Fast Data Acquisition Research Articles

Boolean Spider Monkey Optimization Assisted Routing Integrated with Wireless Sensor Network

The Wireless Sensor Network (WSN) offers greater flexibility in network deployment, especially when powered by batteries. It offers solutions to the sensor nodes' energy consumption as it mainly operates as devices with faster data acquisition. Hence, it is difficult for the sensor nodes to carry such a computational burden from the source nodes to the target base station or the internet gateway. It is essential to maintain the routing paths as well as the balance of the sensor nodes. In this paper, we propose a Boolean Spider Monkey Optimization (BSMO) routing on WSNs is developed to maintain the stable routing path that matches the speed of input data acquisition. The sensor nodes help in data collection and acquisition and WSNs routes the data collected via hops between the source and sink nodes. The BSMO is responsible for controlling the data routing and matches the routing speed with data acquisition speed. Hence, the network is maintained in a stabilized condition. The simulation results are estimated in terms of average delay, throughput, and network energy efficiency. The result shows

Machine Learning-Based Diffractive Image Analysis with Subwavelength Resolution

Far-field analysis of small objects is severely constrained by the diffraction limit. Existing tools achieving subdiffraction resolution often utilize point-by-point image reconstruction via scanning or labeling. Here, we present a new technique capable of fast and accurate characterization of two-dimensional structures with at least λ0/25 theoretical resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving 180 nm-scale features, the smallest available to us, with 845 nm laser light, reaching a resolution of λ0/5. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parametrization, suitable for diffractive configurations and highly tolerant to random noise, was developed. The proposed technique can be applied to new optical characterization tools with high spatial resolution, fast data acquisition, and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscopy.

Towards using convolutional neural network to locate, identify and size defects in phased array ultrasonic testing

Machine learning algorithms are widely used in image recognition. In Phased Array Ultrasonic Testing (PAUT), images are typically formed through constructive and destructive superpositions of signals backscattered from flaws or geometric features. However, all PAUT data acquisition schemes require several emissions and the duration of the acquisition may be too slow in high-speed manufacturing. In this study, the Faster R-CNN was used to identify, locate and size flat bottom holes (FBH) and side-drilled holes (SDH) in an immersed test specimen using a single plane wave insonification. The training was performed on segmented and classified data generated using GPU-accelerated finite element simulations. SDH and FBH of different diameters, depths and lateral positions were included in the training set. The thickness of the test specimen was also variable. An ultrasonic phased array probe of 64 elements was simulated. All elements of the phased array probe were fired at the same time and the time traces from each element were recorded. The individual time traces were concatenated to form a matrix, which was then used in the training. This inspection scenario enables fast acquisition of data at the expense of poor lateral resolution in the resulting image. The trained neural network was initially tested using finite element simulations. Results were assessed in terms of the intersection of the union (IoU) between the ground truth geometry and the predicted geometry. With the simulated cases, the thickness of the test specimen was detected in all cases. When using a 40% IoU threshold, the detection rate of the FBH was 87% while only 20% for the SDH. The smallest detected FBH had a 0.56 wavelength depth and a lateral extent of 1.04 wavelength. Drawing a box using the −6dB drop method around the FBH always led to an IoU under 15%. On average, the lateral extent of the FBH using the −6dB method was three times larger than the diameter predicted by the proposed method. Then, the training was continued with a small augmented dataset of experiments (equivalent to 3% of the simulated dataset). In experiments, the results show that the test specimen was always correctly identified. When using a 40% IoU threshold the experimental detection rate of the FBH was 70%. The smallest detected defect in experiments had a depth of 2 wavelengths.

Single Femtosecond Laser-Pulse-Induced Superficial Amorphization and Re-Crystallization of Silicon.

Superficial amorphization and re-crystallization of silicon in <111> and <100> orientation after irradiation by femtosecond laser pulses (790 nm, 30 fs) are studied using optical imaging and transmission electron microscopy. Spectroscopic imaging ellipsometry (SIE) allows fast data acquisition at multiple wavelengths and provides experimental data for calculating nanometric amorphous layer thickness profiles with micrometric lateral resolution based on a thin-film layer model. For a radially Gaussian laser beam and at moderate peak fluences above the melting and below the ablation thresholds, laterally parabolic amorphous layer profiles with maximum thicknesses of several tens of nanometers were quantitatively attained. The accuracy of the calculations is verified experimentally by high-resolution transmission electron microscopy (HRTEM) and energy dispersive X-ray spectroscopy (STEM-EDX). Along with topographic information obtained by atomic force microscopy (AFM), a comprehensive picture of the superficial re-solidification of silicon after local melting by femtosecond laser pulses is drawn.

