into the Multivariate Gaussian Distribution and Its Entropy and Mutual Information.

In this paper, we provide geometric insights with visualization into the multivariate Gaussian distribution and its entropy and mutual information. In order to develop the multivariate Gaussian distribution with entropy and mutual information, several significant methodologies are presented through the discussion, supported by illustrations, both technically and statistically. The paper examines broad measurements of structure for the Gaussian distributions, which show that they can be described in terms of the information theory between the given covariance matrix and correlated random variables (in terms of relative entropy). The content obtained allows readers to better perceive concepts, comprehend techniques, and properly execute software programs for future study on the topic's science and implementations. It also helps readers grasp the themes' fundamental concepts to study the application of multivariate sets of data in Gaussian distribution. The simulation results also convey the behavior of different elliptical interpretations based on the multivariate Gaussian distribution with entropy for real-world applications in our daily lives, including information coding, nonlinear signal detection, etc. Involving the relative entropy and mutual information as well as the potential correlated covariance analysis, a wide range of information is addressed, including basic application concerns as well as clinical diagnostics to detect the multi-disease effects.

A Unified Power Amplifier Representation-based Receiver Equalization Technique for Nonlinear OFDM Signal Detection

A Unified Power Amplifier Representation-based Receiver Equalization Technique for Nonlinear OFDM Signal Detection

Hong-Ou-Mandel interferometry and spectroscopy using entangled photons

Optical interferometry has been a long-standing setup for characterization of quantum states of light. Both linear and the nonlinear interferences can provide information regarding the light statistics and underlying detail of the light-matter interactions. Here we demonstrate how interferometric detection of nonlinear spectroscopic signals may be used to improve the measurement accuracy of matter susceptibilities. Light-matter interactions change the photon statistics of quantum light, which are encoded in the field correlation functions. Application is made to the Hong-Ou-Mandel two-photon interferometer that reveals entanglement-enhanced resolution that can be achieved with existing optical technology.

Deep Learning Based Nonlinear Signal Detection in Millimeter-Wave Communications

For millimeter-wave (mm-Wave) communications, signal detection in the presence of the power amplifier (PA) nonlinearity and unknown multipath channel has remained one challenging task in single-input single-output (SISO) communication system. Besides, the PA nonlinearity in multiple-input multiple-output (MIMO) communication system also has severe effects upon the signal detection in receiver-end.In this paper, firstly, we suggest a deep-learning (DL) framework, i.e. integrating feedforward neural network (FNN) and recurrent neural network (RNN), to combat both the nonlinear distortion and linear inter-symbol-interference (ISI) from a global point of view, thereby accomplishing nonlinear equalization and signal detection at the receiver-end in SISO communication system.Utilizing the powerful mapping and learning capability of DL, our new method is able to detect symbols via the received signals corrupted by both nonlinear distortion and linear ISI, avoiding both the explicit nonlinear pre-distorter in transmitter and the channel state information (CSI) estimator.Secondly, our DL-based framework can also successfully cope with the joint nonlinear distortion and space-time decoding problem in MIMO communication system, without explicitly pre-calibrating nonlinear distortion and estimating CSI.Numerical experiments demonstrate our DL-based detector is more effective in alleviating the performance degradation both from the coupled nonlinear distortion and linear ISI in SISO communication system, and the coupled nonlinear distortion and linear space-time decoding in MIMO communication system.Compared with the state-of-the-art methods, e.g. pre-distorter and post-equalizer, our DL-based scheme effectively improves the detection performance.

