Search results for: visual features

Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Jaehong Kim, Sunhee Park, HongSeop Shin, Kyongho Lim, Suhyun Kwon, Don-Gyou Lee, Pureum Kim, Moojong Lim, JongSang Baek

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Transparent displays with OLEDs are the most commonly produced forms of see-through displays on the market or in development. In order to block the visual interruption caused by the light coming from the background, the special panel is combined with transparent displays with OLEDs. There is, however, few studies performance of optical shutter panel for transparent displays until now. This paper, therefore, describes the performance of optical shutter panels. The novel evaluation method was developed by measuring the amount of light which can form a transmitted background image. The new proposed method could tell how recognizable transmitted background images cannot be seen, and is consistent with viewer’s perception.

Keywords: optical shutter panel, optical performance, transparent display, visual interruption

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Authors: Al Sayed Al Soudy, Valeria Maselli, Gianluca Polese, Anna Di Cosmo

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Cephalopods are considered as a model organism with a rich behavioral repertoire. Sophisticated behaviors were widely studied and described in different species such as Octopus vulgaris, who has evolved the largest and more complex nervous system among invertebrates. In O. vulgaris, cognitive abilities in problem-solving tasks and learning abilities are associated with long-term memory and spatial memory, mediated by highly developed sensory organs. They are equipped with sophisticated eyes, able to discriminate colors even with a single photoreceptor type, vestibular system, ‘lateral line analogue’, primitive ‘hearing’ system and olfactory organs. They can recognize chemical cues either through direct contact with odors sources using suckers or by distance through the olfactory organs. Cephalopods are able to detect widespread waterborne molecules by the olfactory organs. However, many volatile odorant molecules are insoluble or have a very low solubility in water, and must be perceived by direct contact. O. vulgaris, equipped with many chemosensory neurons located in their suckers, exhibits a peculiar behavior that can be provocatively described as 'smell by touch'. The aim of this study is to establish the priority given to chemical vs. visual perception in food choice. Materials and methods: Three different types of food (anchovies, clams, and mussels) were used, and all sessions were recorded with a digital camera. During the acclimatization period, Octopuses were exposed to the three types of food to test their natural food preferences. Later, to verify if food preference is maintained, food was provided in transparent screw-jars with pierced lids to allow both visual and chemical recognition of the food inside. Subsequently, we tested alternatively octopuses with food in sealed transparent screw-jars and food in blind screw-jars with pierced lids. As a control, we used blind sealed jars with the same lid color to verify a random choice among food types. Results and discussion: During the acclimatization period, O. vulgaris shows a higher preference for anchovies (60%) followed by clams (30%), then mussels (10%). After acclimatization, using the transparent and pierced screw jars octopus’s food choices resulted in 50-50 between anchovies and clams, avoiding mussels. Later, guided by just visual sense, with transparent but not pierced jars, their food preferences resulted in 100% anchovies. With pierced but not transparent jars their food preference resulted in 100% anchovies as first food choice, the clams as a second food choice result (33.3%). With no possibility to select food, neither by vision nor by chemoreception, the results were 20% anchovies, 20% clams, and 60% mussels. We conclude that O. vulgaris uses both chemical and visual senses in an integrative way in food choice, but if we exclude one of them, it appears clear that its food preference relies on chemical sense more than on visual perception.

Keywords: food choice, Octopus vulgaris, olfaction, sensory organs, visual sense

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Hassan Sabo

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Language is a system of conventionally spoken, manual and written symbols by human beings that members of a certain social group and participants in its culture express themselves. Communication, expression of identity and imaginative expression are among the functions of language. Dialect is a form of language, or a regional variety of language that is spoken in a particular geographical setting by a particular group of people. Hausa is one of the major languages in Africa, in terms of large number of people for whom it is the first language. Hausa is one of the western Chadic groups of languages. It constitutes one of the five or six branches of Afro-Asiatic family. The predominant Hausa speakers are in Nigeria and they live in different geographical locations which resulted to variety of dialects within the Hausa language apart of the standard Hausa language, the Hausa language has a variety of dialect that distinguish from one another by such features as phonology, grammar and vocabulary. This study intends to examine such features that serve as means of identification among Hausa speakers who are set off from others, geographically or socially.