A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams

Dam deformation monitoring can directly identify the safe operation state of a dam in advance, which plays an important role in dam safety management. Three-dimensional (3D) terrestrial laser scanning technology is widely used in the field of deformation monitoring due to its fast, complete, and high-density 3D data acquisition capabilities. However, 3D point clouds are characterized by rough surfaces, discrete distributions, which affect the accuracy of deformation analysis of two states data. In addition, it is impossible to directly extract the correspondence points from an irregularly distributed point cloud to unify the coordinates of the two states’ data, and the correspondence lines and planes are often difficult to obtain in the natural environment. To solve the above problems, this paper studies a displacement change detection method for arch dams based on two-step point cloud registration and contour model comparison method. In the environment around a dam, the stable rock is used as the correspondence element to improve the registration accuracy, and a two-step registration method from rough to fine using the iterative closest point algorithm is present to describe the coordinate unification of the two states’ data without control network and target. Then, to analyze the displacement variation of an arch dam surface in two states and improve the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud, the contour model fitting the point clouds is used to compare the change in distance between models. Finally, the method of this paper is applied to the Xiahuikeng Arch Dam, and the displacement changes of the entire dam in different periods are visualized by comparing with the existing methods. The results show that the displacement change in the middle area of the dam is generally greater than that of the two banks, increasing with the increase in elevation, which is consistent with the displacement change behavior of the arch dam during operation and can reach millimeter-level accuracy.

Synchrotron X-ray phase contrast micro tomography to explore the morphology of abdominal organs in Pterostichus melas italicus Dejean, 1828 (Coleoptera, Carabidae)

Micro-computer tomography imaging is a fast and non-destructive data acquisition technique which can replace or complement the traditional investigation methodologies used in entomology to study morphology. In this paper, Synchrotron Radiation X-ray Phase-Contrast micro tomography (SR-PhC micro-CT) was combined with histology and scanning electron microscopy (SEM) observations to describe the abdominal organs of Pterostichus melas italicus Dejean, 1828 (Coleoptera, Carabidae). This species was used as a representative model because of its ecological role as a generalist predator in agroecosystems. SR-PhC micro-CT allowed us to identify in situ abdominal structures including dorsal vessel, digestive tract with Malpighian tubules, male reproductive system, ganglia, fat bodies, pygidial glands, muscles and tracheae. The histology was performed to define the tissue organization of the digestive and reproductive systems. SR-PhC micro-CT and 3D rendering provided more accurate information on shape and size of organs than histological and SEM analyses, respectively. The finding of this study was to describe the anatomy and histology of organs involved in crucial life history traits, such as reproduction, nutrition and excretion. High quality images and the supplementary video represent a significant advance in knowledge of the carabid anatomy and are a baseline for future research.

Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry

The last decade witnessed considerable progress in the development of laser ablation/ionisation time-of-flight mass spectrometry (LI-TOFMS). The improvement of both the laser ablation ion sources employing femtosecond lasers and the method of ion coupling with the mass analyser led to highly sensitive element and isotope measurements, minimisation of matrix effects, and reduction of various fractionation effects. This improvement of instrumental performance can be attributed to the progress in laser technology and accompanying commercialisation of fs-laser systems, as well as the availability of fast electronics and data acquisition systems. Application of femtosecond laser radiation to ablate the sample causes negligible thermal effects, which in turn allows for improved resolution of chemical surface imaging and depth profiling. Following in the footsteps of its predecessor ns-LIMS, fs-LIMS, which employs fs-laser ablation ion sources, has been developed in the last two decades as an important method of chemical analysis and will continue to improve its performance in subsequent decades. This review discusses the background of fs-laser ablation, overviews the most relevant instrumentation and emphasises their performance figures, and summarizes the studies on several applications, including geochemical, semiconductor, and bio-relevant materials. Improving the chemical analysis is expected by the implementation of laser pulse sequences or pulse shaping methods and shorter laser wavelengths providing current progress in mass resolution achieved in fs-LIMS. In parallel, advancing the methods of data analysis has the potential of making this technique very attractive for 3D chemical analysis with micrometre lateral and sub-micrometre vertical resolution.

