Grayscale Research Articles

Robust secret color image sharing anti-cropping and tampering in shares

Secret image sharing (SIS) has excellent properties such as loss tolerance and relatively low computational complexity, providing a brand-new solution for image security protection. However, there has been little research on the robustness of SIS systems, such as how to resist cropping or malicious tampering in shares. Existing related schemes generally focus on grayscale images and suffer from issues such as lossy recovery, weak robustness, and serious pixel expansion, which are challenging to meet the requirements of high-quality sensitive image applications. In this regard, a robust SIS scheme for color images is proposed, which can resist large-scale cropping and malicious tampering in shares. According to the idea of “breaking up the whole cropped area into parts, repairing the shares independently”, the proposed scheme can realize lossless recovery of secret images through organic fusion of secret sharing, error-correcting code, and pixel re-arrangement techniques. Even if all the shares are cropped by 25%, it can still achieve lossless recovery regardless of whether the cropping positions intersect or overlap. It can also resist various malicious tampering (such as marking, defacing, and copy-move forgery) as well as image noise. Moreover, it avoids pixel expansion and requires no auxiliary encryption or preprocessing. Theoretical analysis and experimental results demonstrate that the proposed scheme is superior to existing schemes in terms of robustness and comprehensive performance, and is expected to promote the practical application of SIS.

HDCCT: Hybrid Densely Connected CNN and Transformer for Infrared and Visible Image Fusion

Multi-modal image fusion is a methodology that combines image features from multiple types of sensors, effectively improving the quality and content of fused images. However, most existing deep learning fusion methods need to integrate global or local features, restricting the representation of feature information. To address this issue, a hybrid densely connected CNN and transformer (HDCCT) fusion framework is proposed. In the proposed HDCCT framework, the network of the CNN-based blocks obtain the local structure of the input data, and the transformer-based blocks obtain the global structure of the original data, significantly improving the feature representation. In the fused image, the proposed encoder–decoder architecture is designed for both the CNN and transformer blocks to reduce feature loss while preserving the characterization of all-level features. In addition, the cross-coupled framework facilitates the flow of feature structures, retains the uniqueness of information, and makes the transform model long-range dependencies based on the local features already extracted by the CNN. Meanwhile, to retain the information in the source images, the hybrid structural similarity (SSIM) and mean square error (MSE) loss functions are introduced. The qualitative and quantitative comparisons of grayscale images with infrared and visible image fusion indicate that the suggested method outperforms related works.

A Comprehensive Study on Electrospun Cholesteric Liquid Crystal Core Fibers for Detecting Volatile Organic Compounds

Encapsulation of the cholesteric liquid crystal (CLC) into the core of poly(vinylpyrrolidone) (PVP) microfibers is accomplished via coaxial electrospinning technology and is applied to detect volatile organic compounds (VOCs). The impacts of various process parameters on the morphology of CLC fibers are deeply explored. The periodic arrangement of CLC molecules within the fibers enables the reflection of specific wavelengths. Consequently, changes in the reflected color of the CLC fiber membrane allow for the measurement and analysis of targeted gas molecules. The dynamic response of CLC fibers upon exposure to organic vapor is observed by a polarizing optical microscope, and its gray scale is used to quantitatively analyze the changes of reflected light signals of CLC fibers. Furthermore, the study investigates the reaction time for the failure of CLC molecular planar alignment in the CLC fiber membrane when subjected to perturbations induced by organic gases. The CLC fiber sensor exhibits heightened sensitivity to gases characterized by greater polarity. In light of potential future advancements, it is suggested that the spun fibers imbued with responsive properties through the incorporation of a liquid crystal core exhibit promise as a next-generation sensor technology in textile form, well-suited for applications in wearable technology.

Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

Plant Monitoring System Using Gray Level Co-occurrence Matrix and Weight Based Artificial Neural Network Algorithm over Internet of Things

