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Rice farming in Italy accounts for about 50% of the EU's rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the "Riserva San Massimo" (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists' expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching. [ABSTRACT FROM AUTHOR]

With the continued exponential growth of digital images, concerns about the security and confidentiality of visual data have increased. In this session, a new developed approach was presented for image security and confidentiality by taking advantage of deep learning (DL) technology and producing data hierarchies. Due to the development taking place in the field of images and the large circulation of them through modern applications, it has become necessary to maintain their security. DL technology was used to encrypt and decrypt images, and based on hierarchical variables to complicate the encryption process. Convolutional neural networks are used in automatic learning to extract hierarchical features from an image, and to ensure adaptability, the model is trained on a variety of images. In order to encrypt the image, multi-layered hierarchical processes are used, and there are layers added during the work for complexity and to thwart attacks. Manipulating the layers of the neural network in a hierarchical manner to benefit from the outputs of the layers in feedback reflects the importance of the contributions here. Likewise, scattering the columns and rows of the image in a descending or ascending manner increases the efficiency of the contribution in this study. The use of hierarchical parameters facilitates encryption and decryption for authorized users. The evaluation of the research was conducted using established picture metrics and compared to pre-existing encryption techniques. The experimental findings substantiated the efficacy of the proposed approach in upholding image security, with the inclusion of hierarchical information further bolstering its ability to thwart attacks. Consequently, it emerges as a very promising strategy for ensuring image security. The proposed method is a significant advancement in creating an image security strategy using DL and a hierarchical variable creation process. The study provides a good and adaptable solution to evolving image security challenges in the digital age.

Underwater images frequently exhibit color deviation and blurring. Color deviation arises from the selective spectral absorption of light, whereas blurring results from particle scattering within the water. These challenges impede the performance of high-level visual perception tasks. To improve the underwater image quality, we propose an adaptive method for underwater image enhancement that employs color deviation detection-guided peak flattening. Utilizing six features identified through statistical analysis of the latest underwater real-image datasets, we develop a color deviation detection model for underwater images, which employs ensemble learning to quantify the extent of color deviation. Subsequently, we devise the peak flattening algorithm to achieve histogram adaptive partition conversion. The conversion range is determined by the estimated color deviation value, eliminating the need for iteration. For channels with severe color deviation, we employ the weighted fusion to restore partial gray distribution. Comparisons with state-of-the-art methods demonstrate that the proposed method significantly improves contrast and color fidelity, particularly in images with severe degradation. [ABSTRACT FROM AUTHOR]

Тhe article describes the core selection technologies used in Ukraine in deep wells. Technical solutions of “NDIKB drilling tools” for core selection in vertical, deviated and horizontal wells, are presented. The solutions of “NAUKAGEOTECH” LLC to increase the removal of the core and increase the route penetration are highlighted. The company has developed simple in design, convenient in operation and moderate in cost core receiving devices SCAN, which have been used by AT “Ukrgazvydobuvannya” for more than 25 years. The analysis of the operation of SCAN devices showed that with their help, 100% of the core was taken before 2011, and after 2011 and up to 2015 inclusive − about 70%. Acording to the results of data processing, the statistical characteristics of the yield of the core output for individual departments of drilling works of the BU “Ukrburgaz” are given, as well as histograms of the yield of a core with a diameter of 80 mm per drilling and the distribution of core yield by quartiles. The reasons for the use of SCAN 168/80 and SCAN 133/67 core receiving devices by Ukrgazvydobuvanny JSC in smaller volumes in 2016−2022 are named. This is related, in particular, to the company’s purchase of foreign core receiving devices and the involvement of service organizations in the provision of core selection services. In the conditions of using top-drive drilling rigs in combination with screw motors and PDC bits in order to minimize core erosion when passing weakly cemented rocks, the need to conduct research on the carrying capacity of drilling fluids, rheological properties and fluid flow regime becomes an urgent task.

Digital histogram generation for time-resolved measurements with single-photon avalanche diode (SPAD) sensors requires the storage of many timestamp signals. This work presents a mixed-signal time-to-digital converter (TDC) that uses analog storage to achieve an area-efficient design that can be integrated in large SPAD arrays. Fabricated using a 150 nm CMOS process, the prototype occupies an area of only 18.3 µm × 36.5 µm, a notable size reduction compared to conventional designs. The experimental results demonstrated high performance, with an integral nonlinearity (INL) of 0.35/0.14 least significant bit (LSB) and a differential nonlinearity (DNL) of 0.14/−0.12 LSB. In addition, the proposed TDC can support the construction of histograms comprising up to 512 bins, making it an effective solution to accommodate a wide range of resolution requirements. Validated in a point-of-care (PoC) device for fluorescence lifetime measurements, it distinguished between lifetimes of approximately 4.1 ns, 3.6 ns and 80 ns with the Alexa Fluor (AF) 546 and 568 dyes and Quantum Dot (QD) 705, respectively. The analog storage design and area-efficient architecture offer a novel approach to integrating TDCs in SPAD-based systems, with potential applications in medical diagnostics and beyond. [ABSTRACT FROM AUTHOR]

