Phase Of Waveform Research Articles

Flux Reversal Permanent Magnet Machines With Single-Layer Non-Overlapping Windings

Single-layer (SL) non-overlapping windings are applied to flux reversal permanent magnet (FPRM) machines, to extend the family of stator-PM machines. The machine topology and operating principle are introduced, and the general rules of symmetrical phase flux-linkages in machines with various slot/pole number combinations are found. The even order coil flux-linkage harmonics can be cancelled in the resultant phase waveforms due to opposite polarities of either magnets or coils. Moreover, based on finite element (FE) analysis, the FRPM machines with different rotor pole numbers, equipped with SL or double-layer (DL) windings, are comprehensively compared. The results reveal that compared to their DL counterparts, the FRPM machines with SL windings (FRPM-SL) exhibit similar torques and iron losses, as well as enhanced flux-weakening and fault-tolerant capabilities due to higher self-inductances but lower mutual-inductances. A pair of FRPM-SL prototype machines are manufactured and tested to validate the predictions.

Experimental study on the axial growth characteristics of streamer stem during dark period in long spark discharge

This paper presents an original investigation into the axial evolution of streamer stem during a dark period in long spark discharge. To obtain thermodynamic morphology and temperature distribution of stems, we set up a quantitative schlieren system with the temporal and spatial resolutions of 0.37 μs and 31 μm/pixel, respectively. The quantitative schlieren observation experiments of positive leader discharge with a 1.0 m rod-plate gap were carried out, and the time-resolved quantitative schlieren images were captured. Furthermore, the temperature distribution of stems and its morphology evolution in the axial direction during a dark period were obtained. Due to the dispersion of first streamer discharge, the gas temperature in stem roots shows two evolutionary trends, namely, rising and falling. It was found that the gas temperature in stem decreased along the axis with the increase in the distance from stem root, and the gas temperature of a thermal thin channel was between 400 and 800 K. There is a significant dependency between axial development parameters of thermal thin channels and the first streamer discharge parameters. The phenomenon of channel abrupt elongation triggered by secondary streamer discharge was observed by the schlieren system, and the influence of characteristic parameters on the inception of secondary streamer was statistically analyzed. The ion current waveform in leader relaxation phase was measured, and it is clarified that the generation mechanism of thermal thin channels is due to the energy transfer between positive ions and neutral particles, which finally leads to the increase in gas temperature in the channels.

Improved seismic envelope full-waveform inversion

The envelope full-waveform-inversion method has the potential to greatly improve the estimation of long-wavelength components of subsurface seismic velocity, and simultaneously avoid or reduce inversion cycle skipping, by fitting the envelopes of observed and modeled seismic waveform data, rather than fitting the waveforms directly. However, some issues such as instability of the adjoint source and gradient term make it challenging to use in practice for many seismology applications. We examine the envelope inversion (EI) methods with different envelope function exponent p values from an adjoint source analysis perspective and reveal the adverse effects of the adjoint source weighting term on the data residuals, which degrade or mute important inversion gradient contributions, especially from reflected or scattered phases in the seismic data. We develop the theory and implementation for an improved EI (IEI) method by adding an upshift constant (zero-frequency direct current [DC] bias term) to the seismic waveform data before calculating the envelope functions, which can help solve the instability issue of the adjoint source of the EI with power value p = 1. Our approach is different than the common but simple addition of a “water-level” damping constant in the denominator of the data residual-weighting term, which can ignore important information contained in the data residuals. Our IEI method becomes a mixed envelope-waveform objective function, which introduces a new adjoint source weighting term that can preserve all of the information contained in the data residuals, by boosting the low-amplitude phases of the seismic waveform, while simultaneously preserving the correct envelope (energy shape) of the high-amplitude phases. We test the performance of all three methods in detail using the highly realistic SEAM4D model and data and find the advantages of our new IEI method compared with conventional EI and full-waveform inversion methods.