The potential deployment of the plant monitoring system in cutting-edge technologies is currently generating a great deal of interest. A new IoT-based technology is used to track both the development and wellness of the plant. Setting up cross-device connectivity over the internet is the idea underlying IoT devices. It is a sizable network that links people and various interconnected elements in order to gather and transmit data. This research article aims to generate a manufacturing Internet of Things (IoT) based plant monitoring system that uses IoT sensors to detect environmental conditions. The IoT term is used to link objects to the internet and make it easier for consumers to obtain data. The technology can accurately perceive the surroundings in agriculture and convey the data to users. The system keeps track of several factors like soil moisture, temperature, and light intensity. The method begins with the collection of plant images and noise reduction pre-processing. Once the images have been segmented, they are done so utilizing the Region based K-Means Clustering (RKMC) technique. Following that, Gray Level Co-occurrence Matrix (GLCM) is utilized to extract features, with a focus on extracting the more informative characteristics like color, texture, and shape features. The classifying process is completed by utilizing Weight based Artificial Neural Network (WANN) algorithm for improving the plant monitoring system performance significantly. It offers IoT-based solutions to categorize plant illnesses and observe variables including soil moisture, air temperature, and pH values. Te findings of the suggested GLCM-WANN algorithm show better performers than existing methods for obtained values of computation complexity, accuracy, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

A Fault Diagnosis Method for Electric Check Valve Based on ResNet-ELM with Adaptive Focal Loss

Under the trend of carbon neutrality, the adoption of electric mineral transportation equipment is steadily increasing. Accurate monitoring of the operational status of electric check valves in diaphragm pumps is crucial for ensuring transportation safety. However, accurately identifying the operational characteristics of electric check valves under complex excitation and noisy environments remains challenging. This paper proposes a monitoring method for the status of electric check valves based on the integration of Adaptive Focal Loss (AFL) with residual networks and Extreme Learning Machines (AFL-ResNet-ELMs). Firstly, to address the issue of unclear feature representation in one-dimensional vibration signals, grayscale operations are employed to transform the one-dimensional data into grayscale images with more distinct features. Residual networks are then utilized to extract the state features of the check valve, with Extreme Learning Machines serving as the feature classifier. Secondly, to overcome the issue of imbalanced industrial data distribution, a new Adaptive Focal Loss function is designed. This function focuses the training process on difficult-to-classify data samples, balancing the recognition difficulty across different samples. Finally, experimental studies are conducted using industrially measured vibration data of the electric check valve. The results indicate that the proposed method achieves an average accuracy of 99.60% in identifying four health states of the check valve. This method provides a novel approach for the safety monitoring of slurry pipeline transportation processes.

CT radiomics-based biomarkers can predict response to immunotherapy in hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) remains a leading cause of cancer deaths. Despite the rise of immunotherapies, many HCC patients don't benefit. There's a clear need for biomarkers to guide treatment decisions. This research aims to identify such biomarkers by combining radiological data and machine learning. We analyzed clinical and CT imaging data of 54 HCC patients undergoing immunotherapy. Radiologic features were examined to develop a model predicting short-term immunotherapy effects. We utilized 9 machine learning and 2 ensemble learning techniques using RapidMiner for model construction. We conducted the validation of the above feature combination using 29 HCC patients who received immunotherapy from another hospital, and tested and validated it using XGBoost combined with a genetic algorithm. In 54 HCC patients, radiomics features varied significantly between those with partial response (PR) and stable disease (SD). Key features in Gray Level Run Length Matrix (GLRLM) and in adjacent tissues' Intensity Direct, Neighborhood Gray Tone Difference Matrix (NGTDM), and Shape correlated with short-term immunotherapy efficacy. Selected feature combinations of 15, 19, and 8/15 features yielded better outcomes. Deep learning, random forest, and naive bayes outperformed other methods, with Bagging being more reliable than Adaboost. In the validation set of 29 HCC patients, the mentioned feature combination also demonstrated favorable performance. Furthermore, we achieved improved results when employing XGBoost in conjunction with a genetic algorithm for testing and validation. The machine learning model built with CT image features derived from GLCM, GLRLM, IntensityDirect, NGTDM, and Shape can accurately forecast the short-term efficacy of immunotherapy for HCC.

Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features

Background adaptive PosMarker based on online generation and detection for locating watermarked regions in photographs

Robust watermarking technology can embed invisible messages in screens to trace the source of unauthorized screen photographs. Locating the four vertices of the embedded region in the photograph is necessary, as existing watermarking methods require geometric correction of the embedded region before revealing the message. Existing localization methods suffer from a performance trade-off: either causing unaesthetic visual quality by embedding visible markers or achieving poor localization precision, leading to message extraction failure. To address this issue, we propose a background adaptive position marker, PosMarker, based on the gray level co-occurrence matrix and the noise visibility function. Besides, we propose an online generation scheme that employs a learnable generator to cooperate with the detector, allowing joint optimization between the two. This simultaneously improves both visual quality and detection precision. Extensive experiments demonstrate the superior localization precision of our PosMarker-based method compared to others.