Objective: This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses. Methods: Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects. Results: In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern. Conclusions: Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment. [ABSTRACT FROM AUTHOR]

Objectives. This retrospective study was designed to evaluate the value of histogram analysis on the apparent diffusion coefficient (ADC) map in distinguishing between low-grade and high-grade brainstem glioma (BSG). In this article, we used the two VOI (volume of interest) placements of the entire tumour method and the entire solid part method, thus aiming to compare the diagnostic value between these two performances. Methods. A total of 28 patients (8 low-grade BSGs and 20 highgrade BSGs) with histological diagnosis of BSG. All victims underwent contrast-enhanced magnetic resonance imaging (MRI). We measured ADC histogram parameters (mean, median, SD, max, min, Kurtosis, Skewness, Entropy, Uniformity, and Variance) and calculated the ratios between tumour and normal brain parenchyma parameters in two methods. Independent samples test, Mann-Whitney U test, and ROC curve were used to determine each value's cut-off point, sensitivity, and specificity. Results. Among the method of VOI placing the entire tumour, the values of ADC_;min, rADC_;mean, rADC_;median, and rADC_;min are significantly different between these two neoplasms with cut-off values (sensitivity %, specificity %) of 776 x10-6 m2/s (62.5%, 90%), 2.1765 (62.5%, 95%), 2.1588 (50%, 100%), 1.0535 (100%, 50%), respectively. On the other hand, the method of VOI placing the entire solid part of the tumour showed significantly different in ADC_;mean, ADC_;median, ADC_;min, rADC_;mean, rADC_;median, rADC_;min at the cut-off values (sensitivity%, specificity %) of 1491 x10-6 m2/s (62.5%, 95%), 1438.9 x10-6 m²/s (62.5%, 90%), 862.5 x10-6 m²/s (75%, 100%), 2,112 (62.5%, 95%), 1.9748 (62.5%, 90%), 1.3735 (87.5%, 90%), respectively. Conclusions. The ADC histogram analysis is a promising approach to distinguishing low-grade BSG and high-grade BSG. The entire solid part VOI placement has a superior value compared to the whole tumour VOI placement. The rADC_;mean showed the best performance in differentiating between these two entities, followed by ADC_;min, rADC_;mean, rADC_;median, ADC_;mean, and ADC_;median. Clin Ter 2024; 175 (5):296-306 doi: 10.7417/CT.2024.5134 [ABSTRACT FROM AUTHOR]

The proliferation of the Internet's infrastructure, extending even to rural areas, has propelled images to the forefront of multimedia content, necessitating the need for robust methods to safeguard confidential data. Image steganography has emerged as a pivotal technique for concealing sensitive information within images, amidst growing threats to copyright protection and content integrity. To address these challenges, this research proposes a novel approach that combines the dynamic 8-bit XOR algorithm with the AES crypto algorithm, aiming to fortify the outcomes of image steganography. The method entails encrypting messages using AES encryption, followed by embedding them within images using the dynamic XOR method, ensuring both security and imperceptibility. The study's objectives include assessing the impact of secret message size and stego image dimensions on algorithm efficiency, evaluating through metrics like PSNR, MSE, NPCR, and entropy. Notably, histogram analysis reveals minimal differences between original and stego images, underscoring the difficulty in detecting hidden information. Moreover, the study delves into the influence of secret message size on algorithm performance, highlighting a trade-off between text size and PSNR, where smaller sizes exhibit stronger concealment capabilities. Additionally, the impact of stego image dimension size is examined, revealing varied performance metrics based on different dimensions. Comparisons with existing encryption methods indicate favourable outcomes for the proposed algorithm in terms of PSNR, NPCR, and entropy. Notably, PSNR values range from 74.17 to 80.17 dB, while NPCR values vary from 87.98 to 99.81%. Moreover, MSE values range from 0.000621 to 0.04, and entropy values range from 7.39 to 7.64 dB. The study contributes valuable insights into image steganography, emphasizing the need for robust techniques to safeguard sensitive data in multimedia content. Through comprehensive evaluation and comparison, the proposed method demonstrates promising potential for real-world applications, addressing critical concerns surrounding content protection and data security in the digital age. [ABSTRACT FROM AUTHOR]