Analysis and Control of Chaos in Current Mode Controlled Superbuck Converter for Photovoltaic Systems

Background: Superbuck converter is a strong nonlinear system. However, with the changes in parameters, Superbuck converter performance may be reduced due to nonlinear phenomena, such as bifurcation, chaos, etc. Current mode controlled Superbuck converters have been extensively applied in photovoltaic (PV) systems that require a stable output voltage. Superbuck converters, as nonlinear circuits, exhibit plentiful nonlinear phenomena, such as bifurcation and chaos. In this study, we analyze and control the nonlinear phenomena in the current mode controlled Superbuck converter. The piecewise switching model and discrete iteration mapping model of the Superbuck converter are established. The evolution process of the Superbuck converter from steady state to bifurcation and chaos is revealed when the reference current, input voltage, and load are used as bifurcation parameters, and the bifurcation forms of the converter are all period-doubling bifurcations. The stability criterion of Superbuck converter under current mode has been deduced from reference current by analyzing the stability of the converter. Through the resonant parametric perturbation (RPP) method, the chaos control of the Superbuck converter has been realized for the first time. The converter changes from a chaotic state to a stable single-cycle state about 0.4ms after control and the output voltage ripple is reduced from 3.905V to 0.679V, which effectively improves the output stability of the converter. Objective: This study aimed to analyze and control nonlinear phenomena in current-mode controlled Superbuck converters. Method: Bifurcation diagram, time-domain waveform diagram, phase portrait and Poincare section are used as analytical methods for the nonlinear dynamic characteristics of Superbuck converters. The chaotic control method of the Superbuck converter is Resonant Parameter Perturbation (RPP). Results: After applying RPP control to the Superbuck converter in the chaotic state, it is converted into a single-cycle stable state in about 0.4ms. After the converter is controlled, the output current ripple is reduced from 0.71479A to 0.31A, and the output voltage ripple is reduced from 3.905V to 0.679V. Conclusion: With the increase in the reference current, the decrease in the input voltage, or the increase in the load, the system enters the chaotic state through the period-doubling bifurcation path. In this study, the chaos control of the Superbuck converter is carried out for the first time, and the control method used is the resonant parametric perturbation method. After RPP control, the converter is changed from an unstable, chaotic state to a stable period state in about 0.4ms.

Multi-sinusoidal waveform shaping for integrated data and energy transfer in aging channels

Integrated data and energy transfer (IDET) is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything (IoE) devices. We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer (WET) and orthogonal-frequency-division-multiplexing (OFDM) signals for wireless data transfer (WDT). The outdated channel state information (CSI) in aging channels is employed by the transmitter to shape IDET waveforms. With the constraints of transmission power and WDT requirement, the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average direct-current (DC) among a limited number of transmission frames with the existence of carrier-frequency-offset (CFO). For the amplitude optimisation, the original non-convex problem can be transformed into a reversed geometric programming problem, then it can be effectively solved with existing tools. As for the phase optimisation, the artificial bee colony (ABC) algorithm is invoked in order to deal with the non-convexity. Iteration between the amplitude optimisation and phase optimisation yields our joint design. Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.

Coexisting Behavior and Status Transition of the Hodgkin-Huxley Cardiac Purkinje Fiber Model Under External AC Injection

The membrane current I_m of the Hodgkin-Huxley (HH) cardiac Purkinje fiber (CPF) model is usually calculated as direct current (DC). In this paper, a conventional alternating current (AC) is used, namely I_{AC} = textrm{Asin}(2 pi ft), as the impressed injection. Dynamic characters of the model with different AC parameters and various initial conditions are observed through phase plane orbits, waveforms, bifurcation diagrams, and Lyapunov exponent spectra, which reveal multiple coexisting membrane action potential patterns, corresponding to the coexisting attractors of the same or different periods in the phase diagram. Meanwhile, the model undergoes period, quasi-period and local forward or inverse period-doubling bifurcations with changes in amplitude A or frequency f, which further proves the complex nonlinear property of the AC-injected model. In addition, by changing the external current I_m, the sodium-ion and potassium-ion equilibrium potentials, i.e. E_{Na} and E_{K}, respectively, the regularity of the CPF heartbeat frequency is observed. The state transformations of CPF are found between normal, abnormal and sudden cardiac arrest, and the method adjusting from the dangerous state to the normal heartbeat frequency range is investigated. This study may provide a reference for exploring the evolution of nonlinear dynamics in HH CPF model and protecting the health of life and heart.

