High-resolution Detection Research Articles

A method for diagnosing the implosion symmetry in inertial confinement fusion with wide-angle VISAR

Abstract A method for diagnosing implosion symmetry in inertial confinement fusion(ICF) with wide-angle velocity interferometer system for any reflector(VISAR)was proposed in this study. The method considers the object-image relationship of the wide-angle VISAR, characteristics of the fringe pattern, and the symmetry theory; the fringe pattern must be continuous in space to apply this method. Hydrodynamic calculations showed that the radiation-temperature distribution on the surface of capsule was spatially continuous, indirectly proving the applicability of the method. The evolution of P2 asymmetry at different positions of the capsule was examined through an eight-beam laser indirect-drive experiment at a 10 kJ-level laser facility, proving the feasibility of the method. The application of wide-angle VISAR for the diagnosis of implosion symmetry in ICF was more accurate and intuitive and demonstrated high spatiotemporal resolution and wide detection range. This method can provide reference data for the micro-mechanism study of hydrodynamic instability and radiation-driven asymmetry, as well as support for optimizing the implosion compression process to achieve efficient and stable ignition.

Highly Fluorescent Bio-Synthesized Carbon Quantum Dots for Latent Fingerprint Detection.

This study introduces an innovative approach to high-resolution latent fingerprint detection using carbon quantum dots (CQDs) biosynthesized from spent coffee grounds, enhanced with nitrogen doping. Conventional fingerprinting methods frequently use hazardous chemicals and are costly, highlighting the need for eco-friendly, affordable alternatives that preserve detection quality. The biosynthesized nitrogen-doped CQDs exhibit strong photoluminescence and high stability, offering a sustainable, effective alternative for fingerprint imaging. Using a one-step hydrothermal synthesis method, nitrogen-doped CQDs were developed, displaying intense cyan fluorescence under UV light at 365nm. The quantum yield was measured to be 19.73%. Characterization through UV-Visible and photoluminescence spectroscopy, FTIR, XRD and TEM confirmed the morphology, surface properties, optical properties and stability. The average particle size diameter was calculated to be around 8.71 ± 0.14nm. These CQDs were tested on various non-porous surfaces including marble, glass, aluminium, and metal where they provided detailed visualization of fingerprint ridge patterns, sweat pores, and minutiae with high fluorescence. Notably, the CQDs demonstrated excellent adherence and sustained fluorescence, maintaining photostability for up to 60 days under dark storage conditions at 2-8°C. This sustainable synthesis method thus produces nitrogen-doped CQDs with robust fluorescent properties, establishing a promising, eco-friendly solution for high-resolution latent fingerprint detection in forensic investigations.

Characterization of main degradation products from dendrobine under stress conditions by multistage cleavage of UPLC-ESI-IT-TOF.

Dendrobine is a sesquiterpene alkaloid primarily used in the treatment of inflammatory diseases, immune system disorders, and conditions related to oxidative stress. To understand the possible degradation pathways of dendrobine for its quality control, we conducted an in-depth investigation of its degradation products using forced degradation methods. The separation of dendrobine and its degradation products was achieved on a Shim-pack XR-ODS III (75 mm × 2 mm, 1.6 µm) column with a methanol-water mixture as the mobile phase under isocratic conditions, the isolated compounds were examined in positive ion mode with an ion trap-time of flight mass spectrometer (IT-TOF). In order to obtain in-depth structural information about the degradation products, mass spectrometry was performed using a five-stage fragmentation approach. This method allowed for thorough structural clarification via several rounds of selective fragmentation and high-resolution detection. System control and data acquisition were managed using LCMSsolution 3.81 software. The results showed that dendrobine undergoes significant degradation under oxidative, acidic, hydrolytic and thermal conditions, resulting in the formation of several degradation products with notable structural changes. Under oxidative conditions, dendrobine primarily generates two degradation products with mass increases of 16 Da and 32 Da, indicating mono-oxidation and di-oxidation reactions. Acidic degradation led to the identification of three degradation products, including a novel compound with an 18 Da mass increase, suggesting potential hydrolysis or dehydration reactions. Hydrolytic and thermal conditions resulted in the formation of two and three degradation products, respectively, with structural changes indicating possible molecular cleavage and reorganization mechanisms. In contrast, dendrobine exhibited strong stability under alkaline and photolytic conditions, with no significant degradation products detected. Detailed characterization of the degradation products via multi-stage mass spectrometry revealed key reaction pathways and mechanisms involved in dendrobine's degradation, providing critical insights for assessing its chemical stability and optimizing storage conditions.

