Common Waveform Research Articles

Deep-prior ODEs augment fluorescence imaging with chemical sensors

To study biological signalling, great effort goes into designing sensors whose fluorescence follows the concentration of chemical messengers as closely as possible. However, the binding kinetics of the sensors are often overlooked when interpreting cell signals from the resulting fluorescence measurements. We propose a method to reconstruct the spatiotemporal concentration of the underlying chemical messengers in consideration of the binding process. Our method fits fluorescence data under the constraint of the corresponding chemical reactions and with the help of a deep-neural-network prior. We test it on several GCaMP calcium sensors. The recovered concentrations concur in a common temporal waveform regardless of the sensor kinetics, whereas assuming equilibrium introduces artifacts. We also show that our method can reveal distinct spatiotemporal events in the calcium distribution of single neurons. Our work augments current chemical sensors and highlights the importance of incorporating physical constraints in computational imaging.

THREE-LEVEL DIRECT TORQUE CONTROL BASED ON COMMON MODE VOLTAGE REDUCTION STRATEGY FED TWO PARALLEL CONNECTED FIVE-PHASE INDUCTION MACHINE

This survey proposes a mitigation approach for common mode voltage produced in a three-level five-phase inverter fed two parallel-connected five-phase induction machines controlled by direct torque control. The switching frequencies used in the inverter fed these types of drives are characterized by a large amplitude of the produced common mode voltage waveforms (), leading to prominent issues, especially with the near introduction of wide-gap semiconductor technologies. In this context, the introduced approach is an extension to a multi-motor drive of the TATTE method, exploiting the concept of virtual voltage vectors to mitigate the resulting common mode voltage. The collected results critically evaluate the reported approach by improving the drive’s generated common mode voltage status to () compared to () in the conventional method, in addition to a significant reduction in the flux and torque ripples by 22 % and 39 %, respectively, at the cost of an insignificant increase in the current harmonic content by 6 %.

Multiple receiver specific emitter identification

AbstractSpecific emitter identification (SEI) is a technique for identifying emitters based on the principle that the hardware chain is not ideal, causing the emitted signal to contain emitter‐specific information. However, the receiver is also non‐ideal, which affects recognition accuracy and introduces receiver‐specific information that makes SEI difficult to generalise across receiving systems. In this work, a new multi‐receiver receiving and processing system (MR‐SEI) scheme is proposed to mitigate the influence of receivers based on the analysis of receiver distortion models. After receiving and processing in a specific manner, recognition performance can be enhanced. Therefore, extracted features can be shared among different receivers and platforms, and can even be applied to newly added receivers. The concept of common waveform (CW) is first defined, referring to the received signal without receiver distortions. Different receiving devices are working synchronously, and the CW is estimated using multiple copies of the signal obtained from multiple receivers through the iterative reweighted least squares (IRLS) method. For each receiver, a maximum linear correlation algorithm is proposed to calculate the received signal without being affected by distortions. Experimental results show that the proposed scheme can enhance identification performance. With the increase in the number of receivers, the improvement is more noticeable. Using 10 distorted receivers operating under an SNR of 25 dB, the proposed algorithm can significantly improve the identification performance, achieving over 95% and approaching the ideal scenario of no receiver distortion. Meanwhile, influences caused by receiver distortions can be effectively eliminated, and the database can be shared with new receivers, overperforming other SEI methods that eliminate the receiver.

A machine learning toolbox for the analysis of sharp-wave ripples reveals common waveform features across species

The study of sharp-wave ripples has advanced our understanding of memory function, and their alteration in neurological conditions such as epilepsy is considered a biomarker of dysfunction. Sharp-wave ripples exhibit diverse waveforms and properties that cannot be fully characterized by spectral methods alone. Here, we describe a toolbox of machine-learning models for automatic detection and analysis of these events. The machine-learning architectures, which resulted from a crowdsourced hackathon, are able to capture a wealth of ripple features recorded in the dorsal hippocampus of mice across awake and sleep conditions. When applied to data from the macaque hippocampus, these models are able to generalize detection and reveal shared properties across species. We hereby provide a user-friendly open-source toolbox for model use and extension, which can help to accelerate and standardize analysis of sharp-wave ripples, lowering the threshold for its adoption in biomedical applications.

Associations Between Systolic Pressure, Vascular Waveform, and Volume Flow with Debridement or Amputation Treatment Decisions in Diabetic Foot Ulcer Patients.

