Search results for: binary segmentation
861 Segmentation Using Multi-Thresholded Sobel Images: Application to the Separation of Stuck Pollen Grains
Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie
Abstract:
Being able to identify biological particles such as spores, viruses, or pollens is important for health care professionals, as it allows for appropriate therapeutic management of patients. Optical microscopy is a technology widely used for the analysis of these types of microorganisms, because, compared to other types of microscopy, it is not expensive. The analysis of an optical microscope slide is a tedious and time-consuming task when done manually. However, using machine learning and computer vision, this process can be automated. The first step of an automated microscope slide image analysis process is segmentation. During this step, the biological particles are localized and extracted. Very often, the use of an automatic thresholding method is sufficient to locate and extract the particles. However, in some cases, the particles are not extracted individually because they are stuck to other biological elements. In this paper, we propose a stuck particles separation method based on the use of the Sobel operator and thresholding. We illustrate it by applying it to the separation of 813 images of adjacent pollen grains. The method correctly separated 95.4% of these images.Keywords: image segmentation, stuck particles separation, Sobel operator, thresholding
Procedia PDF Downloads 96860 The Feasibility of Online, Interactive Workshops to Facilitate Anatomy Education during the UK COVID-19 Lockdowns
Authors: Prabhvir Singh Marway, Kai Lok Chan, Maria-Ruxandra Jinga, Rachel Bok Ying Lee, Matthew Bok Kit Lee, Krishan Nandapalan, Sze Yi Beh, Harry Carr, Christopher Kui
Abstract:
We piloted a structured series of online workshops on the 3D segmentation of anatomical structures from CT scans. 33 participants were recruited from four UK universities for two-day workshops between 2020 and 2021. Open-source software (3D-Slicer) was used. We hypothesized that active participation via real-time screen-sharing and voice-communication via Discord would enable improved engagement and learning, despite national lockdowns. Written feedback indicated positive learning experiences, with subjective measures of anatomical understanding and software confidence improving.Keywords: medical education, workshop, segmentation, anatomy
Procedia PDF Downloads 153859 The Effect of Head Posture on the Kinematics of the Spine During Lifting and Lowering Tasks
Authors: Mehdi Nematimoez
Abstract:
Head posture is paramount to retaining gaze and balance in many activities; its control is thus important in many activities. However, little information is available about the effects of head movement restriction on other spine segment kinematics and movement patterns during lifting and lowering tasks. The aim of this study was to examine the effects of head movement restriction on relative angles and their derivatives using the stepwise segmentation approach during lifting and lowering tasks. Ten healthy men lifted and lowered a box using two styles (stoop and squat), with two loads (i.e., 10 and 20% of body weight); they performed these tasks with two instructed head postures (1. Flexing the neck to keep contact between chin and chest over the task cycle; 2. No instruction, free head posture). The spine was divided into five segments, tracked by six cluster markers (C7, T3, T6, T9, T12, and L5). Relative angles between spine segments and their derivatives (first and second) were analyzed by a stepwise segmentation approach to consider the effect of each segment on the whole spine. Accordingly, head posture significantly affected the derivatives of the relative angles and manifested latency in spine segments movement, i.e., cephalad-to-caudad or caudad-to-cephalad patterns. The relative angles for C7-T3 and T3-T6 increased over the cycle of all lifting and lowering tasks; nevertheless, in lower segments increased significantly when the spine moved into upright standing. However, these effects were clearer during lifting than lowering. Conclusively, the neck flexion can unevenly increase the flexion angles of spine segments from cervical to lumbar over lifting and lowering tasks; furthermore, stepwise segmentation reveals potential for assessing the segmental contribution in spine ROM and movement patterns.Keywords: head movement restriction, spine kinematics, lifting, lowering, stepwise segmentation
Procedia PDF Downloads 194858 Object-Based Image Analysis for Gully-Affected Area Detection in the Hilly Loess Plateau Region of China Using Unmanned Aerial Vehicle
Authors: Hu Ding, Kai Liu, Guoan Tang
Abstract:
The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest
Procedia PDF Downloads 183857 An Accurate Brain Tumor Segmentation for High Graded Glioma Using Deep Learning
Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan
Abstract:
Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG
Procedia PDF Downloads 212856 The Bicoid Gradient in the Drosophila Embryo: 3D Modelling with Realistic Egg Geometries
Authors: Alexander V. Spirov, David M. Holloway, Ekaterina M. Myasnikova
Abstract:
Segmentation of the early Drosophila embryo results from the dynamic establishment of spatial gene expression patterns. Patterning occurs on an embryo geometry which is a 'deformed' prolate ellipsoid, with anteroposterior and dorsal-ventral major and minor axes, respectively. Patterning is largely independent along each axis, but some interaction can be seen in the 'bending' of the segmental expression stripes. This interaction is not well understood. In this report, we investigate how 3D geometrical features of the early embryo affect the segmental expression patterning. Specifically, we study the effect of geometry on formation of the Bicoid primary morphogenetic gradient. Our computational results demonstrate that embryos with a much longer ventral than dorsal surface ('bellied') can produce curved Bicoid concentration contours which could activate curved stripes in the downstream pair-rule segmentation genes. In addition, we show that having an extended source for Bicoid in the anterior of the embryo may be necessary for producing the observed exponential form of the Bicoid gradient along the anteroposterior axis.Keywords: Drosophila embryo, bicoid morphogenetic gradient, exponential expression profile, expression surface form, segmentation genes, 3D modelling
Procedia PDF Downloads 235855 Local Directional Encoded Derivative Binary Pattern Based Coral Image Classification Using Weighted Distance Gray Wolf Optimization Algorithm
Authors: Annalakshmi G., Sakthivel Murugan S.
Abstract:
This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization
Procedia PDF Downloads 130854 Low Density Parity Check Codes
Authors: Kassoul Ilyes
Abstract:
The field of error correcting codes has been revolutionized by the introduction of iteratively decoded codes. Among these, LDPC codes are now a preferred solution thanks to their remarkable performance and low complexity. The binary version of LDPC codes showed even better performance, although it’s decoding introduced greater complexity. This thesis studies the performance of binary LDPC codes using simplified weighted decisions. Information is transported between a transmitter and a receiver by digital transmission systems, either by propagating over a radio channel or also by using a transmission medium such as the transmission line. The purpose of the transmission system is then to carry the information from the transmitter to the receiver as reliably as possible. These codes have not generated enough interest within the coding theory community. This forgetfulness will last until the introduction of Turbo-codes and the iterative principle. Then it was proposed to adopt Pearl's Belief Propagation (BP) algorithm for decoding these codes. Subsequently, Luby introduced irregular LDPC codes characterized by a parity check matrix. And finally, we study simplifications on binary LDPC codes. Thus, we propose a method to make the exact calculation of the APP simpler. This method leads to simplifying the implementation of the system.Keywords: LDPC, parity check matrix, 5G, BER, SNR
Procedia PDF Downloads 121853 Improved Feature Extraction Technique for Handling Occlusion in Automatic Facial Expression Recognition
Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade
Abstract:
The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection
Procedia PDF Downloads 129852 Binary Metal Oxide Catalysts for Low-Temperature Catalytic Oxidation of HCHO in Air
Authors: Hanjie Xie, Raphael Semiat, Ziyi Zhong
Abstract:
It is well known that many oxidation reactions in nature are closely related to the origin and life activities. One of the features of these natural reactions is that they can proceed under mild conditions employing the oxidant of molecular oxygen (O₂) in the air and enzymes as catalysts. Catalysis is also a necessary part of life for human beings, as many chemical and pharmaceutical industrial processes need to use catalysts. However, most heterogeneous catalytic reactions must be run at high operational reaction temperatures and pressures. It is not strange that, in recent years, research interest has been redirected to green catalysis, e.g., trying to run catalytic reactions under relatively mild conditions as much as possible, which needs to employ green solvents, green oxidants such O₂, particularly air, and novel catalysts. This work reports the efficient binary Fe-Mn metal oxide catalysts for low-temperature formaldehyde (HCHO) oxidation, a toxic pollutant in the air, particularly in indoor environments. We prepared a series of nanosized FeMn oxide catalysts and found that when the molar ratio of Fe/Mn = 1:1, the catalyst exhibited the highest catalytic activity. At room temperature, we realized the complete oxidation of HCHO on this catalyst for 20 h with a high GHSV of 150 L g⁻¹ h⁻¹. After a systematic investigation of the catalyst structure and the reaction, we identified the reaction intermediates, including dioxymethylene, formate, carbonate, etc. It is found that the oxygen vacancies and the derived active oxygen species contributed to this high-low-temperature catalytic activity. These findings deepen the understanding of the catalysis of these binary Fe-Mn metal oxide catalysts.Keywords: oxygen vacancy, catalytic oxidation, binary transition oxide, formaldehyde
