Gray Channels Research Articles

Towards Functional Safety in Dynamic Distributed Systems

Functional safety (FS) is a well-established concept to avoid technical systems to cause harm during operation. Since FS is based on information exchange, the communication infrastructure plays a vital role to enable FS. Black channel or grey channel approaches are the basis for achieving effective and efficient FS schemes. While simple safety functions (SFs) can be implemented using point-to-point (P2P) transmission protocols, they are usually not suitable to provide FS in dynamic distributed systems (DDSs). This paper discusses time-sensitive networking (TSN) as an important approach for providing FS in wireless TSN (W-TSN) and evaluates the achievable safety integrity levels (SILs) for applications based on PROFISafe running over W-TSN. A discussion on initial ideas for providing FS in DDSs reveals that FS concepts have to be designed and optimized jointly with communication protocols beyond P2P transmission to improve the resulting efficiency required for applying the concepts in industrial processes.

Color Texture Analysis as a Tool for Quantitative Evaluation of Radiation-Induced Skin Injuries

Background: Color texture analysis was applied as a tool for quantitative evaluation of radiation-induced skin injuries.Materials and Methods: We prospectively selected 20 breast cancer patients who underwent whole-breast radiotherapy after breast-conserving surgery. Color images of skin surfaces for irradiated breasts were obtained by using a mobile skin analyzer. The first skin measurement was performed before the first fraction of radiotherapy, and the subsequent measurement was conducted approximately 10 days after the completion of the entire series of radiotherapy sessions. For comparison, color images of the skin surface for the unirradiated breasts were measured similarly. For each color image, six co-occurrence matrices (red-green [RG], red-blue [RB], and green-blue [GB] from color channels, red [R], green [G], blue [B] from gray channels) can be generated. Four textural features (contrast, correlation, energy, and homogeneity) were calculated for each co-occurrence matrix. Finally, several statistical analyses were used to investigate the performance of the color textural parameters to objectively evaluate the radiation-induced skin damage.Results and Discussion: For the R channel from the gray channel, the differences in the values between the irradiated and unirradiated skin were larger than those of the G and B channels. In addition, for the RG and RB channels, where R was considered in the color channel, the differences were larger than those in the GB channel. When comparing the relative values between gray and color channels, the ‘contrast’ values for the RG and RB channels were approximately two times greater than those for the R channel for irradiated skin. In contrast, there were no noticeable differences for unirradiated skin.Conclusion: The utilization of color texture analysis has shown promising results in evaluating the severity of skin damage caused by radiation. All textural parameters of the RG and RB cooccurrence matrices could be potential indicators of the extent of skin damage caused by radiation.

Synergetic application of thermal imaging and CCD imaging techniques to detect mutton adulteration based on data-level fusion and deep residual network

To improve the performance of single thermal imaging and single CCD imaging in detecting unknown adulterated meat samples, these two imaging techniques combined with a deep residual network were synergistically applied to detect mutton adulteration. Considering the importance of spatial and detailed information in improving stability and accuracy, three data-level fusion methods, namely, colour image stitching, grey image stitching and grey channel stacking, were proposed for the fusion of thermal images and CCD images. Classification and prediction models were further developed based on fusion images. The results showed that the models with colour image stitching achieved the best performance. For the external validation set, the accuracy of the best classification model in discriminating five categories was 99.30%. In predicting pork proportions, the R2, RMSE, RPD and RER of the best prediction model were 0.9717, 0.0238, 7.8696 and 21.28, respectively. The best prediction model for duck proportions had a R2 of 0.9616, RMSE of 0.0277, RPD of 5.1015, and RER of 14.44. Therefore, the synergetic application of thermal imaging and CCD imaging can provide a novel and promising tool to detect mutton adulteration and the quality of other food items.

Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach.

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.