Enhancing Diagnostic Accuracy for Skin Cancer and COVID-19 Detection: A Comparative Study Using a Stacked Ensemble Method

In recent years, COVID-19 and skin cancer have become two prevalent illnesses with severe consequences if untreated. This research represents a significant step toward leveraging machine learning (ML) and ensemble techniques to improve the accuracy and efficiency of medical image diagnosis for critical diseases such as COVID-19 (grayscale images) and skin cancer (RGB images). In this paper, a stacked ensemble learning approach is proposed to enhance the precision and effectiveness of diagnosis of both COVID-19 and skin cancer. The proposed method combines pretrained models of convolutional neural networks (CNNs) including ResNet101, DenseNet121, and VGG16 for feature extraction of grayscale (COVID-19) and RGB (skin cancer) images. The performance of the model is evaluated using both individual CNNs and a combination of feature vectors generated from ResNet101, DenseNet121, and VGG16 architectures. The feature vectors obtained through transfer learning are then fed into base-learner models consisting of five different ML algorithms. In the final step, the predictions from the base-learner models, the ensemble validation dataset, and the feature vectors extracted from neural networks are assembled and applied as input for the meta-learner model to obtain final predictions. The performance metrics of the stacked ensemble model show high accuracy for COVID-19 diagnosis and intermediate accuracy for skin cancer.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.

Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations. This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images. The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%). Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

Characterization of Growth Sectors in Gallium Nitride Substrate Wafers

During crystal growth processes, growth sectors are formed due to growth along different crystallographic directions. Although the crystal structure in the different growth sectors is unchanged, strain induced topography contrast is observed by synchrotron X-ray topography. In this study, synchrotron monochromatic beam X-ray topography (SMBXT), synchrotron X-ray plane wave topography (SXPWT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and secondary ion mass spectrometry (SIMS) are used to characterize growth sectors in gallium nitride (GaN) substrate wafers grown by patterned hydride vapor phase epitaxy (HVPE). The SMBXT images reveal the boundaries of {0001} and {1122} type growth sectors. Strain maps generated from SXPWT shows that the out-of-plane strains in different growth sectors have a difference of the order of 10-5. SEM images from SE2 signal shows no contrast of growth sector boundaries while images from Robinson detector (RBSD) show different growth sectors as different grey scale contrast, indicating a strain effect. SIMS analysis shows that the different oxygen impurity levels in the growth sectors, which is the origin of the strain. A formation mechanism of growth sectors in patterned HVPE grown GaN wafers is proposed.

Efficacy of ultrasound in the evaluation of iliac vein stenting.

Objectives: The current study evaluated the efficacy of iliac and IVC ultrasound alone for follow-up evaluation of iliac vein stents in patients with pelvic venous disorders or iliac occlusion from chronic deep vein thrombosis. Methods: A retrospective single site cohort study was conducted by evaluating the most recent 100 iliac vein and inferior vena cava ultrasounds in patients who had undergone iliac vein stenting. Inclusion criteria included a history of iliac vein stent placement. Exclusion criteria included patients under 18-years-old, duplicates of the same patient, and pregnancy at the time of ultrasound. The degree of visualization for color flow, gray scale, and phasicity were determined and classified into the following categories: complete, partial, or none. In addition, each chart was assessed for external compression, in-stent narrowing, and requirements for further imaging. Results: Of the 100 iliac vein and IVC ultrasounds assessed in this review, 99 of the ultrasounds were sufficient for evaluation during follow-up visits without requiring further investigation. Within this study cohort, the average follow-up time was 22months. The average participant body mass index was 27.6. One iliac vein and IVC ultrasound was considered inadequate for follow-up evaluation and required further imaging. Conclusion: The use of iliac and IVC venous doppler ultrasound alone in the follow-up evaluation of iliac stent patency is effective and noninvasive and avoids unnecessary radiation exposure and cost.

Low-Power Driving Waveform Design for Improving the Display Effect of Electrophoretic Electronic Paper.

