Full Signal Research Articles

Ultrasonic evaluation of wire-to-terminal joints: integrating XGBoost machine learning with finite element feature analysis

ABSTRACT A new scheme for ultrasonic non-destructive evaluation of wire-to-terminal joints was developed in this study. A finite element simulation model of 2D porous media based on X-CT images of an NG sample was proposed to analyse acoustic scattering. The simulation signal features were extracted in the time domain, frequency domain, and time-frequency domain, and entropy analysis was conducted to explore the relationship between different characteristic values and porosity, thereby revealing prominent trends in these features. By controlling parameters, 28 welding samples labelled OK and NG were produced, and their echoes were acquired by an ultrasonic Full Matrix Capture (FMC) system. Features of the full matrix signals were extracted, and the combined XGBoost machine learning was used to classify the quality and order the attribution of features. The result highlighted the significance of the waveform factor, margin factor, and kurtosis which are consistent with simulation results. The accuracy of weld quality identification can reach 84%. The three factors may be performance criteria for ultrasonically welded wire-to-terminal joints.

Imaging small geological structure in coal mines based on reflected acoustic logging of underground cross-seam boreholes

Accurate identification of small geological structures is the key and difficult point in coal mine geological survey. The reflected acoustic logging technology has the advantages of large detection range and high imaging resolution, and can realize the accurate investigation of small structural interfaces. Therefore, the idea of identifying small geological structures in coal mines by using cross-seam boreholes reflected acoustic logging is proposed. By placing acoustic logging tools in underground cross-seam boreholes, waveforms generated at coal-rock interfaces around the borehole are collected. The reflection wave information is then inverted to obtain interface images of small structures. First, COMSOL Multiphysics software is utilized to build finite element models for reflected acoustic logging in coal-bearing formations containing coal seam fractures, small normal faults, and coal seam thinning zones, respectively. Then forward modeling is conducted to analyze the spatiotemporal variation patterns of the full waveform signals and velocity snapshots. Finally, the reflected acoustic logging data is inverted to realize the offset imaging of small structures in coal mines. The forward modeling results show that: the arrival times of reflected waves varying with the source longitudinal coordinates exhibit good consistency with the interface morphology of borehole vicinity small structures. The inversion results show that: when fractures exist in the coal seam, the imaged dip angle of the fracture is 54.14°, with an error rate of 2.07% compared to the analytical model, and the imaged fracture length is 15.32 m, with an error rate of 4.57%. When small faults exist in the coal seam, the imaged dip angle of the fault plane is 70.63°, with an error rate of 1.41% compared to the analytical model, and the imaged fault plane length is 13.75 m, with an error rate of 3.03%. When thinning zones exist in the coal seam, the imaged dip angles of the upper and lower interfaces of the thinning zone are 44.61° and 57.80°, with error rates of 0.31% and 0.40% respectively compared to the analytical model. The imaged lengths of the upper and lower interfaces are 9.32 m and 18.73 m respectively, with error rates of 0.32% and 1.08% respectively. Acoustic reflection logging technology can effectively invert the locations and morphological characteristics of small structures in coal mines. This study can provide fundamental theoretical support for the application of acoustic reflection logging technology in cross-seam boreholes for identification of small structures in coal seams.