Ion Pair Chromatography for Endogenous Metabolite LC-MS Analysis in Tissue Samples Following HGH Resolution Untargeted Acquisition.

A protocol for the preparation of tissue extracts for the targeted analysis ca. 150 polar metabolites, including those involved in central carbon metabolism, is described, using a reversed phase ion pair U(H)PLC-MS method. Data collection enabled in high-resolution mass spectrometry detection provides highly specific and sensitive acquisition of metabolic intermediates with wide range physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. At last general comments are given to help the assessment of data quality and system performance.

Simplified Eye-safe Pulsed ToF LiDAR with Modified Newtonian Telescope for Long-Distance High-Resolution Detection in Rain Atmosphere

Simplified Eye-safe Pulsed ToF LiDAR with Modified Newtonian Telescope for Long-Distance High-Resolution Detection in Rain Atmosphere

Organic Manganese Halides with Near-Unity Quantum Efficiency for High-Resolution and Flexible X-Ray Detection and Imaging.

In this paper, two new organic manganese halides (pe-ted)2MnX4 (pe-ted+ = ethylphenyltriethylenediamine cation, X = Cl, Br) are synthesized, exhibiting green luminescence with near-unity photoluminescence quantum efficiency. (pe-ted)2MnBr4 shows good linear response to X-ray dose rate, with a high light yield of ≈62 000photonsMeV-1 and low detection limit of 1.376µGys-1. Flexible scintillator film is prepared by blending with polydimethylsiloxane for high-resolution imaging, with a low detection limit (5.173µGys-1), an excellent X-ray imaging spatial resolution (18.0lpmm-1) and an impressive X-ray irradiation switching stability (3.6mGys-1). WLED of high color rendering index hasalso been fabricated. These findings represent an example of shapeable X-ray scintillators, providing new possibilities for curved and 3D X-ray imaging.

Design of unexploded ordnance detection sensor based on frequency-domain electromagnetic method

ABSTRACT Detecting underground metal targets (e.g. unexploded ordnance) using simple devices and seeking to improve the detection accuracy and depth is a hot issue in electromagnetic detection in recent years. Electromagnetic detection based on the frequency-domain method has a simple excitation method, a wide excitation range, and high detection resolution and frequency flexibility. However, the primary field generated by the excitation coil and the secondary field generated by the unexploded ordnance exist at the same time, which has a large impact on the effective signal; and the current detection accuracy is not high, and the effective eigenvalues are less. To address these situations, in this paper, we use the analytical solution of the rectangular coil and finite element simulation to design a new type of electromagnetic sensor with differential multi-component reception, and the influence of the primary field can be eliminated with the help of its coupling characteristics. Based on the unique sensor structure, we propose a four-quadrant fast horizontal localisation as well as a method for target identification and a method for depth fixing of the target by SNR eigenvalue fitting. In a laboratory environment with 10 V low-power excitation, the sensor has a farthest detection depth of about 1.2 m and a positioning error of about 14.43 mm within a depth of 0.9 m, and it can be detected as far as 0.6 m in an outdoor environment.

High-resolution and instability-driven image reconstruction based on wave localization in azobenzene polymer

Transverse modulation instability (MI) has been proved useful for reconstructing noisy images. However, the signal-noise resonances for high-frequency modes are always suppressed during the generation of instability, resulting in the blurring of output images. By controlling of photo-birefringence and isomerization of azobenzene-derivative polymer, we proposed an instability-driven reconstruction by re-growing high-frequency modes via localizing wave response. The agreement between the experimental results and numerical simulations proves its effectiveness. This work provides a general and flexible way for high-resolution target detection.