Noninvasive calibrated tissue temperature estimation using backscattered energy of acoustic harmonics

PurposeA real-time and non-invasive thermometry technique is essential in thermal therapies to monitor and control the treatment. Ultrasound is an attractive thermometry modality due to its relatively high sensitivity to change in temperature and fast data acquisition and processing capabilities. A temperature-sensitive acoustic parameter is required for ultrasound thermometry in order to track the changes in that parameter during the treatment. Currently, the main ultrasound thermometry methods are based on variation in the attenuation coefficient, the change in backscattered energy of the signal (CBE), the backscattered radio-frequency (RF) echo-shift due to change in the speed of sound and thermal expansion of the medium, and change in the amplitudes of the acoustic harmonics. In this work, an ultrasound thermometry method based on second harmonic CBE (CBEh2) and combined fundamental and second harmonic CBE (CBEcomb) is used to produce 2D temperature maps, detect localized heated region generated by low intensity focused ultrasound (LIFU), and control temperature in the heated region. Materials and methodsEx vivo pork muscle tissue samples were exposed to localized LIFU heating source and 2D temperature maps were produced from the RF data acquired by a 4.2 MHz linear array probe using a Verasonics Vantage™ ultrasound scanner (Verasonics Inc., Redmond, WA) after the exposure. Calibrated needle thermocouples were also placed in the ex vivo tissue sample close to the LIFU focal zone for temperature calibration purposes. The estimated temperature maps were the established echo-shift technique. A tissue motion compensation algorithm was also used to reduce the susceptibility to motion artifacts. Results2D temperature maps were generated using CBE of acoustic harmonic and echo-shift techniques. The results show a direct correlation between the CBE of acoustic harmonics and focal tissue temperature for a range of temperatures from 37 °C (baseline) to 47 °C. ConclusionsThe findings of this study show that the CBE of acoustic harmonics technique can be used to noninvasively estimate temperature change in tissue in the hyperthermia temperature range.

Nonlinear calibration of frequency modulated continuous wave LIDAR based on a microresonator soliton comb.

Traditional frequency modulated continuous wave (FMCW) LIDAR ranging is based on heterodyne detection, calculating unknown distance by extracting the frequency of the interference signal, while the main error source is frequency modulation (FM) nonlinearity. In this paper, a ranging system based on a microresonator soliton comb is demonstrated to correct the nonlinearity by sampling the ranging signals at equal frequency intervals, producing a ranging error lower than 20 µm, while at the range of 2 m. Advantages of fast data acquisition, light computation requirements, and a simple optical path, without long optical fiber, give this method a high practical value in precision manufacturing.

Compressed sensing MRI to characterize sodium alginate deposits during cross-flow filtration in membranes with a helical ridge

Cross-flow membrane ultrafiltration of the polysaccharide sodium alginate in aqueous solution was investigated in polymeric Helix membranes by in-situ magnetic resonance imaging (MRI) non-invasively and non-destructively. MRI allows velocity and NMR intensity measurements during filtration. In the present case and under the conditions used in the experiments, signal intensity is mainly generated by 1H of water. Lower intensities can therefore be understood as a measure for material accumulation. Compressed sensing allows faster data acquisition, enabling a reliable, time resolved measurement of the ultrafiltration progress. Even under cross-flow filtration conditions, the membrane filtration is impaired by the increasing filtration resistance due to gel layer formation during the filtration of aqueous sodium alginate with CaCl2. The Helix membrane has an additional helical ridge along the membrane wall to provide fouling inhibition, resulting in a complex three-dimensional flow field that was measured via compressed sensing MRI. Furthermore, the behavior of the deposit was classified more precisely by backwashing in the Helix membrane. Summarizing, the flow field, the deposit formation during cross-flow filtration in a Helix membrane and its backwashing was monitored and investigated by fast MRI methods.

Characterization and performance evaluation of the XSPA-500k detector using synchrotron radiation.