Optics and quantitative assessment of corneal transparency

While some optical properties of cornea like its stromal birefringence had been known since the early nineteenth century, theories explaining its transparency remained inconclusive until the publication of David Maurice' emblematic paper in 1957 Maurice made a model calculation of light scattering by a single collagen fibril as a “dielectric needle”. The volume density of the fibrils being known, Maurice could demonstrate that in the absence of any order among the fibrils the cornea would be opaque. Maurice postulated the hexagonal structure which since has been confirmed by electron microscopy. In modern terms, the order among the collagen fibrils within a stromal collagen lamella is described by a “structure factor” in the radiative transfer equation. This term corresponds to a description of the structure in reciprocal space and is thus equivalent to the representation of crystalline lattices in the first Brillouin zone used for structural modelling in crystallography.In a recent study we further analysed the collagen structure of healthy and oedematous corneas on a nanometric level observed by transmission electron microscopy (TEM). By automated image analysis we could retrieve the so‐called “pair correlation function” and calculate light attenuation within the corresponding cornea. Our findings show that the 1/e light penetration depth within healthy cornea could be calculated as 7300 ± 800 μm, which explains well the transparency of healthy cornea. However, the photon mean free path of oedematous cornea turned out to be from two to three times that value. In oedematous cornea, the “dilution” effect of the scatters is in fact stronger than the degradation of transparency by the perturbation of the structure. The actual loss of transparency in oedematous cornea may therefore be attributed to scattering processes at the micrometric level induced by the augmented optical contrast at the sites of the keratocytes between the collagen lamellae.Perturbations in corneal transparency do not only hamper vision, they also impact optical imaging methods on cornea. This, in turn, permits to study the transparency properties of the corneal tissue. One of the main research interests of our group is to analyse corneal images recorded by optical coherence tomography (OCT) and to extract quantitative parameters like photon mean path length and the fraction of coherently transmitted light.Our partners could show that in the light scattering regime, a term in the radiation transfer equation may be neglected and the attenuation of the full field OCT signal in the volume of the tissue reduces to a Lambert–Beer law. We developed a data analysis algorithm based on Bayesian inference to extract the attenuation coefficient and used it for the analysis of ex vivo corneal high‐resolution FF‐OCT images.In a second step, we developed an image processing algorithm for clinical OCT images removing typical artefacts and compensating for inhomogeneous illumination and corneal curvature. We presently are able to extract corneal transparency parameters from routine clinical OCT images and to provide post‐op follow‐up after keratoplasty or other surgical interventions.

In-situ test of full field deformation and chip formation mechanism during machining of Ti2AlNb intermetallic with digital image correlation method

Ti2AlNb intermetallic, as a relatively new high temperature-resistant structural material, belongs to typical difficult-to-machine material due to its excellent mechanical properties. This paper aims to investigate the dynamic deformation behavior in chip shear deformation zone and associated chip formation mechanism during the machining of Ti2AlNb intermetallic. A specialized cutting platform combined with a high speed imaging system is developed to realize in-situ detection of full field deformation and corresponding signal processing during the machining process. The high speed imaging system can capture in-situ material deformation images with a frequency of 100 kHz during cutting process while the digital image correlation method is adopted to characterize chip dynamic deformation signals quantitatively. A series of orthogonal cutting experiments for Ti2AlNb is conducted at different cutting depths and cutting speeds, based on which the influences of cutting parameters on chip morphologies and material removal process are studied. The distribution of material flowing velocity, as well as the strain rate fields within shear deformation zone in chip removal process of Ti2AlNb are analyzed. Finally, the formation mechanism of periodic built-up edge formed in the cutting speed range of 60–120 m·min−1 is elucidated.

A Hybrid Energy-Efficient, Area-Efficient, Low-Complexity Switching Scheme in SAR ADC for Biosensor Applications.

A hybrid energy-efficient, area-efficient, low-complexity switching scheme in SAR ADC for biosensor applications is proposed. This scheme is a combination of the monotonic technique, the MSB capacitor-splitting technique, and a new switching method. The MSB capacitor-splitting technique, as well as the reference voltage Vaq allow for more options for reference voltage conversion, resulting in higher area savings and higher energy efficiency. In a capacitor array, the circuit performs unilateral switching during all comparisons except for the second and last two comparisons, reducing the difficulty in designing the drive circuit. The proposed switching scheme saves 98.4% of the switching energy and reduces the number of unit capacitors by 87.5% compared to a conventional scheme. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low-sampling error-sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrated that the proposed SAR ADC achieves 61.51 dB SNDR, 79.21 dB SFDR and consumes 0.278 μW of power in a 180 nm process with a 1 V power supply, a full swing input signal frequency of 23.33 kHz, and a sampling rate of 100 kS/s.

FlexKnot and Gaussian Process for 21 cm global signal analysis and foreground separation

ABSTRACT The cosmological 21 cm signal is one of the most promising avenues to study the Epoch of Reionization. One class of experiments aiming to detect this signal is global signal experiments measuring the sky-averaged 21 cm brightness temperature as a function of frequency. A crucial step in the interpretation and analysis of such measurements is separating foreground contributions from the remainder of the signal, requiring accurate models for both components. Current models for the signal (non-foreground) component, which may contain cosmological and systematic contributions, are incomplete and unable to capture the full signal. We propose two new methods for extracting this component from the data: First, we employ a foreground-orthogonal Gaussian Process to extract the part of the signal that cannot be explained by the foregrounds. Secondly, we use a FlexKnot parametrization to model the full signal component in a free-form manner, not assuming any particular shape or functional form. This method uses Bayesian model selection to find the simplest signal that can explain the data. We test our methods on both, synthetic data and publicly available EDGES low-band data. We find that the Gaussian Process can clearly capture the foreground-orthogonal signal component of both data sets. The FlexKnot method correctly recovers the full shape of the input signal used in the synthetic data and yields a multimodal distribution of different signal shapes that can explain the EDGES observations.