Objective Oxygen saturation profiles generated by pulse oximetry are used as a clinical tool in the neonatal intensive care unit (NICU). There is limited evidence on normal oxygen saturation profile values in term infants. This study aimed to determine oxygen saturation profiles over an 8-hour monitoring period among healthy term neonates between 24 and 48 hours after birth. Study Design A prospective cohort study of healthy term neonates born at 37 to 41 weeks of gestation. Preductal oxygen saturations were continuously monitored for an 8-hour period between 24 and 48 hours of life using pulse oximetry. Oxygen profile histograms were recorded for analysis. The average percent oxygen saturation (SpO 2) was measured over the entire study duration for each neonate and was characterized as the fraction of time of their SpO 2 reading was in each of five intervals: ≤80, 81 to 84, 85 to 89, 90 to 94, and 95 to 100%. Results Seventy-five neonates were included in the study. Median SpO 2 was 95.4%. Percentage time spent in each of the five SpO 2 intervals was as follows: 0.07 (≤80), 0.15 (81–84), 0.88 (85–89), 26.9 (90–94), and 67.3% (95–100%). Eighteen infants (24%) spent the highest percentage of time in SpO 2 of 90 to 94%. Conclusion This study provides reference ranges for oxygen profiles in healthy term neonates during 24 to 48 hours of life. Nearly one-quarter of newborns spent the highest percentage of time in SpO 2 of 90 to 94%. This data is important when interpreting oxygen saturation profiles of term neonates admitted to the NICU. Key Points This study provide reference ranges for oxygen profiles in healthy term neonates during 24 to 48 hours. Median SpO 2 was 95.4%. Nearly one quarter of newborns spent the highest percentage of time in SpO 2 of 90 to 94%. [ABSTRACT FROM AUTHOR]

This paper presents the results of the research carried out by the authors in the process of forming the report "Equivalence of random data distribution densities", delivered at the International Scientific and Practical Conference "Information Technologies and Computer Modeling (ITСM-24)", held in Ivano-Frankivsk in the period from May 21 to 24, 2024, in which the method for the synthesis of a statistical estimate of the equivalent density of a random data distribution is proposed, which does not require "histogram alignment" using the Pearson χ² -distribution. At the first stage, the method proposed by the authors uses the crunching of a histogram constructed using a relatively small sample of random data, followed by cumulative summation to determine the equivalent distribution function of these data on a set of their available values. At the second stage of the method proposed by the authors, the constructed equivalent distribution function of random data is interpolated by cubic splines according to the algorithm that uses the mathematical relations derived by the authors. And at the third stage of the method proposed by the authors, by differentiating the equivalent mathematical model of the distribution of random data, obtained at the previous stages of the implementation of this method, an equivalent mathematical model of the density of the distribution of random data is synthesized, using which the primary base histogram was created. The paper contains or mentions all the Python programs necessary for the implementation of all stages of the proposed method of synthesis of the equivalent density model of the random data distribution, as well as the example of solving a specific practical problem with explanations of all actions necessary to perform at each of the stages implementation of the proposed method [ABSTRACT FROM AUTHOR]

The National Institutional Ranking Framework (NIRF), evaluates universities and institutions based on key parameters such as "Teaching, Learning, and Resources (TLR)," "Research and Professional Practices (RPP)," "Graduation Outcomes (GO)," "Outreach and Inclusivity (OI)," and "Perception (PR)." The study examines the top 100 Engineering institutes of NIRF 2023, focusing on their major parameter categories and their effectiveness in ranking processes. The study will be useful to the engineering institutes to clearly understand the areas of improvement and to have an action plan for better rankings. Descriptive statistics reveal that among five major categories RPP & PR and in the sub-parameters Financial Resources and their Utilisation (FRU), Footprint of Projects and Professional Practice (FPPP), Metric for Number of Ph. D. Students Graduated (GPHD), and Economically and Socially Challenged Students (ESCS) have the lowest effectiveness among others, indicating their minimal contribution in the total score. Consequently, Institutes must focus on enhancing these parameters to enhance their total score and position in the NIRF rankings. The analysis of histograms and descriptive statistics reveals that 75% of ranked institutions score below 60% in RPP and PR parameters, indicating suboptimal performance in perception and research aspects. The study also highlights the importance of prioritizing efforts to improve RPP and PR scores to enhance the overall performance and rankings of engineering institutions in NIRF, as TLR, GO, and OI show relatively consistent performance. [ABSTRACT FROM AUTHOR]