CSI-Independent Non-Linear Signal Detection in Molecular Communications

Molecular communications rely on diffusive propagation to transport information, which is attractive for a variety of nano-scale applications. Due to the long-tail channel response, spatial-temporal coding of information may lead to severe inter-symbol interference (ISI). Classical linear signal processing in wireless communications is usually operating with high complexity and high signal-to-noise ratios, whereas signal processing in molecular communication system requires operating in opposite conditions. In this work, we propose a novel signal processing paradigm inspired by the biological principle, which enables low-complexity signal detection in extremely noisy environments. We first propose a non-linear filter inspired by stochastic resonance, which is found in a variety of biological systems, and it can significantly improve the output SNR by converting noise to useful signals. Then, we design a non-coherent detection method, one which exploits the generally transient trend of observed signals (i.e. quick-rising and slow-decaying) rather than hidden channel state information (CSI), thus excluding CSI estimation and involving only summations. Implementation issues are also discussed, including parameters configuration and adaptive threshold. Numerical results show that the proposed bio-inspired scheme can improve the performance remarkably over classical approaches. Even compared with the optimal linear methods, the required SNR of the proposed scheme can be reduced by 7 dB, which reaffirms why it can be used in noisy biological environments. As the first attempt to design bio-inspired molecular signal detectors, the proposed non-linear processing paradigm may provide the great promise to the emerging nano-machine applications.

Going with the flow: hydrodynamic cues trigger directed escapes from a stalking predator.

In the coevolution of predator and prey, different and less well-understood rules for threat assessment apply to freely suspended organisms than to substrate-dwelling ones. Particularly vulnerable are small prey carried with the bulk movement of a surrounding fluid and thus deprived of sensory information within the bow waves of approaching predators. Some planktonic prey have solved this apparent problem, however. We quantified cues generated by the slow approach of larval clownfish ( Amphiprion ocellaris) that triggered a calanoid copepod ( Bestiolina similis) to escape before the fish could strike. To estimate water deformation around the copepod immediately preceding its jump, we represented the body of the fish as a rigid sphere in a hydrodynamic model that we parametrized with measurements of fish size, approach speed and distance to the copepod. Copepods of various developmental stages (CII-CVI) were sensitive to the water flow caused by the live predator, at deformation rates as low as 0.04 s-1. This rate is far lower than that predicted from experiments that used artificial predator-mimics. Additionally, copepods localized the source, with 87% of escapes directed away (greater than or equal to 90°) from the predator. Thus, copepods' survival in life-threatening situations relied on their detection of small nonlinear signals within an environment of locally linear deformation.

Label-Free Multiphoton Microscopy: The Origin of Fluorophores and Capabilities for Analyzing Biochemical Processes

Multiphoton microscopy (MPM) is a method of molecular imaging and specifically of intravital imaging that is characterized by high spatial resolution in combination with a greater depth of penetration into the tissue. MPM is a multimodal method based on detection of nonlinear optical signals - multiphoton fluorescence and optical harmonics - and also allows imaging with the use of the parameters of fluorescence decay kinetics. This review describes and discusses photophysical processes within major reporter molecules used in MPM with endogenous contrasts and summarizes several modern experiments that illustrate the capabilities of label-free MPM for molecular imaging of biochemical processes in connective tissue and cells.

Saturated excitation microscopy using differential excitation for efficient detection of nonlinear fluorescence signals

We report a method to increase the efficiency of detecting nonlinear fluorescence signals in saturated excitation (SAX) microscopy. With this method, we compare fluorescence signals obtained under different degrees of saturated excitation to extract the nonlinear fluorescent signal induced by saturated excitation. Compared to conventional SAX microscopy using the harmonic demodulation technique, the detection efficiency of the fluorescence signal can be increased up to 8 and 32 times in imaging using the second-order and the third-order nonlinear fluorescence signals, respectively. We combined this approach with pulsed excitation, which is effective to reduce photobleaching effects, and achieved super-resolution imaging using third-order nonlinear fluorescence signals induced by saturated excitation of an organic dye. The resolution improvement was confirmed in the observations of fluorescent beads, actin-filaments in HeLa cells, and a spine in mouse brain tissue.

