Search Results (199)

This study proposes a high-contrast photoacoustic (PA) imaging methodology based on a dual-wavelength confocal metalens, designed to monitor the dissemination of cancer cells and to inform subsequent cancer treatment strategies. The metalens is composed of two metasurfaces that perform filtering and focusing functions, effectively reducing the cross-talk between the two wavelengths of light in space and achieving a confocal effect. Furthermore, to minimize process complexity, a uniform material system of silicon dioxide (SiO₂) and titanium dioxide (TiO₂) is employed across the different metasurfaces of the metalens. The designed metalens has a radius of 25 µm and an operational focal length of 98.5 µm. The results confirm that this dual-metasurface design achieves high focusing efficiency alongside precise focusing capability, with the deviations of the actual focal lengths for both beams from the design values being within 1.5 µm. Additionally, this study developed a skin tissue model and simulated multi-wavelength photoacoustic imaging of cancer cells within the human body by integrating theories of radiative transfer, photothermal conversion, and the wave equation. The results demonstrate that the enhancement trend of the reconstructed signal closely matches the original signal, confirming the model’s excellent fitting performance. The sound pressure values generated by cancer cells are significantly higher than those of normal cells, proving that this method can effectively distinguish cancerous tissue from healthy tissue. This research provides new theoretical support and methodological foundations for the clinical application of multi-wavelength photoacoustic imaging technology. Full article

A lateral overflow integration capacitor (LOFIC) CMOS image sensor (CIS) can achieve high-dynamic-range (HDR) imaging by combining a low-conversion-gain (LCG) signal with a high-conversion-gain (HCG) signal. However, the signal-to-noise ratio (SNR) drops at the switching point from HCG signal to LCG signal due to the significant pixel noise in the LCG signal. To address this issue, a triple-gain LOFIC CIS with a middle-conversion-gain (MCG) signal has been introduced. In this work, we propose an area-efficient readout circuit for the triple-gain LOFIC CIS, using amplifier and capacitor sharing techniques to process the HCG, MCG, and LCG signals. A test chip of the proposed readout circuit was fabricated using the $0.18\mathsf{\mu }$m CMOS process. The area overhead was only $7.6\%$, and the SNR drop was improved by $8.05$ dB compared to the readout circuit for a dual-gain LOFIC CIS. Full article

High parasitic capacitance from poly-insulator-poly capacitors in complementary metal oxide semiconductor (CMOS) processes presents a major bottleneck to achieving high-resolution successive approximation register (SAR) analog-to-digital converters (ADCs) in imaging systems. This study proposes a Phase-Adjustable SAR ADC that addresses this limitation through a reconfigurable architecture. The design utilizes a phase-adjustable logic unit to switch between a conventional SAR mode for high-speed operation and a noise-shaping (NS) SAR mode for high-resolution conversion, actively suppressing in-band quantization noise. An improved SAR logic unit facilitates the insertion of an adjustable phase while concurrently achieving an 86% area reduction in the core logic block. A prototype was fabricated and measured in a 0.35-µm CMOS process. In conventional mode, the ADC achieved a 7.69-bit effective number of bits at 2 MS/s. By activating the noise-shaping circuitry, performance was significantly enhanced to an 11.06-bit resolution, corresponding to a signal-to-noise-and-distortion ratio (SNDR) of 68.3 dB, at a 125 kS/s sampling rate. The results demonstrate that the proposed architecture effectively leverages the trade-off between speed and accuracy, providing a practical method for realizing high-performance ADCs despite the inherent limitations of non-ideal passive components. Full article

To break through the bottleneck in the mapping of the mechanoluminescent (ML) intensity field to the strain field, a quantification method for full-field strain measurement based on pixel-level data fusion is proposed, integrating ML imaging with digital image correlation (DIC) to achieve precise reconstruction of the strain field. Experiments are conducted using aluminum alloy specimens coated with ML film sensor on their surfaces. During the tensile process, ML images of the films and speckle images of the specimen backsides are simultaneously acquired. Combined with DIC technology, high-precision full-field strain distributions are obtained. Through spatial registration and region matching algorithms, a quantitative calibration model between ML intensity and DIC strain is established. The research results indicate that the ML intensity and DIC strain exhibit a significant linear correlation (R² = 0.92). To verify the universality of the model, aluminum alloy notched specimen tests show that the reconstructed strain field is in good agreement with the DIC and finite element analysis results, with an average relative error of 0.23%. This method enables full-field, non-contact conversion of ML signals into strain distributions with high spatial resolution, providing a quantitative basis for studying ML response mechanisms under complex loading. Full article