This paper introduces the Color Histogram Contouring (CHC) method, a new training-less approach to object detection that emphasizes the distinctive features in chrominance components. By building a chrominance-rich feature vector with a bin size of 1, the proposed CHC method exploits the precise information in chrominance features without increasing bin sizes, which can lead to false detections. This feature vector demonstrates invariance to lighting changes and is designed to mimic the opponent color axes used by the human visual system. The proposed CHC algorithm iterates over non-zero histogram bins of unique color features in the model, creating a feature vector for each, and emphasizes those matching in both the scene and model histograms. When both model and scene histograms for these unique features align, it ensures the presence of the model in the scene image. Extensive experiments across various scenarios show that the proposed CHC technique outperforms the benchmark training-less Swain and Ballard method and the algorithm of Viola and Jones. Additionally, a comparative experiment with the state-of-the-art You Only Look Once (YOLO) technique reveals that the proposed CHC technique surpasses YOLO in scenarios with limited training data, highlighting a significant advancement in training-less object detection. This approach offers a valuable addition to computer vision, providing an effective training-less solution for real-time autonomous robot localization and mapping in unknown environments. [ABSTRACT FROM AUTHOR]

Processing underwater digital images is critical in ocean engineering, biology, and environmental studies, focusing on challenges such as poor lighting, image de-scattering, and color restoration. Due to environmental conditions on the sea floor, improving image contrast and clarity is essential for underwater navigation and obstacle avoidance. Particularly in turbid, low-visibility waters, we require robust computer vision techniques and algorithms. Over the past decade, various models for underwater image enrichment have been proposed to address quality and visibility issues under dynamic and natural lighting conditions. This research article aims to evaluate various image improvement methods and propose a robust model that improves image quality, addresses turbidity, and enhances color, ultimately improving obstacle avoidance in autonomous systems. The proposed model demonstrates high accuracy compared to traditional models. The result analysis indicates the proposed model produces images with greatly improved visibility and exceptional color accuracy. Furthermore, research can unlock new possibilities for underwater exploration, monitoring, and intervention by advancing the state-of-the-art models in this domain.

The results of determining the stability and variability of the main characteristic of glass capillary reference viscometers — conversion coefficient — by analyzing a set of data on viscometers having different diameters of the measuring capillary are presented. It is shown that the mean values of the sample differ from the mean values of the symmetric normal distribution curves within (2–4) %, as well as the correspondence of statistics to the normal distribution law by means of the inverse standard function and histograms showing the degree of correspondence of the sample distribution to the theoretical one.

We evaluate the role of apparent diffusion coefficient (ADC) histogram metrics in stratifying pediatric and young adult rhabdomyosarcomas. We retrospectively evaluated baseline diffusion-weighted imaging (DWI) from 38 patients with rhabdomyosarcomas (Not otherwise specified: 2; Embryonal: 21; Spindle Cell: 2; Alveolar: 13, mean ± std dev age: 8.1 ± 7.76 years). The diffusion images were obtained on a wide range of 1.5 T and 3 T scanners at multiple sites. FOXO1 fusion status was available for 35 patients, nine of whom harbored the fusion. 13 patients were TNM stage 1, eight had stage 2 disease, nine were stage 3, and eight had stage 4 disease. 23 patients belonged to Clinical Group III and seven to Group IV, while two and five were CG I and II, respectively. Nine patients were classified as low risk, while 21 and five were classified as intermediate and high risk respectively. Histogram parameters of the apparent diffusion coefficient (ADC) map from the entire tumor were obtained based on manual tumor contouring. A two-tailed Mann–Whitney U test was used for all two-group, and the Kruskal–Wallis's test was used for multiple-group comparisons. Bootstrapped receiver operating characteristic (ROC) curves and areas under the curve (AUC) were generated for the statistically significant histogram parameters to differentiate genotypic and phenotypic parameters. Alveolar rhabdomyosarcomas had a statistically significant lower 10th Percentile (586.54 ± 164.52, mean ± std dev, values are in × 10–6 mm2/s) than embryonal rhabdomyosarcomas (966.51 ± 481.33) with an AUC of 0.85 (95%CI. 0.73–0.95) for differentiating the two. The 10th percentile was also significantly different between FOXO1 fusion-positive (553.87 ± 187.64) and negative (898.07 ± 449.38) rhabdomyosarcomas with an AUC of 0.83 (95% CI 0.71–0.94). Alveolar rhabdomyosarcomas also had statistically significant lower Mean, Median, and Root Mean Squared ADC histogram values than embryonal rhabdomyosarcomas. Four, five, and seven of the 18 histogram parameters evaluated demonstrated a statistically significant increase with higher TNM stage, clinical group, assignment, and pretreatment risk stratification, respectively. For example, Entropy had an AUC of 0.8 (95% CI. 0.67–0.92) for differentiating TNM stage 1 from ≥ stage 2 and 0.9 (95% CI. 0.8–0.98) for differentiating low from intermediate or high-risk stratification. Our findings demonstrate the potential of ADC histogram metrics to predict clinically relevant variables for rhabdomyosarcoma, including FOXO1 fusion status, histopathology, Clinical Group, TNM staging, and risk stratification. [ABSTRACT FROM AUTHOR]