Digitally tunable dispersion controller using chirped multimode waveguide gratings

We propose a digitally tunable dispersion controller (DTDC) for dispersion management that shows potential for realizing phase correction, waveform generation, beamforming, and pulse sculpting in many photonic systems. The controller consists of N stages of cascaded chirped multimode waveguide gratings (MWGs) as well as (N+1) Mach–Zehnder switches (MZSs) on silicon. We introduce MWG technology so that the reflected light can be separated from the input signal even without a circulator, which makes it convenient for various system applications. All the chirped MWGs are identical so that the photonic circuit design is convenient, while the number, m, of the chirped MWGs in cascade for the nth stage is given by m=2(n−1). The total dispersion from the DTDC is accumulated by all the stages, depending on the states of all the 2×2 optical switches. Since there are 2 N −1 chirped MWGs in total, the total dispersion can be freely tuned from 0 to (2 N −1)D0 by a step of D0, where D0 is the dispersion provided by a single chirped MWG. As an example, we designed a DTDC consisting of four stages of chirped MWGs (N=4) and five MZSs and demonstrated its low loss as well as its high-quality group delay response. A chirped MWG with a 2-mm-long grating section has a dispersion of D0=2.82ps/nm in a 20-nm-wide bandwidth, and accordingly the maximum dispersion is given as 42.8 ps/nm by switching the MZSs appropriately. Our on-chip DTDC provides a brand-promising option for broadband flexible dispersion management in optical systems of microwave photonics and optical communications.

Parametrized post-Einsteinian framework for precessing binaries

In general relativity, isolated black holes obey the no hair theorems, which fix the multipolar structure of their exterior spacetime. However, in modified gravity, or when the compact objects are not black holes, the exterior spacetime may have a different multipolar structure. When two black holes are in a binary, this multipolar structure determines the morphology of the dynamics of orbital and spin precession. In turn, the precession dynamics imprint onto the gravitational waves emitted by an inspiraling compact binary through specific amplitude and phase modulations. The detection and characterization of these amplitude and phase modulations can therefore lead to improved constraints on fundamental physics with gravitational waves. Recently, analytic precessing waveforms were calculated in two scenarios: (i) dynamical Chern-Simons gravity, where the no-hair theorems are violated, and (ii) deformed compact objects with generic mass quadrupole moments. In this work, we use these two examples to propose an extension of the parameterized post-Einsteinian~(ppE) framework to include precession effects. The new framework contains $2n$ ppE parameters $(\mathscr{b}^{\rm ppE}_{(m',n)}, b^{\rm ppE}_{(m',n)})$ for the waveform phase, and $2n$ ppE parameters $(\mathscr{a}^{\rm ppE}_{(m',n)}, a^{\rm ppE}_{(m',n)})$ for the waveform amplitudes. The number of ppE corrections $n$ corresponds to the minimum number of harmonics necessary to achieve a given likelihood threshold when comparing the truncated ppE waveform with the exact one, and $(m',n)$ corresponds to the harmonic numbers of the harmonics containing ppE parameters. We show explicitly how these ppE parameters map to the specific example waveforms discussed above. The proposed ppE framework can serve as a basis for future tests of general relativity with gravitational waves from precessing binaries.

