White Images Research Articles

Parking detection method using quadtree decomposition analysis

Searching for available parking spaces can be a painful experience for drivers due to driving around until finding a vacant space. This study proposes a new method to automatically detect available parking spaces. The proposed system identifies empty parking spaces using grayscale images obtained from any type of video camera. The method was found to successfully identify parking availability under different conditions and scenarios. The method was tested using real-life data and achieved a detection rate of 99.7%. This method can be applied in real-time to monitor parking availability and guide drivers to empty spaces. The method has several advantages, including simple algorithms, the use of low-quality black and white images, and simple configuration. Therefore, the system can provide enormous cost savings for locations with existing black and white surveillance cameras instead of replacing existing cameras with new high-quality cameras. • A new method to detect available parking spaces in outdoor parking was proposed. • The proposed method uses grayscale images obtained from any type of video camera. • Using real-life data, the proposed method achieved a detection rate of 99.7%. • The achieved detection rate is higher than most available methods in the literature.

The Helsinki Twin [Finland - digital twin

Helsinki, a world leader in using digital models as tools to achieve its ambitious and rapid decarbonisation goal by 2030, is looking to harness the potential of geothermal energy with the help of local homeowners. Two-thirds of the world's people are expected to be living in cities by 2050, so urban planning for climate change is vital, both to mitigate extreme climate events and to realise a low-carbon future. At the forefront worldwide of deploying and using a digital twin model of their city is a small team in the Finnish capital Helsinki, led by architect and city planner Jarmo Suomisto. The Helsinki digital twin is in fact two models, a Reality model, and a Semantic City GML - short for Geography Markup Language, an international standard in mapping software. Simply put, the reality model is the pretty pictures - made up of two billion polygons, and the semantic model is the underlying brains, with data layers that can be labelled, interrogated, added to, and changed. At the beginning the models were used to evaluate proposals in architectural competitions. The 1985 3D model took 12 hours to render a simple black and white street view image.

Intelligent Air Quality Monitoring Using Arduino UNO & IOT

The level of pollution was increased due to several factors such as industrialization, urbanization, vehicles, electronic wastes etc. While burning those fuels the ejection of several toxic gases are released into the atmosphere which causes severe harmful effects to the people. The watching of weather is basically useful in numerous applications like in important scientific systems or for simulation purposes. Weather sensing is one among the most important functions in region applications to examine suited weather environments of alternative planets too. LCD screens are more energy efficient and offer safer disposal than CRTs. Their low watt consumption allows them to be used in powered electronic devices (backlight) or reflector to provide color or black and white images.

Taxonomic studies on Afrotropical Nolinae: review of the Afrotropical taxa of the genus Hampsonola Lszl, Ronkay amp; Ronkay, 2015, descriptions of five new genera and five new species and introduction of a new synonymy (Lepidoptera, Nolidae).

An overview of the Afrotropical species of Hampsonola Lszl, Ronkay Ronkay is given and five new combinations are introduced: Hampsonola inexplicabilis (Hacker, 2012) comb. n., Hampsonola argyropasta (Hampson, 1914) comb. n., Hampsonola nanographa (Hacker, 2012) comb. n. are transferred from Meganola Dyar, and Hampsonola aulombardiella (Hacker, 2012) comb. n. and Hampsonola transecta (Hampson, 1901) comb. n. are transferred from Nola Leach to Hampsonola. Based on integrative taxonomic analyses, five new monotypic genera and their type species are described: Gabonola gen. n. smithi sp. n., Bellanola gen. n. mikongo sp. n., Brunneonola gen. n. nimba sp. n., Ivindonola gen. n. ipassa sp. n. and Cryptonola gen. n. confundata sp. n. Additionally, Negeta franeyae Viette, 1987 is transferred to the genus Leucobaeta Lszl, Ronkay Witt, 2010: Leucobaeta franeyae (Viette, 1987) comb. n. representing a senior synonym of Leucobaeta malagassa Lszl, 2022 syn. n. The paper is illustrated with 21 colour and 16 black and white diagnostic images.

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.

Disentangling the effects of Alzheimer's and small vessel disease on white matter fibre tracts.