Diabetic foot ulcer disease, affecting 6.3% of the global population, necessitates crucial decisions regarding debridement and amputation, with substantial cost, morbidity, and mortality implications. This study's primary goal is to determine effective vascular examination modalities, including systolic blood pressure, vascular waveforms, and blood volume flow to guide optimal treatments for diabetic foot patients. This cross-sectional study at Cipto Mangunkusumo General Hospital aimed to identify effective vascular examination modalities, such as systolic blood pressure, vascular waveforms, and blood volume flow, to guide treatment choices for diabetic foot patients. The study included 38 subjects, equally split between debridement and amputation groups. Notably, the presence of a biphasic pattern in popliteal artery vascular examinations was associated with the decision for amputation in diabetic foot patients (p < 0.05). The Chi-square test revealed that a biphasic ultrasound pattern served as a predictive factor for amputation among diabetic foot patients, with three times more biphasic patients choosing amputation over debridement. Conversely, assessments of popliteal artery systolic pressure, common femoral artery waveform, popliteal artery volume flow, and common femoral artery volume flow showed no significant correlations with the choice of debridement or amputation treatment for diabetic foot patients. In summary, ultrasound assessment of vascular waveform in the popliteal artery emerges as a predictive factor for amputation or debridement in diabetic foot patients.

Comparison among baseband codes based on MATLAB

The thesis is mainly concerned with baseband codes. First the paper will introduce the significance and application of baseband codes research. Then comparison between unipolar and bipolar signals capabilities resisting noise will be demonstrated. Moreover, calculation of channel capacity is utilized to demonstrate the difference between NRZ and RZ waveforms. After four kinds of waveforms, they are matched into groups and contrast them in frequency domain. Lastly, few common baseband waveforms are generated so that the discussed factors application can be illustrated more vividly. The thesis is to illustrate fundamental theories more reasonable and figurative.

Radar Range Deception With Time-Modulated Scatterers

Modern radar systems are designed to have high Doppler tolerance to detect fast-moving targets. This means range and Doppler estimations are inevitably coupled, opening pathways to concealing objects by imprinting artificial Doppler signatures on the reflected echoes. Proper temporal control of the backscattered phase can cause the investigating radar to estimate wrong range and velocity, thus cloaking the real position and trajectory of the scatterer. This deception method is exploited here theoretically for arbitrary Doppler tolerant waveforms and then tested experimentally on an example of the linear frequency modulated radar, which is the most common waveform of that class used in practice. The method allows retaining radio silence with a semi passive (battery assisted) approach that can work well with time-dependent metasurfaces. Furthermore, as an insight into new capabilities, we demonstrate that temporally concealed objects could even be made to appear closer than they truly are without violating the laws of relativity.

Neuromorphic Integrated Sensing and Communications

Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless Internet-of-Things (IoT) networks. In this letter, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and to detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metrics for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.

Sensing Using Coded Communications Signals

A key challenge for common waveforms for Integrated Sensing and Communications – widely regarded as a resource-efficient way to achieve high performance for both functionalities – lies in leveraging information-bearing channel-coded communications signal(s) (c.c.s) for sensing. In this paper, we investigate the range-Doppler sensing performance of c.c.s in multi-user interference-limited scenarios, and show that it is affected by sidelobes whose form depends on whether the c.c.s modulates a single-carrier or OFDM waveform. While uncoded signals give rise to asymptotically zero sidelobes due to the law of large numbers, it is not obvious that the same holds for c.c.s, as structured codes (e.g., linear block codes) induce dependence across codeword symbols. In this paper, we show that c.c.s also give rise to asymptotically zero sidelobes – for both single-carrier and OFDM waveforms – by deriving upper bounds for the tail probabilities of the sidelobe magnitudes that decay as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\exp ( - O(\text {code rate} \times \text {block length}))$ </tex-math></inline-formula> . Consequently, for any code rate, c.c.s are effective sensing signals that are robust to multi-user interference at sufficiently large block lengths, with negligible difference in performance based on whether they modulate a single-carrier or OFDM waveform. We verify the latter implication through simulations, where we observe the sensing performance (i.e., the detection and false-alarm probabilities) of a QPSK-modulated c.c.s (code rate = 120/1024, block length = 1024 symbols) to match that of a comparable interference-free FMCW waveform even at high interference levels (SIR of −11dB), for both single-carrier and OFDM waveforms.

Dynamic functional and structural remodeling during retinal regeneration in zebrafish.

