Sparse frequency waveform analysis and design based on ambiguity function theory

According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate) and LPI (low probability of intercept). The simulation results verify the effectiveness of this method.

The variation of device characteristics is a challenge to present and future large-scale integrations. One of the origins of the variation is line width roughness (LWR). To facilitate the efforts to cope with LWR, we developed a method to accurately characterize LWR basing on the analysis of power spectral densities (PSDs). Because experimental PSDs are intrinsically discrete, we derive simple analytic formulas of the discrete PSDs by assuming that the autocorrelation function (ACF) exponentially decays with distance. The PSDs calculated by using the formulas agree excellently with experimentally obtained PSDs of photoresist LWR. From the result we find that the photoresist LWR of this study has a standard deviation of 2.5 nm and an exponentially-decaying autocorrelation function with a correlation length of 35 nm. Although the experimental PSDs inevitably contain a component produced by scanning-electron-microscope (SEM)-image noise, the two components of LWR and noise are separately determined by the method of this study. Due to this feature, the resultant variance of LWR is independent of the noise intensity. However, it is still important to reduce the noise component in order to maintain the accuracy of analysis especially in the case of LWR that has an unknown functional form of PSD. This is because even the PSDs that have different functional forms sometimes look alike in the presence of large noise. To reduce the noise effect, it is effective to average the SEM images perpendicularly to fine lines before edge detections. The procedure does not reduce the variance unlike averaging along the lines. The method of this study is applicable not only to LWR but also to other cases as far as the ACF exponentially decays with distance, or equivalently the spectral line shape is Lorentzian. Accordingly, it forms the basis for spectral analysis of most experimental results.

Designing radar waveforms with notched spectral regions can mitigate mutual interference with other proximate RF users. However, this capability comes at the cost of degraded range-Doppler sidelobe performance. To evaluate the limitations of correlation-based processing, the null-constrained power spectral density that minimizes correlation sidelobe levels is determined for comparison with waveform and pulse compression filter design methods. Existence of the least-squares (LS) global optimum indicates a fundamental dynamic range limitation for notched power spectra (notwithstanding further receive compensation or range resolution spoiling). Recent work investigated spectrally notched random FM (RFM) waveform design where ad-hoc tapering was incorporated into the null shape as a heuristic means of reducing range sidelobes. Here, waveforms designed according to the optimal null-constrained spectral template are demonstrated to have improved sidelobe performance after pulse compression and slow-time processing. Further, because these waveforms are designed according to the LS optimal spectral template, application of the LS mismatched filter provides additional sidelobe reduction (toward the global limit) with minimal mismatch loss.

We present model light curves for accreting black hole candidates (BHCs) based on a recently proposed model for their spectro-temporal properties. According to this model, the observed light curves and aperiodic variability of BHCs are due to a series of soft photon injections at random (Poisson) intervals near the compact object and their reprocessing into hard radiation in an extended but nonuniform hot plasma corona surrounding the compact object. We argue that the majority of the timing characteristics of these light curves are due to the stochastic nature of the Comptonization process in the extended corona, whose properties, most notably its radial density dependence, are imprinted in them. We compute the corresponding power spectral densities (PSD), autocorrelation functions, time skewness of the light curves, and time lags between the light curves of the sources at different photon energies and compare our results to observation. Our model light curves compare well with observations, providing good fits to their overall morphology, as manifest by the autocorrelation and skewness functions. The lags and PSDs of the model light curves are also in good agreement with those observed (the model can even accommodate the presence of quasi-periodic oscillations). Finally, while most of the variability power resides at timescales approximately a few seconds, at the same time, the model allows also for shots of a few milliseconds in duration, in accordance with observation. We suggest that refinements of this type of model along with spectral and phase lag information can be used to probe the structure of this class of high-energy sources.