Knowledge of the liquid–gas flow regime is important for the proper control of many industrial processes (e.g., in the mining, nuclear, petrochemical, and environmental industries). The latest publications in this field concern the use of computational intelligence methods for flow structure recognition, which include, for example, expert systems and artificial neural networks. Generally, machine learning methods exploit various characteristics of sensors signals in the value, time, frequency, and time–frequency domain. In this work, the convolutional neural network (CNN) VGG-16 is applied for analysis of histogram images of signals obtained for water–air flow by using gamma-ray absorption. The experiments were carried out on the laboratory hydraulic installation fitted with a radiometric measurement system. The essential part of the hydraulic installation is a horizontal pipeline made of metalplex, 4.5 m long, with an internal diameter of 30 mm. The radiometric measurement set used in the investigation consists of a linear Am-241 radiation source with an energy of 59.5 keV and a scintillation detector with a NaI(Tl) crystal. In this work, four types of water–air flow regimes (plug, slug, bubble, and transitional plug–bubble) were studied. MATLAB 2022a software was used to analyze the measurement signal obtained from the detector. It was found that the CNN network correctly recognizes the flow regime in more than 90% of the cases. [ABSTRACT FROM AUTHOR]

The article describes the core selection technologies used in Ukraine in deep wells. Technical solutions of "NDIKB drilling tools" for core selection in vertical, deviated and horizontal wells, are presented. The solutions of "NAUKAGEOTECH" LLC to increase the removal of the core and increase the route penetration are highlighted. The company has developed simple in design, convenient in operation and moderate in cost core receiving devices SCAN, which have been used by AT "Ukrgazvydobuvannya" for more than 25 years. The analysis of the operation of SCAN devices showed that with their help, 100% of the core was taken before 2011, and after 2011 and up to 2015 inclusive -- about 70%. Acording to the results of data processing, the statistical characteristics of the yield of the core output for individual departments of drilling works of the BU "Ukrburgaz" are given, as well as histograms of the yield of a core with a diameter of 80 mm per drilling and the distribution of core yield by quartiles. The reasons for the use of SCAN 168/80 and SCAN 133/67 core receiving devices by Ukrgazvydobuvanny JSC in smaller volumes in 2016-2022 are named. This is related, in particular, to the company's purchase of foreign core receiving devices and the involvement of service organizations in the provision of core selection services. In the conditions of using top-drive drilling rigs in combination with screw motors and PDC bits in order to minimize core erosion when passing weakly cemented rocks, the need to conduct research on the carrying capacity of drilling fluids, rheological properties and fluid flow regime becomes an urgent task. [ABSTRACT FROM AUTHOR]

In image analysis and processing, image segmentation is one of the most important functions. The outcomes of segmentation have such significance on all subsequent image analysis operations, covering object tracking and description, feature measurement, and even higher-level tasks like object recognition. Malicious code detection is becoming increasingly significant, and current models must be improved. Hence forth the Image segmentation in the field of Malware image classification is a significant task. The sectional structure or region of interest must be identified and extracted during the segmentation process so that it can be evaluated independently. There are various reviews stating the traditional approach of image segmentation in various fields. The necessity of image segmentation in malicious image is extracting data for classification using CNN is discussed. In this work we use malimg_paper_dataset_imgs with 9,339 malware images. Various segmentation techniques were used to enhance the malware images. Those enhanced malicious image were applied in CNN architecture and Mal_CNN for classification and a comparative result is been discussed. The malicious images in dataset after incorporating segmentation have achieved 95% of accuracy in CNN architecture and 97% with Mal_CNN. [ABSTRACT FROM AUTHOR]

A video enhancement method based on applying gamma correction is proposed. Its realization implies processing all video frames using a single algorithm. The proposed algorithm, due to the preservation of inter-frame coherence throughout the entire video, significantly reduces the time spent searching for the optimal value of the gamma parameter, which ensures the highest quality of the frame reaching the maximum possible level in the automatic mode in the sense of both visual observation and the determination of key points and the selection of contours of objects in images. The method is characterized by high adaptability to sudden changes in the lighting of the scene, preservation of interframe coherence, and the absence of any side negative artifacts in the enhanced video. A toolkit for automatically determining the optimal value of the gamma parameter for video frames is developed. It significantly increases the efficiency of video analytics systems, image and video segmentation, and processing due to reducing the negative impact of the scene lighting mode on image quality. [ABSTRACT FROM AUTHOR]