Using PLS-SEM for assessing negative impact and cooperation as antecedents of gray market in FMCG supply chains: an analysis on Spanish wholesale distributors

PurposeWithin the fast-moving consumer goods (FMCG) supply chain, one of the problems facing the distribution channel strategy is the presence of the gray market. The article shows two novel antecedents of the participation of official distributors in this gray channel: Negative impact on distributor performance and the relationship with their supplier. Knowledge of this background helps to preserve the strategy outlined for the official distribution channel.Design/methodology/approachData were collected from 172 Spanish wholesale distributors and analyzed using PLS-SEM.FindingsThe authors found that the damage through negative affectation in the official distributor's performance and the cooperation provided by the manufacturer, have different effects. While affectation is shown to be a powerful antecedent of participation in the gray market, the effect of perceived manufacturer cooperation does not show strong results.Practical implicationsIn business practice, these findings lead the manufacturer to keep transactions carried out in the gray market at low levels and provide cooperation to official distributors to guarantee the official channel strategy aimed at efficiency in the distribution of branded goods.Originality/valueThe background of the gray market discussed in the study has not been previously analyzed in the literature. In this way, the authors contribute to the knowledge of such a common problem as the presence of the gray market in the segmentation of distribution channels of high-demand products.

Implementation of a Human Motion Capture System Based on the Internet of Things Machine Vision

The classification of the stereo matching comprehensive analysis related algorithm model can be subdivided into local stereo matching based on the entire acquisition and global stereo matching based on the entire local. But it can have a higher capture efficiency because the log-likelihood variance cost calculation function can have a faster feature convergence capture speed than the ordinary log-mean-square error cost function. Through the combination of gray channel and frame difference channel, a better network structure and parameters on the KTH data set are obtained, which can ensure the classification effect while greatly reducing the number of parameters, improving training efficiency and improving classification accuracy. The article uses dual-channel 3D convolutional human neural network technology to achieve 92.5% accuracy of human feature capture, which is significantly better than many traditional feature extraction techniques proposed in the literature.

Retinal Blood Vessel Extraction Using a New Enhancement Technique of Modified Convolution Filters and Sauvola Thresholding

The retinal blood vessels in humans are major components with different shapes and sizes. The extraction of the blood vessels from the retina is an important step to identify the type or nature of the pattern of the diseases in the retina. Furthermore, the retinal blood vessel was also used for diagnosis, detection, and classification. The most recent solution in this topic is to enable retinal image improvement or enhancement by a convolution filter and Sauvola threshold. In image enhancement, gamma correction is applied before filtering the retinal fundus. After that, the image should be transformed to a gray channel to enhance pictorial clarity using contrast-limited histogram equalization. For filter, this paper combines two convolution filters, namely sharpen and smooth filters. The Sauvola threshold, the morphology, and the medium filter are applied to extract blood vessels from the retinal image. This paper uses DRIVE and STARE datasets. The accuracies of the proposed method are 95.37% for DRIVE with a runtime of 1.77[Formula: see text]s and 95.17% for STARE with 2.05[Formula: see text]s runtime. Based on the result, it concludes that the proposed method is good enough to achieve average calculation parameters of a low time quality, quick, and significant.

Assessing the Formal and Informal Waste Recycling Business Processes through a Stakeholders Lens in Pakistan

With this study, we aim to assess integration possibilities of formal and informal waste management approaches in a low-income country context by using recycling as a tool towards sustainability. Using a holistic approach, this study examines both formal and informal aspects of recycling value chains through a stakeholder lens. Pakistan is far from being a circular economy, as evident from the dismal levels of municipal solid waste collection and recycling. This problem calls for the effective integration of formal and informal recycling business processes to fill the void. With this study, we seek to assess various aspects of formal and informal waste recycling in Pakistan through an inductive qualitative research design by examining the case of one of the most populous cities, Lahore, in the Punjab province of Pakistan. Equal representation to various stakeholders is accorded, with 25 interviews each with managers of private companies, public companies, waste pickers and scrap dealers in the target area. The study highlights that recycling sector in Pakistan is completely informal and unregulated with no or little support available due to a lack of funds and systematic planning. The empirical evidence suggests that the formal sector lacks the capacity for proper waste disposal of total waste generated. The recycling aspects are left to informal sector stakeholders who improvise to extract value from MSW in an entrepreneurial manner. The formal sector holds a bias towards informal stakeholders due to the fact that their work also serves as a theft bargain and resale platform under the guise of waste collection and recycling. The study highlights the most and least sought after recyclables, identifies gray channel markets of spare parts, metal recycling and counterfeit products, and practices of adulteration in the target area. This study recommends as a policy input, to overcome bias towards the informal sector, the creation of a central recycling commission to overlook and regulate the affairs of hitherto unregulated and informal municipal solid waste and recycling value chains.