PAPR analysis in OTFS using the centre phase sequence matrix based PTS method

JPT Forum articles are limited to 1,500 words including 250 words for each table and figure, or a maximum of two pages in JPT. A Forum article may present preliminary results or conclusions of an investigation that the present preliminary results or conclusions of an investigation that the author wishes to publish before completing a full study; it may impart general technical information that does not warrant publication as a full-length paper. All Forum articles are subject to approval by a editorial committee. Letters to the editor are published under Dialogue, and may cover technical or nontechnical topics. SPE-AIME reserves the right to edit letters for style and content. A wave-follower buoy has many advantages over nearly all other types of wave-measuring devices, particularly on the open sea or far offshore. The buoy requires only the earth's gravitational field as a frame of reference instead of a pier, a piling, or perhaps a large spar buoy. Any of the ordinary-capacity or resistive-wave gauges must be attached to a solid place. Any type of wave-follower (accelerometer) buoy will exhibit its own resonant dynamic behavior in the presence of a wave field. The engineer in need of wave information generally needs to know its power spectral density to be able to apply the data, whether the problem is wave forces on a structure or something else. It is not sufficient to simply double-integrate the vertical acceleration of the wave buoy and claim to have the desired wave-height information. The objective of this paper is to compare an actual power spectral density in a wave field with that of the power spectral density in a wave field with that of the accelerometer-output power spectral density produced in the same wave field. We will show an empirical spectral transfer function that can be applied to the observed accelerometer spectrum to yield the desired power spectral density of the driving waves. For this purpose, an ideal linear model for the buoy was proposed to keep the system design simple. Even with this incorrect model, the comparison of the precise spectrum with the compensated spectrum out of the buoy is quite good. The buoy used for this study was a Model WF-100 buoy manufactured by Coastal Data Service, Inc. Let x(t) be the actual vertical displacement of the sea surface as a function of time. If y(t) is the output from the buoy accelerometer, then x(t) and Y\y(t) are related as in Eq. 1. x(t) * h(t) = y(t),...................................(1) where h(t) is the time domain representative of desired transfer function and * denotes convolution. In the frequency domain, we have Eq. 2. X (f) x H(f) = Y(f)...................................(2) Using the Fourier transform theory, one obtains Eq. 3. Gy(f) H(f) 2 = ------------, Gx(f) where Gx(f) = X(f) 2 ..................................(3) In this equation, means absolute value, means expected value, Gx(f) is the power spectral density of waves, and Gy(f) is the power spectral density of the accelerometer output. In this experiment, the two desired power spectral densities were generated by a minicomputer, power spectral densities were generated by a minicomputer, and the ratio of the two in Eq. 3 leads to the compensation function, H(f). The linear model is shown in Eq. 4. 1 1/2 sin (1.26f) H(f) = 32.2 [w2] -------- x ------------- 1 + 2f2 1.26f I II III ...................................(4) Factor I in Eq. 4 includes the accelerometer scale constant and w 2 corresponds to straight double integration of sinusoidal components. P. 652

Data-driven prediction of aerodynamic noise of transonic buffeting over an airfoil

Background:Non-nutritive suck (NNS) is used to promote ororhythmic patterning and assess oral feeding readiness in preterm infants in the neonatal intensive care unit (NICU). While time domain measures of NNS are available in real time at cribside, our understanding of suck pattern generation in the frequency domain is limited. The aim of this study is to model the development of NNS in the frequency domain using Fourier and machine learning (ML) techniques in extremely preterm infants (EPIs).Methods:A total of 117 EPIs were randomized to a pulsed or sham orocutaneous intervention during tube feedings 3 times/day for 4 weeks, beginning at 30 weeks post-menstrual age (PMA). Infants were assessed 3 times/week for NNS dynamics until they attained 100% oral feeding or NICU discharge. Digitized NNS signals were processed in the frequency domain using two transforms, including the Welch power spectral density (PSD) method, and the Yule-Walker PSD method. Data analysis proceeded in two stages. Stage 1: ML longitudinal cluster analysis was conducted to identify groups (classes) of infants, each showing a unique pattern of change in Welch and Yule-Walker calculations during the interventions. Stage 2: linear mixed modeling (LMM) was performed for the Welch and Yule-Walker dependent variables to examine the effects of gestationally-aged (GA), PMA, sex (male, female), patient type [respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD)], treatment (NTrainer, Sham), intervention phase [1, 2, 3], cluster class, and phase-by-class interaction.Results:ML of Welch PSD method and Yule-Walker PSD method measures revealed three membership classes of NNS growth patterns. The dependent measures peak_Hz, PSD amplitude, and area under the curve (AUC) are highly dependent on PMA, but show little relation to respiratory status (RDS, BPD) or somatosensory intervention. Thus, neural regulation of NNS in the frequency domain is significantly different for each identified cluster (classes A, B, C) during this developmental period.Conclusions:Efforts to increase our knowledge of the evolution of the suck central pattern generator (sCPG) in preterm infants, including NNS rhythmogenesis will help us better understand the observed phenotypes of NNS production in both the frequency and time domains. Knowledge of those features of the NNS which are relatively invariant vs. other features which are modifiable by experience will likewise inform more effective treatment strategies in this fragile population.