Phase Noise Suppression for φ-OTDR With Optimized Minima Controlled Recursive Averaging Method

We propose a phase noise suppression method for phase-sensitive optical time-domain reflectometer ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR) system. The signal-to-noise ratio (SNR) of the demodulated phase is improved by the optimized minima controlled recursive averaging (MCRA) method. First, the original phase waveform is divided into frames. Then, the minimum control is used to track the minimum power spectrum of phase noise with Gaussian distribution. The prior variance of noise is corrected by recursive averaging. The attenuation factor is determined by appropriate threshold parameters and the noise prior variance. Ultimately, the phase waveform is reconstructed in the time–frequency domain. In this way, the SNR of the reconstructed waveform is intensified remarkably. Experimental results show that the SNR of the demodulated phase is improved by 9.12–19.63 dB in the range of 20–1200 Hz. Furthermore, the enhancement of acoustic extracted from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varphi $ </tex-math></inline-formula> -OTDR could also be verified experimentally with the proposed approach.

Comparison of discrete and continuous analysis approaches for evaluating gait biomechanics in individuals with anterior cruciate ligament reconstruction

BackgroundAberrant gait biomechanics contribute to post-traumatic knee osteoarthritis development following anterior cruciate ligament reconstruction (ACLR). Walking gait biomechanics are typically evaluated post-ACLR by identifying discrete, peak values in the load acceptance phase of gait (i.e. first 50 %). As these approaches evaluate a single time instant during the gait cycle, functional data analysis (FDA) techniques that evaluate the entire stance phase waveform are becoming more common in the literature. However, it is unclear if these analysis approaches identify the same biomechanical phenomena. Research questionThe purpose of this study was to determine whether four gait biomechanics analysis approaches identify the same aberrant gait characteristics in individuals with ACLR. MethodsTwenty-four individuals with ACLR and 24 healthy controls completed gait analyses on an instrumented treadmill. Four analysis approaches were employed to compare the vertical ground reaction force and sagittal knee angles and moments during the first 50 % of the stance phase between groups and between limbs in the ACLR cohort: 1) comparison of peak values from individual trials (Peak), 2) comparison of peak values from time-normalized ensemble waveforms (Ensemble Peak), 3) FDA via functional ANCOVA (FANCOVA), and 4) FDA evaluating overlap of the 95 % confidence intervals for each waveform (FDA-CI). ResultsThe Peak, Ensemble Peak, and FANCOVA approaches identified highly similar group and limb differences in the biomechanics outcomes with respect to both magnitude and temporal location. However, the FANCOVA approach indicated that these differences were distributed across large portions of the load acceptance phase and that differences existed outside the first 50 % of stance. The FDA-CI approach was generally not effective for identifying aberrant gait biomechanics. SignificancePeak and FANCOVA approaches to gait analysis provide similar findings. Future research is necessary to determine if the additional information afforded by FANCOVA provides insight regarding the mechanical pathogenesis of post-traumatic knee osteoarthritis.

A 2-D Method Based on Nonlinear Frequency Modulation Waveform and Phase Coding for Range Ambiguity Suppression

Range ambiguity suppression is a key technical challenge for synthetic aperture radar (SAR) systems. Waveform diversity technology is a potential solution due to its low system complexity. In this letter, a 2-D method based on orthogonal nonlinear frequency modulation (NLFM) waveform and azimuth phase coding (APC) for range ambiguity suppression is proposed. This approach not only suppresses range ambiguity energies instead of dispersing them, but also works for multiple consecutive orders of range ambiguity energies. The imaging processing and range ambiguity suppression performance are described in detail. In addition, the range-ambiguity-to-signal radio (RASR) is analyzed, and the simulation results for the point target and distribution scenarios are given to verify the effectiveness and practicality of the proposed scheme.

Alpha Rhythm Wavelength of Electroencephalography Signals as a Diagnostic Biomarker for Alzheimer's Disease.

