Fast Field Research Articles

Fast direct multi-person radiance fields from sparse input with dense pose priors

Volumetric radiance fields have been popular in reconstructing small-scale 3D scenes from multi-view images. With additional constraints such as person correspondences, reconstructing a large 3D scene with multiple persons becomes possible. However, existing methods fail for sparse input views or when person correspondences are unavailable. In such cases, the conventional depth image supervision may be insufficient because it only captures the relative position of each person with respect to the camera center. In this paper, we investigate an alternative approach by supervising the optimization framework with a dense pose prior that represents correspondences between the SMPL model and the input images. The core ideas of our approach consist in exploiting dense pose priors estimated from the input images to perform person segmentation and incorporating such priors into the learning of the radiance field. Our proposed dense pose supervision is view-independent, significantly speeding up computational time and improving 3D reconstruction accuracy, with less floaters and noise. We confirm the advantages of our proposed method with extensive evaluation in a subset of the publicly available CMU Panoptic dataset. When training with only five input views, our proposed method achieves an average improvement of 6.1% in PSNR, 3.5% in SSIM, 17.2% in LPIPSvgg, 19.3% in LPIPSalex, and 39.4% in training time.

Cell-class-specific electric field entrainment of neural activity

Electric fields affect the activity of neurons and brain circuits, yet how this happens at the cellular level remains enigmatic. Lack of understanding of how to stimulate the brain to promote or suppress specific activity significantly limits basic research and clinical applications. Here, we study how electric fields impact subthreshold and spiking properties of major cortical neuronal classes. We find that neurons in the rodent and human cortex exhibit strong, cell-class-dependent entrainment that depends on stimulation frequency. Excitatory pyramidal neurons, with their slower spike rate, entrain to both slow and fast electric fields, while inhibitory classes like Pvalb and Sst (with their fast spiking) predominantly phase-lock to fast fields. We show that this spike-field entrainment is the result of two effects: non-specific membrane polarization occurring across classes and class-specific excitability properties. Importantly, these properties are present across cortical areas and species. These findings allow for the design of selective and class-specific neuromodulation.

PTME: A Regular Expression Matching Engine Based on Speculation and Enumerative Computation on FPGA

Fast regular expression matching is an essential task for deep packet inspection. In previous works, the regular expression matching engine on FPGA struggled to achieve an ideal balance between resource consumption and throughput. Speculation and enumerative computation exploits the statistical properties of deterministic finite automata, allowing for more efficient pattern matching. Existing related designs mostly revolve around vector instructions and multiple processors/cores or SIMD instruction sets, with a lack of implementation on FPGA platforms. We design a parallelized two-character matching engine on FPGA for efficiently fast filtering off fields with no pattern features. We transform the state transitions with sequential dependencies to the existing problem of elements in one set, enabling the proposed design to achieve high throughput with low resource consumption and support dynamic updates. Results show that compared with the traditional DFA matching, with a maximum resource consumption of 25% for on-chip FFs (74323/1045440) and LUTs (123902/522720), there is an improvement in throughput of 8.08-229.96 × speedup and 87.61-99.56% speed-up(percentage improvement) for normal traffic, and 11.73-39.59 × speedup and 91.47-97.47% speed-up(percentage improvement) for traffic with high-frequency match hits. Compared with the state-of-the-art similar implementation, our circuit on a single FPGA chip is superior to existing multi-core designs.

A new approach for fast field calculation in electrostatic electron lens design and optimization

