Acquisition Of High-resolution Images Research Articles

Assessing the combined impact of fatty liver-induced TGF-β1 and LPS-activated macrophages in fibrosis through a novel 3D serial section methodology.

Non-alcoholic steatohepatitis (NASH), caused by fat buildup, can lead to liver inflammation and damage. Elucidation of the spatial distribution of fibrotic tissue in the fatty liver in NASH can be immensely useful to understand its pathogenesis. Thus, we developed a novel serial section-3D (SS3D) technique that combines high-resolution image acquisition with 3D construction software, which enabled highly detailed analysis of the mouse liver and extraction and quantification of stained tissues. Moreover, we studied the underexplored mechanism of fibrosis progression in the fatty liver in NASH by subjecting the mice to a high-fat diet (HFD), followed by lipopolysaccharide (LPS) administration. The HFD/LPS (+) group showed extensive fibrosis compared with control; additionally, the area of these fibrotic regions in the HFD/LPS (+) group was almost double that of control using our SS3D technique. LPS administration led to an increase in Tnfα and Il1β mRNA expression and the number of macrophages in the liver. On the other hand, transforming growth factor-β1 (Tgfβ1) mRNA increased in HFD group compared to that of control group without LPS-administration. In addition, COL1A1 levels increased in hepatic stellate cell (HSC)-like XL-2 cells when treated with recombinant TGF-β1, which attenuated with recombinant latency-associated protein (rLAP). This attenuation was rescued with LPS-activated macrophages. Therefore, we demonstrated that fatty liver produced "latent-form" of TGF-β1, which activated by macrophages via inflammatory cytokines such as TNFα and IL1β, resulting in activation of HSCs leading to the production of COL1A1. Moreover, we established the effectiveness of our SS3D technique in creating 3D images of fibrotic tissue, which can be used to study other diseases as well.

Learning content-aware feature fusion for guided depth map super-resolution

RGB-D data including paired RGB color images and depth maps is widely used in downstream computer vision tasks. However, compared with the acquisition of high-resolution color images, the depth maps captured by consumer-level sensors are always in low resolution. Within decades of research, the most state-of-the-art (SOTA) methods of depth map super-resolution cannot adaptively tune the guidance fusion for all feature positions by channel-wise feature concatenation with spatially sharing convolutional kernels. This paper proposes JTFNet to resolve this issue, which simulates the traditional Joint Trilateral Filter (JTF). Specifically, a novel JTF block is introduced to adaptively tune the fusion pattern between the color features and the depth features for all feature positions. Moreover, based on the variant of JTF block whose target features and guidance features are in the cross-scale shape, the fusion for depth features is performed in a bi-directional way. Therefore, the error accumulation along scales can be effectively mitigated by iteratively HR feature guidance. Compared with the SOTA methods, the sufficient experiment is conducted on the mainstream synthetic datasets and real datasets, i.e., Middlebury, NYU and ToF-Mark, which shows remarkable improvement of our JTFNet.

SHAPE'S IMPACT ON DIMENSIONAL PRECISION IN 3D PRINTED COMPONENTS

In this comprehensive exploration, the study explores the relationship between shape and the dimensional accuracy of components manufactured through additive manufacturing processes. The methodology involves the adept utilization of Autodesk Inventor Software, strategically embossing capital letters from A to O onto a rectangular plate. The resulting models are exported in STL format, laying the foundation for rapid prototyping. The investigation unfolds with the application of a Prusa İ3 desktop 3D printer, where specific settings, including layer height (ranging from 90 to 300 microns), 20% infill density, and a heated bed temperature of 60 ºC, are scrupulously chosen. Three different embossing methods are examined in this study in order to see how each affects dimensional correctness. These methods are join, cut half, and cut through. Through a meticulous comparative analysis, facilitated by high-resolution image acquisition and advanced processing techniques like binarizing and edge detection, the study discerns that embossing with join yields shapes characterized by higher dimensional accuracy, a conclusion substantiated by correlation coefficient analysis. This research stands as a significant contribution, offering valuable insights into optimizing additive manufacturing processes and elevating dimensional precision in 3D printed components.

OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method

With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8

INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. 
 OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. 
 METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation.
 RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. 
 CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.

A deep neural network approach with attention mechanism to improve the quality of target observation for UAVs

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However, with the development of deep learning, super-resolution reconstruction has become a popular technique for enhancing image resolution. Though, current super-resolution reconstruction methods can enhance UAV target observation images to some extent, most of them are unable to strike a balance between the number of parameters, computational cost, and reconstruction effect. Furthermore, limited research has been conducted for overcoming the cascade coupling problem in UAV target observation images. To solve these problems and provide a lightweight, fast, and high-quality super-resolution method for UAV target observation images, an enhanced attention dense cross network (EADCN) is proposed. This method includes excellent attention mechanisms, such as the double large kernel attention module (D-LKA) and multi-aware channel attention distillation block (MAADB). In addition to benchmark datasets, a UAV target observation dataset (UAV-TOD) is created to evaluate the performance of EADCN. The experimental results demonstrate that the EADCN has fewer parameters, low computational complexity, high accuracy, and fast processing speed, making it suitable for deployment in UAVs.

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI.

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasetsto fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

Inference of highly time-resolved melt pool visual characteristics and spatially-dependent lack-of-fusion defects in laser powder bed fusion using acoustic and thermal emission data

With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic sensing, photodiode sensing, and high-speed imaging. However, real-time acquisition of high-resolution melt pool images in particular remains computationally demanding in practice due to the high variability of melt pool morphologies and the limitation of data caching and transfer, making it challenging to detect the local lack-of-fusion (LOF) defect occurrences. In this work, we propose a new acoustic and thermal information-based monitoring method that can robustly infer critical LPBF melt pool morphological features in image forms and detect spatially-dependent LOF defects within a short time period. We utilize wavelet scalogram matrices of acoustic and photodiode clip data to identify and predict highly time-resolved (within a 1.0 ms window) visual melt pool characteristics via a well-trained data-driven pipeline. With merely the acoustic and photodiode-collected thermal emission data as the input, the proposed pipeline enables data-driven inference and tracking of highly variable melt pool visual characteristics with R2≥0.8. We subsequently validate our proposed approach to infer local LOF defects between two adjacent scanlines, showing that our proposed approach can outperform our selected baseline theoretical model based on previous literature. Revealing the physical correlation between airborne acoustic emission, thermal emission, and melt pool morphology, our work demonstrates the feasibility of creating an efficient and cost-effective acoustic- and thermal-based approach to facilitate online visual melt pool characterization and LOF defect detection. We believe that our work can further contribute to the advances in quality control for LPBF.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Development of a Powder Analysis Procedure Based on Imaging Techniques for Examining Aggregation and Segregation Phenomena.

In this manuscript, a method that utilizes classical image techniques to assess particle aggregation and segregation, with the primary goal of validating particle size distribution determined by conventional methods, is presented. This approach can represent a supplementary tool in quality control systems for powder production processes in industries such as manufacturing and pharmaceuticals. The methodology involves the acquisition of high-resolution images, followed by their fractal and textural analysis. Fractal analysis plays a crucial role by quantitatively measuring the complexity and self-similarity of particle structures. This approach allows for the numerical evaluation of aggregation and segregation phenomena, providing valuable insights into the underlying mechanisms at play. Textural analysis contributes to the characterization of patterns and spatial correlations observed in particle images. The examination of textural features offers an additional understanding of particle arrangement and organization. Consequently, it aids in validating the accuracy of particle size distribution measurements. To this end, by incorporating fractal and structural analysis, a methodology that enhances the reliability and accuracy of particle size distribution validation is obtained. It enables the identification of irregularities, anomalies, and subtle variations in particle arrangements that might not be detected by traditional measurement techniques alone.

A 48-Channel High-Resolution Ultrasound Beamforming System for Ultrasound Endoscopy Applications

We introduce a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver that allows for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high-voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces the area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed a Shift Coherence Factor (SCF) algorithm that is hardware-friendly. This approach delivers a lateral resolution of over 20% better than that of the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy (AP50) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively.