Detectability of the impacts of ozone-depleting substances and greenhouse gases upon stratospheric ozone accounting for nonlinearities in historical forcings

Abstract. We perform a formal attribution study of upper- and lower-stratospheric ozone changes using observations together with simulations from the Whole Atmosphere Community Climate Model. Historical model simulations were used to estimate the zonal-mean response patterns (“fingerprints”) to combined forcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases (GHGs), as well as to the individual forcing by each factor. Trends in the similarity between the searched-for fingerprints and homogenized observations of stratospheric ozone were compared to trends in pattern similarity between the fingerprints and the internally and naturally generated variability inferred from long control runs. This yields estimated signal-to-noise (S∕N) ratios for each of the three fingerprints (ODS, GHG, and ODS + GHG). In both the upper stratosphere (defined in this paper as 1 to 10 hPa) and lower stratosphere (40 to 100 hPa), the spatial fingerprints of the ODS + GHG and ODS-only patterns were consistently detectable not only during the era of maximum ozone depletion but also throughout the observational record (1984–2016). We also develop a fingerprint attribution method to account for forcings whose time evolutions are markedly nonlinear over the observational record. When the nonlinearity of the time evolution of the ODS and ODS + GHG signals is accounted for, we find that the S∕N ratios obtained with the stratospheric ODS and ODS + GHG fingerprints are enhanced relative to standard linear trend analysis. Use of the nonlinear signal detection method also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. Furthermore, by explicitly considering nonlinear signal evolution, the complete observational record can be used in the S∕N analysis, without applying piecewise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in either the upper- or lower-stratospheric SWOOSH data, irrespective of the signal detection method used. In the WACCM simulations of future climate change, the GHG signal is statistically identifiable between 2020 and 2030. Our findings demonstrate the importance of continued stratospheric ozone monitoring to improve estimates of the contributions of ODS and GHG forcing to global changes in stratospheric ozone.

Traction Inverter Open Switch Fault Diagnosis Based on Choi–Williams Distribution Spectral Kurtosis and Wavelet-Packet Energy Shannon Entropy

In this paper, a new approach for fault detection and location of open switch faults in the closed-loop inverter fed vector controlled drives of Electric Multiple Units is proposed. Spectral kurtosis (SK) based on Choi–Williams distribution (CWD) as a statistical tool can effectively indicate the presence of transients and locations in the frequency domain. Wavelet-packet energy Shannon entropy (WPESE) is appropriate for the transient changes detection of complex non-linear and non-stationary signals. Based on the analyses of currents in normal and fault conditions, SK based on CWD and WPESE are combined with the DC component method. SK based on CWD and WPESE are used for the fault detection, and the DC component method is used for the fault localization. This approach can diagnose the specific locations of faulty Insulated Gate Bipolar Transistors (IGBTs) with high accuracy, and it requires no additional devices. Experiments on the RT-LAB platform are carried out and the experimental results verify the feasibility and effectiveness of the diagnosis method.

Outer Race Bearing Fault Identification of Induction Motor based on Nonlinear Signal Detection and Time Frequency Representation

Outer Race Bearing Fault Identification of Induction Motor based on Nonlinear Signal Detection and Time Frequency Representation

Enhanced nonlinear spectroscopy for monolayers and thin films in near-Brewster’s angle reflection pump-probe geometry

We experimentally demonstrate and theoretically explicate a method that greatly enhances the detection of third-order nonlinear signals from monolayers and thin films on dielectric substrates. Nonlinear infrared signals, including two dimensional infrared (2D IR) vibrational echo signals, were detected from a functionalized alkyl chain monolayer on a dielectric SiO2 surface in a near-Brewster’s angle reflection pump-probe geometry. We observed a tremendous enhancement of the signal-to-noise (S/N) ratio in this geometry compared with a conventional transmission pump-probe geometry signal. The S/N enhancement is achieved by the greatly increased modulation of the local oscillator (LO) field that is induced by the nonlinear signal field. By reducing the LO field without loss of the signal field, the modulation amplitude acquired in this geometry was enhanced by more than a factor of 50. The incident angle dependence of the enhancement was measured and the result agreed remarkably well with theoretical calculations. We combined this geometry with a germanium acousto-optic modulator pulse shaping system to apply 2D IR spectroscopy to the monolayer. The enhanced and phase-stable 2D IR spectra gave detailed dynamical information for the functionalized alkyl chain monolayer. The application of the method to films with finite thickness was described theoretically. The range of film thicknesses over which the method is applicable is delineated, and we demonstrate that accurate dynamical information from thin films can be obtained in spite of dispersive contributions that increase with the film thickness. While we focus on infrared experiments in this article, the method and the theory are applicable to visible and ultraviolet experiments as well.