In electron optics, calculation of the electric field plays a major role in all computations and simulations. Accurate field calculation methods such as the finite element method (FEM), boundary element method and finite difference method, have been used for years. However, such methods are computationally very expensive and make the computer simulation challenging or even infeasible when trying to apply automated design of electrostatic lens systems with many free parameters. Hence, for years, electron optics scientists have been searching for a fast and accurate method of field calculation to tackle the aforementioned problem in the design and optimization of electrostatic electron lens systems. This paper presents a novel method for fast electric field calculation in electrostatic electron lens systems with reasonably high accuracy to enable the electron-optical designers to design and optimize an electrostatic lens system with many free parameters in a reasonably short time. The essence of the method is to express the off-axis potential in an axially symmetrical coordinate system in terms of derivatives of the axial potential up to the fourth order, and equate this to the potential of the electrode at that axial position. Doing this for a limited number of axial positions, we get a set of equations that can be solved to obtain the axial potential, necessary for calculating the lens properties. We name this method the fourth-order electrode method because we take the axial derivatives up to the fourth order. To solve the equations, a quintic spline approximation of the axial potential is calculated by solving three sets of linear equations simultaneously. The sets of equations are extracted from the Laplace equation and the fundamental equations that describe a quintic spline. The accuracy and speed of this method is compared with other field calculation methods, such as the FEM and second order electrode method (SOEM). The new field calculation method is implemented in design/optimization of electrostatic lens systems by using a genetic algorithm based optimization program for electrostatic lens systems developed by the authors. The effectiveness of this new field calculation method in optimizing optical parameters of electrostatic lens systems is compared with FEM and SOEM and the results are presented. It should be noted that the formulation is derived for general axis symmetrical electrostatic electron lens systems, however the examples shown in this paper are with cylindrical electrodes due to the simplicity of the implementation in the software.

Integrated microcavity electric field sensors using Pound-Drever-Hall detection

Discerning weak electric fields has important implications for cosmology, quantum technology, and identifying power system failures. Photonic integration of electric field sensors is highly desired for practical considerations and offers opportunities to improve performance by enhancing microwave and lightwave interactions. Here, we demonstrate a high-Q microcavity electric field sensor (MEFS) by leveraging the silicon chip-based thin film lithium niobate photonic integrated circuits. Using the Pound-Drever-Hall detection scheme, our MEFS achieves a detection sensitivity of 5.2 μV/(mHz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sqrt{{{{{{{{\\rm{Hz}}}}}}}}}$$\\end{document}), which surpasses previous lithium niobate electro-optical electric field sensors by nearly two orders of magnitude, and is comparable to atom-based quantum sensing approaches. Furthermore, our MEFS has a bandwidth that can be up to three orders of magnitude broader than quantum sensing approaches and measures fast electric field amplitude and phase variations in real-time. The ultra-sensitive MEFSs represent a significant step towards building electric field sensing networks and broaden the application spectrum of integrated microcavities.

Application of the Z-transform on the deconvolution of lightning electric field waveforms obtained from single-station measurements

Fast electric field measurement systems used in the field of atmospheric electricity often have decay time constants of the order of 1 ms, which means that slower processes – such as long continuing currents – are not visible in the raw electric field records. This paper presents a practical approach to compensate for the time decay caused by the system response and regain the original lightning electric field waveforms. To accomplish this task, the transfer function of the measurement system is modeled in the Z-domain. Then, from the inverse Z-transform of the transfer function, the compensated electric field is obtained in discrete time domain. Based on data obtained at Nova Gameleira campus of CEFET-MG (Belo Horizonte, Brazil), we present and discuss results of typical electric field waveforms that include some slower lightning processes, which are not readily observable in the raw data.

Fast flow field prediction approach of supersonic inlet in wide operating range based on deep learning

The flow in a supersonic inlet at wide operation range is quite complicated and contains complex shock reflections and shock wave/boundary layer interactions (SWBLI). So it is difficult to achieve the operating state monitoring in real-time by the sensor signals only. The recently booming deep learning (DL) is promising to provide a new way for flow field reconstruction due to its excellent data mining capability. For these, a fast flow field prediction model based on deep learning is proposed for the supersonic inlet in a wide operation range. To establish the data set for model training, numerical simulations at various Mach number and flight altitude obtained by Latin Hypercube Sampling (LHS) method are carried out. The trained model successfully established the mapping relationship between the inlet flow field and the input features. Both the start and unstart flows can be reconstructed by the model with high accuracy and efficiency. Then, two different types of input features are compared to test the influence of different input features on the model performance. The results indicated that the pressure distribution inputs perform better than that of operating conditions, which is because the wall pressure distribution contains more information and characteristics of the flow. Furthermore, compared with the traditional Computational Fluid Dynamics (CFD) simulations and wind tunnel experiments, the model has higher efficiency in predicting the inlet flow field with high accuracy. Finally, interpolation and extrapolation tests are conducted and the results revealed that the model is of high robustness and generalization ability.