To address the high power consumption associated with image refresh operations in EPDs, this paper proposes a low-power driving waveform that reduces the refresh power of EPDs by lowering the system's peak power. Compared to traditional waveforms, this waveform first activates the particles before erasing them, thus reducing voltage polarity changes. Additionally, it introduces a specific duration of 0 V voltage during the activation phase based on the physical characteristics of the electrophoretic particles to reduce the voltage span. Finally, a particular duration of 0 V voltage is introduced during the erasure phase to minimize the voltage span while ensuring the stability and consistency of the reference gray scale. The experimental results demonstrate that, in standard power tests, the new driving waveform reduces the power fluctuation value by 1.33% and the energy fluctuation value by 37.24% compared to the traditional driving waveform. This reduction in refresh power also mitigates screen flicker and ghosting phenomena.

Research on Characteristics Matching of Micro-LED Devices

This paper presents the design of a 40 × 40 micro-light-emitting (micro-LED) test array based on a 20 mm × 20 mm substrate. A study of the relationship between luminous brightness, driving current, and driving voltage revealed that the data voltages of the red, green, and blue micro-LED array from 0 to 255 grey levels are 0.31 V, 0.29 V, and 0.30 V, respectively, under the condition that the target brightness of the white field is 1000 nits and the color temperature is 9300 K. The brightness range of the red micro-LED array is 64.8–101.2%, the brightness range of the green micro-LED array is 66.5–102.8%, and the brightness range of the blue micro-LED array is 53.5–129.2%. In order to overcome the luminance nonuniformity, a grey level depth of 12 -bit is required. A 10T3C pixel driver circuit based on low-temperature polysilicon (LTPS) with a depth of 12 bits greyscale is designed and fabricated into the micro-LED display. A brightness uniformity of 84.1–97.1% can be achieved by brightness correction combined with a 12-bit greyscale depth system for micro-LED display. This provides a valuable reference point for subsequent improvements in the quality of micro-LED displays.

Mixed-integer disciplined convex programming approach applied to the optimal energy supply of near-zero energy buildings

Energy needs in the buildings sector accounts for 40 % of global energy demand. Therefore, the implementation of several renewable energy sources is necessary to reduce this demand. The design stage of a decentralized generation project requires quantifying the power to be installed and the energy forecast for each source throughout the useful life of the building. This study develops a novel optimization algorithm for a long-term economic function based on mixed-integer disciplined convex programming (MIDCP) which guarantees the sustainability of the building and its energy systems. The robust algorithm integrates risk management of intermittent sources, technical and economic parameters of selected technologies, and life cycle analysis (LCA) of different energy systems, including storage. Furthermore, the penetration of green hydrogen into the distributed generation mix is evaluated as an important contribution. Meteorological and energy demand variables of two antagonistic scenarios were also used as inputs to the algorithm. As a result, the optimal energy supply sizing for tertiary buildings in the two defined locations was obtained. The results of the simulations have achieved an optimal convergence of 100 % in the proposed scenarios, with a resolution time of 14 s and using a memory of about 183 MB. The simulations suggest a higher penetration of green hydrogen in scenarios where supply and investment costs decrease to gray hydrogen supply levels, reaching up to 81 % coverage of the thermal demand of the building. Hybrid energy systems under favorable conditions show a penetration of about 92 % within the distributed generation mix. The developed tool could enable decision-makers to optimally plan distributed generation projects in buildings based on economic, policy, and geographic conditions.

Conversion of images of 3D friction maps to study the coupling between coefficient of friction, velocity and contact temperature of the disc brake

The coefficient of friction during braking is not constant. Determining its variability requires measurements in real conditions. This paper presents a methodology of conversion of the measured time profiles of the sliding velocity Veq, contact temperature T, and coefficient of friction f, into analytical formulation of two variables z = f(x,y). This was executed in the following steps: 1) creating a three-column table Veq, T, f from experimental measurements; 2) interpolation using Kriging; 3) creating a contour map of the coefficient of friction dependent on the sliding velocity and temperature, 4) import of a grey scale image of the friction map into the finite element (FE) software, and 5) definition of a function z = f(x,y). The studies were carried out for five composite organic friction materials and cast-iron brake disc of a railway vehicle. The developed five friction maps for each friction pair were based the data from the full-scale bench tests during braking from the initial vehicle velocity of 80 kmh−1, 120 kmh−1, 140 kmh−1, 160 kmh−1, 200 kmh−1 to a stop. In the second part of the study the initial-value problem for the equation of motion combined with the heat conduction problem were formulated and solved numerically using special functionalities of the FE software, e.g. Deformed Geometry, Mathematics. Changes in the vehicle velocity and time profiles of the coefficient of friction were determined from the solution of the coupled thermal problem. The computational FE model was verified by comparing the measured and calculated changes in the vehicle velocity, coefficient of friction and evolutions of the brake disc temperature during single braking. The maximum calculated temperature 1 mm under the contact surface of the brake disc, during braking from initial vehicle velocity of 80 kmh−1, was obtained for the base friction material and was equal to T1,3,5Num.= 72 °C. The corresponding experimental value was T1-6Exp.= 66.1 °C. When braking from 140 kmh−1, for the same material it was T1,3,5Num.= 129.7 °C and T1-6Exp.= 130.7 °C. The obtained differences in braking times calculated and measured did not exceed 1.7 %. The corresponding temperature differences of the disc at the stopping time were lower than 5.5 %.