Physiological sensor data cleaning with autoencoders

Objective. Physiological sensor data (e.g. photoplethysmograph) is important for remotely monitoring patients’ vital signals, but is often affected by measurement noise. Existing feature-based models for signal cleaning can be limited as they might not capture the full signal characteristics. Approach. In this work we present a deep learning framework for sensor signal cleaning based on dilated convolutions which capture the coarse- and fine-grained structure in order to classify whether a signal is noisy or clean. However, since obtaining annotated physiological data is costly and time-consuming we propose an autoencoder-based semi-supervised model which is able to learn a representation of the sensor signal characteristics, also adding an element of interpretability. Main results. Our proposed models are over 8% more accurate than existing feature-based approaches with half the false positive/negative rates. Finally, we show that with careful tuning (that can be improved further), the semi-supervised model outperforms supervised approaches suggesting that incorporating the large amounts of available unlabeled data can be advantageous for achieving high accuracy (over 90%) and minimizing the false positive/negative rates. Significance. Our approach enables us to reliably separate clean from noisy physiological sensor signal that can pave the development of reliable features and eventually support decisions regarding drug efficacy in clinical trials.

Design and Fabrication of a 3D-Printed Microfluidic Immunoarray for Ultrasensitive Multiplexed Protein Detection.

Microfluidic technology has revolutionized device fabrication by merging principles of fluid dynamics with technologies from chemistry, physics, biology, material science, and microelectronics. Microfluidic systems manipulate small volumes of fluids to perform automated tasks with applications ranging from chemical syntheses to biomedical diagnostics. The advent of low-cost 3D printers has revolutionized the development of microfluidic systems. For measuring molecules, 3D printing offers cost-effective, time, and ease-of-designing benefits. In this paper, we present a comprehensive tutorial for design, optimization, and validation for creating a 3D-printed microfluidic immunoarray for ultrasensitive detection of multiple protein biomarkers. The target is the development of a point of care array to determine five protein biomarkers for aggressive cancers. The design phase involves defining dimensions of microchannels, reagent chambers, detection wells, and optimizing parameters and detection methods. In this study, the physical design of the array underwent multiple iterations to optimize key features, such as developing open detection wells for uniform signal distribution and a flap for covering wells during the assay. Then, full signal optimization for sensitivity and limit of detection (LOD) was performed, and calibration plots were generated to assess linear dynamic ranges and LODs. Varying characteristics among biomarkers highlighted the need for tailored assay conditions. Spike-recovery studies confirmed the assay's accuracy. Overall, this paper showcases the methodology, rigor, and innovation involved in designing a 3D-printed microfluidic immunoarray. Optimized parameters, calibration equations, and sensitivity and accuracy data contribute valuable metrics for future applications in biomarker analyses.

Fast and Reliable Detection of Significant Solitons in Signals With Large Time-Bandwidth Products

We present a fast method to calculate the significantly large solitonic components of signals with large time-bandwidth products governed by the nonlinear Schrödinger equation, for which the computation typically becomes prohibitively expensive and/or numerically unstable. We partition the full signal in both frequency and time to obtain short signals with a constant number of samples, independent of the size of the full signal. The solitons within each short signal are computed using a conventional nonlinear Fourier transform (NFT) algorithm. The partitioning in general leads to spurious solitons not present in the full signal. We therefore design an acceptance scheme that removes spurious solitons. The remaining solitons are attributed to the full signal. Solitons that are too wide to fit into the short signals cannot be detected by this approach, but since wide solitons must be of low amplitude, the significant solitons will be found. This approach only requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> floating point operations, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> the number of signal samples. It can furthermore be applied to signals with large time-bandwidth products for which conventional NFT algorithms become unreliable or even fail. When applying our proposed method to a signal of 15,000 samples, the significant solitonic components were computed 14 times faster than when considering the whole signal, for which the conventional algorithm furthermore provided wrong results. We found that time-partitioning yields accurate results, while frequency-partitioning causes a small loss in accuracy. Combined frequency-time partitioning leads to the fastest computation, but also suffers from the same loss in accuracy as with frequency-partitioning. As time-partitioning yields a significant speed-up at nearly no loss in accuracy, we regard this as the method of choice in most practical scenarios.