A fast three-dimensional flow field prediction around bluff bodies using deep learning

This study presents a deep learning approach for predicting the flow field in the incompressible turbulent three-dimensional (3D) external flow around right-rhombic prism-shaped bluff bodies. The approach involves treating the nodes of the unstructured grid in the computational fluid dynamics domain as a point cloud, which is used as an input for a neural network. The neural network is trained to map the spatial coordinates of the nodes to the corresponding velocity and pressure values in the domain. The PointNet, a reliable solution in 3D vision tasks, is selected as the neural network architecture. Implementing this architecture makes it feasible to use irregular positions of the nodes of an unstructured grid as an input without needing interpolation. A dataset, comprising 3511 cases, is generated for training and testing the network. This is achieved by changing the geometric parameters of a right rhombic prism and varying its angle to the flow stream. Then, the continuity and momentum equations for turbulent flow are solved using a solver. Given the need for a larger number of points to accurately represent a 3D flow, the architecture of PointNet is modified. This modification involves adding extra layers and adjusting the number of neurons inside the layers to overcome this challenge. Once the training is completed, given the unseen samples from the test dataset to the model, our model can predict the velocity and pressure of the flow field at a speed that exceeds our conventional solver by several orders of magnitude with a maximum relative error of 4.58%.

A two-layer Timepix3 stack for improved charged particle tracking and radiation field decomposition

Abstract We characterize a novel instrument designed for radiation field decomposition and particle trajectory reconstruction for application in harsh radiation environments. The device consists of two Timepix3 assemblies with 500 µm thick silicon sensors in a face-to-face geometry. These detectors are interleaved with a set of neutron converters: 6LiF for thermal neutrons, polyethylene (PE) for fast neutrons above 1 MeV, and PE with an additional aluminum recoil proton filter for neutrons above ∼4 MeV. Application of the coincidence and anticoincidence technique together with pattern recognition allows improved separation of charged and neutral particles, their discrimination against γ-rays and assessment of the overall directionality of the fast neutron field. The instrument's charged particle tracking and separation capabilities were studied at the Danish Center for Particle Therapy (DCPT), the Proton Synchrotron, and Super Proton Synchrotron with protons (50–240 MeV), pions (1–10 GeV/c and 180 GeV/c). After developing temporal and spatial coincidence assignment methodology, we determine the relative amount of coincident detections as a function of the impact angle, present the device's impact angle resolving power (both in coincidence and anticoicidence channels). The detector response to neutrons was studied at the Czech Metrology Institute (CMI), at n_ToF and the Los Alamos Neutron Science Center (LANSCE), covering the entire spectrum from thermal up to 600 MeV. The measured tracks were assigned to their corresponding neutron energy by application of the time of flight technique. We present the achieved neutron detection efficiency as a function of neutron kinetic energy and demonstrate how the ratio of events found below the different converters can be used to assess the hardness of the neutron spectrum. As an application, we determine the neutron content within a PMMA phantom just behind the Bragg-peak during clinical irradiation condition with protons of 160 MeV.

Utility of Prostate Health Index Density for Biopsy Strategy in Biopsy-Naïve Patients With PI-RADS v2.1 Category 3 Lesions.

Category 3 lesions in PI-RADSv2.1 pose diagnostic challenges, complicating biopsy decisions. Recent biomarkers like prostate health index (PHI) have shown higher specificity in detecting clinically significant prostate cancer (csPCa) than prostate-specific antigen (PSA). Yet their integration with MRI remains understudied. To evaluate the utility of PSA and PHI with its derivatives for detecting csPCa in biopsy-naïve patients with category 3 lesion on initial prostate MRI scan. Retrospective. One hundred ninety-three biopsy-naïve patients who underwent MRI, PSA, and PHI testing, followed by both targeted and systematic biopsies. Turbo spin-echo T2-weighted imaging, diffusion-weighted single-shot echo-planar imaging, and dynamic contrast-enhanced T1-weighted fast field echo sequence imaging in 3 T. PHI density (PHID) and PSA density (PSAD) derived by dividing serum PHI and PSA with prostate volume (MRI based methodology suggested by PI-RADSv2.1). Risk-stratified models to evaluate the utility of markers in triaging patients for biopsy, including low-, intermediate-, and high-risk groups. Independent t-test, Mann-Whitney U test, Mantel-Haenszel test, generalized estimating equation, and receiver operating characteristic (ROC) curve analysis were used. Statistical significance defined as P < 0.05. CsPCa was found in 16.6% (32/193) of patients. PHID had the highest area under the ROC curve (AUROC) of 0.793, followed by PHI of 0.752, PSAD of 0.750, and PSA of 0.654. PHID with two cut-off points (0.88/mL and 1.82/mL) showed the highest potential biopsy avoidance of 47.7% (92/193) with 5% missing csPCa, and the lowest intermediate-risk group (borderline decision group) at 38.9% (75/193), compared to PSA and PHI. PHID demonstrated better potential in triaging patients with category 3 lesions, possibly aiding more selective and confident biopsy decisions for csPCa detection, than traditional markers. 4 TECHNICAL EFFICACY: Stage 5.