Zebrafish regenerate their retinas following damage, resulting in restoration of visual function. Here we evaluate recovery of retinal function through qualitative and quantitative analysis of the electroretinogram (ERG) over time following retinal damage, in correlation to histological features of regenerated retinal tissue. Retinas of adult zebrafish were lesioned by intravitreal injection of 10 μM (extensive lesion; destroys all neurons) or 2 μM (selective lesion; spares photoreceptors) ouabain. Unlesioned contralateral retinas served as controls. Function of retinal circuitry was analyzed at selected timepoints using ERG recordings from live zebrafish, and whole eyes were processed for histological analyses immediately thereafter. Qualitative and quantitative assessment of waveforms during retinal regeneration revealed dynamic changes that were heterogeneous on an individual level within each sampling time, but still followed common waveform recovery patterns on a per-fish and population-level basis. Early in the regeneration period (13-30 days post injury; DPI), for both lesion types, b-waves were essentially not detected, and unmasked increased apparent amplitudes, implicit times, and half-widths of a-waves (vs. controls). In control recordings, d-waves were not obviously detected, but apparent d-waves (OFF-bipolar responses) from regenerating retinas of several fish became prominent by 30DPI and dominated the post-photoreceptor response (PPR). Beyond 45DPI, b-waves became detectable, and the ratio of apparent d- to b-wave contributions progressively shifted with most, but not all, fish displaying a b-wave dominated PPR. At the latest timepoints (extensive, 90DPI; selective, 80DPI), recordings with measurable b-waves approached a normal waveform (implicit times and half-widths), but amplitudes were not restored to control levels. Histological analyses of the retinas from which ERGs were recorded showed that as regeneration progressed, PKCa + ON-bipolar terminals and parvalbumin + amacrine cell processes became more stereotypically positioned within the deep sublaminae of the INL over recovery time after each lesion type, consistent with the shift in PPR seen in the ERG recordings. Taken together, these data suggest that photoreceptor-OFF-bipolar component/connectivity may functionally recover and mature earlier during regeneration compared to the photoreceptor-ON-bipolar component, though the timeframe in which such recovery happens is heterogeneous on a per-fish basis. Collectively our studies suggest gradual restoration of ON-bipolar functional circuitry during retinal regeneration.

Adaptive filtering of microseismic data for monitoring a water-conducting fractured zone in a mine

Water-conducting fractured zones in a rock mass can cause problems in mining. Attempts have been made to monitor their development using microseismic signals. However, due to the lack of prior information, it is difficult to filter out mixed low-frequency interference with traditional denoising methods. In this work, the proposed adaptive filtering algorithm is applied after the wavelet packets are decomposed. It is based on a cross-correlation analysis. The algorithm takes a high-quality signal in the common source waveform as prior information and applies the corresponding correlation coefficients between subbands as a threshold. The algorithm was verified with simulations. The results show that low-frequency interference can be effectively suppressed by filtering. For single-frequency interference, the signal-to-noise ratio increased from − 10.18 to 13.97, and the root-mean-square error was 43.88. For multi-frequency interference, it increased from − 10.01 and − 2.63 to 13.50 and 7.99. The root-mean-square errors were 46.31 and 138.07. The narrower the main frequency band of the interference signal and the less the overlap of the main frequency band of the interference signal and the effective signal, the better the filtering effect. When the algorithm was applied to microseismic data collected in the field, the number of effective channels increased and the accuracy improved. The development of a water-conducting fractured zone in the field was consistent with the microseismic location obtained after interference was removed by the algorithm, which indicates that it is feasible to monitor a water-conducting fractured zone by analyzing microseismic waveforms with the adaptive filtering algorithm.

Novel combined waveform temperature modulation method of NiO-In2O3 based gas sensor for measuring and identifying VOC gases