Speech-recognition in landslide predictive modelling: A case for a next generation early warning system

Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling architectures typical of speech-recognition tasks. We aim at classifying the Turkish landscape into 5 km grids assigned with dynamic landslide susceptibility estimates. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long Short-Term Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model: 1) by randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008–2020) and 2) by assessing the effect of different time windows used to pass the rainfall signal to the neural network. Results show that the inclusion of the full precipitation signal rather than its scalar approximation leads to a substantial increase in prediction power (approximately 20%). This may potentially pave the road for a new generation of speech-recognition-based landslide early warning systems.

Fibrous mats based skin sensor with ultra-sensitivity and anti-overshooting dynamic stability enhanced with artificial intelligence

Next generation epidermal and wearable strain sensors are rapidly emerged as promising candidates for personalized medicine, soft machines, and human–machine interfaces. However, overshooting issues of these flexible strain sensors are major concerns during their dynamic strain monitoring, which often cause signal distortion and non-monotonic sensing characteristics. In addition, achieving a high sensitivtiy of these strain sensors within a full-body motion strain range (0–200%) remains a critical challenge. Herein, a conductive electrospun fibrous mats which are consisted of graphene anchored into fibrous thermoplastic polyurethane is proposed to endow strain sensors with anti-overshooting performance (or a good dynamic stability) and high sensitivity within full-body motion monitoring range. An overshooting-to-stable transition for the sensor’s electrical performance is observed by controlling the contents of conductive agents and substrate, which is elucidated by the kinetic changing behaviors of conductive pathways along the longitudinal and transverse strain directions. By strengthening the interaction forces between fillers and matrix, the conduction pathways are capable of rapidly switching to disconnection in response to external strains, thus achieving an ultrahigh sensitivity. The developed skin sensing platform is capable of detecting full range physiological signals and achieving healthcare Internet of Things in achilles tendon rehabilitation. Furthermore, a wearable Morse code-to-speech translation system powered with deep learning algorithm was demonstrated, with high recognition accuracy (>98.5%) and fast response time (∼16 ms).

Impacts of non-linear ITRF2020 on reference frame alignment

The geometric similarity between two sets of coordinates should be assumed for frame alignment. When aligning Global Navigation Satellite System (GNSS) positions with seasonal variations to the linear Terrestrial Reference Frame (TRF), non-negligible aliasing errors of transformation parameters can appear and bias the station positions. The recent release of the seasonally augmented International Terrestrial Reference Frame 2020 (ITRF2020) necessitates an investigation into the impacts of seasonal terms on the alignment of regional networks. Using four-year of GPS data for Crustal Movement Observation Network of China (CMONOC) stations, we realize five sets of regional reference frames (RFs) aligned to the linear ITRF2020 (REF-LIN), the seasonal ITRF2020 (REF-FRQ), the linear ITRF2020 augmented either with seasonal signals or with full signals from loading models (REF-FIT and REF-NTL), and the combination of seasonal displacements from ITRF2020 and sub-seasonal signals from loading model (REF-CMB). Our analysis revealed that the augmented ITRF2020 had significant impact on the global stations used for frame alignment. Aligning daily solutions to the augmented ITRF2020 instead of the linear ITRF2020 resulted in a noticeable root mean square (RMS) decrease for over 70% of global stations. The REF-CMB method was the most preferred for frame alignment of global networks, with the largest decreased RMS of 0.3 and 1.0 mm in horizontal and vertical components. This indicates that most true signals are preserved in the aligned daily solutions. For regional network, we found only marginal impacts on regional stations in terms of linear velocity, residuals of time series stacking between the augmented solutions and REF-LIN. However, the larger discrepancies are noticed between annual estimates and loading model for the vertical component of REF-CMB and REF-FRQ, compared to RER-LIN solutions.