Keywords: dialect, features, geographical location, Hausa language

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Rishabh Beri, Sahil Shah

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We have applied a range of filters and processing in order to extract out the various features of the football game, like the field lines of a football field. Another important aspect was the detection of the players in the field and tagging them according to their teams distinguished by their jersey colours. This extracted information combined about the players and field helped us to create a virtual field that consists of the playing field and the players mapped to their locations in it.

Keywords: Detect, Football, Players, Virtual

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

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This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

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Authors: Moulana Mohammed

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Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: Ebenezer Obaloluwa Olaniyi

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In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

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Authors: Lenka Mynaříková, Hedvika Boukalová

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The study looks at the ability to detect nonverbal deception among police officers and management students in the Czech Republic. Respondents from police departments (n=197) and university students of human resources (n=161) completed a deception detection task and evaluated veracity of the statements of suspects in 21 video clips from real crime investigations. Their evaluations were based on nonverbal behavior. Voices in the video clips were modified so that words were not recognizable, yet paraverbal voice characteristics were preserved. Results suggest that respondents have a tendency to lie bias based on their profession. In the evaluation of video clips, stereotypes also played a significant role. The statements of suspects of a different ethnicity, younger age or specific visual features were considered deceitful more often. Research might be beneficial for training in professions that are in need of deception detection techniques.

Keywords: deception detection, police officers, human resources, forensic psychology, forensic studies, organizational psychology

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Muriel Canas-Walker

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The 2012 TV series "The Legend of Zhen Huan" is one of the most popular and influential historical dramas on Chinese television and is regularly discussed on Chinese social media such as Weibo. Set in the Qing dynasty, the 76 episodes series features palace intrigues focused on female characters. In the Forbidden City, concubines must survive the cruelty of an extreme polygamy system, constantly competing against each other. The patriarchal oppression of the women sequestred in the harem relies on fierce female competition and does not leave much room for compassion. Using Michel Foucault’s theory of power, feminist theories, and visual anthropology, this paper analyzes the complex relationships between the female characters, from their rise to power to their fall from grace, from alliances to betrayals, from sorority to sororicide. This analysis aims to understand what makes this series particularly popular with young female audiences in China and explain its importance in Chinese media.

Keywords: Chinese TV Drama, feminism, popular culture, Theory of Power

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Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

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Authors: Vassilis Argyropoulos, Magda Nikolaraizi, Maria Papazafiri

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This study describes a series of learning workshops, which took place within CUIDAR project. The workshops aimed to empower children to share their experiences and views in relation to natural hazards and disasters. The participants in the workshops were ten primary school students who had severe visual impairments or multiple disabilities and visual impairments (MDVI). The main objectives of the workshops were: a) to promote access of the children through the use of appropriate educational material such as texts in braille, enlarged text, tactile maps and the implementation of differentiated instruction, b) to make children aware regarding their rights to have access to information and to participate in planning and decision-making especially in relation to disaster education programs, and c) to encourage children to have an active role during the workshops through child-led and experiential learning activities. The children expressed their views regarding the meaning of hazards and disasters. Following, they discussed their experiences and emotions regarding natural hazards and disasters, and they chose to place the emphasis on a hazard, which was more pertinent to them, their community and their region, namely fires. Therefore, they recalled fires that have caused major disasters, and they discussed about the impact that these fires had on their community or on their country. Furthermore, they were encouraged to become aware regarding their own role and responsibility to prevent a fire or get prepared and know how to behave if a fire occurs. They realized that prevention and preparation are a matter of personal responsibility. They also felt the responsibility to inform their own families. Finally, they met important people involved in fire protection such as rescuers and firefighters and had the opportunity to carry dialogues. In conclusion, through child led workshops, experiential and accessible activities, the students had the opportunity to share their own experiences, to express their views and their questions, to broaden their knowledge and to realize their personal responsibility in disaster risk reduction, specifically in relation to fires.