Ultrahigh-Resolution Reconstruction of Shale Digital Rocks from FIB-SEM Images Using Deep Learning

Summary Accurate characterization of shale pore structures is of paramount importance in elucidating the distribution and migration mechanisms of fluids within shale rocks. However, the acquisition of high-resolution (HR) images of shale rocks is limited by the precision of the scanning equipment. Even with higher-precision devices, compromising the image field of view becomes inevitable, making it challenging to faithfully represent the actual conditions of shale. We propose a stepwise 3D super-resolution (SR) reconstruction method for shale digital rocks based on the widely used focused-ion-beam scanning electron microscope (FIB-SEM) technique. This method effectively addresses the issues of inconsistent horizontal and vertical resolutions as well as low 3D image resolution in FIB-SEM images. By adopting this approach, we significantly enhance image details and clarity, enabling successful observations of pores smaller than 10 nm within shale and laying a foundation for further pore-scale flow simulations. Furthermore, we extract the pore network model (PNM) from the SR reconstructed digital rock to analyze the pore size distribution, coordination number, and pore-throat ratio of shale samples from the Jiyang Depression. The results demonstrate a pore radius distribution in the range of 0 nm to 40 nm, which aligns with the results from nitrogen adsorption experiments. Notably, pores with radii smaller than 10 nm account for 50% of the total connected pores. The proportion of isolated pores in the SR reconstructed shale PNM is significantly reduced, with the coordination number mainly distributed between 1 and 4. The pore-throat ratio of shale ranges from 1 to 3, indicating a relatively uniform development of pores and throats. This study introduces a novel method for accurately characterizing the shale pore structure, which aids researchers in evaluating the pore size distribution and connectivity of shales.

Stainless steel cylindrical pot outer surface defect detection method based on cascade neural network

Addressing issues such as surface geometric distortion, high reflection, and the challenge of detecting minor defects on the external surfaces of stainless steel pots, this paper presents a novel approach for detecting defects on the external surfaces of cylindrical stainless steel pots based on high-resolution line-scan imaging. The method begins by creating a real-time inspection system that includes a line-scan camera and a strip light source. This system achieves distortion-free, high-resolution image acquisition of the outer surfaces of stainless steel cylindrical pots by establishing linear constraints on pot size, rotary table rotation speed, and line-scan frame rate. Subsequently, a You Only Look Once and fully convolutional network cascade neural network surface defect detection strategy is introduced, utilizing dual-channel images of the original and enhanced images as inputs. This approach enables the characterization of subtle defects in high-resolution image data. Finally, we conducted experiments using the proposed method on the provided dataset, and the results demonstrate the effectiveness of this approach in detecting various types of product defects. The mean pixel accuracy achieved a remarkable 91.69%, while the mean intersection over union score reached an impressive 83.59%. These findings provide an effective technical means for the qualitative detection of the types of defects on the surface of stainless steel pots and the quantitative measurement of the size of the defects.

Left atrial glucose metabolism by 18F-FDG PET/CT in persistent atrial fibrillation and after return to sinus rhythm