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12mm, compared to 2.6% for the CoM method and 8.5% for no correction. We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

BEYONDPLANCK

We present a Bayesian calibration algorithm for cosmic microwave background (CMB) observations as implemented within the global end-to-end BEYONDPLANCKframework and applied to thePlanckLow Frequency Instrument (LFI) data. Following the most recentPlanckanalysis, we decomposed the full time-dependent gain into a sum of three nearly orthogonal components: one absolute calibration term, common to all detectors, one time-independent term that can vary between detectors, and one time-dependent component that was allowed to vary between one-hour pointing periods. Each term was then sampled conditionally on all other parameters in the global signal model through Gibbs sampling. The absolute calibration is sampled using only the orbital dipole as a reference source, while the two relative gain components were sampled using the full sky signal, including the orbital and Solar CMB dipoles, CMB fluctuations, and foreground contributions. We discuss various aspects of the data that influence gain estimation, including the dipole-polarization quadrupole degeneracy and processing masks. Comparing our solution to previous pipelines, we find good agreement in general, with relative deviations of −0.67% (−0.84%) for 30 GHz, 0.12% (−0.04%) for 44 GHz and −0.03% (−0.64%) for 70 GHz, compared toPlanckPR4 andPlanck2018, respectively. We note that the BEYONDPLANCKcalibration was performed globally, which results in better inter-frequency consistency than previous estimates. Additionally, WMAP observations were used actively in the BEYONDPLANCKanalysis, which both breaks internal degeneracies in thePlanckdata set and results in an overall better agreement with WMAP. Finally, we used a Wiener filtering approach to smoothing the gain estimates. We show that this method avoids artifacts in the correlated noise maps as a result of oversmoothing the gain solution, which is difficult to avoid with methods like boxcar smoothing, as Wiener filtering by construction maintains a balance between data fidelity and prior knowledge. Although our presentation and algorithm are currently oriented toward LFI processing, the general procedure is fully generalizable to other experiments, as long as the Solar dipole signal is available to be used for calibration.

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging (dMRI). Many analysis frameworks using dMRI data have been proposed to describe exchange, often using a double diffusion encoding (DDE) stimulated echo sequence. Techniques such as diffusion exchange weighted imaging (DEWI) and the filter exchange and rapid exchange models, use a specific subset of the full space DDE signal. In this work, a general representation of the DDE signal was employed with different sampling schemes (namely constant b1, diagonal and anti-diagonal) from the data reduction models to estimate exchange. A near-uniform sampling scheme was proposed and compared with the other sampling schemes. The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes. These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner. In synthetic data, the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth. In experimental data, the shifted diagonal and near-uniform sampling schemes outperformed the others, yielding the most consistent estimates with the full space estimation. The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes. In future studies, algorithms could be further developed for the optimization of sampling schemes, as well as incorporating additional properties, such as geometry and diffusion anisotropy, into exchange frameworks.

Miniaturized Pattern-Reconfigurable Multimode Antennas With Continuous Beam-Steering Capability

In this paper, two miniaturized multi-mode pattern steering antennas for Internet of Things (IoTs) applications are proposed. A compact multi-mode cavity functions both the hybrid and radiator. A continuous 360° pattern scanning in the azimuth plane is controlled by the amplitude and phase of three excitation ports. The first design achieves the smallest size with TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">02</sub> and TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> modes. In contrast, the second design has improved impedance bandwidth and mode isolation by introducing TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">02</sub> and high-order TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</sub> modes. Eight dipole chokes are attached to the cavity for surface current suppression. This approach eliminates the unwanted patch mode radiation. The antennas support multibeam applications with digital beamforming and analog beamforming with the 0°/180° phase switch and variable gain amplifier (VGA). The direction of arrival (DOA) can be estimated by directly measuring the amplitude and phase of the receiving signal. No full phase shifter and signal classification algorithm are required. The prototypes are fabricated and measured at 2.4 GHz. The diameters of the two antennas are 0.4 λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and 0.9 λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . The achieved antenna size is very miniature as shown by the literature comparison. The radiation and DOA performance is evaluated. The peak system efficiency is better than 60% after calibration and a good DOA estimation result is obtained.

Importance of Electrode Selection and Number in Reconstructing Standard Twelve Lead Electrocardiograms.

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG and either report less information or employ algorithms to reconstruct a full twelve-lead signal. We assessed the optimal electrode selection and number that minimizes redundant information collection while maximizing reconstruction accuracy. We employed a validated deep learning model to reconstruct ECG signals from 250 different patients in the PTB database. Different numbers and combinations of electrodes were removed from the ECG before reconstruction to measure the effect of electrode inclusion on reconstruction accuracy. The Left Leg (LL) electrode registered the largest drop in average reconstruction accuracy, from an R2 of 0.836 when the LL was included to 0.737 when excluded. Additionally, we conducted a correlation analysis to identify leads that behave similarly. We demonstrate that there exists a high correlation between leads I, II, aVL, aVF, V4, V5, and V6, which all occupy the bottom right quadrant in an ECG axis interpretation, and likely contain redundant information. Based on our analysis, we recommend the prioritization of electrodes RA, LA, LL, and V3 in any future lead collection devices, as they appear most important for full ECG reconstruction.