Keywords: accessibility, children, disasters, visual impairment

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Authors: Vikram Singh, Parmod Kumar Sethi

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The objective of the study was to study the effectiveness of common yoga protocol on reaction time (choice visual reaction time, measured in milliseconds/seconds) of male football players in the age group of 16 to 21 years. The 40 boys were measured initially on parameters of years of experience, level of participation. They were randomly assigned into two groups i.e. control and experimental. CVRT for both the groups was measured on day-1 and post intervention (common yoga protocol here) was measured after 45 days of training to the experimental group after they had finished with their regular fitness and soccer skill training. One way ANOVA (Univariate analysis) and Independent t-test using SPSS 23 statistical package were applied to get and analyze the results. The experimental yoga protocol group showed a significant reduction in CVRT, whereas the insignificant difference in reaction times was observed for control group after 45 days. The effect size was more than 52% for CVRT indicating that the effect of treatment was large. Power of the study was also found to be high (> .80). There was a significant difference after 45 days of yoga protocol in choice visual reaction time of experimental group (p = .000), t (21.93) = 6.410, p = .000 (two-tailed). The null hypothesis (that there would be no difference in reaction times of control and experimental groups) was rejected. Where p< .05. Therefore alternate hypothesis was accepted.

Keywords: reaction time, yoga protocol, t-test, soccer players

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Authors: Nengi A. H. Ejiobih

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Conflict is one of the defining features of human societies. Often, the use or misuse of language in interaction is the genesis of conflict. As such, it is expected that when people use language they do so in socially determined ways and with almost predictable social effects. The objective of this paper was to examine the interest at work as manifested in language choice and collocations in conflict discourse. It also scrutinized the implications of linguistic features in conflict discourse as it concerns ideology and power relations in political discourse in Nigeria. The methodology used for this paper is an approach from Critical discourse analysis because of its multidisciplinary model of analysis, linguistic features and its implications were analysed. The datum used is a text from the Sunday Sun Newspaper in Nigeria, West Africa titled Akunyili Memo: State of the Nation. Some of the findings include; different ideologies are inherent in conflict discourse, there is the presence of power relations being produced, exercised, maintained and produced throughout the discourse and the use of pronouns in conflict discourse is valuable because it is used to initiate and maintain relationships in social context. This paper has provided evidence that, taking into consideration the nature of the social actions and the way these activities are translated into languages, the meanings people convey by their words are identified by their immediate social, political and historical conditions.

Keywords: conflicts, discourse, language, linguistic features, social context

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Authors: Gulay Aras, Isil Kutluturk Karagoz, Z. Candan Algun

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Background: Hypermetropia in school-age is a pathology that responds to treatment. In the literature, there has been no study of exercise practice in hypermetropia treatment. Objective: The purpose of this study was to investigate the effects of eye exercises and convergence exercise on visual acuity in school-age children with hypermetropia. Methods: Forty volunteer school-age children with hypermetropia (30 girls, 30 boys, between 7-17 years of age) were included in the study. Sociodemographic information and clinical characteristics were evaluated. 40 participants were randomly divided into two groups: eye exercises and convergence exercises. Home exercise protocols were given to all groups for six weeks, and regular phone calls were made once a week. Individuals performed eye exercises 10 times, convergence exercises 5 min. for two sessions per day for six weeks. The right and left eyes of all the subjects participating in the study were assessed separately by the eye doctor with a Snellen chart. The participants' quality of life was assessed using Pediatric Quality of Life Inventory Version 4.0. Physical health total score (PHTS) and scale total score (STS), which were obtained by evaluating Psychosocial health total score (PSHTS) school, emotional and social functioning, were calculated separately in the scores. At the end of the exercise program, the assessment tests applied at the beginning of the study were reapplied to all individuals. Results: There was no statistically significant difference between the pre- and post-Snellen chart measurements and quality of life in the eye exercises group (p > 0,05). There was a statistically significant difference in visual acuity of right and left eyes (p=0,004, p=0,014) and quality of life in PHTS, PSHTS and STS in the convergence exercise group (p=0,001, p=0,017, p=0,001). Conclusions: In school-age children, convergence exercises were found to be effective on visual acuity and health-related quality of life. Convergence exercises are recommended for the treatment of school-aged children with hypermetropia.