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation, at 32 Gbd, using only directly detected data.

Principal curve-based monitoring chart for anomaly detection of non-linear process signals

This study proposes a monitoring chart for anomaly detection of non-linear process signals generated by semiconductor manufacturing processes. In these manufacturing processes, fault detection and classification (FDC) and statistical process control (SPC) have been established as fundamental techniques to improve production efficiency and yield. Non-linear process signals are collected through automatic sensing during each operation cycle of a manufacturing process. As these cyclic signals non-linearly vary on the process state, the usage of the prevalent SPC chart is limited. Therefore, we propose a more efficient monitoring chart considering non-linear and time-variant characteristics. Using the principal curve, a non-linear smoothing algorithm, we construct a time-variant centerline that represents the standard pattern of the process. Then, control limits are calculated with time-variant variances over the course of the process. To evaluate performance, the proposed method was applied to industrial data for chemical vapor deposition (CVD), a semiconductor manufacturing process. We employed the misdetection ratio of signals to evaluate the performance. The proposed method demonstrated superior performance compared to other existing methods.

Extending lock-in methods: term isolation detection of nonlinear signals.

We show that components of a nonlinear signal can be measured using phase-sensitive detection at unconventional demodulation frequencies, allowing us to isolate individual terms from the signal. To demonstrate this technique, autocorrelation measurements of an ultrafast pulsed laser were performed using two-photon absorption. In this example, the isolation of individual autocorrelation terms may provide internal consistency checks to improve the precision and accuracy of pulse characterization. More generally, this scheme can be extended to a range of nonlinear measurements. As a demonstration, we analyze a three-photon autocorrelation model, showing that many nonlinear signals can be studied with this method. We anticipate that term isolation detection will find application in a broad range of experiments, such as multidimensional Fourier transform spectroscopy or coherent anti-Stokes Raman spectroscopy.

Nonlinear Plasmonic Sensing.

We introduce the concept of nonlinear plasmonic sensing, relying on third harmonic generation from simple plasmonic nanoantennas. Because of the nonlinear conversion process we observe a larger sensitivity to a local change in the refractive index as compared to the commonly used linear localized surface plasmon resonance sensing. Refractive index changes as small as 10(-3) can be detected. In order to determine the spectral position of highest sensitivity, we perform linear and third harmonic spectroscopy on plasmonic nanoantenna arrays, which are the fundamental building blocks of our sensor. Furthermore, simultaneous detection of linear and nonlinear signals allows quantitative comparison of both methods, providing further insight into the working principle of our sensor. While the signal-to-noise ratio is comparable, nonlinear sensing gives about seven times higher relative signal changes.

Compact Nonlinear Yagi-Uda Nanoantennas.

Nanoantennas have demonstrated unprecedented capabilities for manipulating the intensity and direction of light emission over a broad frequency range. The directional beam steering offered by nanoantennas has important applications in areas including microscopy, spectroscopy, quantum computing, and on-chip optical communication. Although both the physical principles and experimental realizations of directional linear nanoantennas has become increasingly mature, angular control of nonlinear radiation using nanoantennas has not been explored yet. Here we propose a novel concept of nonlinear Yagi-Uda nanoantenna to direct second harmonic radiation from a metallic nanosphere. By carefully tuning the spacing and dimensions of two lossless dielectric elements, which function respectively as a compact director and reflector, the second harmonic radiation is deflected 90 degrees with reference to the incident light (pump) direction. This abnormal light-bending phenomenon is due to the constructive and destructive interference between the second harmonic radiation governed by a special selection rule and the induced electric dipolar and magnetic quadrupolar radiation from the two dielectric antenna elements. Simultaneous spectral and spatial isolation of scattered second harmonic waves from incident fundamental waves pave a new way towards nonlinear signal detection and sensing.