Evaluation of the microscale structure and performance of asphalt mixtures under different design methods

Distributed acoustic sensing (DAS) is an ideal tool for ambient noise tomography owing to the dense spatial measurements and the ability to continuously record in harsh environments, such as underwater. Although the fine spatial sampling of DAS facilitates the imaging of small-scale lateral velocity heterogeneities, efforts relying on dispersion-based ambient noise tomography are hampered by the underlying premise of negligible lateral variations across the segment used for dispersion curve extraction. To image small-scale structures, this method should be augmented with objective and independent approaches that are not scale limited. Here, we show that power spectral densities (PSDs) and autocorrelations (ACs) of DAS data reveal extremely detailed frequency-dependent resonance and wave propagation characteristics. These observations contain crucial information on lateral and vertical wave propagation. We use these methods to demonstrate the ability to image a complex underwater basin using ambient noise recorded on a fiber deployed offshore Greece. A 2D shear-wave velocity model was derived by analyzing Scholte-wave dispersion. The PSD and AC reveal significant lateral variations across the short 2.5 km long fiber segment, including basin edge effects and scattered waves. These were used to further constrain and modify the velocity model. The modified model is supported by waveform simulations that qualitatively reproduce the PSD and AC observations. Our results demonstrate the advantages of incorporating PSD and AC observations into ambient noise-based imaging. The spatially continuous observation of resonance modes across the basin highlights the benefit of DAS acquisitions for ground-motion estimation.

Chapter 12 presents a detailed analysis of continuous-time signals and systems in the frequency domain, including the theory of Fourier series and Fourier transforms, and key examples relevant for the analysis and synthesis of signals processed in the digital transceiver blocks of a communication system. The amplitude, magnitude, phase, and power spectra are defined and calculated for typical signals. In particular, the Fourier transform of periodic signals is presented, due to its importance in communication systems theory and practice. Using a unique notation that distinguishes energy and power signals, the correlation, power, and energy spectral density functions are inter-related by proving the Wiener–Khintchine theorem. A comprehensive analysis of a linear-time-invariant system, using the concepts of impulse response, system correlation function, and power spectral density, both for power signals and energy signals, is presented. In addition, Parseval’s theorem and the Rayleigh theorem are proven.

In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiring sophisticated a priori knowledge of environmental parameters which may be difficult to obtain in real-time scenarios. In this paper, we propose a novel hybrid model-driven and data-driven architecture that adapts to the ever changing environment and allows for adaptive unimodular waveform design. In particular, the approach lays the groundwork for developing extremely low-cost waveform design and processing frameworks for radar systems deployed in autonomous vehicles. The proposed model-based deep architecture imitates a well-known unimodular signal design algorithm in its structure, and can quickly infer statistical information from the environment using the observed data. Our numerical experiments portray the advantages of using the proposed method for efficient radar waveform design in time-varying environments.

We model the spectral signatures or spectral reflectance curves representing colors as power spectral density (PSD) functions, instead of considering them signals. Assuming that colors are generated by stationary random processes and deploying the Wiener-Khinchin theorem, we link the PSD of a color, thus the physical measurement of reflected or radiant energy, to the information provided by such spectrum. From the PSD, through the inverse Fourier transform, we recover the autocorrelation function of the corresponding random process. We propose ranking the spectral signatures of colors by imposing a lexicographic ordering of the autocorrelation function values. Such ranking can be deployed for the construction of nonlinear operators like morphological ones. We validate our approach on reference colors of a standard color set widely-used in colorimetry. We show the usefulness of our approach by computing morphological profiles for pixel classification using a widely-known hyperspectral image.

The shared waveform design for integrated radar detection and jamming signal is an important research component of modern multifunctional integrated Radio Frequency (RF) systems. The integrated signals designed by the established research are only capable of forming incoherent jamming, which has a great demand on power. This paper proposes a new waveform design method for integrated radar detection and coherent jamming signal based on Linear Frequency Modulation (LFM) signal, which can form advanced and delayed false target jamming to enemy. In order to reduce the jamming of enemy LFM signals, an integrated signal receiving and processing method was proposed based on prior information to identify and suppress enemy LFM signals. Theoretical analysis and simulation results show that the integrated signal designed in this paper can form multi-order false target jamming to the enemy, and the enemy LFM signal is well suppressed after receiving and processing, thus achieving good radar detection performance.

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system's target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.