Sustainability of Recycling Waste Picker Sustainopreneurs for Prevention and Mitigation of Municipal Solid Waste in Swat

This study assessed the role of waste picker sustainopreneurs (WPS) by examining their recycling contributions in a special case of District Swat, Pakistan. Using a transformative worldview, this study acknowledges their contributions to recycling. The study envisages how entrepreneurial agency works in unanticipated ways where the poorest of the poor enterprise without resources demonstrate unexpected agency to improve the environment. An inductive research design in conjunction with grounded theory was used to analyze data from 37 interviews in three tehsils of District Swat. The recycling sector uses primitive measures, which are completely informal, self-organized, and self-controlled. Surprisingly, we discovered that informal recycling contributes unfavorably to waste, energy, and food (WEF) security due to intermixing and adulteration, as well as acts as a gray channel for illicit practices that have taken advantage of tax amnesty in the area until 2023. The uncontrolled welding of half cut and nose cut car parts has skyrocketed the motorization index and CO2 emissions; however, it has also resulted in alternative sources of livelihoods, as these accidental environmentalists had found modern sources of income. This is similar to low-tech innovation and business creation that takes advantage of tax holidays due to the special status of District Swat. The study highlights the most and least valuable recyclables and identifies the gray channel markets of spare parts, metal recycling, counterfeit products, fuel intermixing, and adulteration. The study contributes by untangling the understanding of a legal gambit of tax amnesty as a critical pre-policy input as well as advocates for rights of invisible stakeholders of waste management in Pakistan.

Bays International Private Limited: Evolving Through Market Dynamics

Bays International was incorporated in 1996. Over the years it faced numerous challenges including: high employee turnover (particulary saleforce), limited expansion capabilities, low penetration in semi-urban markets, high operating expenses like rentals, increasing duties (on luxury products), low cash-flows, low budgets for advertising and marketing as compared to competitors, less awareness amongst consumers, volatile consumer preferences (especially by millennial), weak brands’ loyalties, counterfeits/fake products, smuggling and infiltration from grey channels, high number of foreign and local chains entering the market, price war especially with local chains, and uncertain political situation. Four interviews were conducted from management and two interviews from ex employees. Moreover, a lot of open access documents of the company were reviewed. Pertinent literature reveiew also revealed interesting insights about emerging consumer trends and cosmetic industry along with interviews of some marketing directors of leading global brands. The Bays leadership believes that key parameters are improving over time. The case covers how the company maneuvered itself since its inception and launched numerous other brands targeted towards different segments of the society, in order to steer towards growth in changing internal and external environment. The case is based on a scenario when there is yet another increase of taxes by the government in 2019 and presents troublesome situations to branding cum marketing strategy for the company to consider.

Automatic Detection of Diabetic Retinopathy Using Support Vector Machine

Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method. SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.

Orthogonal microscopy image acquisition analysis technique for rock sections in polarizer angle domain

The microscopic identification of rock sections is one of the most basic research methods of petromineralogy, which is important to the research of mineral composition, pore throat characteristics, interstitial filling etc. The traditional manual identification method was time-consuming, and also burdened with errors due to the subjective nature of human judgment, which can bring some uncertainties to the further results. Therefore, this paper proposes a technique for image acquisition and analysis of rock sections, based on sequential images of rock sections in angle domain under polarizing microscope. The analysis includes the following three steps. Firstly, based on the multi-angles extinction feature of rock section, the direction-adaptive correlation coefficient method was used to characterize the edge of the rock sections and segment the rock grains. A series of parameters, including grain size, roundness, convexity, flatness and roughness, were also extracted to characterize the morphological features of rock grains quantitatively. Secondly, the variation characteristics of multi-channel parameters (RGB and Gray channel) with angle domain were analyzed for different minerals and then the mineral composition was accurately identified. On the basis of grain segmentation, the pore structures, including intergranular pores, dissolved pores and throat, were extracted. And the parameters of pore structure were also obtained. Finally, the distribution of the interstitials was characterized based on the change of the extinction characteristics of the interstitials in the pores. Image analysis technique based on rock sections with polarizing microscope provides an effective and convenient method for grain segmentation, mineral composition classification, pore identification and interstitial analysis.