Fast Non-Rigid Radiance Fields from Monocularized Data.

The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as monocularized data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360° inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views. Our code and dataset are available online: https://github.com/MoritzKappel/MoNeRF.

External testing of an established glaucoma screening AI on a glaucoma clinic population from Finland

Purpose: To assess the external performance of an existing deep learning regression model for glaucoma detection (G‐RISK) in a glaucoma clinic population from Finland.Methods: Colour fundus images were retrospectively collected from the Tampere University Hospital (Tays), Finland. Eyes that had a visit in 2019 or 2020 were included based on the availability of ground truth labels. Ground truth labels for glaucoma were obtained through clinical evaluation of the optic nerve head (ONH) from colour fundus photos, clinical evaluation of retinal nerve fibre layer (RNFL) defects from fundus photos imaged with a cobalt filter, and clinical evaluation of the SITA Fast visual field (VF) test. Glaucoma diagnosis at Tays followed the ‘2 out of 3 rule’ recommended by the Finnish Evidence‐Based Guideline for Glaucoma, which requires at least 2 positive indications of glaucoma tests for referral [1]. Preprocessed colour fundus images were used as input to a previously trained deep learning model (G‐RISK) [2]. Performance was evaluated using the area under the receiver operating characteristic curve (AUC), accompanied by 95% confidence intervals. Patient aggregated results were obtained by comparing the maximum of both prediction and ground truth label.Results: Over 3000 patients had at least one ground truth available for a visit in 2019 or 2020. Clinical evaluation of structural features (ONH, RNFL) triggered more glaucomatous symptoms than the SITA Fast VF test. When thresholding the AI prediction against a single ground truth type, the best AUC was obtained on the ONH label (0.90 [0.89–0.91]), followed by RNFL (0.84 [0.83–0.86]) and VF (0.83 [0.81–0.84]). When implementing the 2 out of 3 rule, AUC reached 0.88 [0.82–0.90].Conclusion: The model performed best when using the ONH label and incorporating the ‘2 out of 3 rule’, reaching an AUC of 0.88. These findings suggest that the G‐RISK model could be a valuable tool for screening in clinical practice.

Fast flow field prediction of three-dimensional hypersonic vehicles using an improved Gaussian process regression algorithm

Conducting large-scale numerical computations to obtain flow field during the hypersonic vehicle engineering design phase can be excessively costly. Although deep learning algorithms enable rapid flow field prediction with high-precision, they require a significant investment in training samples, contradicting the motivation of reducing the cost of acquiring flow field. The combination of feature extraction algorithms and regression algorithms can also achieve high-precision prediction of flow fields, which is more suitable to tackle three-dimensional flow prediction with a small dataset. In this study, we propose a reduced-order model (ROM) for the three-dimensional hypersonic vehicle flow prediction utilizing proper orthogonal decomposition to extract representative features and Gaussian process regression with improved automatic kernel construction (AKC-GPR) to perform a nonlinear mapping of physical features for prediction. The selection of variables is based on sensitivity analysis and modal assurance criterion. The underlying relationship is unveiled between flow field variables and inflow conditions. The ROM exhibits high predictive accuracy, with mean absolute percentage error (MAPE) of total field less than 3.5%, when varying altitudes and Mach numbers. During angle of attack variations, the ROM only effectively reconstructs flow distribution by interpolation with a MAPE of 7.02%. The excellent small-sample fitting capability of our improved AKC-GPR algorithm is demonstrated by comparing with original AKC-GPRs with a maximum reduction in a MAPE of 35.28%. These promising findings suggest that the proposed ROM can serve as an effective approach for rapid and accurate vehicle flow predicting, enabling its application in engineering design analysis.