Keywords: convergence exercise, eye exercises, hypermetropia, school-age children

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Authors: E. Olmos-Raya, A. Cascales Martínez, N. Minto de Sousa, M. Alcañiz Raya

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The late detection and subjectivity of the assessment of Autism Spectrum Disorder (ASD) imposed a difficulty for the children’s clinical and familiar environment. The results showed in this paper, are part of a research project about the assessment and training of social skills in children with ASD, whose overall goal is the use of virtual environments together with physiological measures in order to find a new model of objective ASD assessment based on implicit brain processes measures. In particular, this work tries to contribute by studying the differences and changes in the Skin Conductance Response (SCR) and Eye Tracking (ET) between a typical development group (TD group) and an ASD group (ASD group) after several combined stimuli using a low cost Immersive Virtual Environment (IVE). Subjects were exposed to a virtual environment that showed natural scenes that stimulated visual, auditory and olfactory perceptual system. By exposing them to the IVE, subjects showed natural behaviors while measuring SCR and ET. This study compared measures of subjects diagnosed with ASD (N = 18) with a control group of subjects with typical development (N=10) when exposed to three different conditions: only visual (V), visual and auditory (VA) and visual, auditory and olfactory (VAO) stimulation. Correlations between SCR and ET measures were also correlated with the Autism Diagnostic Observation Schedule (ADOS) test. SCR measures showed significant differences among the experimental condition between groups. The ASD group presented higher level of SCR while we did not find significant differences between groups regarding DF. We found high significant correlations among all the experimental conditions in SCR measures and the subscale of ADOS test of imagination and symbolic thinking. Regarding the correlation between ET measures and ADOS test, the results showed significant relationship between VA condition and communication scores.

Keywords: autism, electrodermal activity, eye tracking, immersive virtual environment, virtual reality

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Authors: M. Ibrahim Ali, M. El Dawi, M. A. Mohamed Ali

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In this study, the use of magnetic gradient survey, and the geoelectrical ground methods used together to explore archaeological features in Nuri’s pyramids area. Research methods used and the procedures and methodologies have taken full right during the study. The magnetic survey method was used to search for archaeological features using (Geoscan Fluxgate Gradiometer (FM36)). The study area was divided into a number of squares (networks) exactly equal (20 * 20 meters). These squares were collected at the end of the study to give a major network for each region. Networks also divided to take the sample using nets typically equal to (0.25 * 0.50 meter), in order to give a more specific archaeological features with some small bipolar anomalies that caused by buildings built from fired bricks. This definition is important to monitor many of the archaeological features such as rooms and others. This main network gives us an integrated map displayed for easy presentation, and it also allows for all the operations required using (Geoscan Geoplot software). The parallel traverse is the main way to take readings of the magnetic survey, to get out the high-quality data. The study area is very rich in old buildings that vary from small to very large. According to the proportion of the sand dunes and the loose soil, most of these buildings are not visible from the surface. Because of the proportion of the sandy dry soil, there is no connection between the ground surface and the electrodes. We tried to get electrical readings by adding salty water to the soil, but, unfortunately, we failed to confirm the magnetic readings with electrical readings as previously planned.

Keywords: archaeological features, independent grids, magnetic gradient, Nuri pyramid