High-resolution airborne magnetic detection of iron ore deposits

High-resolution airborne magnetic detection of iron ore deposits

Bayesian modeling of incompatible spatial data: A case study involving Post-Adrian storm forest damage assessment

Modeling incompatible spatial data, i.e., data with different spatial resolutions, is a pervasive challenge in remote sensing data analysis. Typical approaches to addressing this challenge aggregate information to a common coarse resolution, i.e., compatible resolutions, prior to modeling. Such pre-processing aggregation simplifies analysis, but potentially causes information loss and hence compromised inference and predictive performance. To avoid losing potential information provided by finer spatial resolution data and improve predictive performance, we propose a new Bayesian method that constructs a latent spatial process model at the finest spatial resolution. This model is tailored to settings where the outcome variable is measured on a coarser spatial resolution than predictor variables—a configuration seen increasingly when high spatial resolution remotely sensed predictors are used in analysis. A key contribution of this work is an efficient algorithm that enables full Bayesian inference using finer resolution data while optimizing computational and storage costs. The proposed method is applied to a forest damage assessment for the 2018 Adrian storm in Carinthia, Austria, that uses high-resolution laser imaging detection and ranging (LiDAR) measurements and relatively coarse resolution forest inventory measurements. Extensive simulation studies demonstrate the proposed approach substantially improves inference for small prediction units.

Two-Step Adaptive Target Detection Scheme Using Beam-Steering FMCW Radar Sensors

This letter proposes a two-stage adaptive target detection approach using a beam-steered frequency-modulated continuous wave (FMCW) radar sensor. In the first step, coarse estimation was performed using multiple sharped beams, where the presence of a target was determined using only a portion of the sampled beat signals. In the second step, a high-resolution range-Doppler (RD) map was generated using the entire information about the beat signal pertaining to the transmitted beams estimated to contain the target. Angle estimation was accomplished through digital beamforming based on the enhanced RD map. Furthermore, to verify the effectiveness of the proposed algorithm, high-resolution target detection experiments were conducted using a 24-GHz FMCW radar, while complexity reduction was evaluated in terms of the required memory size.

Satellite-Based Assessment of Rocket Launch and Coastal Change Impacts on Cape Canaveral Barrier Island, Florida, USA

The Cape Canaveral Barrier Island, home to the National Aeronautics and Space Administration (NASA)’s Kennedy Space Center and the United States (U.S.) Space Force’s Cape Canaveral Space Force Station, is situated in a unique ecological transition zone that supports diverse wildlife. This study evaluates the recent changes in vegetation cover (2016–2023) and dune elevation (2007–2017) within the Cape Canaveral Barrier Island using high-resolution optical satellite and light detection and ranging (LiDAR) data. The study period was chosen to depict the time period of a recent increase in rocket launches. The study objectives include assessing changes in vegetation communities, identifying detectable impacts of liquid propellant launches on nearby vegetation, and evaluating dune elevation and tide level shifts near launchpads. The results indicate vegetation cover changes, including mangrove expansion in wetland areas and the conversion of coastal strands to denser scrubs and hardwood forests, which were likely influenced by mild winters and fire management. While detectable impacts of rocket launches on nearby vegetation were observed, they were less severe than those caused by solid rocket motors. Compounding challenges, such as rising tide levels, beach erosion, and wetland loss, potentially threaten the resilience of launch operations and the surrounding habitats. The volume and scale of launches continue to increase, and a balance between space exploration and ecological conservation is required in this biodiverse region. This study focuses on the assessment of barrier islands’ shorelines.

