Resolution Performance Research Articles

Modification of reconstruction threshold algorithm in two-dimensional LFMCW compressive radar

Nowadays, the Compressive Sensing theory has an important effect on many communication system’s performances, and one of these applications is the radar system. Applying CS in the radar such as Linear Frequency Modulation Continuous Wave (LFMCW) radar signals has many advantages but suffers from the processing time in the two-dimensional processing. The performance of the LFMCW radar signals is achieved by using both conventional Complex Approximate Message Passing (CAMP) and adaptive recovery algorithms using a suitable reduction factor in both range and Doppler directions. In this paper, a modification is made to the adaptive CAMP algorithm to enhance the radar detection performance compared to both the conventional and adaptive algorithms under the same conditions. One of the main problems in radar detection is off-pin targets, which can be overcome using a certain filter in both range and Doppler directions. A comparison is achieved among these algorithms concerning detection performance using Receiver Operating Characteristic curves and the resolution performance in both range and Doppler directions. During the system analysis, it was found that an enhancement was achieved using the modified algorithm in the radar performance without any degradation in both range and Doppler resolution compared with the other algorithms.

OPTIMAL LINEAR COMBINATIONS SELECTION BASED ON FUZZY CLUSTERING ANALYSIS FOR MULTI-FREQUENCY GNSS

Abstract. Currently, satellite navigation and positioning systems, such as BDS, GPS, and Galileo, can broadcast 3 or more navigation signals. In theory, multi-frequency signals can form more high-quality carrier-phase linear combinations, which is conducive to improving the performance of multi-frequency carrier ambiguity resolution (MCAR). In this paper, focusing on the selection of the optimal combinations in the multi-frequency GNSS carrier phase ambiguity resolution, according to the long wavelength of the combinations, the influence of weak ionospheric delay, and low noise, the fuzzy clustering analysis method is used to realize objective selection of multi-frequency optimal linear combinations (e.g., GPS, BDS-2, BDS-3, Galileo) and introduced the total noise level (TNL) for the analysis of the combinations characteristics under different baseline length scenarios. In order to verify the ambiguity resolution performance of the extra-wide lane combinations selected by the fuzzy clustering analysis method, the GF_MCAR model was used to solve the BDS-2, BDS-3 triple-frequency combination ambiguities and the BDS-3 five-frequency combination ambiguities of the baseline JNFG and WUH2. The results manifest that the number of high-quality linear combinations of multi-frequency GNSS increases sharply with the increase of the frequencies number, which is conducive to the formation of combinations with a lower total noise level, and the experiments show that the difference between adjacent epochs of the combination ambiguities of the extra-wide lane obtained by GF_MCAR is basically less than ±0.5 cycles, and the single epoch rounding can be used to directly fix the ambiguity of the extra-wide lane.

A Novel Clock Parameterization and Its Implications for Precise Point Positioning and Ionosphere Estimation.

By convention, IGS precise clock products are computed using the ionosphere-free linear combination. Due to the broad use of IGS products, this convention is exploited in PPP-RTK models not using such a linear combination. So, in different carrier phase combinations, the code hardware biases are contained in different combinations, thus making the problem of separating biases from integer ambiguities more complicated. In this paper, we proposed a novel clock parameterization which allows facilitating this problem. Based on the proposed parameterization, we derived a dual-frequency PPP-RTK model for the undifferenced measurements and assessed this model for the static positioning case in terms of positioning accuracy, convergence, and ambiguity resolution performance. The results showed that a cm-level accuracy level is achievable with the derived models with nearly instant convergence and almost 100% successfully resolved ambiguities. We demonstrated the use of this parameterization for slant ionosphere estimation. We derived the analog of the equation linking the wide-lane, geometry-free, and ionosphere-free biases from the Fast-PPP system and used it to retrieve slant ionosphere information. Our TEC estimates showed some evidence of capability to reach an agreement of 1–2 TECU and the standard deviation of 3–4 TECU with GIM TEC values.