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Authors: Maria Ledinskaya

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This paper is part case study and part literature review. It seeks to introduce Agile, Lean, and Hybrid project management concepts from business, software development, and manufacturing fields to museum conservation by looking at their practical application on a recent conservation project at the Sainsbury Centre for Visual Arts. The author outlines the advantages of leaner and more agile conservation practices in today’s faster, less certain, and more budget-conscious museum climate where traditional project structures are no longer as relevant or effective. The Morris Collection Conservation Project was carried out in 2019-2021 in Norwich, UK, and concerned the remedial conservation of around 150 Abstract Constructivist artworks bequeathed to the Sainsbury Centre by private collectors Michael and Joyce Morris. It was a medium-sized conservation project of moderate complexity, planned and delivered in an environment with multiple known unknowns – unresearched collection, unknown conditions and materials, unconfirmed budget. The project was later impacted by the COVID-19 pandemic, introducing indeterminate lockdowns, budget cuts, staff changes, and the need to accommodate social distancing and remote communications. The author, then a staff conservator at the Sainsbury Centre who acted as project manager on the Morris Project, presents an incremental, iterative, and value-based approach to managing a conservation project in an uncertain environment. The paper examines the project from the point of view of Traditional, Agile, Lean, and Hybrid project management. The author argues that most academic writing on project management in conservation has focussed on a Traditional plan-driven approach – also known as Waterfall project management – which has significant drawbacks in today’s museum environment due to its over-reliance on prediction-based planning and its low tolerance to change. In the last 20 years, alternative Agile, Lean and Hybrid approaches to project management have been widely adopted in software development, manufacturing, and other industries, although their recognition in the museum sector has been slow. Using examples from the Morris Project, the author introduces key principles and tools of Agile, Lean, and Hybrid project management and presents a series of arguments on the effectiveness of these alternative methodologies in museum conservation, including the ethical and practical challenges to their implementation. These project management approaches are discussed in the context of consequentialist, relativist, and utilitarian developments in contemporary conservation ethics. Although not intentionally planned as such, the Morris Project had a number of Agile and Lean features which were instrumental to its successful delivery. These key features are identified as distributed decision-making, a co-located cross-disciplinary team, servant leadership, focus on value-added work, flexible planning done in shorter sprint cycles, light documentation, and emphasis on reducing procedural, financial, and logistical waste. Overall, the author’s findings point in favour of a hybrid model, which combines traditional and alternative project processes and tools to suit the specific needs of the project.

Keywords: agile project management, conservation, hybrid project management, lean project management, waterfall project management

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Mayank Raj, V. G. Narendra

Abstract:

Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

Abstract:

To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

Abstract:

Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

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Authors: Olivier Chaigneau, Damianos Oikonomidis, Marie-Cecile Delmas

Abstract:

GAMBAS (Galileo Advanced features for the Maritime domain: Breakthrough Applications for Safety and security) is a project funded by the European Space Program Agency (EUSPA) aiming at identifying the search-and-rescue and ship security alert system needs for maritime users (including operators and fishing stakeholders) and developing operational concepts to answer these needs. The general objective of the GAMBAS project is to support the deployment of Galileo exclusive features in the maritime domain in order to improve safety and security at sea, detection of illegal activities and associated surveillance means, resilience to natural and human-induced emergency situations, and develop, integrate, demonstrate, standardize and disseminate these new associated capabilities. The project aims to demonstrate: improvement of the SAR (Search And Rescue) and SSAS (Ship Security Alert System) detection and response to maritime distress through the integration of new features into the beacon for SSAS in terms of cost optimization, user-friendly aspects, integration of Galileo and OS NMA (Open Service Navigation Message Authentication) reception for improved authenticated localization performance and reliability, and at sea triggering capabilities, optimization of the responsiveness of RCCs (Rescue Co-ordination Centre) towards the distress situations affecting vessels, the adaptation of the MCCs (Mission Control Center) and MEOLUT (Medium Earth Orbit Local User Terminal) to the data distribution of SSAS alerts.

Keywords: Galileo new advanced features, maritime, safety, security

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Authors: Qazi Mahdia Ghyas

Abstract:

Electronic Government Procurement (e-GP) has implemented in Bangladesh to ensure the good Governance. e-GP has transformed Bangladesh's procurement process electronically. But, to our best knowledge, there is no study to understand the key features of e-GP in Bangladesh. So, this study tries to identify the features of performance improvement after implementing an e-GP system that will help for further improvements. Data was collected from the PROMIS Overall Report (Central Procurement Technical Unit website) for the financial year from Q1 _July- Sep 2015-16 to Q4 _Apr- Jun 2021-22. This study did component factor analysis on KPIs and found nineteen KPIs that are statistically significant and represent time savings, efficiency, accountability, anti-corruption and compliance key features in procurement activities of e-GP. Based on the analysis, some practical measures have been recommended for better improvement of e-GP. This study has some limitations. Because of having multicollinearity issues, all the 42 KPIs (except 19) did not show a good fit for component factor analysis.

Keywords: public procurement, electronic government procurement, KPI, performance evaluation

Procedia PDF Downloads 62