Alzheimer's disease and cerebral small vessel disease are the two leading causes of cognitive decline and dementia and coexist in most memory clinic patients. White matter damage as assessed by diffusion MRI is a key feature in both Alzheimer's and cerebral small vessel disease. However, disease-specific biomarkers of white matter alterations are missing. Recent advances in diffusion MRI operating on the fixel level (fibre population within a voxel) promise to advance our understanding of disease-related white matter alterations. Fixel-based analysis allows derivation of measures of both white matter microstructure, measured by fibre density, and macrostructure, measured by fibre-bundle cross-section. Here, we evaluated the capacity of these state-of-the-art fixel metrics to disentangle the effects of cerebral small vessel disease and Alzheimer's disease on white matter integrity. We included three independent samples (total n = 387) covering genetically defined cerebral small vessel disease and age-matched controls, the full spectrum of biomarker-confirmed Alzheimer's disease including amyloid- and tau-PET negative controls and a validation sample with presumed mixed pathology. In this cross-sectional analysis, we performed group comparisons between patients and controls and assessed associations between fixel metrics within main white matter tracts and imaging hallmarks of cerebral small vessel disease (white matter hyperintensity volume, lacune and cerebral microbleed count) and Alzheimer's disease (amyloid- and tau-PET), age and a measure of neurodegeneration (brain volume). Our results showed that (i) fibre density was reduced in genetically defined cerebral small vessel disease and strongly associated with cerebral small vessel disease imaging hallmarks; (ii) fibre-bundle cross-section was mainly associated with brain volume; and (iii) both fibre density and fibre-bundle cross-section were reduced in the presence of amyloid, but not further exacerbated by abnormal tau deposition. Fixel metrics were only weakly associated with amyloid- and tau-PET. Taken together, our results in three independent samples suggest that fibre density captures the effect of cerebral small vessel disease, while fibre-bundle cross-section is largely determined by neurodegeneration. The ability of fixel-based imaging markers to capture distinct effects on white matter integrity can propel future applications in the context of precision medicine.

Reference database of teeth images from the Family Bovidae

Researchers typically rely on fossils from the Family Bovidae to generate African paleoenvironmental reconstructions due to their strict ecological tendencies. Bovids have dominated the southern African fauna for the past four million years and, therefore, dominate the fossil faunal assemblages, especially isolated teeth. Traditionally, researchers reference modern and fossil comparative collections to identify teeth. However, researchers are limited by the specific type and number of bovids at each institution. B.O.V.I.D. (Bovidae Occlusal Visual IDentification) is a repository of images of the occlusal surface of bovid teeth. The dataset currently includes extant bovids from 7 tribes and 20 species (~3900). B.O.V.I.D. contains two scaled images per specimen: a color and a black and white (binarized) image. The database is a useful reference for identifying bovid teeth. The large sample size also allows one to observe the natural variation that exists in each taxa. The binarized images can be used in statistical shape analyses, such as taxonomic classification. B.O.V.I.D. is a valuable supplement to other methods for taxonomically identifying bovid teeth.

Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder.

While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.

Dual‐Mode Switching E‐Paper by Negative Electrorheological Fluid with Reversible Silica Networks

AbstractHuman–machine interaction will be revolutionarily different in the future Internet of Things (IoT) environments. Many displays will be adopted onto electronic devices to enhance human–device communication, even under a very bright sunlight ambience. Thus, power consumption and sunlight visibility are important attributes for this application. Electrophoretic displays (EPDs) have the inherent advantages of ultra‐low power consumption and high sunlight visibility, which are perfectly suitable for IoT applications. The low power consumption resulted from the balance of viscosity, gravity, and other complicated forces involved in the electrophoretic dispersion. This force balance is generally termed “bistability,” meaning the particle‐packing can be stable without external power at black and white image states. However, good bistability implies a slow image updating rate, significantly degrades users’ experience. In this work, a 3D network structure that undergoes disruption and reorganization with the particles’ movement is utilized in the electrophoretic ink dispersion. Dynamic viscosity modulation enables the bistable and fast‐response dual‐working modes. The newly developed design can increase the response speed of EPDs by a factor of 2.38, simultaneously maintaining the bistability. The electronic ink with this reversible network provides a promising solution for the future video‐rate e‐paper displays.

A highly automated apparatus for ultra-fast laser ablation studies.

We present a novel experimental apparatus that can be used for extensive systematic studies of (single- and multi-shot) ultra-short laser pulse ablation. It is fully automated and generates a large number of ablation sites in a short time on a small sample surface area. For each site, the apparatus takes four in situ images: an image of the incident ablation beam (to determine pulse energy), a white light reference image of the pristine sample site, an image of the reflected ablation spot, and a white light image of the ablated sample site. The setup can perform ablation experiments as a function of many parameters, including pulse energy, pulse duration, number of pulses, time between pulses, and focus size. As a proof of concept, we present example results on single-shot ablation off crystalline silicon. Using only data acquired in situ in the presented setup, we determine the single-shot ablation threshold as a function of pulse duration and verify the threshold value using optical interferometric profilometry. The values we found agree well with literature values.