Time resolution and high-counting rate performance of plastic scintillation counter with multiple MPPC readout

We have been developing a plastic scintillation counter for the WASA detector. The performance of a plastic scintillation counter with Multi-Pixel Photon Counter (MPPC) readout has been systematically investigated for minimum ionizing particles in terms of the time resolution and the signal amplitude stability. The performance was evaluated under various incident conditions of 2.5 GeV/c proton beams and with different MPPC settings. A plastic scintillator with a size of 550×38×8mm3 was optically coupled with multiple MPPCs at each plane with a size of 38×8mm2 for detecting the scintillation photons. The observed time resolution ranges in 37–80 ps (σ) depending on the incident positions and angles for the case of three MPPCs attached to each end. We found the time resolution and the signal amplitude fairly consistent up to the counting rate of 0.2 MHz.

Smartphone‐based Electrochemical On‐site Quantitative Detection Device for Nonenzyme Lactate Detection

AbstractOn‐site quantitative detection provides the opportunity for accurate and timely health care, clinical diagnosis, environmental monitoring, and food analysis. A variety of portable electrochemical detection instruments have been developed at present for on‐site quantitative detection use, including commercially available portable potentiostats, but the reality is that these electrochemical detection devices still have the problem of narrow output voltage range and detection current range, and their performance of output voltage resolution and detection current resolution is still unsatisfactory. The narrow voltage or current range makes it difficult for some chemical reactions with peaks outside the range to proceed, and the insufficiency of voltage resolution and detection current resolution are key parameters restricting the detection accuracy of existing devices. In this regard, it is urgent to carry out partial transformation of the existing device to increase the use range and detection accuracy of the device. In the current work, a smartphone‐based electrochemical on‐site quantitative detection device was developed. The device comprises a portable small potentiostat, a smartphone equipped with a specially designed Android electrochemical detection application, and a disposable sensor (NiONP modified screen‐printed electrode [SPE]). The potentiostat can realize electrochemical detection methods, including cyclic voltammetry, chronoamperometry, and differential pulse voltammetry. In addition, the potentiostat can output a potential range −2.047∼+2.047 V with a current detection sensitivity scale 1×10−8∼1×10−3 A, and the current detection range could reach ±10 nA∼±10 mA. Most importantly, the current detection resolution of the potentiostat can reach 0.003 % (3.125 pA on ±10 nA range). The smartphone was used for communicating with the potentiostat, setting detection parameters, and mapping voltammograms in real time. The SPE was modified with nickel oxide nanoparticles and perfluorinated resin solution, which were employed as sensors to convert and amplify the electrochemical current response during the on‐site quantitative detection. Results indicated that the device features excellent performance for the lactate detection. The device covered a linear range 0.1∼30 mM on lactate detection with the correlation coefficient (R2) reaching 0.997.

Performance Analysis of Root Anycast Nodes Based on Active Measurement

The root server is at the top of the domain name hierarchical structure. To improve root service performance, each root deploys anycast nodes worldwide. What is the actual service performance of these nodes after deployment? We analyze the service performance of the root anycast nodes deployed in China based on the active measurement data detected by the VPs of different ISPs in different geographical locations. From the analysis, we find that the resolution performance of the roots with anycast nodes deployed in China is higher than that of roots without deployment. However, users of different operators have significant differences in accessing the root servers, such as parsing time, hitting anycast nodes, and most anycast nodes only providing services for one operator, limiting the service scope and reducing the service performance. The analysis results can help the root management and introduction institutions master the actual service status of the root servers, which can be used to optimize the performance of the existing root anycast nodes and provide a basis for deploying new root anycast nodes in the next step. Finally, we find that 67 top-level domain names are hijacked on the resolution path based on the measured data.

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

ABSTRACT There are many factors outlined in the signal processing pipeline that impact brain–computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 ± 6.09%, non-VR 73.5 ± 5.89%, p = 0.0096; 12 electrodes: VR 73.26 ± 5.2%, non-VR 70.87 ± 4.96%, p = 0.0129; 7 electrodes: VR 66.74 ± 5.92%, non-VR 63.09 ± 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and non-immersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).

Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification.

The ability to discern subtle image changes over time is useful in applications such as product quality control, civil engineering structure evaluation, medical video analysis, music entertainment, and so on. However, tiny yet useful variations are often combined with large motions, which severely distorts current video amplification methods bounded by external constraints. This paper presents a novel use of spectra to make motion magnification robust to large movements. By exploiting spectra, artificial limitations and the magnification of small motions are avoided at similar frequency levels while ignoring large ones at distinct spectral pixels. To achieve this, this paper constructs spline-kerneled chirplet transform (SCT) into an empirical Bayesian paradigm that applies to the entire time series, giving powerful spectral resolution and robust performance to noise in nonstationary nonlinear signal analysis. The important advance reported is Bayesian-rule embedded SCT (BE-SCT); two numerical experiments show its superiority over current approaches. For applying to spectrum-aware motion magnification, an elaborate analytical framework is established that captures global motion, and use of the proposed BE-SCT for dynamic filtering enables a frequency-based motion isolation. Our approach is demonstrated on real-world and synthetic videos. This approach shows superior qualitative and quantitative results with less visual artifacts and more local details over the state-of-the-art methods.

High‐Resolution Multicolor Patterning of Metal Halide Perovskite Nanocrystal Thin Films through Rapid‐Evaporation‐Assisted Strategy

AbstractMaterial integrations with the desired shape, size, and dimension allow for the fabrication of high resolution and high performance devices. However, the patterning of all‐inorganic halide perovskite nanocrystals (NCs), promising building blocks for optics and optoelectronics, has been a fundamental challenge owing to their unstable and reactive structures and properties. Here, a rapid‐evaporation‐assisted patterning strategy is reported that simultaneously pattern CsPbBr3 NC thin films and tune the color by selectively printing the anion‐exchange ink of micro‐sized droplets. The rapid evaporation time is investigated according to the droplet size, falling into a range that is faster than the structural degradation time but slower than the chemical reaction time of anion exchange. Structural, chemical, optical analyses, and analytical estimation of droplet evaporation are conducted. As a result, high‐resolution multicolor patterns with minimum feature size down to 5 µm and a pitch of <2 µm, covering the entire visible emission range, are achieved. It is believed that the multicolor patterning technique of PeNCs will significantly contribute to the development of next‐generation devices, such as image sensors, displays, and other fields.

A Novel Elastomer-Based Inclinometer for Ultrasensitive Bridge Rotation Measurement.

Bridge deformation consists of cross-section rotation and deflection, which are crucial parameters for bridge capacity evaluation and damage detection. The maximum value of deflection usually happens at mid-span while for rotation it happens at two-ends. Therefore, compared with deflection, rotation is more convenient for in-situ measurement since the bridge pier can be the reference point. In this study, a high-precision inclinometer for bridge rotation measurement was conceptualized, designed, and validated. The proposed inclinometer converted the small rotation of bridge section into the deformation of an elastomer. Strain gauges were then utilized to measure the elastomer deformation and thus the bridge rotation can be obtained. The dimensions and modulus of the elastomer were designed and chosen based on the theoretical analysis. Characteristics of the inclinometer were calibrated in lab and in-situ experiments at an in-service bridge were conducted to validate its feasibility and robustness. Test results showed that the proposed inclinometer had excellent performance in resolution and accuracy, which indicate its great potential for future bridge health monitoring.