This study analyzes confessionally conditioned self-image and mutual perception of the Catholic and Evangelical Church of the Augsburg Confession in Upper Hungary in the context of the second confessionalization process. Based on comparative research of the contemporary press, including either the printed or handwritten homiletic and catechetical literature, predominantly from the area of Upper Hungary, the study examines which phenomena and processes taking place since the 1830s until the end of the 1850s signaled a renewal in confessional identities in both the Catholic and Evangelical Churches. A particular emphasis has been placed on indicators of the second confessionalization, such as the debate on mixed marriages, a rising number of conversions, or legislative interventions in the freedom of religion. Through discourse analysis, the study explores how the image and self-image of the Catholic and Evangelical Church of the Augsburg Confession evolved as a result of the expansion of the catechetical literature and apologetic works and identifies the narrative strategies employed in their respective confessional discourses. The Catholic discourse stressed maintaining dogmatic integrity and Church authority in particular, whereas the Evangelicals more frequently accentuated a thorough biblicality and rationality as a counterposition to Catholic piety. Both traditions claimed exclusive access to “true religion” and used apologetic genres to defend and enhance their identity. Polemical texts also served as tools to form confessionally conditioned collective consciousness, as well as a part of contemporary cultural and political debates. Full article

In light of the computational efficiency bottleneck and inadequate regional feature representation in traditional global data approximation methods, this paper introduces the concept of non-uniform partition to transform global continuous approximation into multi-region piecewise approximation. Additionally, we propose an image representation algorithm based on linear canonical transformation and non-uniform partitioning, which enables the regional representation of sub-signal features while reducing computational complexity. The algorithm first demonstrates that the two-dimensional linear canonical transformation series has a least squares solution within each region. Then, it adopts the maximum likelihood estimation method and the scale transformation characteristics to achieve conversion between the nonlinear and linear expressions of the two-dimensional linear canonical transformation series. It then uses the least squares method and the recursive method to convert the image information into mathematical expressions, realize image vectorization, and solve the approximation coefficients in each region more quickly. The proposed algorithm better represents complex image texture areas while reducing image quality loss, effectively retains high-frequency details, and improves the quality of reconstructed images. Full article

Ultrafast lasers operating at 740 nm and 820 nm have attracted widespread attention as two-photon light sources for the detection of biological metabolism. Here, we report on a solid-like quartz-encapsulated femtosecond laser with a repetition rate of 80 MHz, delivering 740 nm and 820 nm femtosecond laser pulses. This home-built laser system was realized by employing an erbium-doped 1560 nm fiber laser as the fundamental laser source. A quartz-encapsulated nonlinear frequency conversion stage, consisting of a second-harmonic generation (SHG) stage and self-phase modulation (SPM)-based nonlinear spectral broadening stage, was utilized to deliver 30 mW, 53.7 fs, 740 nm laser pulses and the 15 mW, 60.8 fs, 820 nm laser pulses. Further imaging capabilities of both wavelengths were validated using a custom-built inverted two-photon microscope. Clear imaging results were obtained from mouse kidney sections and pollen samples by collecting the corresponding fluorescence signals. The achieved results demonstrate the great potential of this laser source for advanced two-photon microscopy in metabolic detection. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article

Human memory is inherently susceptible to errors, including the formation of false memories—instances where individuals mistakenly recall information they were never exposed to. While prior research has largely focused on neural activity associated with false memory, the structural brain correlates of this phenomenon remain relatively unexplored. This study bridges that gap by investigating gray matter structure as it relates to individual differences in false memory performance. Using publicly available magnetic resonance imaging datasets, we analyzed cortical thickness (CT) in neural regions implicated in memory processes. To assess false memory, we applied signal detection theory, which provides a robust framework for differentiating between true and false memory. Our findings reveal that increased CT in the parietal lobe and middle occipital gyrus correlates with greater susceptibility to false memories, highlighting its role in integrating and manipulating memory information. Conversely, CT in the middle frontal gyrus and occipital pole was associated with enhanced accuracy in memory recall, emphasizing its importance in perceptual processing and encoding true memories. These results provide novel insights into the structural basis of memory errors and offer a foundation for future investigations into the neural underpinnings of memory reliability. Full article

