Search results for: orientation features

Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: So-Young Lee, Myoung-Won Oh, Soon-Cheol Eom, Yeon-Won Suh

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Buildings produce more than one thirds of the total energy consumption and CO₂ emissions. Korean government agency pronounced and initiated Zero Energy Buildings policy for construction as of 2025. The net zero energy design features include passive (daylight, layout, materials, insulation, finishes, etc.) and active (renewable energy sources) elements. The Zero Energy House recently built in Nowon-gu, Korea is provided for 121 households as a public rental housing complex. However most of public rental housing did not include sustainable features which can reduce housing maintaining cost significantly including energy cost. It is necessary to implement net zero design features to the obsolete public rental housing during the remodeling procedure since it can reduce housing cost in long term. The purpose of this study is to investigate sustainable design elements implemented in Net Zero Energy House in Korea and passive and active housing design features in order to apply the sustainable features to the case public rental apartment for remodeling. Housing complex cases in this study are Nowan zero Energy house, Gangnam Bogemjari House, and public rental housings built in more than 20 years in Seoul areas. As results, energy consumption in public rental housing built in 5-years can be improved by exterior surfaces. Energy optimizing in case housing built in more than 20 years can be enhanced by renovated materials, insulation, replacement of windows, exterior finishes, lightings, gardening, water, renewable energy installation, Green IT except for sunlight and layout of buildings. Further life costing analysis is needed for energy optimizing for case housing alternatives.

Keywords: affordable housing, remodeling, sustainable design, zero-energy house

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Authors: Mohamed S. Selim, Nesreen A. Fatthallah, Shimaa A. Higazy, Zhifeng Hao, Xiang Chen

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After the prohibition of tin-based fouling-prevention coatings in 2003, the researchers were directed toward eco-friendly coatings. Because of their nonstick, environmental, and economic benefits, foul-release nanocoatings have received a lot of attention. They use physical anti-adhesion terminology to deter any fouling attachment.Natural bioinspired surfaces have micro/nano-roughness and low surface free energy features, which may inspire the design of dynamic antifouling coatings. Graphene-based nanocomposite surfaces were designed to combat marine-fouling adhesion with ecological as well as eco-friendly effects rather than biocidal solutions. Polymer–graphenenanofiller hybrids are a novel class of composite materials in fouling-prevention applications. The controlled preparation of nanoscale orientation, arrangement, and direction along the composite building blocks would result in superior fouling prohibition. This work representsfoul-release nanocomposite top coats for marine coating applications with superhydrophobicity, surface inertness against fouling adherence, cost-effectiveness, and increased lifetime.

Keywords: foul-release nanocoatings, graphene-based nanocomposite, polymer, nanofillers

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Authors: Rubens P. Costa, Marcelo A. Leigui de Oliveira

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The comprehension of features on the energy spectra, the chemical compositions, and the origins of Ultra-High Energy Cosmic Rays (UHECRs) - mainly atomic nuclei with energies above ~1.0 EeV (exa-electron volts) - are intrinsically linked to the problem of determining the magnitude of their deflections in cosmic magnetic fields on cosmological scales. In addition, as they propagate from the source to the observer, modifications are expected in their original energy spectra, anisotropy, and the chemical compositions due to interactions with low energy photons and matter. This means that any consistent interpretation of the nature and origin of UHECRs has to include the detailed knowledge of their propagation in a three-dimensional environment, taking into account the magnetic deflections and energy losses. The parameter space range for the magnetic fields in the universe is very large because the field strength and especially their orientation have big uncertainties. Particularly, the strength and morphology of the Extragalactic Magnetic Fields (EGMFs) remain largely unknown, because of the intrinsic difficulty of observing them. Monte Carlo simulations of charged particles traveling through a simulated magnetized universe is the straightforward way to study the influence of extragalactic magnetic fields on UHECRs propagation. However, this brings two major difficulties: an accurate numerical modeling of charged particles diffusion in magnetic fields, and an accurate numerical modeling of the magnetized Universe. Since magnetic fields do not cause energy losses, it is important to impose that the particle tracking method conserve the particle’s total energy and that the energy changes are results of the interactions with background photons only. Hence, special attention should be paid to computational effects. Additionally, because of the number of particles necessary to obtain a relevant statistical sample, the particle tracking method must be computationally efficient. In this work, we present an analysis of the propagation of ultra-high energy charged particles in the intergalactic medium. The EGMFs are considered to be coherent within cells of 1 Mpc (mega parsec) diameter, wherein they have uniform intensities of 1 nG (nano Gauss). Moreover, each cell has its field orientation randomly chosen, and a border region is defined such that at distances beyond 95% of the cell radius from the cell center smooth transitions have been applied in order to avoid discontinuities. The smooth transitions are simulated by weighting the magnetic field orientation by the particle's distance to the two nearby cells. The energy losses have been treated in the continuous approximation parameterizing the mean energy loss per unit path length by the energy loss length. We have shown, for a particle with the typical energy of interest the integration method performance in the relative error of Larmor radius, without energy losses and the relative error of energy. Additionally, we plotted the distance amplification from rectilinear propagation as a function of the traveled distance, particle's magnetic rigidity, without energy losses, and particle's energy, with energy losses, to study the influence of particle's species on these calculations. The results clearly show when it is necessary to use a full three-dimensional simulation.