Performance Evaluation of Feature Matching Techniques for Detecting Reinforced Soil Retaining Wall Displacement

Image registration technology is widely applied in various matching methods. In this study, we aim to evaluate the feature matching performance and to find an optimal technique for detecting three types of behaviors—facing displacement, settlement, and combined displacement—in reinforced soil retaining walls (RSWs). For a single block with an artificial target and a multiblock structure with artificial and natural targets, five popular detectors and descriptors—KAZE, SURF, MinEigen, ORB, and BRISK—were used to evaluate the resolution performance. For comparison, the repeatability, matching score, and inlier matching features were analyzed based on the number of extracted and matched features. The axial registration error (ARE) was used to verify the accuracy of the methods by comparing the position between the estimated and real features. The results showed that the KAZE method was the best detector and descriptor for RSWs (block shape target), with the highest probability of successfully matching features. In the multiblock experiment, the block used as a natural target showed similar matching performance to that of the block with an artificial target attached. Therefore, the behaviors of RSW blocks can be analyzed using the KAZE method without installing an artificial target.

Extended depth of focus by self-imaging wavefront division with the mirror tunnel.

The mirror tunnel is a component used to extend the depth of focus for compact imaging probes used in endoscopic optical coherence tomography (OCT). A fast and accurate method for mirror tunnel probe simulation, characterization, and optimization is needed, with the aim of reconciling wave- and ray-optics simulation methods and providing a thorough description of the physical operating principle of the mirror tunnel. BeamLab software, employing the beam propagation method, was used to explore the parameter space and quantify lateral resolution and depth of focus extension. The lateral resolution performance was found to depend heavily on the metric chosen, implying that care should be taken in the interpretation of optimization and simulation results. Interpreting the mirror tunnel exit face as an extended object gives an understanding of the probe operation, decoupling it from the focusing optics and potentially helping to reduce the parameter space for future optimization.

Impact of the training loss in deep learning-based CT reconstruction of bone microarchitecture.

Computed tomography (CT) is a technique of choice to image bone structure at different scales. Methods to enhance the quality of degraded reconstructions obtained from low-dose CT data have shown impressive results recently, especially in the realm of supervised deep learning. As the choice of the loss function affects the reconstruction quality, it is necessary to focus on the way neural networks evaluate the correspondence between predicted and target images during the training stage. This is even more true in the case of bone microarchitecture imaging at high spatial resolution where both the quantitative analysis of bone mineral density (BMD) and bone microstructure is essential for assessing diseases such as osteoporosis. Our aim is thus to evaluate the quality of reconstruction on key metrics for diagnosis depending on the loss function that has been used for training the neural network. We compare and analyze volumes that are reconstructed with neural networks trained with pixelwise, structural, and adversarial loss functions or with a combination of them. We perform realistic simulations of various low-dose acquisitions of bone microarchitecture. Our comparative study is performed with metrics that have an interest regarding the diagnosis of bone diseases. We therefore focus on bone-specific metrics such as bone volume and the total volume (BV and TV), resolution, connectivity assessed with the Euler number, and quantitative analysis of BMD to evaluate the quality of reconstruction obtained with networks trained with the different loss functions. We find that using norm as the pixelwise loss is the best choice compared to or no pixelwise loss since it improves resolution without deteriorating other metrics. Visual Geometry Group (VGG) perceptual loss, especially when combined with an adversarial loss, allows to better retrieve topological and morphological parameters of bone microarchitecture compared to Structural SIMilarity (SSIM) index. This however leads to a decreased resolution performance. The adversarial loss enhances the reconstruction performance in terms of BMD distribution accuracy. In order to retrieve the quantitative and structural characteristics of bone microarchitecture that are essential for postreconstruction diagnosis, our results suggest to use norm as part of the loss function. Then, trade-offs should be made depending on the application: VGG perceptual loss improves accuracy in terms of connectivity at the cost of a deteriorated resolution, and adversarial losses help better retrieve BMD distribution while significantly increasing the training time.

Recent advances in aggregation-induced emission luminogens in photoacoustic imaging.