Abstract Background Atrial Fibrillation (AF) is strongly associated with the development of Left Atrial (LA) remodeling. Animal studies pointed to an important role of metabolic deregulation in AF, yet few studies have assessed LA myocardial metabolism in vivo in patients with AF. 18-fluorodeoxyglucose (FDG) Positron Emission tomography (PET) has been widely used for evaluation of left ventricular (LV) metabolism, but its application to study metabolism of LA has not yet been explored. Purpose The aim of this study was to evaluate LA glucose metabolism and contractile efficiency in patients with persistent AF, following our successful implementation of a protocol enabling the acquisition of high-resolution PET images of the LA. Methods 36 nondiabetic patients (64±7 years, 29 men) with persistent non valvular AF (peAF) undergoing a first AF ablation and 5 healthy age matched volunteers (69±5 years, 3 men, p=0.114) without AF underwent FDG PET, speckle tracking echocardiography (STE) and cardiac MRI (cMR). PET was performed after overnight fasting and after administration of a nicotinic acid derivative (Acipimox 250 mg) to maximally stimulate myocardial glucose uptake. The 3 tests were repeated 3 months later in 26 patients in whom return to permanent sinus rhythm (SR) could be obtained after the ablation procedure. PET images were registered with cMR LA angiography, and the mean partial volume and spillover corrected standardized FDG uptake (SUV) in the whole LA, the left atrial appendage (LAA), and the left ventricle (LV) were computed. Measurements were compared to global Peak Atrial Longitudinal Strain (GPALS) and to left atrial ejection fraction (LAEF) measured by STE and LA contractile efficiency was computed as the ratio of LA SUV to LAEF. Results Hemodynamics (syst. BP mmHg: 128±11 vs 129±5 mmHg, p=0.663) and metabolic parameters (glycemia: 99±14 vs 96±7 mg/dl, p=0.480) were similar in peAF patients and healthy controls. While 18F-FDG SUV in the LV was comparable between pEAF patients and healthy controls, patients in peAF exhibited significantly increased SUV in LA and LAA compared to controls. Following a return to SR, LA-SUVmean and LAA-SUVmean in patients peAF decreased significantly after 3 months (20.3%, p<0.002 and 34.1%, p<0.001), whereas the LV-SUVmean remained unchanged (p=0.847). The Global Peak Atrial Longitudinal Strain (GPALS) increased by 54.2%(p<0.001), and the Left Atrial Ejection Fraction (LAEF) by 39.2% (p<0.001), post-restoration of SR. (Figure 1). Accordingly contractile efficiency of LA SUV/LAEF in peAF patients improved significantly upon their return to SR (47%, p<0.001). Conclusion LA glucose metabolism is significantly increased in peAF and return after restoration of SR. This could indicate oxygen wasting and reduced contractile efficiency of AF or metabolic shift towards preferred glucose metabolism in AF. These findings support the notion that LA glucose metabolism plays a role in the pathophysiology of AF.Figure 1Figure 2

Whole-Mount Imaging of Adipose Tissue Macrophages.

The adipose tissue comprises highly heterogeneous macrophage populations, which play critical roles in the regulation of adipose tissue function and dysfunction during health and disease. Whole-amount staining is a powerful technique for macrophage characterization within the 3D environment of the adipose tissue, enabling the visualization of different macrophage populations and their interaction with other cells within their in vivo niche. Due to the high-fat content and softness, freezing and sectioning of adipose tissue is difficult, and distortion of tissue morphology typically occurs, especially in the case of white adipose tissue. We describe here a whole-mount staining alternative for adipose tissue imaging that preserves all structures and allows high-resolution image acquisition. We address in a step-by-step manner how to perform immunofluorescence staining of different fat pads and how to optimally visualize cellular and acellular (extracellular matrix) constituents of the adipose tissue and its vasculature, as well as resident and infiltrating macrophage populations.

Optical-scanning evanescent sensor for high-speed broadband photoacoustic microscopy

Photoacoustic microscopy with high spatial resolution and fast imaging acquisition allows observing dynamic processes of optical absorption-based microanatomic structures in three dimensions. An evanescent sensor accesses ultrasonic detection with high sensitivity and broad bandwidth while suffering from limited field of view (FOV), thus compromising the photoacoustic imaging acquisition rate. Here, we develop an optical-scanning evanescent sensor by fast deflection of the interrogation light along the interface of prism and water using a one-dimensional galvanometer, demonstrating excellent detection sensitivity of ∼132 Pa with a broadband frequency response of >140-MHz at an enlarged FOV of ∼2.90 × 0.19 mm2. Incorporating the optical-scanning evanescent sensor in photoacoustic microscopy, a volumetric image (∼3.0 × 0.25 × 1.0 mm3) with micrometer-scale spatial resolution is acquired within ∼2.5 s by synergistically scanning both photoacoustic illumination laser and sensor's interrogation light. High-speed imaging of flowing microparticles within a capillary tube offers the visualizations of the traveling processes in three dimensions. Potentially, the optical-scanning evanescent sensor allows photoacoustic microscopy accommodating to dynamic imaging at cellular level such as in vivo flow cytometry of circulating tumor cells.