The extremely widely applied metal oxide semiconductor (MOS) gas sensors suffer from underdeveloped selectivity, and dynamic measurement methods are expected to solve this issue. In this paper, the In 2 O 3 and NiO-In 2 O 3 gas sensors were prepared, and then their common gas sensing performances were measured. The response and response time of the NiO-In 2 O 3 gas sensor were more excellent, and thus it was chosen for dynamic measurement. We perform periodic temperature modulated dynamic measurement of six common VOCs. By comparing and analyzing the response curves under common heating waveforms, a combined waveform of rectangular wave and triangular wave is customized as the heating waveform. We can obtain response curves with rich features and high amplitude, which is beneficial to the qualitative and quantitative identification of VOCs. The gas types are first identified employing a support vector machine (SVM) with 100% accuracy with a stepwise identification strategy. Subsequently, the data set is processed applying principal component analysis (PCA) and the principal components with a cumulative contribution above 90% are selected for concentration prediction. Polynomial fitting is used for quantitative analysis of VOCs with a relative error of regarding 5%. The results are indicative that the combination of rectangular-triangular wave temperature modulation and stepwise identification scheme can effectively solve the issue of underdeveloped selectivity of gas sensors. • The sensing properties of In 2 O 3 and NiO-In 2 O 3 composites to VOCs were compared and NiO-In 2 O 3 was more excellent. • A combined waveform of rectangular and triangular was customized as the heating waveform. • The gas types were identified employing a support vector machine (SVM) with 100% accuracy. • Concentration prediction was performed applying a combined algorithm with a relative error of regarding 5%.

Using generalized additive models to decompose time series and waveforms, and dissect heart–lung interaction physiology

Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles’ effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data.

Diagnostic Performance of Pulsed Doppler Ultrasound of the Common Femoral Vein to Detect Elevated Right Atrial Pressure in Pulmonary Hypertension.

Right atrial pressure (RAP) is an important prognostic criterion in pulmonary hypertension (PH). The main goals were to evaluate the following: (i) the accuracy of Doppler assessment of common femoral vein flow waveform to detect elevated RAP and (ii) the diagnostic accuracy of RAP assessed by echocardiography (eRAP). Fifty-seven patients, addressed for right heart catheterization, were included in a retrospective cross-sectional study during a 6-month period. Forty-five patients (78.9%) had PH confirmed by RHC. Elevated RAP was defined by RAP ≥ 10mmHg. Femoral venous stasis index (FVSI) was highly correlated to RAP on both univariate (p < 0.001) and multivariate analysis (p = 0.003), and showed good diagnostic performances to detect elevated RAP (specificity: 92.3% [80.0-99.3], diagnosis accuracy: 90.4 [77.4-97.3], positive likelihood ratio: 12.5 [3.01-51.97]). Diagnosis accuracy of eRAP was only 51.2% (36.2-66.1). FVSI is independently correlated to RAP and a useful tool to predict elevated RAP in PH patients.

Ambulatory factors influencing pulmonary artery pressure waveforms and implications for clinical practice.

CardioMEMS, a remote pulmonary artery pressure monitoring system, provides waveform patterns for the ambulatory heart failure patient. These waveforms provide significant insights into patient volume and clinical management. We aim to provide a foundation for understanding the determinants of waveform characteristics and provide practical examples illustrating how to interpret and integrate common scenario waveforms into clinical decision-making. A total of three groups of relevant scenarios were included namely (a) location and activity at time of waveform transmission, (b) impact of contemporary interventions, and (c) arrhythmias. We illustrate that waveform analysis can be individualized to each patient's care strategy in the appropriate clinical context to help guide clinical decision-making.

Toward Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication, and Security

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in a spectrum/energy/cost-efficient way. ISAC systems communicate and sense for targets using a common waveform, a common hardware platform, and ultimately the same network infrastructure. Nevertheless, the inclusion of information signaling in the probing waveform for target sensing raises challenges from the perspective of information security. At the same time, the sensing capability incorporated in ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview article discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to ensure security. We then identify the new opportunity of using the sensing capability to obtain knowledge target information as an enabling approach against the known weak-nesses of PHY security. Finally, we illustrate some low-cost secure ISAC architectures, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices.

Establishing an equivalent circuit for a quasihomogeneous discharge atmospheric-pressure plasma jet with a breakdown-voltage-controlled breaker and power supply circuit

To more accurately simulate the I–V diagram of a homogeneous and a filamentary plasma discharge with an equivalent circuit model, this study employed a breaker and passive circuit components and calculated the discharge parameters, such as equivalent discharge resistances and potential distribution, in an atmospheric pressure plasma jet (APPJ). In addition, we calculated the gas-gap and dielectric capacitances of the APPJ and added a power supply equivalent circuit. Compared with other circuit models that use switches or a time-controlled current source to simulate the discharges, our present circuit model uses a breakdown-voltage-controlled breaker for the homogeneous discharge and resistors with high-frequency switches for the filamentary discharge. We employed potential simulation to obtain the equivalent dielectric capacitance in the APPJ and then derived the gas-gap capacitance. We also replaced the ideal sine wave power supply with the equivalent circuit of the common double-peak waveform power supply. MATLAB Simulink was used to construct an equivalent circuit model and the discharge area ratio, breakdown voltage and filamentary equivalent resistance were obtained via I–V waveform fitting. We measured the plasma I–V waveform with a 20 kHz frequency, various voltages (6, 12 and 15 kV), a gas flow rate of 30 standard liter per minute (SLM) and two types of gas (Ar and He). The simulated and experimental I–V waveforms were very close under the different conditions. In summary, the proposed equivalent circuit model more meaningfully describes the plasma physics to simulate homogeneous and filamentary discharges, achieving results that are compatible with our experimental observations. The findings can help with investigating plasma discharge mechanisms and full-model simulations of plasma.