Photoacoustic imaging (PAI) is a rapidly emerging modality in biomedical research with the advantages of noncontact operation, high optical resolution, and deep penetration. Great efforts and progress in the development of PAI agents with improved imaging resolution and sensitivity have been made over the past 2 decades. Among them, organic agents are the most promising candidates for preclinical/clinical applications due to their outstanding in vivo properties and facile biofunctionalities. Motivated by the unique properties of aggregation-induced emission (AIE) luminogens (AIEgens), various optical probes have been developed for bioanalyte detection, multimodal bioimaging, photodynamic/photothermal therapy, and imaging-guided therapeutics. In particular, AIE-active contrast agents have been demonstrated in PAI applications with excellent performance in imaging resolution and tissue permeability in vivo. This paper presents a brief overview of recent progress in AIE-based agents in the field of photoacoustic imaging. In particular, we focus on the basic concepts, data sorting and comparison, developing trends, and perspectives of photoacoustic imaging. Through numerous typical examples, the way each system realizes the desired photoacoustic performance in various biomedical applications is clearly illustrated. We believe that AIE-based PAI agents would be promising multifunctional theranostic platforms in clinical fields and will facilitate significant advancements in this research topic.

ISAR Resolution Enhancement Method Exploiting Generative Adversarial Network

Deep learning has been used in inverse synthetic aperture radar (ISAR) imaging to improve resolution performance, but there still exist some problems: the loss of weak scattering points, over-smoothed imaging results, and the universality and generalization. To address these problems, an ISAR resolution enhancement method of exploiting a generative adversarial network (GAN) is proposed in this paper. We adopt a relativistic average discriminator (RaD) to enhance the ability of the network to describe target details. The proposed loss function is composed of feature loss, adversarial loss, and absolute loss. The feature loss is used to get the main characteristics of the target. The adversarial loss ensures that the proposed GAN recovers more target details. The absolute loss is adopted to make the imaging results not over-smoothed. Experiments based on simulated and measured data under different conditions demonstrate that the proposed method has good imaging performance. In addition, the universality and generalization of the proposed GAN are also well verified.

Time Resolution Studies of Thallium Based Cherenkov Semiconductors

In the context of improving the detector performance of time-of-flight positron emission tomography (TOF-PET), the combination of charge induction readout and prompt Cherenkov photon production in semiconductor materials can lead to an outstanding detector performance in energy, timing, and spatial resolution. Energy resolutions as good as 1.2% at 662 keV and 5% at 122 keV are reported for pixel thallium bromide (TlBr) detectors. The high refractive index of Tl-based materials, between 2.3 and 2.6, leads to a high Cherenkov photon generation yield but can also challenge photon extraction, potentially affecting the time performance. In this work, the timing properties of TlBr and thallium chloride (TlCl) crystals of different geometries are measured using an optimized test setup with high-frequency readout electronics. A coincidence time resolution (CTR) value of 167 ± 6 ps FWHM is achieved using a 3 × 3 × 3 mm3black-painted TlBr crystal. In order to assess potential improvements, a Geant4-based simulation tool kit is developed and validated against experimental measurements. The simulation tool kit is used to predict the contributions limiting the time resolution regarding the crystal and photodetector properties, highlighting the potential of such materials. Finally, paths to further improve the detector performance in TOF-PET are discussed.

Distributed Optical Fiber Sensing System for Large Infrastructure Temperature Monitoring

In this article, a distributed optical fiber sensing system for large infrastructure temperature monitoring is proposed. To meet the requirements of monitoring networks in terms of measurement accuracy, spatial resolution, and real-time or quasireal-time performance, a quaternion wavelet transform (QWT) image denoising algorithm is proposed to address the original edge node data for the structural monitoring networks of large infrastructures. A distributed Brillouin optical time-domain analysis (BOTDA) sensing system with a 40-km sensing fiber is established. The raw Brillouin gain spectrum (BGS) image is decomposed into one magnitude image and three phase images by QWT. The phase images of the OWT are distributed randomly and disorderly with respect to the noise, while the magnitude image of the quaternion wavelet is greatly affected by the noise. The useful message energy of the magnitude image is concentrated on a small number of coefficients with large amplitude, while the noise mainly corresponds to the coefficients with smaller amplitude. Then, the Bayes shrink threshold method is introduced to filter out noise in the magnitude image. The results indicate that the signal-to-noise ratio (SNR) and the frequency uncertainty have been improved significantly. The accuracy of the retrieved Brillouin frequency shift from denoised BGS images reaches 0.2 MHz, which corresponds to a temperature error of ±0.1 °C. Less than 4 s are required to process a BGS image with 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 40 000 pixels by the QWT denoising technique. The uploaded data obtained from 40 M bytes of raw data are reduced to 0.08 M bytes for each measurement. We hope that with technological progress and algorithm optimization, the distributed optical fiber sensing system based on the QWT image denoising algorithm will have an important role in the real-time application of large-scale infrastructure structural health monitoring for the Internet of Things.