Mataram Fault – New Active Fault Crosses East-West in the Centre of Yogyakarta City

Yogyakarta has experienced two devastating earthquake disasters: May 26, 2006, Mw6.4, and the penultimate event, June 10, 1867 Mw7.7. The active fault that is thought to be responsible for the two earthquake events is the Opak Fault. However, the Opak Fault has still not been thoroughly well mapped yet. The lack of a high-resolution image, dense vegetation cover, high sediment flux from the Merapi volcano, and human activities eroding the original landscape challenge studying the on-land fault in this area. Our recent study, however, indicates that the Opak Fault is not the only active fault that can cause a major disaster, but another fault strand exists in the area. We mapped the fault using the best available data of DEMNAS assisted by more detailed DEM and Orthophoto, developed from drone survey, and we also conducted Earth Resistivity Tomography (ERT) survey. We found that inferred new active fault is oriented East-West across the center of the high-populated city. The fault runs parallel and close to the famous Mataram channel (Selokan Mataram). The fault is likely continue the previously recognized Dengkeng Fault, east of the Opak fault. The fault strand is indicated by morphological lineaments and a few steam offsets. Our ERT 2D sections have revealed the fault zone in several locations along the inferred fault line. The fault line cut the Young Quaternary rocks; hence, it is an active fault. Further studies are needed to get further details of this newly recognized active fault, such as conducting paleoseismological studies, detailed seismological survey, geodetic GPS measurement, and acquiring a high-resolution image acquisition survey (LiDAR – Light Detection and Ranging Survey).

Automatic Fruit Quality Inspection System Using Image Processing

The demand for high-quality fruits has increased significantly, driven by consumers' growing emphasis on health and nutrition. To ensure consistent quality control and efficient fruit grading processes, an automatic fruit quality inspection system using image processing techniques has been developed. This system leverages Convolutional Neural Network (CNN) algorithms to achieve an impressive accuracy rate of 99% in fruit quality assessment. The proposed system involves a multi-step process starting with the acquisition of high-resolution fruit images. These images undergo pre-processing to enhance clarity and eliminate noise or artifacts. Subsequently, the pre-processed images are fed into a trained CNN model for feature extraction and classification based on learned patterns. The CNN model has been trained on a large dataset of labeled fruit images, enabling it to recognize quality attributes such as colour, size, shape, and defects. The system's evaluation involved a diverse range of fruits, encompassing various species, varieties, and maturity stages. The output demonstrate the system's exceptional accuracy, with a 99% success rate in correctly identifying and grading fruit quality attributes. This automatic fruit quality inspection system offers several advantages, including real-time processing, high efficiency, and reduced labor costs, making it suitable for integration into fruit processing facilities and supply chains. [1]

4D Light-sheet imaging and interactive analysis of cardiac contractility in zebrafish larvae.

Despite ongoing efforts in cardiovascular research, the acquisition of high-resolution and high-speed images for the purpose of assessing cardiac contraction remains challenging. Light-sheet fluorescence microscopy (LSFM) offers superior spatiotemporal resolution and minimal photodamage, providing an indispensable opportunity for the in vivo study of cardiac micro-structure and contractile function in zebrafish larvae. To track the myocardial architecture and contractility, we have developed an imaging strategy ranging from LSFM system construction, retrospective synchronization, single cell tracking, to user-directed virtual reality (VR) analysis. Our system enables the four-dimensional (4D) investigation of individual cardiomyocytes across the entire atrium and ventricle during multiple cardiac cycles in a zebrafish larva at the cellular resolution. To enhance the throughput of our model reconstruction and assessment, we have developed a parallel computing-assisted algorithm for 4D synchronization, resulting in a nearly tenfold enhancement of reconstruction efficiency. The machine learning-based nuclei segmentation and VR-based interaction further allow us to quantify cellular dynamics in the myocardium from end-systole to end-diastole. Collectively, our strategy facilitates noninvasive cardiac imaging and user-directed data interpretation with improved efficiency and accuracy, holding great promise to characterize functional changes and regional mechanics at the single cell level during cardiac development and regeneration.