Hybrid photon counting (HPC) detectors are widely used at both synchrotron facilities and in-house laboratories. The features of HPC detectors, such as no readout noise, high dynamic range, high frame rate, excellent point spread function, no blurring etc. along with fast data acquisition, provide a high-performance detector with a low detection limit and high sensitivity. Several HPC detector systems have been developed around the world. A number of them are commercially available and used in academia and industry. One of theimportant features of an HPC detector is a fast readout speed. Most HPC detectors can easily achieve over 1000 frames s-1, one or two orders of magnitude faster than conventional CCD detectors. Nevertheless, advanced scientific challenges require ever faster detectors in order to study dynamical phenomena in matter. The XSPA-500k detector can achieve 56 kframes s-1 continuously, without dead-time between frames. Using `burst mode', a special mode of the UFXC32k ASIC, the frame rate reaches 1 000 000 frames s-1. XSPA-500k was fully evaluated at the Metrology beamline at Synchrotron SOLEIL (France) and its readout speed was confirmed by tracking the synchrotron bunch time structure. The uniformity of response, modulation transfer function, linearity, energy resolution and other performance metrics were also verified either with fluorescence X-rays illuminating the full area of the detector or with the direct beam.

Visualizing Dynamic Processes with rapidFLIMHiRes: Ultra Fast Flim with Outstanding 10 PS Time Resolution

Fluorescence Lifetime Imaging (FLIM) has become an essential tool in Life Sciences over the last decade. However, up to now, users had to choose between high timing precision or fast data acquisition when using Time-Correlated Single Photon Counting (TCSPC) electronics. This was a drawback when investigating fast processes in cells or tissues such as protein interactions, FRET dynamics, or chemical reactions.

Development of a fast characterization setup for radionuclide generators demonstrated by a 227Ac-based generator

Abstract The development of a setup for a fast online characterization of radionuclide generators is reported. A generator utilizing the mother nuclide 227Ac sorbed on a cation exchange resin is continuously eluted by using a peristaltic pump. To allow continuous and pulse-free elution of a large volume over extended time periods a 3D-printed interface designed to remove pressure-oscillations induced by the pump was placed between pump and generator column to ensure undisturbed generator elution. The eluate of the generator is passed through a 3D printed flow cell placed inside a borehole Na(Tl)-scintillation detector for high counting efficiency. Alternatively, a HPGe detector suitable for nuclide identification was used to demonstrate the validity of the online method. The detection system combines conventional gamma-ray spectrometry with fast list mode data acquisition in the Matlab software package. Elution experiments were performed at different flow rates of hydrochloric acid, separating 211Bi (t 1/2 = 2.14 min) free from its parent nuclides. In addition, to prove the versatility of the setup, experiments at different hydrochloric acid concentrations were performed resulting in the elution of pure 211Pb (t 1/2 = 36.1 min) and 223Ra (t 1/2 = 11.43 d), respectively.

Depth Evaluation for Metal Surface Defects by Eddy Current Testing Using Deep Residual Convolutional Neural Networks

Eddy current testing (ECT) is an effective technique for evaluating depth of metal surface defects. However, in practice, evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this article, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are threefold. First, a highly integrated portable ECT device is developed, taking the advantage of an advanced field-programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Second, a dataset, termed metal defects of different depths by ECT (MDDECT), is constructed using the ECT device by human operators and made openly available. It contains 48000 scans from 18 defects of different depths and liftoffs. Third, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-D residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-D ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet with depths from 0.3 to 2.0 mm in the resolution of 0.1 mm. In addition, the results show that the trained ResNeXt1D-38 model is immune to liftoff signals.

Electromagnetic Induction Imaging at Multiple Depths With a Single Coil

Electromagnetic induction imaging (EII) is an attractive imaging modality due to its contactless operation, low cost, and good penetration depth. To implement real-time imaging, in applications such as object tracking or functional biomedical imaging, fast data acquisition is required. To proceed toward this goal, in this article, we propose the use of a single coil for 3-D EII along with a fast inversion approach. To collect sufficient data that allow 3-D imaging, we propose two approaches: collecting data at multiple coil’s drive current levels and collecting data with multiple tank circuit’s capacitor values. We show that the use of such data when using a single coil allows for 3-D EII. For proof-of-concept experiments, we use a commercial data acquisition board, that is, LDC1614EVM module, and multilayer planar coils made by Texas Instruments.

A Deep Learning-Based Model That Reduces Speed of Sound Aberrations for Improved In Vivo Photoacoustic Imaging.

Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images in terms of image resolution, imaging depth, or processing speed. Here, we demonstrate a novel deep learning PAI that uses multiple speed of sound (SoS) inputs. With this novel method, we achieved SoS aberration mitigation, streak artifact removal, and temporal resolution improvement all at once in structural and functional in vivo PA images of healthy human limbs and melanoma patients. The presented method produces high-contrast PA images in vivo with reduced distortion, even in adverse conditions where the medium is heterogeneous and/or the data sampling is sparse. Thus, we believe that this new method can achieve high image quality with fast data acquisition and can contribute to the advance of clinical PAI.

Rosette Trajectories for Fast MRI Based on an Adaptive Reconstruction Method

Non-Cartesian MRI k-space trajectories provide faster and more motion-robust data acquisitions than those of Cartesian trajectories. In this paper, we focus on the rosette trajectory and generalize the weighted rosette trajectories for fast undersampled k-space data acquisition. However, single-slice imaging using the rosette trajectory will be affected by the off-resonance effect. To reduce the artifacts from off-resonance slices, this paper introduced an adaptive iterative reconstruction method derived from the TV and nuclear norm regularization terms for raw MRI data reconstruction. This reconstruction problem was separated into two subproblems and solved with an adaptive regularization parameter. The results show that the weighted rosette trajectory that offers short acquisition times and good off-resonance behaviors with little blurring can be used for reconstruction with adaptive regularization parameters and achieve a superior performance.

Lipid-Nanodiscs Formed by Paramagnetic Polymers for Fast NMR Data Acquisition

Lipid-Nanodiscs Formed by Paramagnetic Polymers for Fast NMR Data Acquisition

High-bandwidth, variable-resistance differential noise thermometry.

We have developed Johnson noise thermometry applicable to mesoscopic devices with variable source impedance with high bandwidth for fast data acquisition. By implementing differential noise measurement and two-stage impedance matching, we demonstrate noise measurement in the frequency range of 120 MHz-250MHz with a wide sample resistance range of 30 Ω-100kΩ tuned by gate voltages and temperature. We employed high-frequency, single-ended low noise amplifiers maintained at a constant cryogenic temperature in order to maintain the desired low noise temperature. We have achieved thermometer calibration with temperature precision up to 650 μK measuring a 200 mK temperature modulation on a 10K background with 30s of averaging. Using this differential noise thermometry technique, we measured thermal conductivity on a bilayer graphene sample spanning the metallic and semiconducting regimes in a wide resistance range, and we compared it to the electrical conductivity.

Mutifocal image scanning microscopy based on double-helix point spread function engineering

Confocal laser scanning microscopy (CLSM) is a powerful imaging tool providing high resolution and optical sectioning. In its standard optical configuration, a pair of confocal pinholes is used to reject out-of-focus light. The diffraction limited resolution can be broken by reducing the confocal pinhole size. But this comes at the cost of extremely low signal-to-noise ratio (SNR). The limited SNR problem can be solved by image scanning microscopy (ISM), in which the single-point detector of a regular point-scanning confocal microscopy is substituted with an array detector such as CCD or CMOS, thus the two-fold super-resolution imaging can be achieved by pixel reassignment and deconvolution. However, the practical application of ISM is challenging due to its limited image acquisition speed. Here, we present a hybrid microscopy technique, named multifocal refocusing after scanning using helical phase engineering microscopy (MRESCH), which combines the double-helix point spread function (DH-PSF) engineering with multifocal structured illumination to dramatically improve the image acquisition speed. In the illumination path, sparse multifocal illumination patterns are generated by a digital micromirror device for parallel imaging information acquisition. In the detection path, a phase mask is introduced to modulate the conventional PSF to the DH-PSF, which provides volumetric information, and meanwhile, we also present a digital refocusing strategy for processing the collected raw data to recover the wild-filed image from different sample layers. To demonstrate imaging capabilities of MRESCH, we acquire the images of mitochondria in live HeLa cells and make a detailed comparison with those from the wide-field microscopy. In contrast to the conventional wide-field approach, the MRESCH can expand the imaging depth in a range from –1 μm to 1 μm. Next, we sample the F-actin of bovine pulmonary artery endothelial cells to characterize the lateral resolution of the MRESCH. The results show that the MRESCH has a better resolution capability than the conventional wide-field illumination microscopy. Finally, the proposed image scanning microscopy can record three-dimensional specimen information from a single multi-spot two-dimensional scan, which ensures faster data acquisition and larger field of view than ISM.