Postrelease Survival and Migration Behavior of Adult Walleye Following Intracoelomic Transmitter Implantation Using Two Methods of Electro‐Immobilization

AbstractThe use of electricity as a method of fish restraint (i.e., electro‐immobilization) during field and hatchery operations has increased recently, and a need exists for field‐based assessments of the effects of these techniques on the postrelease physiology, behavior, and survival of wild fish to develop best handling practices. Two common waveforms used in electro‐immobilization are continuous and pulsed DC (cDC and pDC, respectively). With cDC, fish are immobilized through exposure to a continuous weak current and recover almost immediately once the current ceases. With pDC, fish are incapacitated via brief exposure to a strong current and often require several minutes to recover (i.e., regain equilibrium). Here, we present a comparison of cDC and pDC electro‐immobilization techniques and their effects on the postrelease survival and spawning migration behavior of adult Walleye Sander vitreus in a Lake Erie tributary. Fish were intracoelomically implanted with acoustic transmitters and then tracked by a network of acoustic receivers throughout Lake Erie that provided extensive spatiotemporal coverage of posttagging movements. Survival did not differ between the two electro‐immobilization treatments but was somewhat lower than in a control group consisting of fish tagged several years earlier (e.g., 50‐d survival = 69% for the pDC group, 61% for the cDC group, and 90% for control fish). We did not find any plausible or biologically meaningful effects of treatment on downstream movement or migratory behavior over subsequent months postrelease. Our results suggest that the two electro‐immobilization techniques did not differ in their effects on adult Walleye behavior, but more work is needed to elucidate the extent to which electro‐immobilization in general contributes to potential decreases in survival versus other aspects of capture and handling associated with performing surgical procedures.

Classification and Identification of Power Quality Disturbances Based on Stochastic Forest Model

With the emergence and use of a large number of new power electronic equipment, the power supply department has begun to pay extensive attention to the problem of power quality, and the majority of users have put forward higher requirements for the quality of power supply. This paper studies the classification and identification method of power quality disturbances based on random forest model. In this paper, according to IEEE power quality standard, the normal waveform and 16 common power quality disturbance waveforms are mathematically modeled, and the power quality disturbance signal is analyzed by S-transform. In this paper, the power quality disturbance identification algorithm based on random forest is optimized. Experimental data show that the optimized method has higher disturbance recognition accuracy and better anti noise ability. Therefore, using the power quality disturbance identification method proposed in this paper to monitor the power quality of power grid is of great significance to ensure the safe and stable operation of power grid and improve economic benefits.

ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds): A pipeline for sample-specific computational modeling of electrical stimulation of peripheral nerves.

Electrical stimulation and block of peripheral nerves hold great promise for treatment of a range of disease and disorders, but promising results from preclinical studies often fail to translate to successful clinical therapies. Differences in neural anatomy across species require different electrodes and stimulation parameters to achieve equivalent nerve responses, and accounting for the consequences of these factors is difficult. We describe the implementation, validation, and application of a standardized, modular, and scalable computational modeling pipeline for biophysical simulations of electrical activation and block of nerve fibers within peripheral nerves. The ASCENT (Automated Simulations to Characterize Electrical Nerve Thresholds) pipeline provides a suite of built-in capabilities for user control over the entire workflow, including libraries for parts to assemble electrodes, electrical properties of biological materials, previously published fiber models, and common stimulation waveforms. We validated the accuracy of ASCENT calculations, verified usability in beta release, and provide several compelling examples of ASCENT-implemented models. ASCENT will enable the reproducibility of simulation data, and it will be used as a component of integrated simulations with other models (e.g., organ system models), to interpret experimental results, and to design experimental and clinical interventions for the advancement of peripheral nerve stimulation therapies.