Current Issue : July-September Volume : 2025 Issue Number : 3 Articles : 5 Articles
In this study, the effect of ammonia on the Acipenser gueldenstaedtii was investigated using non-invasive methods. Different concentrations (100, 200, and 400 mg·lt−1) of ammonium chloride (NH4Cl) were added to the experimental groups to simulate ammonia in aquaculture systems, and the movements of the fish were monitored, recorded, and analyzed using image processing techniques and statistical methods. For image processing operations, the optical flow Farneback object-tracking algorithm and necessary image development algorithms were implemented using Python 3.9.13 Programming language codes in the Visual Studio Code software 1.98.2 development environment. At low concentrations, it was observed that the fish made circular movements, while at high concentrations, their movements were restricted and concentrated in areas close to the water’s surface. It was observed that with the increase in ammonia concentration, the movement distances of the fish decreased, and their movements became irregular. This shows that the Acipenser gueldenstaedtii is sensitive to ammonia concentrations and that these concentrations affect the behavior of the fish. These findings are significant for aquaculture conditions and water quality management of the endangered Acipenser gueldenstaedtii, which is protected from the threat of extinction....
In this paper, we propose a novel deep dilated convolutional neural network (DDCNN) architecture to reconstruct periodic rough surfaces, including their periodic length, dielectric constant, and shape. Historically, rough surface problems were addressed through optimization algorithms. However, these algorithms are computationally intensive, making the process very time-consuming. To resolve this issue, we provide measured scattered fields as training data for the DDCNN to reconstruct the periodic length, dielectric constant, and shape. The numerical results demonstrate that DDCNN can accurately reconstruct rough surface images under high noise levels. In addition, we also discuss the impacts of the periodic length and dielectric constant of the rough surface on the shape reconstruction. Notably, our method achieves excellent reconstruction results compared to DCNN even when the period and dielectric coefficient are unknown. Finally, it is worth mentioning that the trained network model completes the reconstruction process in less than one second, realizing efficient real-time imaging....
Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research....
Quality inspection in the manufacturing of car air conditioning vents has traditionally relied on human operators, a process prone to subjectivity, inconsistency, and inefficiency due to factors like fatigue and human error. To overcome these limitations, this study proposes an automated quality inspection system using image processing techniques to detect defects such as missing parts and scratches. Using MATLAB, the system integrates image acquisition, enhancement, segmentation, and defect analysis for consistent and accurate inspection. Images are captured under controlled lighting with optimal camera positioning to minimize distortion, and preprocessing techniques such as contrast adjustment, morphological operations, and adaptive thresholding are applied to refine image quality and highlight defects. Extensive validation of the system demonstrated over 90% accuracy in defect detection, particularly when vent positions and angles were fixed. This study highlights the potential of combining image processing and machine vision to improve quality control processes in the automotive industry, offering a reliable alternative to traditional manual inspections....
This paper demonstrates the impact of 3D effects on performance parameters in small-sized Time Delay Integration (TDI) image sensor pixels. In this paper, 2D and 3D simulation models of 3.5 μm × 3.5 μm small-sized TDI pixels were constructed, utilizing a three-phase pixel structure integrated with a lateral anti-blooming structure. The simulation experiments reveal the limitations of traditional 2D pixel simulation models by comparing the 2D and 3D structure simulation results. This research validates the influence of the 3D effects on the barrier height of the anti-blooming structure and the full well potential and proposes methods to optimize the full well potential and the operating voltage of the anti-blooming structure. To verify the simulation results, test chips with pixel sizes of 3.5 μm × 3.5 μm and 7.0 μm × 7.0 μm were designed and manufactured based on a 90 nm CCD-in-CMOS process. The measurement results of the test chips matched the simulation data closely and demonstrated excellent performance: the 3.5 μm × 3.5 μm pixel achieved a full well capacity of 9 ke- while maintaining a charge transfer efficiency of over 0.99998....
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