Dual colorimetric platforms for direct detection of glyphosate based on Os-Rh nanozyme with peroxidase-like activity

BackgroundTo minimize the impact of pesticide residues in food on human health, it is necessary to enhance their detection. Recently, many nanozyme-based colorimetric methods for pesticides detection have been developed, however, they often required the assistance of natural enzymes, which made the process and result of methods susceptible to the stability and activity of natural enzymes. To overcome these drawbacks, methods for direct detection of pesticides using nanozymes have been developed, and there are few studies in this field currently. Thus, it is of great research and practical significance to develop more nanozymes-based colorimetric methods for direct detection of pesticides. ResultsDual colorimetric platforms based on Os-Rh nanozyme with excellent peroxidase-like activity were constructed for directly detection of glyphosate in this work. Results showed that glyphosate was able to sensitively and selectively inhibit the peroxidase-like activity of Os-Rh nanozyme through hindering the decomposition of H2O2 by Os-Rh nanozyme to produce HO∙. Based on this, the dual colorimetric platforms achieved highly sensitive detection for glyphosate over a wide linear concentration range (50–1000 μg L−1 in solution platform and 200–1000 μg L−1 in paper platform), with the detection limits of 28.37 μg L−1 in solution platform and 400 μg L−1 (naked-eye detection limit)/123.25 μg L−1 (gray scale detection limit) in paper platform, respectively. Moreover, the dual colorimetric platforms possessed satisfactory reliability and accuracy for practical applications, and has been successfully applied to the detection of real samples with the spiked recoveries of 92.78–102.75 % and RSD of 1.17–3.88 %. SignificanceThe dual colorimetric platforms for glyphosate direct detection based on Os-Rh nanozyme developed in this work not only owned considerable practical application potential, but also could provide more inspirations and ideas for the rational design and development of colorimetric sensing methods for the rapid detection of pesticides based on nanozymes.

DCM-GIFT: An Android malware dynamic classification method based on gray-scale image and feature-selection tree

Context:The boom of Android market makes mobile products more popular and convenient. However, in the face of the complex Android application market, how to efficiently and accurately identify malware has become one of the focuses of research. Various new types of disguised malware lurk in the web pages, links and major application malls. Therefore, people’s privacy and property security have become a major obstacle to the continued development of mobile devices. Objective:Most of the existing malware classification methods are fixed on one or several types of characteristics of Android devices, such as static characteristics, dynamic characteristics and traffic characteristics. Single feature detection or fixed feature fusion models limit the dimension of detection software, and also cause imbalanced classification results. This paper proposes an Android Malware Dynamic Classification Method based on Gray-scale Image and Feature-selection Tree (DCM-GIFT), which aims to improve and stabilize the precision of Android software classification and enhance the robustness of malware classification. Method:In this paper, we construct gray-scale images for the original Android traffic to retain the characteristics of the time series and spatial structure of the original network traffic. At the same time, we take the dynamic information and static information of Android software as auxiliary features to build a feature selection tree. The feature-selection algorithm helps the classifier dynamically select the optimal feature fusion scheme, and the resulting fusion feature vector will be trained and predicted using machine learning clusters for model training. Results:We evaluate the performance of DCM-GIFT on multiple datasets published at the Canadian Institute for Cybersecurity, the area under the accuracy, precision, recall and F1measure. The results show that the proposed DCM-GIFT model has significantly better prediction performance compared to other software classification models. Conclusion:It can be concluded that: (1) In terms of accuracy, precision, recall and F1measure, the DCM-GIFT model has a higher average value. (2) The DCM-GIFT model effectively solves the problem of imbalanced classification results in Android software. (3) The DCM-GIFT model achieves the goal of dynamic feature fusion and significantly improves the utilization of system resources.