Objectives. We assessed the value of histogram analysis (HA) of apparent diffusion coefficient (ADC) maps for grading low-grade (LGG) and high-grade (HGG) gliomas. Methods. We compared the diagnostic performance of two region-of-interest (ROI) placement methods (ROI 1: the entire tumor; ROI 2: the tumor excluding cystic and necrotic portions). We retrospectively evaluated 54 patients with supratentorial gliomas (18 LGG and 36 HGG). All subjects underwent standard 3T contrast-enhanced magnetic resonance imaging. Histogram parameters of ADC maps calculated with the two segmentation methods comprised mean, median, maximum, minimum, kurtosis, skewness, entropy, standard deviation (sd), mean of positive pixels (mpp), uniformity of positive pixels, and their ratios (r) between lesion and normal white matter. They were compared using the independent t-test, chi-square test, or Mann-Whitney U test. For statistically significant results, receiver operating characteristic curves were constructed, and the optimal cutoff value, sensitivity, and specificity were determined by maximizing Youden’s index. Results. The ROI 1 method resulted in significantly higher rADC mean, rADC median, and rADC mpp for LGG than for HGG; these parameters had value for predicting the histological glioma grade with a cutoff (sensitivity, specificity) of 1.88 (77.8%, 61.1%), 2.25 (44.4%, 97.2%), and 1.88 (77.8%, 63.9%), respectively. The ROI 2 method resulted in significantly higher ADC mean, ADC median, ADC mpp, ADC sd, ADC max, rADC median, rADC mpp, rADC mean, rADC sd, and rADC max for LGG than for HGG, while skewness was lower for LGG than for HGG (0.27 [0.98] vs 0.91 [0.81], p = 0.014). In ROI 2, ADC median, ADC mpp, ADC mean, rADC median, rADC mpp, and rADC mean performed well in differentiating glioma grade with cutoffs (sensitivity, specificity) of 1.28 (77.8%, 88.9%), 1.28 (77.8%, 88.9%), 1.25 (77.8%, 91.7%), 1.81 (83.3%, 91.7%), 1.74 (83.3%, 91.7%), and 1.81 (83.3%, 91.7%), respectively. Conclusions. HA parameters had value for grading gliomas. Excluding cystic and necrotic portions of the tumor for measuring HA parameters was preferable to using the entire tumor as the ROI. In this segmentation, rADC median showed the highest performance in predicting histological glioma grade, followed by rADC mpp, rADC mean, ADC median, ADC mpp, and ADC mean. [ABSTRACT FROM AUTHOR]

. A video enhancement method based on applying gamma correction is proposed. Its realization implies processing all video frames using a single algorithm. The proposed algorithm, due to the preservation of inter-frame coherence throughout the entire video, significantly reduces the time spent searching for the optimal value of the gamma parameter, which ensures the highest quality of the frame reaching the maximum possible level in the automatic mode in the sense of both visual observation and the determination of key points and the selection of contours of objects in images. The method is characterized by high adaptability to sudden changes in the lighting of the scene, preservation of interframe coherence, and the absence of any side negative artifacts in the enhanced video. A toolkit for automatically determining the optimal value of the gamma parameter for video frames is developed. It significantly increases the efficiency of video analytics systems, image and video segmentation and processing processes due to reducing the negative impact of the scene lighting mode on image quality. [ABSTRACT FROM AUTHOR]

CubeSats are designed to optimize applications within the strict constraints of space and power. This paper presents an On-Board Image Enhancement technique for remote sensing payloads, focusing on achieving Auto White Balance (AWB) with limited resources and enhancing the capabilities of small/microsatellites. The study introduces hardware-based techniques, including histogram adjustment, De-Bayer processing, and AWB, all tailored to minimize hardware resource consumption on CubeSats. The integrated 1U CubeSat system comprises a sensor board, an Image Data Processor (IDP) unit, and onboard computing, with a total power consumption estimated at 2.2 W. This system facilitates image capture at a resolution of 1920 × 1200 and utilizes the proposed algorithm for image enhancement on remote sensing payloads to improve the quality of images captured in low-light environments, thereby demonstrating significant advancements in satellite image processing and object-detection capabilities. [ABSTRACT FROM AUTHOR]