Procedia PDF Downloads 96851 Potassium-Phosphorus-Nitrogen Detection and Spectral Segmentation Analysis Using Polarized Hyperspectral Imagery and Machine Learning
Authors: Nicholas V. Scott, Jack McCarthy
Abstract:
Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization
Procedia PDF Downloads 105850 Effect of Temperature on the Binary Mixture of Imidazolium Ionic Liquid with Pyrrolidin-2-One: Volumetric and Ultrasonic Study
Authors: T. Srinivasa Krishna, K. Narendra, K. Thomas, S. S. Raju, B. Munibhadrayya
Abstract:
The densities, speeds of sound and refractive index of the binary mixture of ionic liquid (IL) 1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([BMIM][Imide]) and Pyrrolidin-2-one(PY) was measured at atmospheric pressure, and over the range of temperatures T= (298.15 -323.15)K. The excess molar volume, excess isentropic compressibility, excess speed of sound, partial molar volumes, and isentropic partial molar compressibility were calculated from the values of the experimental density and speed of sound. From the experimental data excess thermal expansion coefficients and isothermal pressure coefficient of excess molar enthalpy at 298.15K were calculated. The results were analyzed and were discussed from the point of view of structural changes. Excess properties were calculated and correlated by the Redlich–Kister and the Legendre polynomial equation and binary coefficients were obtained. Values of excess partial volumes at infinite dilution for the binary system at different temperatures were calculated from the adjustable parameters obtained from Legendre polynomial and Redlich–Kister smoothing equation. Deviation in refractive indices ΔnD and deviation in molar refraction, ΔRm were calculated from the measured refractive index values. Equations of state and several mixing rules were used to predict refractive indices of the binary mixtures and compared with the experimental values by means of the standard deviation and found to be in excellent agreement. By using Prigogine–Flory–Patterson (PFP) theory, the above thermodynamic mixing functions have been calculated and the results obtained from this theory were compared with experimental results.Keywords: density, refractive index, speeds of sound, Prigogine-Flory-Patterson theory
Procedia PDF Downloads 374849 Heart Murmurs and Heart Sounds Extraction Using an Algorithm Process Separation
Authors: Fatima Mokeddem
Abstract:
The phonocardiogram signal (PCG) is a physiological signal that reflects heart mechanical activity, is a promising tool for curious researchers in this field because it is full of indications and useful information for medical diagnosis. PCG segmentation is a basic step to benefit from this signal. Therefore, this paper presents an algorithm that serves the separation of heart sounds and heart murmurs in case they exist in order to use them in several applications and heart sounds analysis. The separation process presents here is founded on three essential steps filtering, envelope detection, and heart sounds segmentation. The algorithm separates the PCG signal into S1 and S2 and extract cardiac murmurs.Keywords: phonocardiogram signal, filtering, Envelope, Detection, murmurs, heart sounds
Procedia PDF Downloads 106848 Receptiveness of Market Segmentation Towards Online Shopping Attitude: A Quality Management Strategy for Online Passenger Car Market
Authors: Noor Hasmini Abdghani, Nik Kamariah Nikmat, Nor Hayati Ahmad
Abstract:
Rapid growth of the internet technology led to changes in the consumer lifestyles. This involved customer buying behaviour-based internet that create new kind of buying strategy. Hence, it has summoned many of world firms including Malaysia to generate new quality strategy in preparation to face new customer buying lifestyles. Particularly, this study focused on identifying online customer segment of automobile passenger car customers. Secondly, the objective is to understand online customer’s receptiveness towards internet technologies. This study distributed 700 questionnaires whereby 582 were returned representing 83% response rate. The data were analysed using factor and regression analyses. The result from the factor analysis precipitates four online passenger car segmentations in Malaysia, which are: Segment (1)- Automobile Online shopping Preferences, Segment (2)- Automobile Online Brand Comparison, Segment (3)- Automobile Online Information Seeking and Segment (4)- Automobile Offline Shopping Preferences. In understanding the online customer’s receptiveness towards internet, the regression result shows that there is significant relationship between each of four segments of online passenger car customer with attitude towards automobile online shopping. This implies that, for online customers to have receptiveness toward internet technologies, he or she must have preferences toward online shopping or at least prefer to browse any related information online even if the actual purchase is made at the traditional store. With this proposed segmentation strategy, the firms especially the automobile firms will be able to understand their online customer behavior. At least, the proposed segmentation strategy will help the firms to strategize quality management approach for their online customers’ buying decision making.Keywords: Automobile, Market Segmentation, Online Shopping Attitude, Quality Management Strategy