Quantum Sensing of Fast Time-Varying Magnetic Field With Daubechies Wavelets

Quantum Sensing of Fast Time-Varying Magnetic Field With Daubechies Wavelets

Fast field echo resembling CT using restricted echo-spacing (FRACTURE) MR sequence can provide craniocervical region images comparable to a CT in dogs.

Magnetic resonance imaging (MRI) is essential for evaluating cerebellar compression in patients with craniocervical junction abnormalities (CJA). However, it is limited in depicting cortical bone because of its short T2 relaxation times, low proton density, and organized structure. Fast field echo resembling a computed tomography (CT) scan using restricted echo-spacing (FRACTURE) MRI, is a new technique that offers CT-like bone contrast without radiation. This study aimed to assess the feasibility of using FRACTURE MRI for craniocervical junction (CCJ) assessment compared with CT and conventional MRI, potentially reducing the need for multiple scans and radiation exposure, and simplifying procedures in veterinary medicine. CT and MRI of the CCJ were obtained from five healthy beagles. MRI was performed using three-dimensional (3D) T1-weighted, T2-weighted, proton density-weighted (PDW), single echo-FRACTURE (sFRACTURE), and multiple echo-FRACTURE (mFRACTURE) sequences. For qualitative assessment, cortical delineation, trabecular bone visibility, joint space visibility, vertebral canal definition, overall quality, and artifacts were evaluated for each sequence. The geometrical accuracy, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Both sFRACTURE and CT images provided significantly higher scores for cortical delineation and trabecular bone visibility than conventional MRI. Joint space visibility and vertebral canal definition were similar to those observed on CT images, regardless of the MR sequence. In the quantitative assessment, the distances measured on T2-weighted images differed significantly from those measured on CT. There were no significant differences between the distances taken using T1-weighted, PD-weighted, sFRACTURE, mFRACTURE and those taken using CT. T1-weighted and sFRACTURE had a higher SNR for trabecular bone than CT. The CNR between the cortical bone and muscle was high on CT and FRACTURE images. However, the CNR between the cortical and trabecular bones was low in mFRACTURE. Similar to CT, FRACTURE sequences showed higher cortical delineation and trabecular bone visibility than T2-weighted, T1-weighted, and PDW CCJ sequences. In particular, sFRACTURE provided a high signal-to-noise ratio (SNR) of the trabecular bone and a high CNR between the cortical bone and muscle and between the cortical and trabecular bones. FRACTURE sequences can complement conventional MR sequences for bone assessment of the CCJ in dogs.

Fast field echo resembling a CT using restricted echo-spacing (FRACTURE) sequence for shoulder joint in normal dogs.

Shoulder disease is a common cause of forelimb lameness in dogs. Determining the precise underlying cause of shoulder lameness can be challenging, especially in veterinary practice. Computerized tomography (CT) is often the preferred imaging modality for bone evaluation; however, it uses ionizing radiation and provides limited soft tissue contrast. Conversely, magnetic resonance imaging (MRI) offers excellent soft tissue contrast but has limitations in bone imaging. This study aimed to introduce a new technical innovation that enhances cortical and trabecular bone contrast on MRI, which we refer to as Fast Field Echo Resembling a CT Using Restricted Echo-Spacing (FRACTURE). In this prospective pilot study, we aimed to evaluate the use of FRACTURE, CT, and conventional MRI sequences in assessing the normal canine shoulder using a 3.0 Tesla MRI scanner. Five research beagle dogs were included, and the following pulse sequences were acquired for each dog (1): three-dimensional (3D) FRACTURE, (2) T2-weighted (T2W) images using 3D turbo spin echo (TSE), (3) T1-weighted (T1W) images using 3D TSE, (4) PD-weighted (PDW) images using 3D TSE, and (5) CT. Various parameters, including the delineation of cortical bone (intertubercular groove, greater tubercle, and lesser tubercle), conspicuity of the trabecular bone, shoulder joint visualization, and image quality, were measured for each dog and sequence. In all sequences, the shoulder joint was successfully visualized in all planes with mild motion artifacts. The intertubercular groove was best visualized on CT and FRACTURE. Both the greater and lesser tubercles were easily identified on the CT, FRACTURE, and PDW images. The trabecular pattern scored significantly higher in the CT and FRACTURE images compared to the T1W, T2W, and PDW images. Overall, the visualization of the shoulder joint was excellent in all sequences except for T1W. The use of FRACTURE in combination with conventional MRI sequences holds promise for facilitating not only soft tissue evaluation but also cortical and trabecular bone assessment. The findings from this study in normal dogs can serve as a foundation for further FRACTURE studies in dogs with shoulder diseases.