The medical CT sequence images dopes noise for various reasons. Denoising can effectively improve image quality. The common algorithms are used for single image, while the CT sequence images have high similarity between adjacent images. Therefore, this paper proposes a hybrid denoising algorithm based on the structural similarity. Firstly, a histogram is drawn according to the maximum and minimum gray value. Secondly, relevant threshold parameters are set to calculate the window width and window level, and then conduct window adjustment. Thirdly, the structural similarity of the target image and its adjacent images are calculated. Finally, BM3D and Gaussian filtering algorithms are mixed for three images according to structural similarity. Experimental results show that the algorithm can improve the mean square error, peak signal‑to‑noise ratio and structural similarity, which effectively improves the image quality. [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND. Presently actively new direction develops in digital treatment of CТ images – radiomics, that presents the result of co-operation on verge of different sciences (radiology, computer sciences and mathematical statistics). Inaccessible for the unarmed eye additional information of CT images can be got by means of their mathematical treatment and creation of the segmented histograms. Last it is possible to compare and analyse both isolated and with regard to the dynamics of physiopathology descriptions of organs and fabrics at the different human diseases. OBJECTIVE. To define the roentgenologic and morphological features of development of vanishing lung syndrome for patients with non-hospital viral pneumonia. MATERIALS AND METHODS. Data of CТ are analysed in a dynamics for patients with non-hospital viral pneumonia of COVID-19, that were on treatment in SI “National institute of phthisiology and pulmonology named after F.G. Yanovsky of the NAMS of Ukraine” or were directed from other medical establishments. The Dragonfly program from Object Research Systems (Montreal, Canada), which performs micro-X-ray structural analysis of the examined tissues, was used to analyze CT images of chest. Pathomorphological examination was performed in the laboratory of pathomorphology of the institute. RESULTS. Monitoring of CT is conducted in the group, that consisted of 90 patients with non-hospital viral pneumonia of COVID-19. 27 (30,0 %) patients (18 men and 9 women in age from 23 to 68) are educed with the roentgenologic signs of vanishing lung syndrome. 12 from them (9 men and 3 women in age from 23 to 56) were on treatment in the institute in an acute period of disease. Other 15 patients (9 men and 6 women in age from 26 to 68) directed from other curative establishments, where they treated oneself 3-4 months ago. CONCLUSIONS. Micro-X-ray structural analysis of data of CT allows to educe the features of changes of parenchima at development of vanishing lung syndrome. These changes are confirmed by the educed changes at pathomorphological research of postoperative preparations of lungs.

Abstract To explore the relationship between quantitative perfusion histogram parameters of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with the expression of tumor tissue epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF) and EGFR gene mutations in non-small cell lung cancer (NSCLC). A total of 44 consecutive patients with known NSCLC were recruited from March 2018 to August 2021. Histogram parameters (mean, uniformity, skewness, energy, kurtosis, entropy, percentile) of each (Ktrans, Kep, Ve, Vp, Fp) were obtained by Omni Kinetics software. Immunohistochemistry staining was used in the detection of the expression of VEGF and EGFR protein, and the mutation of EGFR gene was detected by PCR. Corresponding statistical test was performed to compare the parameters and protein expression between squamous cell carcinoma (SCC) and adenocarcinoma (AC), as well as EGFR mutations and wild-type. Correlation analysis was used to evaluate the correlation between parameters with the expression of VEGF and EGFR protein. Fp (skewness, kurtosis, energy) were statistically significant between SCC and AC, and the area under the ROC curve were 0.733, 0.700 and 0.675, respectively. The expression of VEGF in AC was higher than in SCC. Fp (skewness, kurtosis, energy) were negatively correlated with VEGF (r = − 0.527, − 0.428, − 0.342); Ktrans (Q50) was positively correlated with VEGF (r = 0.32); Kep (energy), Ktrans (skewness, kurtosis) were positively correlated with EGFR (r = 0.622, r = 0.375, 0.358), some histogram parameters of Kep, Ktrans (uniformity, entropy) and Ve (kurtosis) were negatively correlated with EGFR (r = − 0.312 to − 0.644). Some perfusion histogram parameters were statistically significant between EGFR mutations and wild-type, they were higher in wild-type than mutated (P

We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarser-grained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss. Although information loss with each step is well understood, little guidance exists on the overall information loss when conducting both steps sequentially. We use Monte Carlo simulations to regress information loss on the number of bins and PCs, given few parameters of a dataset related to its scale and correlation structure. A sensitivity study shows that information loss can be approximated well given sufficiently large datasets. Using the number of bins, PCs, and two correlation measures, we derive an empirical loss model with high accuracy. Furthermore, we demonstrate the benefits of estimating information losses and the representativeness of total loss in evaluating the accuracy of k-means clustering for a real-world customer fleet dataset. For preprocessing sensor data which are aggregated from sufficient number of samples, continuously distributed, and can be represented by Beta-distributions, we recommend not to coarsen the histogram binning before PCA.