SRNMSM: A Deep Light-Weight Image Super Resolution Network Using Multi-Scale Spatial and Morphological Feature Generating Residual Blocks

Generating features representing the textures and structures of an image is very important characteristic of a super resolution network. Morphological operations are the nonlinear mathematical operations that can process signals focusing on their structures and textures. In this paper, we propose a novel residual block to generate and process morphological features and fuse them with the conventional spatial features, in order to produce a very rich and highly representational set of residual feature maps. The proposed residual block is then used in a deep convolutional neural network for the task of image super resolution. It is shown that the capability of the proposed block in generating and using the morphological features can significantly improve the super resolution performance of a deep network. The super resolution network employing the proposed residual block is shown to outperform the state-of-the-art low-complexity image super resolution networks on various benchmark datasets.

Estimating PM2.5 surface concentrations from AOD: A combination of SLSTR and MODIS

Compared to surface in-situ observations, satellite data on aerosol optical depth (AOD) enables area-wide monitoring of tropospheric aerosols. However, coverage and reliability of satellite data products depend on atmospheric conditions and surface concentrations have to be retrieved from AOD. This study investigates the potential to produce reliable maps of PM2.5 surface concentrations for Germany and parts of the surrounding countries using AOD based on observations by three different satellite sensors. For the first time, AOD retrievals from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A are used together with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the two NASA platforms Terra and Aqua. We investigate the differences and similarities of the three different satellite products in terms of coverage, resolution and algorithmic performances. Based on this analysis we examine the suitability and advantage of a combination of these data sets. We can substantiate an increase in mean daily coverage from a maximum of 10.2% for the individual products to 16.7% for the ensemble product. Using a semi-empirical linear regression model, we derive surface-level PM2.5 concentrations and attain an overall correlation of 0.76 between satellite-derived and in-situ measured PM2.5 concentrations. By considering surface measurements, the systematic error (bias) and the root mean square error (RMSE) can be significantly reduced. The general model performance is evaluated by a 5-fold cross validation and the relative prediction error (RPE).

All-Fabric-Based Flexible Capacitive Sensors with Pressure Detection and Non-Contact Instruction Capability

The flexible and wearable capacitive sensors have captured tremendous interest due to their enormous potential for healthcare monitoring, soft robotics, and human−computer interface. However, despite recent progress, there are still pressing challenges to develop a fully integrated textile sensor array with good comfort, high sensitivity, multisensing capabilities, and ultra-light detection. Here, we demonstrate a pressure and non-contact bimodal fabric-only capacitive sensor with highly sensitive and ultralight detection. The graphene nanoplatelets-decorated multidimensional honeycomb fabric and nickel-plated woven fabric serve as the dielectric layer and electrode, respectively. Our textile-only capacitive bimodal sensor exhibits an excellent pressure-sensing sensitivity of 0.38 kPa−1, an ultralow detection limit (1.23 Pa), and cycling stability. Moreover, the sensor exhibits superior non-contact detection performance with a detection distance of 15 cm and a maximum relative capacitance change of 10%. The sensor can successfully detect human motion, such as finger bending, saliva swallowing, etc. Furthermore, a 4 × 4 (16 units) textile-only capacitive bimodal sensor array was prepared and has excellent spatial resolution and response performance, showing great potential for the wearable applications.