Search Results (193)

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

Artificial intelligence (AI) is revolutionizing the field of medical imaging, offering unprecedented capabilities in data analysis, image interpretation, and decision support. Transcranial Doppler (TCD) and Transcranial Color-Coded Doppler (TCCD) are widely used, non-invasive modalities for evaluating cerebral hemodynamics in acute and chronic conditions. Yet, their reliance on operator expertise and subjective interpretation limits their full potential. AI, particularly machine learning and deep learning algorithms, has emerged as a transformative tool to address these challenges by automating image acquisition, optimizing signal quality, and enhancing diagnostic accuracy. Key applications reviewed include the automated identification of cerebrovascular abnormalities such as vasospasm and embolus detection in TCD, AI-guided workflow optimization, and real-time feedback in general ultrasound imaging. Despite promising advances, significant challenges remain, including data standardization, algorithm interpretability, and the integration of these tools into clinical practice. Developing robust, generalizable AI models and integrating multimodal imaging data promise to enhance diagnostic and prognostic capabilities in TCD and ultrasound. By bridging the gap between technological innovation and clinical utility, AI has the potential to reshape the landscape of neurovascular and diagnostic imaging, driving advancements in personalized medicine and improving patient outcomes. This review highlights the critical role of interdisciplinary collaboration in achieving these goals, exploring the current applications and future directions of AI in TCD and TCCD imaging. This review included 41 studies on the application of artificial intelligence (AI) in neurosonology in the diagnosis and monitoring of vascular and parenchymal brain pathologies. Machine learning, deep learning, and convolutional neural network algorithms have been effectively utilized in the analysis of TCD and TCCD data for several conditions. Conversely, the application of artificial intelligence techniques in transcranial sonography for the assessment of parenchymal brain disorders, such as dementia and space-occupying lesions, remains largely unexplored. Nonetheless, this area holds significant potential for future research and clinical innovation. Full article

This paper presents an ultrasonic driving and detection system based on a CMUT array using MEMS technology. Among them, the core component CMUT array is composed of 8 × 8 CMUT array elements, and each CMUT array element contains 6 × 6 CMUT units. The collapse voltage of a single CMUT unit obtained through finite element analysis is 95.91 V, and the resonant frequency is 3.16 MHz. The driving section achieves 64-channel synchronous driving, with key parameters including an adjustable excitation signal frequency ranging from 10 kHz to 5.71 MHz, a delay precision of up to 1 ns, and an excitation duration of eight pulse cycles. For the echo reception, a two-stage amplification circuit for high-frequency weak echoes with 32 channels was designed, achieving a gain of 113.72 dB and −3 dB bandwidth of 3.89 MHz. Simultaneously, a 32-channel analog-to-digital conversion based on a self-calibration algorithm was implemented, with a sampling rate of 50 Mbps and a data width of 10 bits. Finally, the experimental results confirm the successful implementation of the driving system’s designed functions, yielding a center frequency of 1.4995 MHz and a relative bandwidth of 127.9%@−6 dB for the CMUT operating in silicone oil. This paper successfully conducted the transmit–receive integrated experiment of the CMUT and applied Butterworth filtering to the echo data, resulting in high-quality ultrasonic echo signals that validate the applicability of the designed CMUT-based system for ultrasonic imaging. Full article

Digital Imaging and Communication in Medicine (DICOM) is a standard format for storing medical images, which are typically represented in higher bit depths (10–16 bits), enabling detailed representation but exceeding the display capabilities of standard displays and human visual perception. To address this, DICOM images are often accompanied by windowing parameters, analogous to tone mapping in High-Dynamic-Range image processing, which compress the intensity range to enhance diagnostically relevant regions. This study evaluates traditional histogram-based methods and explores the potential of deep learning for predicting window parameters in radiographs where such information is missing. A range of architectures, including MobileNetV3Small, VGG16, ResNet50, and ViT-B/16, were trained on high-bit-depth computed radiography images using various combinations of loss functions, including structural similarity (SSIM), perceptual loss (LPIPS), and an edge preservation loss. Models were evaluated based on multiple criteria, including pixel entropy preservation, Hellinger distance of pixel value distributions, and peak-signal-to-noise ratio after 8-bit conversion. The tested approaches were further validated on the publicly available GRAZPEDWRI-DX dataset. Although histogram-based methods showed satisfactory performance, especially scaling through identifying the peaks in the pixel value histogram, deep learning-based methods were better at selectively preserving clinically relevant image areas while removing background noise. Full article