Large-field detection of Metal/CFRP hybrid composites based on air-coupled laser ultrasound

Metal/carbon fiber reinforced polymer (CFRP) hybrid composites are widely used in the aeronautical and aerospace industry due to their excellent mechanical properties. However, traditional nondestructive testing methods are difficult to achieve fast, non-contact, large-field, high resolution, and high contrast detection at the same time, due to the large differences in thickness, density, acoustic impedance, and thermal diffusion between metal and fiber layers. Here, an air-coupled laser ultrasound (ACLU) method was presented for large-field detection of internal defects on the non-homogeneous metal/CFRP interface. Simulation analysis was conducted to analyze the propagation characteristics of laser ultrasound in Al/CFRP hybrid composites without defect and with defect. Test results for ACLU system display that the maximum imaging field of view is 300 mm × 75 mm at the frame rate of 1.5 s/frame. Under a 0.3 mm metal layer, the detecting resolution of the system can still reach up to 200 μm. The system was applied to detect three types of crack and disbond defects of metal/CFRP hybrid composites, and making a side-by-side comparison with phased array ultrasonic testing (PAUT) and radiographic testing (RT). The C-scan results show that the constructed system obtains a high signal-to-noise ratio (SNR) of 23 dB, which improves 4 times and 20 times compared with PAUT and RT, respectively. It indicates that the developed ACLU system hold a great prospect in non-contact large-field inspection of metal/CFRP hybrid composites.

Synergistic Coupling of Multi-Source Remote Sensing Data for Sandy Land Detection and Multi-Indicator Integrated Evaluation

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land.

Temperature–amplitude spectrum for early full-field vibration-fatigue-crack identification

A dynamic structure under vibration loading within its natural frequency range can experience failure due to vibration fatigue. Understanding the causes of such failure requires pinpointing the initiation time and location of fatigue cracks, tracking their propagation, and identifying the frequency range of critical stress responses. This research introduces a novel, thermoelasticity-based method – the Temperature–Amplitude Spectrum (TAS) method – for early-stage, full-field, and non-contact crack detection that operates during uninterrupted vibration testing. This method leverages high-speed infrared imaging to analyze the specimen’s temperature–amplitude spectrum, capturing comprehensive crack-related information, including initiation and propagation, in real time. Experimentally validated on both 3D-printed polymer and aluminum specimens, the TAS method accurately identified crack locations and paths without complex adjustments to the experimental setup or data processing. This new approach advances vibration-fatigue testing by enabling reliable, high-resolution crack detection and analysis while remaining computationally efficient.

High-resolution weld defect detection with RSU-MLP and dynamic kernel supervision

The challenges posed by high-resolution X-ray images of weld defects—including fuzzy features, multi-scale variability, small targets, and sample imbalance—create significant obstacles for accurate defect localization. Traditional deep learning methods typically emphasize global image characteristics, often neglecting local and multi-scale information, which leads to the inaccurate detection of small defects. To address this issue, we propose a novel high-resolution weld defect detection method called RSU-MLP. This method combines the RSU-MLP network with multi-scale feature extraction technology to effectively capture both local and global image features. Additionally, we design a dynamic kernel adaptive segmentation head based on a hybrid extended convolutional attention mechanism, which adaptively adjusts the receptive field size of the convolutional kernel, thereby enhancing detection capabilities for defects of varying scales. Furthermore, an in-depth supervision mechanism is introduced to monitor and learn defects at different levels of the network, leading to improved detection performance. Experimental results on real high-resolution welding datasets demonstrate that the proposed method achieves promising results in welding defect detection. Compared to traditional approaches, this method provides more accurate detection and identification of various welding defects, achieving DICE and Jaccard scores of 75.97% and 64.01%, respectively. This represents the best performance, demonstrating both robustness and generalization capabilities. Consequently, it offers a reliable automated detection method for producing high-quality welded products.