Keywords: cosmic rays propagation, extragalactic magnetic fields, magnetic deflections, ultra-high energy

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: S. S. Panda, Arkesh Chouhan, Yogesh Deshpande

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The present paper deals with designing and fabricating an apparatus for the speedy and accurate manufacturing of fiber reinforced composite lamina of different orientation, thickness and stacking sequences for testing. Properties derived through an analytical approach are verified through measuring the elastic modulus, ultimate tensile strength, flexural modulus and flexural strength of the samples. The 0⁰ orientation ply looks stiffer compared to the 90⁰ ply. Similarly, the flexural strength of 0⁰ ply is higher than to the 90⁰ ply. Sample machinability has been studied by conducting numbers of drilling based on Taguchi Design experiments. Multi Responses (Delamination and Damage grading) is obtained using the desirability approach and optimum cutting condition (spindle speed, feed and drill diameter), at which responses are minimized is obtained thereafter. Delamination increases nonlinearly with the increase in spindle speed. Similarly, the influence of the drill diameter on delamination is higher than the spindle speed and feed rate.

Keywords: delamination, FRP composite, Taguchi design, multi response optimization

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Authors: Madhu Sudan, G. N. Tiwari

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In this paper, an attempt has been made to analyze the effect of wall window orientation on Daylight Illuminance Ratio (DIR) and energy saving in a building known as “SODHA BERS COMPLEX (SBC)” at Varanasi, UP, India. The building has been designed incorporating all passive concepts for thermal comfort as well daylighting concepts to maximize the use of natural daylighting for the occupants in the day to day activities. The annual average DIR and the energy saving has been estimated by using the DIR model for wall window with different orientations under clear sky condition. It has been found that for south oriented window the energy saving per square meter is more compared to the other orientations due to the higher level of solar insolation for the south window in northern hemisphere whereas energy saving potential is minimum for north oriented wall window. The energy saving potential was 26%, 81% and 51% higher for east, south and west oriented window in comparison to north oriented window. The average annual DIR has same trends of variation as the annual energy saving and it is maximum for south oriented window and minimum for north oriented window.

Keywords: clear sky, daylight factor, energy saving, wall window

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Authors: E. Osorio Schmied

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Architects commonly attempt a depiction of organic forms when their works are inspired by nature, regardless of the building site. Nevertheless it is also possible to try matching structures with natural scenery, by applying a phenomenological approach in terms of spatial operations, regarding perceptions from nature through architectural aspects such as protection, views, and orientation. This method acknowledges a relationship between place and space, where intentions towards tangible facts then become design statements. Although spaces resulting from such a process may present an effective response to the environment, they can also offer further outcomes beyond the realm of form. The hypothesis is that, in addition to recognising a bond between architecture and nature, it is also plausible to associate such perceptions with the inner ambient of buildings, by analysing features such as daylight. The case study of a single-family house in a rainforest near Valdivia, Chilean Patagonia is presented, with the intention of addressing the above notions through a discussion of the actual effects of inhabiting a place by way of a series of insights, including a revision of diagrams and photographs that assist in understanding the implications of this design practice. In addition, figures based on post-occupancy behaviour and daylighting performance relate both architectural and environmental issues to a decision-making process motivated by the observation of nature.