When a wide range of luminance levels exist in a scene, an image captured in the scene contains very dark and bright areas with low visibility. Various methods for improving the visibility of such images have been proposed. However, the performance of these methods is not often satisfactory owing to over-enhancement such as noise amplification. Furthermore, some methods have high computational costs. In this paper, we propose an image enhancement method for improving the visibility of images captured in wide-dynamic-range scenes. The proposed method generates a tone curve for brightness and contrast enhancements. To suppress over-enhancement, the tone curve is generated by using the gradient-norm-based weighted histogram. Applying a tone curve in a pixel-wise manner may decrease the local contrast in some regions as any tone curve has a small slope in some areas. To prevent contrast reduction, the local contrast correction for controlling the effect of the tone curve in a region-adaptive manner is introduced as the post-processing of tone curve. By applying the tone curve and local contrast correction, the proposed method can enhance the visibility of an image sufficiently while suppressing over-enhancement. In addition, each processing of the proposed method is simple and suitable for fast implementation.

3D pattern film is a film that makes a 2D pattern appear 3D depending on the amount and angle of light. However, since the 3D pattern film image was developed recently, there is no established method for classifying and verifying defective products, and there is little research in this area, making it a necessary field of study. Additionally, 3D pattern film has blurred contours, making it difficult to detect the outlines and challenging to classify. Recently, many machine learning methods have been published for analyzing product quality. However, when there is a small amount of data and most images are similar, using deep learning can easily lead to overfitting. To overcome these limitations, this study proposes a method that uses an MLP (Multilayer Perceptron) model to classify 3D pattern films into genuine and defective products. This approach entails inputting the widths derived from specific points' heights in the image histogram of the 3D pattern film into the MLP, and then classifying the product as 'good' or 'bad' using optimal hyper-parameters found through the random search method. Although the contours of the 3D pattern film are blurred, this study can detect the characteristics of 'good' and 'bad' by using the image histogram. Moreover, the proposed method has the advantage of reducing the likelihood of overfitting and achieving high accuracy, as it reflects the characteristics of a limited number of similar images and builds a simple model. In the experiment, the accuracy of the proposed method was 98.809%, demonstrating superior performance compared to other models. [ABSTRACT FROM AUTHOR]

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3-30 mm. [ABSTRACT FROM AUTHOR]

This article discusses an alternative method for estimating marginal probability densities of the solution to stochastic differential equations (SDEs). Two algorithms for calculating the numerical–statistical projection estimate for distributions of solutions to SDEs using Legendre polynomials are proposed. The root-mean-square error of this estimate is studied as a function of the projection expansion length, while the step of a numerical method for solving SDE and the sample size for expansion coefficients are fixed. The proposed technique is successfully verified on three one-dimensional SDEs that have stationary solutions with given one-dimensional distributions and exponential correlation functions. A comparative analysis of the proposed method for calculating the numerical–statistical projection estimate and the method for constructing the histogram is carried out. [ABSTRACT FROM AUTHOR]

This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information entropy, differential attacks, noise evaluation, adjacent pixels' correlation index, National Institute of Standards and Technology (NIST) analyses, mean absolute error, and average difference. The experimental results demonstrate that the encryption and decryption time when using the proposed CAST 128-bit algorithm with magic square is less than the time required for the CAST 128-bit algorithm with the matrix. Conversely, the proposed CAST with the matrix is higher than the CAST with the magic square. Both theoretical analysis and experimental results confirm that the two proposed enhancements to CAST perform effectively with sufficient security levels. [ABSTRACT FROM AUTHOR]

Teaching veterinary anatomy using digital platforms requires improved image quality, which may influence the fixation process. This study aimed to compare four embalming solutions for high‐colour‐quality images of different tissues compared to the original image. Four equine left pelvic limbs were cut into metameres and divided equally for application of 10% formaldehyde, 96% glycerine, 33% hypersaturated NaCl solution and modified Larssen solution, respectively, which was maintained for 3 days. After drying for 3 days at room temperature, photographs were obtained at time 0 (T0), without any fixation process (original colour); time 1 (T1), immediately after removal from the solutions; and every 24 h for 3 days (T2–T4). The image colour quality was investigated by digitally evaluating the cortical bone, tendon and bone marrow using histograms and CIEDE2000 as well as by 10 specialists in an online survey. CIEDE2000 and histograms revealed that all fixation solutions changed the original tissue colour at all the time points (p < 0.0001). According to the specialists, the 33% saline solution produced the best results compared to the original one. The modified Larssen solution demonstrated better results for the tendon, marrow and cortical bone at T3 (p = 0.0015). Considering the colour of digital images, the modified Larssen solution provided the best results; however, the visual evaluation by the specialists revealed the 33% saline solution as the best. [ABSTRACT FROM AUTHOR]