Throw distribution across the Dabbahu−Manda Hararo dike-induced fault array: Implications for rifting and faulting

Dike intrusion and formation of overlying dike-induced normal faults facilitate plate extension. The kinematics of these dike-induced normal faults provide an accessible record of subsurface diking. Here, we use high-resolution light detection and ranging (LiDAR) and interferometric synthetic aperture radar (InSAR) data to explore how strain was distributed across a preexisting dike-induced fault array during diking events in the Dabbahu−Manda Hararo magmatic segment (Afar, Ethiopia) in 2008 and 2010. By analyzing throw of the dike-induced normal faults, we show that only a small number of faults were reactivated during each diking event; the distribution of this reactivation likely reflects dike depth, opening, and inclination, as well as fault orientation. We also show fault throw favorably accrued toward fault centers, away from areas of soft- or hard-linkage. Our high-resolution data sets demonstrate the importance of reactivation to rifting, as it means extension can occur at lower extensional forces, and that fault slip (and seismic hazard) may not localize at sites of fault linkage.

Influence of Optical Fiber Parameters on the Speckle Pattern and Spectral Observation in Astronomy

Optical fibers serve as a bridge to transmit starlight into the spectrograph in fiber spectral surveys. Due to the interference between multiple modes supported within the fiber, a granular speckle pattern appears on the end of the fiber and leads to an uneven and random energy distribution in the spectrum. This effect is called mode noise, which reduces the accuracy of high-resolution spectral detection. This work investigates the influence of transmitted mode numbers on speckle patterns by using fibers with different core diameters and numerical apertures. A reciprocating mechanical scrambler is proposed for suppressing near-field speckles with negligible focal ratio degradation. We use centroid offset and radial power spectrum to quantitatively evaluate the characteristics of the speckles with and without scrambling. Experimental results show that more modes in a fiber with a larger core diameter reduce the centroid offset of the speckle and make the energy distribution more uniform. The mechanical mode scrambler significantly reduces the random centroid deviation caused by speckles, which is more obvious for large-core fibers. The standard deviation of centroid offset in 1000-cycle tests for the 160 µm core fiber is only 0.043 µm, which is one-tenth of that for the 16 µm core fiber. However, in solar spectrum measurement using these fibers, small-core fibers can more easily achieve higher spectral resolution and capture more spectral information. Therefore, large-core fibers are suitable for tasks requiring high accuracy, while fibers with a smaller core diameter should be applied for high-precision spectral measurement.

Design of high-precision multi-channel ice detection system

Abstract Effectively detecting aircraft icing caused by supercooled water is essential for ensuring safe flight. This paper utilizes ultrasonic pulse-echo technology to measure ice thickness. Unlike traditional ultrasonic detection devices, which are widely used for flaw detection but are bulky, inconvenient, and have low detection accuracy, this study develops a multichannel, high-resolution pulse-echo wireless detection system with a field-programmable gate array (FPGA) as the central processing unit. The system primarily involves the design of ultrasonic pulse-echo hardware, high-speed data processing, and wireless transmission. Ice detection experiments were conducted on an ice-aluminum double-layer structure. The test results indicate that this system can effectively detect the thickness of ice layers with a resolution of 1 mm.

A Ground Penetrating Radar Array Antenna Designed for Small Targets Detection

Abstract The detection of small targets, especially those made of non-metallic materials, presents significant challenges due to their small size and the difficulty of being detected by traditional metal detectors. Additionally, environmental factors and soil conditions further complicate their detection. Traditional Ground Penetrating Radar (GPR) operate with a single-line detection method, resulting in a low number of scan lines and inefficient detection. This approach makes it difficult to generate slice images and can lead to high missed detection. A miniaturized GPR array antenna suitable for handheld and unmanned platforms has been developed. Utilizing a planar dipole antenna with multiple resistive and capacitive loading, a horizontal oblique polarization array comprising five transmitters and six receivers has been constructed, achieving dual-reception from a single transmission across ten channels, significantly improving upon the limitations of single antenna systems in terms of data volume and sampling interval. Experimental tests demonstrate that the designed system can successfully detect the echo signals of metallic and non-metallic small targets like buried up to 20 cm deep in typical soil conditions, and generate clear slice images, proving its capability for high-resolution imaging detection of shallow buried small targets.