FPGA-Based Low-Visibility Enhancement Accelerator for Video Sequence by Adaptive Histogram Equalization With Dynamic Clip-Threshold

In the natural and practical scenario, the captured video sequence under bad weather situations or low light conditions often suffers from poor visibility and low-contrast problems. This hurts the performance of the high-level processing, e.g. object tracking or recognition. In this paper, we develop an FPGA-based low-visibility enhancement accelerator for video sequence by adaptive histogram equalization with dynamic clip-threshold (AHEwDC) which is determined by the visibility assessment. The main goal is to improve the low visibility with high image quality for both hazy and low-light video sequences in real-time. Firstly, a concept to quantify the visual perception based on supervised learning is to estimate the visibility score. Then, to avoid the problem of noise amplification in the conventional method, we propose a visibility assessment model to find an optimal clip-threshold. The contrast energy of gray channel, yellow-blue channel and red-green channel, average saturation, and gradients are statistical features in the model to describe the visibility of an image. Finally, to meet the speed requirement for video sequence processing, a specified hardware architecture for both visibility assessment and AHEwDC is implemented on FPGA. Besides, a mean spatial filter for cumulative distribution functions (CDFs) of the AHE is developed for suppressing the noise caused by a single-color local region. The demonstration system on the DE1-SoC platform with the Intel Cyclone V FPGA device with the max working frequency of 75.84 MHz is capable of processing 30 fps FHD ( $1920\times 1080$ ) video.

Intelligent trademark similarity analysis of image, spelling, and phonetic features using machine learning methodologies

The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater due diligence to protect intangible assets such as brands and corporate logos which can easily be copied or distributed through grey channels and internet sales sites. Trademarks (TMs) are government registered intellectual property rights (IPRs) used to legally protect a companies’ identities and brand equity. The rapid growth of global trademark (TM) registrations and the number of TM infringement cases pose a great challenge for TM owners to detect infringement and take action to protect TMs, consumer trust, and market share. This research develops advanced TM similarity assessment models using machine learning (ML) approaches. Litigation principles over similarity follow US TM laws which are consistent with global TM protection convention under the World Intellectual Property Organization (WIPO). This research covers the similarity analysis of TM spelling, pronunciation, and images, which are most likely to cause TM confusion among customers. The research focuses on deploying machine learning for natural language (spelling and phonetic features) and image similarity analyses. The vector space modeling algorithms are trained and verified for the similarity analysis of TM wordings in both spelling and pronunciation. The convolutional neural network and Siamese neural network models are trained and verified for TM image similarity comparison. The training and testing sets consist of 250,000 and 20,000 different image pairs respectively. This research provides a significant contribution toward implementing intelligent and automated IPR protection. The system solution supports users (companies, TM attorneys, or IP officers) to identify similar registered TMs before registering new TMs ensuring uniqueness to avoid infringement disputes. The solution also supports automatic screening of online content to detect potential infringement of TM images and wording for effective global IPR protection.

A Novel Hybrid Secure Image Encryption Based on Julia Set of Fractals and 3D Lorenz Chaotic Map.

Chaos-based encryption schemes have attracted many researchers around the world in the digital image security domain. Digital images can be secured using existing chaotic maps, multiple chaotic maps, and several other hybrid dynamic systems that enhance the non-linearity of digital images. The combined property of confusion and diffusion was introduced by Claude Shannon which can be employed for digital image security. In this paper, we proposed a novel system that is computationally less expensive and provided a higher level of security. The system is based on a shuffling process with fractals key along with three-dimensional Lorenz chaotic map. The shuffling process added the confusion property and the pixels of the standard image is shuffled. Three-dimensional Lorenz chaotic map is used for a diffusion process which distorted all pixels of the image. In the statistical security test, means square error (MSE) evaluated error value was greater than the average value of 10000 for all standard images. The value of peak signal to noise (PSNR) was 7.69(dB) for the test image. Moreover, the calculated correlation coefficient values for each direction of the encrypted images was less than zero with a number of pixel change rate (NPCR) higher than 99%. During the security test, the entropy values were more than 7.9 for each grey channel which is almost equal to the ideal value of 8 for an 8-bit system. Numerous security tests and low computational complexity tests validate the security, robustness, and real-time implementation of the presented scheme.