Hall viscosity and hydrodynamic inverse Nernst effect in graphene

Motivated by Hall viscosity measurements in graphene sheets, we study hydrodynamic transport of electrons in a channel of finite width in external electric and magnetic fields. We consider electric charge densities varying from close to the Dirac point up to the Fermi-liquid regime. We find two competing contributions to the hydrodynamic Hall and inverse Nernst signals that originate from the Hall viscous and Lorentz forces. This competition leads to a nonlinear dependence of the full signals on the magnetic field and even a cancellation at different critical field values for both signals. In particular, the hydrodynamic inverse Nernst signal in the Fermi-liquid regime is dominated by the Hall viscous contribution. We further show that a finite channel width leads to a suppression of the Lorenz ratio, while the magnetic field enhances this ratio. All of these effects are predicted in parameter regimes accessible in experiments.

Principles of Logic Design with Nanoscale Thin Film Memristive Systems for High Performance Digital Circuit Applications

The characteristic pinched hysteresis behavior of memristors has been reported by stacks of a variety of materials. This paper aims to examine the principles of logic design using such two terminal memristive systems for high performance digital circuit applications. As against logic design with standard CMOS, the benefits of logic design with memristors have been stated. The realization and operation of memristor based AND and OR hybrid logic gates obtained by integrating memristors with standard CMOS logic have been discussed. The IMPLY and MAGIC logic families have been demonstrated by covering MAGIC NOR and NAND logic gate implementation with MAGIC NOR in detail. A qualitative comparison has been drawn towards the end of the paper to conclude on the suitability and application space for each of the logic families studied in this paper. This work also describes the hybrid CMOS-memristive logic family known as MRL (Memristor Ratioed Logic). With the addition of CMOS inverters, this logic family's OR and AND logic gates, which are based on memristive components, are given a full logic structure and signal restoration. The MRL family, in contrast to earlier memristor-based logic families, is compatible with conventional CMOS logic.

Accelerated submillimeter wave-encoded magnetic resonance imaging via deep untrained neural network.

Wave gradient encoding can adequately utilize coil sensitivity profiles to facilitate higher accelerations in parallel magnetic resonance imaging (pMRI). However, there are limitations in mainstream pMRI and a few deep learning (DL) methods for recovering missing data under wave encoding framework: the former is prone to introduce errors from the auto-calibration signals (ACS) signal acquisition and is time-consuming, while the latter requires a large amount of trainingdata. To tackle the above issues, an untrained neural network (UNN) model incorporating wave-encoded physical properties and deep generative model, named WDGM, was proposed with additional ACS- and trainingdata-free. Generally, the proposed method can provide powerful missing data interpolation capability using the wave physical encoding framework and designed UNN to characterize the MR image (k-space data) priors. Specifically, the MRI reconstruction combining physical wave encoding and elaborate UNN is modeled as a generalized minimization problem. The designation of UNN is driven by the coil sensitivity maps (CSM) smoothness and k-space linear predictability. And then, the iterative paradigm to recover the full k-space signal is determined by the projected gradient descent, and the complex computation is unrolled to the network with optimized parameters by the optimizer. Simulated wave encoding and in vivo experiments are exploited to demonstrate the feasibility of the proposed method. The best quantitative metrics RMSE/SSIM/PSNR of 0.0413, 0.9514, and 37.4862 gave competitive results in all experiments with at least six-fold acceleration, respectively. In vivo experiments of human brains and knees showed that the proposed method can achieve comparable reconstruction quality and even has superiority relative to the comparison, especially at a high resolution of 0.67 mm and fewer ACS. In addition, the proposed method has a higher computational efficiency achieving a computation time of 9.6 s/perslice. The model proposed in this work addresses two limitations of MRI reconstruction in the wave encoding framework. The first is to eliminate the need for ACS signal acquisition to perform the time-consuming calibration process and to avoid errors such as motion during the acquisition procedure. Furthermore, the proposed method has clinical application friendly without the need to prepare large training datasets, which is difficult in the clinical. All results of the proposed method demonstrate more confidence in both quantitative and qualitative metrics. In addition, the proposed method can achieve higher computationalefficiency.