To explore changes in the alpha rhythm wavelength of background electroencephalography in Alzheimer's disease patients with different degrees of dementia in a resting state; examine their correlation with the degree of cognitive impairment; determine whether the alpha rhythm wavelength can distinguish mild Alzheimer's disease patients, moderately severe Alzheimer's disease patients, and healthy controls at the individual level; and identify a cut-off value to differentiate Alzheimer's disease patients from healthy controls. Quantitative electroencephalography signals of 42 patients with mild Alzheimer's disease, 42 patients with moderately severe Alzheimer's disease, and 40 healthy controls during rest state with eyes closed were analyzed using wavelet transform. Electroencephalography signals were decomposed into different scales, and their segments were superimposed according to the same length (wavelength and amplitude) and phase alignment. Phase averaging was performed to obtain average phase waveforms of the desired scales of each lead. The alpha-band wavelengths corresponding to the ninth scale of the background rhythm of different leads were compared between groups. The average wavelength of the alpha rhythm phase of the whole-brain electroencephalography signals in Alzheimer's disease patients was prolonged and positively correlated with the severity of cognitive dysfunction (P < 0.01). The ninth-scale phase average wavelength of each lead had high diagnostic efficacy for Alzheimer's disease, and the diagnostic efficacy of lead P3 (area under the receiver operating characteristic curve = 0.873) was the highest. The average wavelength of the electroencephalography alpha rhythm phase may be used as a quantitative feature for the diagnosis of Alzheimer's disease, and the slowing of the alpha rhythm may be an important neuro-electrophysiological index for disease evaluation.

System for Monitoring Avian Cardiac Output and Breathing Patterns Using Transmission-Type Microwaves

We report the development of a non-contact monitoring device for avian cardiac output and breathing patterns based on the anterior thoracic air sac pressure that uses transmission-type microwaves (2,400−2,500 MHz, continuous wave). Since the phase waveform represents the dielectric constant change, the phase reflects −j/ωc and the dielectric constant change is related to blood flow. The magnitude waveform is reflected from the electronic resistance of tissues due to the expansion of the anterior thoracic air sac, which mainly consists of the thoracic wall. To confirm these waveforms, pigeons and chickens were used for testing. To validate the output waveforms of the developed transmission-type microwave device, data from esophageal catheters and pressure sensors in the anterior thoracic air sac, abdominal air sac, and intraoral cavity were obtained. The waveform for the esophageal catheter, where electrocardiogram electrodes and an angular velocity sensor were installed, correlates with cardiac output. A heart sound microphone was used to confirm the closing sound of the arterial and mitral valves. The experimental results confirm that a linear waveform synchronized with the cardiac blood flow and the anterior thoracic air sac pressure of birds was obtained using transmission-type microwaves. The proposed device, which can monitor cardiac output and respiratory patterns, may enable the early screening of cytokine storms caused by avian influenza viruses. Existing devices use Doppler radar in the 10 to 77 GHz band; these high frequencies are reflected by the chest wall and do not reach deep into body, making it impossible to monitor the blood flow inside the body.

A novel heart sound segmentation algorithm via multi-feature input and neural network with attention mechanism

Objective. Heart sound segmentation (HSS), which aims to identify the exact positions of the first heart sound(S1), second heart sound(S2), the duration of S1, systole, S2, and diastole within a cardiac cycle of phonocardiogram (PCG), is an indispensable step to find out heart health. Recently, some neural network-based methods for heart sound segmentation have shown good performance. Approach. In this paper, a novel method was proposed for HSS exactly using One-Dimensional Convolution and Bidirectional Long-Short Term Memory neural network with Attention mechanism (C-LSTM-A) by incorporating the 0.5-order smooth Shannon entropy envelope and its instantaneous phase waveform (IPW), and third intrinsic mode function (IMF-3) of PCG signal to reduce the difficulty of neural network learning features. Main results. An average F1-score of 96.85 was achieved in the clinical research dataset (Fuwai Yunnan Cardiovascular Hospital heart sound dataset) and an average F1-score of 95.68 was achieved in 2016 PhysioNet/CinC Challenge dataset using the novel method. Significance. The experimental results show that this method has advantages for normal PCG signals and common pathological PCG signals, and the segmented fundamental heart sound(S1, S2), systole, and diastole signal components are beneficial to the study of subsequent heart sound classification.