With the increase in the penetration rate of distributed sources and loads, the sensor monitoring data is increasing dramatically. Power grid maintenance services require a rapid response in power quality data analysis. To achieve a rapid response and highly accurate classification of power quality disturbances (PQDs), this paper proposes an efficient classification algorithm for PQDs based on Lissajous trajectory (LT) and a lightweight DenseNet, which utilizes the concept of Lissajous curves to construct an ideal reference signal and combines it with the original PQD signal to synthesize a feature trajectory with a distinctive shape. Meanwhile, to enhance the ability and efficiency of capturing trajectory features, a lightweight L-DenseNet skeleton model is designed, and its feature extraction capability is further improved by integrating an attention mechanism with L-DenseNet. Finally, the LT image is input into the fusion model for training, and PQD classification is achieved using the optimally trained model. The experimental results demonstrate that, compared with current mainstream PQD classification methods, the proposed algorithm not only achieves superior disturbance classification accuracy and noise robustness but also significantly improves response speed in PQD classification tasks through its concise visualization conversion process and lightweight model design. Full article

Formation MicroScanner Image (FMI) technology is a key method for identifying fractured reservoirs and optimizing oil and gas exploration, but its inherent insufficient resolution severely constrains the fine characterization of geological features. This study innovatively applies a Super-Resolution Generative Adversarial Network (SRGAN) to the super-resolution reconstruction of FMI logging image to address this bottleneck problem. By collecting FMI logging image of glutenite from a well in Xinjiang, a training set containing 24,275 images was constructed, and preprocessing strategies such as grayscale conversion and binarization were employed to optimize input features. Leveraging SRGAN’s generator-discriminator adversarial mechanism and perceptual loss function, high-quality mapping from low-resolution FMI logging image to high-resolution images was achieved. This study yields significant results: in RGB image reconstruction, SRGAN achieved a Peak Signal-to-Noise Ratio (PSNR) of 41.39 dB, surpassing the optimal traditional method (bicubic interpolation) by 61.6%; its Structural Similarity Index (SSIM) reached 0.992, representing a 34.1% improvement; in grayscale image processing, SRGAN effectively eliminated edge blurring, with the PSNR (40.15 dB) and SSIM (0.990) exceeding the suboptimal method (bilinear interpolation) by 36.6% and 9.9%, respectively. These results fully confirm that SRGAN can significantly restore edge contours and structural details in FMI logging image, with performance far exceeding traditional interpolation methods. This study not only systematically verifies, for the first time, SRGAN’s exceptional capability in enhancing FMI resolution, but also provides a high-precision data foundation for reservoir parameter inversion and geological modeling, holding significant application value for advancing the intelligent exploration of complex hydrocarbon reservoirs. Full article

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article

In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments. Full article

In the context of vessel monitoring, the accuracy of vessel speed measurements is contingent on the availability of AIS data. However, the absence, failure, or signal congestion of AIS devices may lead to delays and inaccuracies in the speed information. To address this challenge, this paper proposes a vessel speed detection method based on target detection and tracking to acquire vessel speed in real time. The proposed methodology involves the establishment of a mapping relationship between image coordinates and four real-world coordinates, ensuring precise conversion from pixel velocity to physical velocity. Subsequently, a frame difference method combined with a multi-frame averaging strategy calculates the vessel speed. Furthermore, an advanced M-YOLOv11 detection model is introduced to enhance the detection performance in different vessel shapes and complex environments, thus ensuring the accuracy of speed information is further improved. The experimental results demonstrate that M-YOLOv11 exhibits a significant performance enhancement, with a 13.95% improvement in the average precision metric over the baseline model. Over 60% of the measured vessel speed measurement errors are less than 0.5 knots, with an overall average error below 0.45 knots. These findings substantiate the efficacy and superiority of the proposed method in practical applications. Full article