Mormonicity

Mormonicity

Machine Learning

Abstract: Machine Learning (M.L) is a scientific study of statical modals and algorithms that computer used to perform a certain task. Learning algorithms used in many applications that our used in daily life. Image recognition is a well known for identify an object as a digital image, one of the reason it work so well is because a learning algorithm that has based on the intensity of the pixels black & white images and color images. These algorithms are used for various purposes like predictive analytics, virtual assistants etc. The main advantage of using machine learning is that, once an algorithm grasp what to perform with particular data, then it works automatically.

The Establishment of the Theme of Evil Through Imagery in Othello

Othello, hailed as one of four major Shakespeare’s tragedies, is a play in which various types of imagery can be found. As with most of Shakespeare’s plays, the function of imagery is to aid not only characterization but also the reinforcement of the main themes as it helps to convey meaning, establish the dramatic atmosphere of the play and facilitate the audience’s understanding of the play. This essay tends to examine the symbolic and contrasting imagery employed in Othello and discuss its relevance to the theme of evil in the play. In Othello, the symbolic theft and animal images, loaded with evil content and evil intention, are evilly employed to manipulate and destroy others while the contrasting black and white images as well as Othello’s lofty and low images picturesquely highlight the devastating effect of evil. By this approach, the theme of evil is thereby elucidated.

DETECTION OF SIGNAL TO NOISE RATIO FROM IMAGE CONTOUR -A MATLAB APPLICATION

Image is a combination of thousand number of pixels which is an integral unit of image , every pixel has three primary colors Red,Green,Blue. Contour means border detection of black and white image. Here in this paper our main aim is to reduce noise from black and white image after converting it from color image. Several noises are there, but we have used only few of them as an application to show you the procedure. Here we have added noise then applied filtering method to generate the contour and compared it with original contour of a normal noise free image. In each case original image has been displayed in result section.]

Vibration Analysis of 3D printed runner with CNN for using deep learning in hydropower for condition monitoring

Convolutional Neural Networks are deep learning algorithms that are typically used for the classification of images. A black and white image can be represented as the number of rows and columns along with values of intensity for each of components of these rows and columns. Vibration measured with the help of accelerometer can be used to create an image like data. These data can be used for machine learning applications with the help of deep learning. In deep learning techniques, features do not have to be extracted from the data set. The CNN network can generate a feature-like set which can be further classified with a fully connected neural network to give out the probability over the predefined class. The neural networks model is trained with a data set created in laboratory conditions to monitor the unfractured and single blade fracture condition operation state. The paper deals with showing the proof of concept that CNN algorithms can be applied for conditional monitoring of runner blades. The paper mainly focuses on demonstrating that deep learning algorithms can be used as analysis tools for hydropower runners by applying these techniques in scaled versions of 3D printed runners.

On the definition, construction, and presentation of the human cerebral sulci: A morphology-based approach.

Although the term sulcus is known for almost four centuries, its formal, precise, consistent, constructive, and quantitative definition is practically lacking. As the cerebral sulci (and gyri) are vital in cortical anatomy which, in turn, is central in neuroeducation and neuroimage processing, a new sulcus definition is needed. The contribution of this work is threefold, namely to (1) propose a new, morphology-based definition of the term sulcus (and consequently that of gyrus), (2) formulate a constructive method for sulcus calculation, and (3) provide a novel way for the presentation of sulci. The sulcus is defined here as a volumetric region on the cortical mantle between adjacent gyri separated from them at the levels of their gyral white matter crest lines. Consequently, the sulcal inner surface is demarcated by the crest lines of the gyral white matter of its adjacent gyri. Correspondingly, the gyrus is defined as a volumetric region on the cortical mantle separated from its adjacent sulci at the level of its gyral white matter crest line. This volumetric sulcus definition is conceptually simple, anatomy-based, educationally friendly, quantitative, and constructive. Considering the sulcus as a volumetric object is a major differentiation from other works. Based on the introduced sulcus definition, a method for volumetric sulcus construction is proposed in two, conceptually straightforward, steps, namely, sulcal intersection formation followed by its propagation which steps are to be repeated for every sulcal segment. These sulcal and gyral constructions can be automated by applying existing methods and public tools. As a volumetric sulcus forms an imprint into the white matter, this enables prominent sulcus presentation. Since this type of presentation is novel yet unfamiliar to the reader, also a dual surface presentation was proposed here by employing the spatially co-registered white matter and cortical surfaces. The results were presented as dual surface labeled sulci on eight standard orthogonal views, anterior, left lateral, posterior, right lateral, superior, inferior, medial left, and medial right by using a 3D brain atlas. Moreover, additional 108 labeled images were created with sulcus-oriented views for 27 individual left and right sulci forming 54 dual white matter-cortical surface images strengthening in this way the educational value of the proposed approach. These images were included for public use in the NOWinBRAIN neuroimage repository with over 7700 3D images available at www.nowinbrain.org. The results demonstrated the superiority of white matter surface sulci presentation over the standard cortical surface and cross-sectional presentations in terms of sulcal course, continuity, size, shape, width, depth, side branches, and pattern. To my best knowledge, this is the first work ever presenting the labeling of sulci on all cerebral white matter surfaces as well as on dual white matter-cortical surfaces. Additionally to neuroeducation, three other applications of the proposed approach were discussed, sulcal reference maps, sulcus quantification in terms of new parameters introduced here (sulcal volume, wall skewness, and the number of white matter basins), and an atlas-assisted tool for exploration and studying of cerebral sulci and gyri .

Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants.

Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.

Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis.

Background: Early diagnosis of laryngeal lesions is necessary to begin treatment of patients as soon as possible to preserve optimal organ functions. Imaging examinations are often aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of this study is to investigate diagnostic utility of AI in laryngeal endoscopy. Methods: Five databases were searched for studies implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity, and specificity. Results: All 11 studies included presented an overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997). The accuracy was significantly higher in studies using a larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue were 0.91 and 0.97, respectively. The same values for differentiation between benign and malignant lesions were 0.91 and 0.94, respectively. The comparison of the effectiveness of AI models assessing narrow band imaging and white light endoscopy images revealed no statistically significant differences (p = 0.409 and 0.914). Conclusion: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity.

Taxonomic review of the genus Morabia Hausmann & Tujuba, 2020 with descriptions of two new species and introducing five new generic combinations (Lepidoptera, Geometridae, Ennominae)

The paper contains the description of two new species of the genus Morabia Hausmann Tujuba, 2020: M. cryptica sp. n. and M. smithi sp. n. The descriptions are based on both morphological data and DNA barcoding. Based on integrative taxonomic analyses, five new combinations are proposed: Morabia nigripunctata (Warren, 1897), comb. n. described from Nigeria, Morabia hero (Viette, 1971), comb. n., Morabia pluto (Viette, 1971), comb. n. described from Madagascar, Morabia herbuloti (Orhant, 2003), comb. n. described from Runion and Morabia distinctaria (Joannis, 1915), comb. n. described from Mauritius are transferred from the genus Ectropis Hbner, 1825 to Morabia. The female genitalia of M. nigripunctata, M. brunnea Hausmann Tujuba, 2020, M. hero and M. herbuloti are described and illustrated for the first time. Detailed diagnoses and re-descriptions of all taxa newly transferred to Morabia are given, interspecific pairwise distances of DNA barcodes are calculated and a maximum likelihood tree is compiled. The paper is illustrated with 38 colour and 38 black and white diagnostic images and a distribution map.

DeVun, Leah, The Shape of Sex: Nonbinary Gender from Genesis to the Renaissance

Leah DeVun’s fine book is a major contribution to gender studies. It will remain a key reference work on premodern, nonbinary bodies for at least the next five years, if not decade. Aimed at a wide audience that includes historians of European gender and sexuality, art historians, literary scholars, philosophers, animal studies scholars, medical historians, LGBT scholars and gender activists, The Shape of Sex is an avowedly politically engaged study, not only offering a ‘usable past’ for nonbinary, genderqueer, trans, sex variant and intersex persons today but also imagining for them—and for everyone—‘a more radically transformative future’ (p. 14). Beautifully written and lavishly illustrated with colour and black and white images, The Shape of Sex provides a wide-ranging analysis of how European thinkers from the third- to the fifteenth-century CE—theologians, legal theorists, makers and illustrators of bestiaries and mappaemundi (symbolic maps of the world), travel writers, encyclopaedists, natural philosophers, physicians, surgeons and alchemists—imagined bodies that did not fit within the traditional dimorphic understanding of sexed bodies during this period, and thus raised crucial questions about which bodies counted as human and which did not.