Procedia PDF Downloads 504847 Thermal Image Segmentation Method for Stratification of Freezing Temperatures
Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka
Abstract:
The study uses an image analysis technique employing thermal imaging to measure the percentage of areas with various temperatures on a freezing surface. An image segmentation method using threshold values is applied to a sequence of image recording the freezing process. The phenomenon is transient and temperatures vary fast to reach the freezing point and complete the freezing process. Freezing salt water is subjected to the salt rejection that makes the freezing point dynamic and dependent on the salinity at the phase interface. For a specific area of freezing, nucleation starts from one side and end to another side, which causes a dynamic and transient temperature in that area. Thermal cameras are able to reveal a difference in temperature due to their sensitivity to infrared radiance. Using Experimental setup, a video is recorded by a thermal camera to monitor radiance and temperatures during the freezing process. Image processing techniques are applied to all frames to detect and classify temperatures on the surface. Image processing segmentation method is used to find contours with same temperatures on the icing surface. Each segment is obtained using the temperature range appeared in the image and correspond pixel values in the image. Using the contours extracted from image and camera parameters, stratified areas with different temperatures are calculated. To observe temperature contours on the icing surface using the thermal camera, the salt water sample is dropped on a cold surface with the temperature of -20°C. A thermal video is recorded for 2 minutes to observe the temperature field. Examining the results obtained by the method and the experimental observations verifies the accuracy and applicability of the method.Keywords: ice contour boundary, image processing, image segmentation, salt ice, thermal image
Procedia PDF Downloads 281846 A Picture is worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
Authors: Tal Remez, Or Litany, Alex Bronstein
Abstract:
The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.Keywords: binary pixels, maximum likelihood, neural networks, sparse coding
Procedia PDF Downloads 165845 Colour Segmentation of Satellite Imagery to Estimate Total Suspended Solid at Rawa Pening Lake, Central Java, Indonesia
Authors: Yulia Chalri, E. T. P. Lussiana, Sarifuddin Madenda, Bambang Trisakti, Yuhilza Hanum
Abstract:
Water is a natural resource needed by humans and other living creatures. The territorial water of Indonesia is 81% of the country area, consisting of inland waters and the sea. The research object is inland waters in the form of lakes and reservoirs, since 90% of inland waters are in them, therefore the water quality should be monitored. One of water quality parameters is Total Suspended Solid (TSS). Most of the earlier research did direct measurement by taking the water sample to get TSS values. This method takes a long time and needs special tools, resulting in significant cost. Remote sensing technology has solved a lot of problems, such as the mapping of watershed and sedimentation, monitoring disaster area, mapping coastline change, and weather analysis. The aim of this research is to estimate TSS of Rawa Pening lake in Central Java by using the Lansat 8 image. The result shows that the proposed method successfully estimates the Rawa Pening’s TSS. In situ TSS shows normal water quality range, and so does estimation result of segmentation method.Keywords: total suspended solid (TSS), remote sensing, image segmentation, RGB value
Procedia PDF Downloads 373844 Text Data Preprocessing Library: Bilingual Approach
Authors: Kabil Boukhari
Abstract:
In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on different corpora and gave an interesting result.Keywords: text preprocessing, segmentation, knowledge extraction, normalization, text generation, information retrieval
Procedia PDF Downloads 54843 The Foundation Binary-Signals Mechanics and Actual-Information Model of Universe
Authors: Elsadig Naseraddeen Ahmed Mohamed
Abstract:
In contrast to the uncertainty and complementary principle, it will be shown in the present paper that the probability of the simultaneous occupation event of any definite values of coordinates by any definite values of momentum and energy at any definite instance of time can be described by a binary definite function equivalent to the difference between their numbers of occupation and evacuation epochs up to that time and also equivalent to the number of exchanges between those occupation and evacuation epochs up to that times modulus two, these binary definite quantities can be defined at all point in the time’s real-line so it form a binary signal represent a complete mechanical description of physical reality, the time of these exchanges represent the boundary of occupation and evacuation epochs from which we can calculate these binary signals using the fact that the time of universe events actually extends in the positive and negative of time’s real-line in one direction of extension when these number of exchanges increase, so there exists noninvertible transformation matrix can be defined as the matrix multiplication of invertible rotation matrix and noninvertible scaling matrix change the direction and magnitude of exchange event vector respectively, these noninvertible transformation will be called actual transformation in contrast to information transformations by which we can navigate the universe’s events transformed by actual transformations backward and forward in time’s real-line, so these information transformations will be derived as an elements of a group can be associated to their corresponded actual transformations. The actual and information model of the universe will be derived by assuming the existence of time instance zero before and at which there is no coordinate occupied by any definite values of momentum and energy, and then after that time, the universe begin its expanding in spacetime, this assumption makes the need for the existence of Laplace’s demon who at one moment can measure the positions and momentums of all constituent particle of the universe and then use the law of classical mechanics to predict all future and past of universe’s events, superfluous, we only need for the establishment of our analog to digital converters to sense the binary signals that determine the boundaries of occupation and evacuation epochs of the definite values of coordinates relative to its origin by the definite values of momentum and energy as present events of the universe from them we can predict approximately in high precision it's past and future events.Keywords: binary-signal mechanics, actual-information model of the universe, actual-transformation, information-transformation, uncertainty principle, Laplace's demon
Procedia PDF Downloads 133842 The Influence of the Normative Gender Binary in Diversity Management: A Multi-Method Study on Gender Diversity of Diversity Management
Authors: Robin C. Ladwig
Abstract:
Diversity Management, as a substantial element of Human Resource Management, aims to secure the economic benefit that assumingly comes with a diverse workforce. Consequently, diversity managers focus on the protection of employees and securing equality measurements to assure organisational gender diversity. Gender diversity as one aspect of Diversity Management seems to adhere to gender binarism and cis-normativity. Workplaces are gendered spaces which are echoing the binary gender-normativity presented in Diversity Management, sold under the label of gender diversity. While the expectation of Diversity Management implies the inclusion of a multiplicity of marginalised groups, such as trans and gender diverse people, in current literature and practice, the reality is curated by gender binarism and cis-normativity. The qualitative multi-method research showed a lack of knowledge about trans and gender diverse matters within the profession of Diversity Management and Human Resources. The semi-structured interviews with trans and gender diverse individuals from various backgrounds and occupations in Australia exposed missing considerations of trans and gender diverse experiences in the inclusivity and gender equity of various workplaces. Even if practitioners consider trans and gender diverse matters under gender diversity, the practical execution is limited to gender binary structures and cis-normative actions as the photo-elicit questionnaire with diversity managers, human resource officers, and personnel management demonstrates. Diversity Management should approach a broader source of informed practice by extending their business focus to the knowledge of humanity studies. Humanity studies could include diversity, queer, or gender studies to increase the inclusivity of marginalised groups such as trans and gender diverse employees and people. Furthermore, the definition of gender diversity should be extended beyond the gender binary and cis-normative experience. People may lose trust in Diversity Management as a supportive ally of marginalised employees if the understanding of inclusivity is limited to a gender binary and cis-normativity value system that misrepresents the richness of gender diversity.Keywords: cis-normativity, diversity management, gender binarism, trans and gender diversity