Phase-shifted demodulation scheme for fiber-optic interferometric sensors with combined waveform phase modulation

We propose and demonstrate a demodulation scheme for interferometric optical fiber sensing using combined waveform phase modulation. The method is based on a designed combined waveform and time-division separation technology, which moves the target signal to the sidebands of the carrier signal and eliminates the influence of residual direct current (DC) term and low-frequency intensity noise by filtering the detector signal directly. The effect of the modulation-to-target ratio (MTR) defined as the ratio of the modulation frequency to the target frequency on the demodulation results is also discussed. Both simulations and experiments verify the ability to obtain a pair of quadrature signals. The experimental results indicate that the phase resolution of the system is 1.1 × 10−6 rad/√Hz@2 kHz when the MTR is 625, and 5 × 10−6 rad/√Hz@2 kHz when the MTR is 25, while demonstrating excellent demodulation consistency. This work not only has the advantages of carrier modulation methods, but also does not need to generate analog signals for mixing, which provides guidance for high-sensitivity phase demodulation technology.

Beyond the linear tide: impact of the non-linear tidal response of neutron stars on gravitational waveforms from binary inspirals

ABSTRACT Tidal interactions in coalescing binary neutron stars modify the dynamics of the inspiral and hence imprint a signature on their gravitational wave (GW) signals in the form of an extra phase shift. We need accurate models for the tidal phase shift in order to constrain the supranuclear equation of state from observations. In previous studies, GW waveform models were typically constructed by treating the tide as a linear response to a perturbing tidal field. In this work, we incorporate non-linear corrections due to hydrodynamic three- and four-mode interactions and show how they can improve the accuracy and explanatory power of waveform models. We set up and numerically solve the coupled differential equations for the orbit and the modes and analytically derive solutions of the system’s equilibrium configuration. Our analytical solutions agree well with the numerical ones up to the merger and involve only algebraic relations, allowing for fast phase shift and waveform evaluations for different equations of state over a large parameter space. We find that, at Newtonian order, non-linear fluid effects can enhance the tidal phase shift by $\gtrsim 1\, {\rm radian}$ at a GW frequency of 1000 Hz, corresponding to a $10{{\%}}-20{{\%}}$ correction to the linear theory. The scale of the additional phase shift near the merger is consistent with the difference between numerical relativity and theoretical predictions that account only for the linear tide. Non-linear fluid effects are thus important when interpreting the results of numerical relativity and in the construction of waveform models for current and future GW detectors.

Deep learning model based on a bidirectional gated recurrent unit for the detection of gravitational wave signals

The application of deep learning to the detection of gravitational wave (GW) signals has attracted much attention in recent years, and nearly all of the existing deep learning-based GW detection algorithms are based on the convolutional neural network (CNN), which can be used to identify the GW merger moment and produce responding triggers. However, current CNN-based models are not best optimized tools in modeling the causal logic of sequential data in the time dimension to incorporate the information of the inspiral, merger and ringdown phases of the GW waveform. To address this issue we propose a deep learning model designed based on the bidirectional gated recurrent unit (BiGRU), a variant of the recurrent neural network (RNN) that is sensitive to the sequential logic of GW data. We train the neural network and implement the GW detection task by using the data obtained during the first, second and third observation runs (O1/O2/O3) of the LIGO-VIGRO scientific collaboration. We find that our BiGRU model achieves a false alarm probability (FAP) of $4.88\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$ (corresponding false alarm rate is about 1 per 18.2 hours) and maintains a detection ratio of about 89.6%. We also construct a model ensemble containing two independently trained BiGRU models and find it does not trigger any false alarm events on our testing dataset. Meanwhile, our BiGRU model not only recovers all the reported GW events of O1-O2 runs, including the unique (as of today) confident (with observed electromagnetic counterparts) GW event originating from a binary neutron star (BNS) merger (GW170817), but also achieves a detection ratio of about 80% of the O3 run. By calculating and comparing the receiver operator characteristic (ROC) curves we demonstrate the superiority of the BiGRU model over traditional CNN-based models, and show that the BiGRU model, being optimized to deal with GW waveform that sequentially contains the inspiral, merger and ringdown phases, is a potentially powerful tool to study the GW data in the future.