Objectives: The purpose of this study was to analyze image quality and radiation exposure of thoracic computed tomography angiography (CTA) in children with congenital heart diseases (CHDs) using either manual contrast medium (CM) injection or automated power injectors with bolus tracking. Methods: A total of 137 thoracic CTAs of 120 consecutive pediatric patients were included in this retrospective study. We analyzed the method of CM administration (power injection with bolus tracking (PI) or manual injection (MI)), injection routes, volumes and flow rates of CM. For the evaluation of objective image quality, attenuation values in the heart chambers and great thoracic vessels were determined by region-of-interest (ROI) analysis and signal-to-noise (SNR) and contrast-to-noise (CNR) ratios calculated thereof. Visual image quality was assessed by two blinded readers (four-point Likert-scale) analyzing the presence of artifacts and the depiction of relevant anatomical structures. Effective radiation doses were calculated with dose length products and specific conversion factors. Results: CM administration was performed using PI in 119/137 CTAs, whereas MI was conducted in 18/137. The smallest size of peripheral venous cannulas was 24 gauge in 36/137 (26.3%) cases. Overall mean CM volume was 17 mL ± 16 mL (mean ± SD). In PI, the mean flow rate of CM was 1.52 ± 0.90 mL/s with a range between 0.5 and 5.0 mL/s. When comparing the overall PI population and an age-, size- and weight-matched PI subpopulation (18 cases) with the MI population, attenuation values in Hounsfield units (HU) and CNR values were significantly higher in the PI groups than in the MI group for each relevant cardiac structure (left ventricle, right ventricle, ascending aorta and pulmonary trunk, p = 0.02–0.001). Overall image quality and depiction of cardiac structures were rated significantly better in CTAs with PI (interquartile ranges: “good” to “excellent” (Likert 3–4)) in PI compared with CTAs acquired with MI (interquartile ranges: “fair” to “good” (2–3)) in MI by both readers (p < 0.001). The inter-observer reliability was strong, with a Kendall’s Tau-b correlation coefficient of τ = 0.802 (p < 0.001). The mean effective radiation dose (E) did not differ significantly when comparing the stratified samples (i.e., the matched PI subgroup and the MI group; 0.5 (±0.3) mSv in both, p = 0.76). There were no complications associated with the CM injections for both application approaches. Conclusions: Automated contrast agent applications with power injectors and bolus tracking ensure better image quality in pediatric CTA, even when low volumes and flow rates need to be applied. There is a slight increase in radiation associated with bolus tracking. This approach represents a suitable imaging technique for the work-up of congenital heart disease. Full article

Amorphous lead oxide (a-PbO) X-ray photoconductors show potential for applications in direct conversion medical imaging detectors within the diagnostic energy range. a-PbO enables large-area deposition at low temperatures and exhibits no signal lag. Low dark current can be maintained through specialized blocking layers, similar to those used in multilayer amorphous selenium (a-Se) structures in commercial detectors. However, the current state of a-PbO technology faces challenges in thick layer deposition, leading to crystalline inclusions and cracks. Our proposed stress-induced crystallization model reveals that intrinsic stress in a-PbO layers amplifies with thickness, leading to crystallographic defects. These defects, which are associated with the stable phase of β-PbO, contribute to increased dark current and initiate layer cracking. We calculate the thermal expansion coefficient of a-PbO, indicating a thermomechanical mismatch between the photoconductor and the substrate as the primary source of stress. Furthermore, we demonstrate that layer deposition parameters significantly impact heat accumulation within the growing layer, thereby facilitating temperature-induced crystallization. Our study suggests that relieving stress in grown a-PbO layers by eliminating thermal expansion coefficient mismatches between different layers in a-PbO blocking structures, coupled with optimizing deposition parameters to prevent heat accumulation during layer growth, may inhibit or even prevent stress-induced crystallization and the emergence of structural defects in thick a-PbO layers. Full article

Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure maximum productivity. Recently, DL has been implemented as a data-driven approach for detecting faults in motors. However, due to the limited availability of labeled fault data, the performance of the DL model is constrained. This issue is addressed by leveraging transfer learning (TL), which uses knowledge from a larger source domain to improve performance in a smaller target domain. In this paper, a multiple fault detection (FD) model for EMs is proposed by combining the ideas of deep convolutional neural networks (CNNs) and TL. A one-dimensional signal-to-image conversion technique is suggested for converting the vibration signal to images, and an Inception-ResNet-v2-inspired FD model is proposed for detecting bearing faults in the motor. The proposed method achieved a mean accuracy of more than 99%. Full article