Procedia PDF Downloads 156841 The Influence of Imposter Phenomenon on the Experiences of Intimacy in Non-Binary Young Adults
Authors: Muskan Jain, Baiju Gopal
Abstract:
Objectives: Intimacy in interpersonal relationships is integral to psychological health and everyday wellbeing; the focus is on intimacy, which can be described as feelings of closeness, connection, and belonging within relationships, which is influenced by an individual's gender identity as well as life experiences. The study aims to explore the experiences of intimacy of the non-binary gender; this marginalized community has increased risks of developing the imposter phenomenon. The study explores the influence of IP on the development and sustenance of intimacy in relationships. Methods: The present study accumulates detailed narratives from 10 non-binary young adults ages 18 to 25 in metropolitan cities of India. Thematic analysis was used for the data analysis. Results: Seven major themes have emerged revolving around internalized criticism and self-depreciating behavior, which causes distance between partners. The four themes that result in the internalization of criticism are lack of social stability, invalidation by social units, adverse life experiences, and estrangement due to gender identity. Three themes that encapsulate major difficulties in relationships are limited self-disclosure, inhibition of physical needs, and fear of taking space. The findings have been critically compared and contrasted with the existing body of literature in the domain, which sets the agenda for further inquiry. Conclusion: It is important for future studies to capture the experiences of non-binary genders in India to provide better therapeutic support in order to assist them in forming meaningful and authentic relationships, thus increasing overall wellbeing.Keywords: imposter phenomenon, intimacy, internalized criticism, marginalized community
Procedia PDF Downloads 27840 Nature Writing in Margaret Atwood’s 'The Testaments'
Authors: Natalia Fontes De Oliveira
Abstract:
Nature and women have a long age association that has persisted throughout history, cultures, literature, and arts. Women’s physiological functions of reproduction and childbearing are viewed as closer to nature as a binary opposition to men, who have metaphorically and historically been associated with culture. To liberate from strictures of phallogocentric rhetoric, a radical critique of the categories of nature and culture must be undertaken. This paper proposes that nature writing in Margaret Atwood’s The Testaments is used subversively as a form of rebellion to disrupt the metaphorical relationship between women and nature. In tune with ecofeminist concerns, the imagery rewrites patriarchal paradigms of binary oppositions as the protagonists narrate a complex and plural relationship between nature and women.Keywords: ecofeminism, Margaret Atwood, nature writing, women's writing
Procedia PDF Downloads 126839 A Physical Theory of Information vs. a Mathematical Theory of Communication
Authors: Manouchehr Amiri
Abstract:
This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle
Procedia PDF Downloads 121838 National Directorate of Employment Training and Agricultural-Small and Medium Enterprises Performance in Nigeria
Authors: Festus M. Epetimehin
Abstract:
This study was conducted to identify the effect of National Directorate of Employment (NDE) training on the profit of Agricultural-Small and Medium Enterprises (SMEs) and to evaluate the factors that influenced farmers' participation in NDE training, as well as the type and frequency of training farmers and other agro-allied entrepreneurs in Nigeria. Using a multi-stage sampling procedure, a total of 384 respondents were sampled, including 192 beneficiaries and 192 non-beneficiaries in Oyo and Lagos States, respectively. Data were analysed using Binary Logit regression and Propensity Score Matching techniques. According to the binary logit analysis, respondents’ gender, availability to extension services, and the location of respondent’s operation were determinant factors influencing NDE training enrolment. All identified factors are related to the probability of respondents’ involvement in a positive way. Propensity score matching revealed that Agricultural-SMEs who participated in the NDE program boosted their profit by N341,072.18. The positive outcome of the effect implies that NDE training enhances Agri-SME performance in Nigeria. The study concluded that greater funding should be provided for the NDE for performance-enhancing training of the Agri-SMEs.Keywords: PSM, binary logit model, Agri-SME
Procedia PDF Downloads 62837 Thermodynamic Behaviour of Binary Mixtures of 1, 2-Dichloroethane with Some Cyclic Ethers: Experimental Results and Modelling
Authors: Fouzia Amireche-Ziar, Ilham Mokbel, Jacques Jose
Abstract:
The vapour pressures of the three binary mixtures: 1, 2- dichloroethane + 1,3-dioxolane, + 1,4-dioxane or + tetrahydropyrane, are carried out at ten temperatures ranging from 273 to 353.15 K. An accurate static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P and the excess molar Gibbs energies GE.Keywords: disquac model, dortmund UNIFAC model, excess molar Gibbs energies GE, VLE