Cranial Nerve Thinning Distinguishes RFC1-Related Disorder from Other Late-Onset Ataxias.

RFC1-related disorder (RFC1/CANVAS) shares clinical features with other late-onset ataxias, such as spinocerebellar ataxias (SCA) and multiple system atrophy cerebellar type (MSA-C). Thinning of cranial nerves V (CNV) and VIII (CNVIII) has been reported in magnetic resonance imaging (MRI) scans of RFC1/CANVAS, but its specificity remains unclear. To assess the usefulness of CNV and CNVIII thinning to differentiate RFC1/CANVAS from SCA and MSA-C. Seventeen individuals with RFC1/CANVAS, 57 with SCA (types 2, 3 and 6), 11 with MSA-C and 15 healthy controls were enrolled. The Balanced Fast Field Echo sequence was used for assessment of cranial nerves. Images were reviewed by a neuroradiologist, who classified these nerves as atrophic or normal, and subsequently the CNV was segmented manually by an experienced neurologist. Both assessments were blinded to patient and clinical data. Non-parametric tests were used to assess between-group comparisons. Atrophy of CNV and CNVIII, both alone and in combination, was significantly more frequent in the RFC1/CANVAS group than in healthy controls and all other ataxia groups. Atrophy of CNV had the highest sensitivity (82%) and combined CNV and CNVIII atrophy had the best specificity (92%) for diagnosing RFC1/CANVAS. In the quantitative analyses, CNV was significantly thinner in the RFC1/CANVAS group relative to all other groups. The cutoff CNV diameter that best identified RFC1/CANVAS was ≤2.2 mm (AUC = 0.91; sensitivity 88.2%, specificity 95.6%). MRI evaluation of CNV and CNVIII using a dedicated sequence is an easy-to-use tool that helps to distinguish RFC1/CANVAS from SCA and MSA-C.

Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.

Particle-pair creation by high-harmonic laser fields

We consider the dynamical pair-creation by intense electric fields in vacuum, based on exact solutions of the Dirac equation using the temporal Klein process. This approach gives a simple interpretation of the QED processes associated with arbitrary time-varying electric fields, and leads to the standard quantum kinetic equation. As an application, we consider the case of an intense laser field containing a large spectrum of coherent high-harmonics (HH). The role of the fast field oscillations is discussed. We show that HH pulses eventually lead to an enhancement of pair-creation and can be described as a sequence of dynamically equivalent Sauter pulses.

An improved hybrid photon detector based on a SiC Schottky diode

In order to improve the temporal response of SiC-HPMTs, a new SiC detector with a faster time response (leading edge 1.5 ns, FWHM 4 ns) was developed for constructing an improved SiC-HPMT. Then the properties of the improved SiC-HPMT were studied in detail. Compared with those of the previous SiC-HPMT, the temporal response of the improved SiC-HPMT, with a leading edge of about 2 ns and a FWHM of less than 5ns was effectively improved. The maximum linear current that exceeded 630 mA depends on both the accelerating and focusing voltage (Va) and the bias voltage (VSiC) of the SiC detector, but has a strong dependence on Va. The scintillation detector comprised of the improved SiC-HPMT and an EJ228 scintillator can accurately obtain the multiple peaks characteristic of the pulsed X-ray source, which indicates that the improved SiC-HPMT can well satisfy the requirements for measuring the time information of fast pulsed radiation fields. In addition, the SiC-HPMT should be placed at the position that deviates from the radiation beam to avoid the direct irradiation on the SiC detector. The improved SiC-HPMT will further expand its application in pulsed radiation field measurements and become a supplement for existing photoelectric detectors.