Complete characterization of picosecond optical pulses by the offset frequency intensity modulation.

We present a method for characterizing the intensity waveform, spectrum, frequency chirp, and spectral phase of picosecond pulses at a moderate repetition rate of ∼100 MHz. The proposed method exploits the intensity modulation at ∼10 GHz, which is slightly offset from the integer multiple of the repetition rate of the pulses. The modulated pulses are split into two, and one is measured by an optical spectrum analyzer, whose output is detected by a lock-in amplifier, while the other is directly detected by a photodiode and its output is used as a reference signal of the lock-in amplifier. In the experiment, we demonstrate the measurement of picosecond Ti:sapphire laser pulses to investigate frequency chirp induced by self-phase modulation. We anticipate that the proposed method will be useful for the characterization of various types of picosecond pulses.

Joint design of transmit waveform and passive beamforming for RIS-assisted ISAC system

The joint design of the transmit waveform and passive beamforming is the key issue in the reconfigurable intelligent surface (RIS) assisted integrated sensing and communication (ISAC) systems. The existing methods are the joint waveform design without RIS or the sensing-first joint waveform design with RIS. Different from these methods, the fair sensing-communication joint waveform design with RIS is studied in this paper. For this purpose, we simultaneously optimize the waveform and RIS phase shifts to maximize the sensing Signal-to-Interference-and-Noise-Ratio (SINR) and minimize the communication Multiple User Interference (MUI). Due to the constant modulus (CM) constraint and the fractional programming form of the SINR expression, the resulted optimization problem is hard to solve. To address these issues, an Alternating Gradient-descent on Riemannian Manifold (AGRM) method is proposed. First, a dinkelbatch-GRM algorithm is derived to maximize SINR, by fixing the RIS phase shifts. Then, a GRM algorithm is given to mitigate MUI, while fixing the waveform. Finally, the overall AGRM algorithm is presented to solve the original problem. Simulation results show that the communication achievable rate and the sensing SINR of the proposed method are 2.33 bits/s/Hz and 5.6 dB higher than [17] and [30,31].

Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning.

Estimating physiological parameters - such as blood pressure (BP) - from raw sensor data captured by noninvasive, wearable devices rely on either burdensome manual feature extraction designed by domain experts to identify key waveform characteristics and phases, or deep learning (DL) models that require extensive data collection. We propose the Data-Driven Guided Attention (DDGA) framework to optimize DL models to learn features supported by the underlying physiology and physics of the captured waveforms, with minimal expert annotation. With only a single template waveform cardiac cycle and its labelled fiducial points, we leverage dynamic time warping (DTW) to annotate all other training samples. DL models are trained to first identify them before estimating BP to inform them which regions of the input represent key phases of the cardiac cycle, yet we still grant the flexibility for DL to determine the optimal feature set from them. In this study, we evaluate DDGA's improvements to a BP estimation task for three prominent DL-based architectures with two datasets: 1) the MIMIC-III waveform dataset with ample training data and 2) a bio-impedance (Bio-Z) dataset with less than abundant training data. Experiments show that DDGA improves personalized BP estimation models by an average 8.14% in root mean square error (RMSE) when there is an imbalanced distribution of target values in a training set and improves model generalizability by an average 4.92% in RMSE when testing estimation of BP value ranges not previously seen in training.