Procedia PDF Downloads 225836 Computer Simulation of Hydrogen Superfluidity through Binary Mixing
Authors: Sea Hoon Lim
Abstract:
A superfluid is a fluid of bosons that flows without resistance. In order to be a superfluid, a substance’s particles must behave like bosons, yet remain mobile enough to be considered a superfluid. Bosons are low-temperature particles that can be in all energy states at the same time. If bosons were to be cooled down, then the particles will all try to be on the lowest energy state, which is called the Bose Einstein condensation. The temperature when bosons start to matter is when the temperature has reached its critical temperature. For example, when Helium reaches its critical temperature of 2.17K, the liquid density drops and becomes a superfluid with zero viscosity. However, most materials will solidify -and thus not remain fluids- at temperatures well above the temperature at which they would otherwise become a superfluid. Only a few substances currently known to man are capable of at once remaining a fluid and manifesting boson statistics. The most well-known of these is helium and its isotopes. Because hydrogen is lighter than helium, and thus expected to manifest Bose statistics at higher temperatures than helium, one might expect hydrogen to also be a superfluid. As of today, however, no one has yet been able to produce a bulk, hydrogen superfluid. The reason why hydrogen did not form a superfluid in the past is its intermolecular interactions. As a result, hydrogen molecules are much more likely to crystallize than their helium counterparts. The key to creating a hydrogen superfluid is therefore finding a way to reduce the effect of the interactions among hydrogen molecules, postponing the solidification to lower temperature. In this work, we attempt via computer simulation to produce bulk superfluid hydrogen through binary mixing. Binary mixture is a technique of mixing two pure substances in order to avoid crystallization and enhance super fluidity. Our mixture here is KALJ H2. We then sample the partition function using this Path Integral Monte Carlo (PIMC), which is well-suited for the equilibrium properties of low-temperature bosons and captures not only the statistics but also the dynamics of Hydrogen. Via this sampling, we will then produce a time evolution of the substance and see if it exhibits superfluid properties.Keywords: superfluidity, hydrogen, binary mixture, physics
Procedia PDF Downloads 286835 Structural Performance Evaluation of Segmented Wind Turbine Blade Through Finite Element Simulation
Authors: Chandrashekhar Bhat, Dilifa Jossley Noronha, Faber A. Saldana
Abstract:
Transportation of long turbine blades from one place to another is a difficult process. Hence a feasibility study of modularization of wind turbine blade was taken from structural standpoint through finite element analysis. Initially, a non-segmented blade is modeled and its structural behavior is evaluated to serve as reference. The resonant, static bending and fatigue tests are simulated in accordance with IEC61400-23 standard for comparison purpose. The non-segmented test blade is separated at suitable location based on trade off studies and the segments are joined with an innovative double strap bonded joint configuration. The adhesive joint is modeled by adopting cohesive zone modeling approach in ANSYS. The developed blade model is analyzed for its structural response through simulation. Performances of both the blades are found to be similar, which indicates that, efficient segmentation of the long blade is possible which facilitates easy transportation of the blades and on site reassembling. The location selected for segmentation and adopted joint configuration has resulted in an efficient segmented blade model which proves the methodology adopted for segmentation was quite effective. The developed segmented blade appears to be the viable alternative considering its structural response specifically in fatigue within considered assumptions.Keywords: modularization, fatigue, cohesive zone modeling, wind turbine blade
Procedia PDF Downloads 407834 The Application of Video Segmentation Methods for the Purpose of Action Detection in Videos
Authors: Nassima Noufail, Sara Bouhali
Abstract:
In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.Keywords: video segmentation, action detection, classification, Kmeans, C3D
Procedia PDF Downloads 42833 Restricted Boltzmann Machines and Deep Belief Nets for Market Basket Analysis: Statistical Performance and Managerial Implications
Authors: H. Hruschka
Abstract:
This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models
Procedia PDF Downloads 158832 Sign Language Recognition of Static Gestures Using Kinect™ and Convolutional Neural Networks
Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy
Abstract:
This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.Keywords: sign language, CNN, HCI, segmentation
Procedia PDF Downloads 108