Shoot high-quality color images using dual-lens system with monochrome and color cameras

In the dual-lens system with monochrome and color cameras, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Due to occlusions, the colorization will inevitably fail in some cases. Thus, evaluating the colorization quality is also of great importance. We solve both problems in this paper. For colorization, we propose a gray-color correspondence prior, i.e. in local regions, if two patches are similar in the gray channel, it is very often that the two pixels centered at these two patches have similar colors. Based on this prior, a deep learning based and coarse-to-fine colorization method is proposed. For evaluating the colorization quality, we propose a symmetry colorization based evaluation method. Experimental results show that our method could largely outperform the state-of-the-art methods and is also efficient in computation.

A Novel Unsupervised Two‐Stage Technique in Color Image Segmentation

A new unsupervised two-stage method for color image segmentation is proposed. The method contains coarse segmentation and delicate segmentation. In coarse segmentation, we adaptively choose a gray channel from CIE-lab color space. The Otsu method combined with a refinement to its threshold is applied to get global optimal segmentation. In delicate segmentation, a narrowband based procedure is applied to get more accurate contour of the object and local optimal segmentation is achieved. Our method finally balance the global optimal and the local optimal. The proposed method does not need initial contours or initial labels, thus it is more robust in certain applications. Experimental results of our method in MSRA1000 database show that our method is robust in segmenting objects and backgrounds when possessing weakly heterogeneous color. Our method firstly achieves global optimal and then achieves local optimal which draws a new and prospective outlook for segmenting color images.

Monocular visual-IMU odometry using multi-channel image patch exemplars

In this paper, we propose three sets of multi-channel image patch features for monocular visual-IMU (Inertial Measurement Unit) odometry. The proposed feature sets extract image patch exemplars from multiple feature maps of an image. We also modify an existing visual-IMU odometry framework by using different salient point detectors and feature sets and replacing the inlier selection approach with a self-adaptive scheme. The modified framework is used to examine the proposed feature sets. In addition to the Root Mean Square Error (RMSE) metric, we use the Hausdorff distance to measure the inconsistency between the estimated and ground-truth trajectories. Compared to the point-wise comparison used by RMSE, the Hausdorff distance takes the shape inconsistency of two trajectories into account and is hence more perceptually consistent. Experimental results show that the multi-channel feature sets outperform, or perform comparably to, the single gray level channel feature sets examined in this study. Particularly, the multi-channel feature set that uses integral channels, i.e., ICIMGP (Integral Channel Image Patches), outperforms two state-of-the-art feature sets: SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). Besides, ICIMGP performs better than the two multi-channel feature sets that are designed based on derivative channels and gradient channels respectively. These promising results are attributed to the fact that the multi-channel features encode richer image characteristics than their single gray level channel counterparts.

An Improved Saliency Detection Approach for Flying Apsaras in the Dunhuang Grotto Murals, China

Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature value computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.

Japan’s Island Problem

“Don’t get me wrong,” said Mr. Hasegawa, a fisherman. “I don’t think that the bombing of Hiroshima was a good thing.” Staring at the furious grey channel where the Pacific Ocean meets the Sea of Okhotsk off Hokkaido in northern Japan on a cold, clear day last March, he spoke like a trauma victim reliving the past: “But if the Americans had dropped the atomic bomb a month earlier, those islands out there would still be Japan’s.” Were I unaware of the chronology of the summer of 1945 and had we been anywhere else, such a comment would make the engaging 64-year-old seem insensitive or odd. Yet on the horizon three miles in the distance were the snow-covered banks of one of Russia’s Kuril Islands, known to the Japanese who lived there until 1945 as Suishojima of the Habomai group. Mr. Hasegawa’s father was among the 17,291 Japanese who called it and several other nearby islands home.