Keywords: architecture, design statements, nature, perception

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Authors: Ramy Hammady, Sylvester Arnab

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Significant attention has been directed to adopt game interventions practically to change certain behaviours in many disciplines such as health, education, psychology through many years. That’s due to the intrinsic motivation that games can cause and the substantial impact the games can leave on the player. Many review papers were induced to highlight and measure the effectiveness of the game’s interventions on changing behaviours; however, most of these studies neglected the game design process itself and the game features and elements that can stimuli changing behaviours. Therefore, this paper aims to identify the most game design mechanics and features that are the most influencing on changing behaviour during or after games interventions. This paper also sheds light on the theories of changing behaviours that clearly can led the game design process. This study gives directions to game designers to spot the most influential game features and mechanics for changing behaviour games in order to exploit it on the same manner.

Keywords: behaviour change, game design, serious gaming, gamification, review

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Authors: Kanupriya Rawat, Aleksandra Błachnio, Paweł Izdebski

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Achievement motivation, which drives a person to strive for success, is an important construct in sports psychology. This systematic review aims to analyze the methods of measuring achievement motivation used in previous studies published over the past four decades and to find out which method of measuring achievement motivation is the most prevalent and the most effective by thoroughly examining measures of achievement motivation used in each study and by evaluating most highly cited achievement motivation measures in sport. In order to understand this latent construct, thorough measurement is necessary, hence a critical evaluation of measurement tools is required. The literature search was conducted in the following databases: EBSCO, MEDLINE, APA PsychARTICLES, Academic Search Ultimate, Open Dissertations, ERIC, Science direct, Web of Science, as well as Wiley Online Library. A total of 26 articles met the inclusion criteria and were selected. From this review, it was found that the Achievement Goal Questionnaire- Sport (AGQ-Sport) and the Task and Ego Orientation in Sport Questionnaire (TEOSQ) were used in most of the research, however, the average weighted impact factor of the Achievement Goal Questionnaire- Sport (AGQ-Sport) is the second highest and most relevant in terms of research articles related to the sport psychology discipline. Task and Ego Orientation in Sport Questionnaire (TEOSQ) is highly popular in cross-cultural adaptation but has the second last average IF among other scales due to the less impact factor of most of the publishing journals. All measures of achievement motivation have Cronbach’s alpha value of more than .70, which is acceptable. The advantages and limitations of each measurement tool are discussed, and the distinction between using implicit and explicit measures of achievement motivation is explained. Overall, both implicit and explicit measures of achievement motivation have different conceptualizations of achievement motivation and are applicable at either the contextual or situational level. The conceptualization and degree of applicability are perhaps the most crucial factors for researchers choosing a questionnaire, even though they differ in their development, reliability, and use.

Keywords: achievement motivation, task and ego orientation, sports psychology, measures of achievement motivation

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Authors: Man Singh Meena

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Structural engineers are facing different kind of challenges due to innovative & bold ideas of architects who are trying to design every structure with uniqueness. In RCC frame structures different geometry of columns can be used in design and rectangular columns can be placed with different type orientation. The analysis is design of structures can also be carried out by different type of software available i.e., STAAD Pro, ETABS and TEKLA. In recent times high-rise building modeling & analysis is done by ETABS due to its certain features which are superior to other software. The case study in this paper mainly emphasizes on structural behavior of high rise building for different column shape configurations like Circular, Square, Rectangular and Rectangular with 90-degree Rotation and rectangular shape plan. In all these column shapes the areas of columns are kept same to study the effect on design of concrete area is same. Modelling of 20-storeys R.C.C. framed building is done on the ETABS software for analysis. Post analysis of the structure, maximum bending moments, shear forces and maximum longitudinal reinforcement are computed and compared for three different story structures to identify the effectiveness of geometry of column.

Keywords: high-rise building, column geometry, building modelling, ETABS analysis, building design, structural analysis, structural optimization

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Authors: Lavanya Srinivasan

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Biometric identification is to identify unique features in a person like fingerprints, iris, ear, and voice recognition that need the subject's permission and physical contact. Gait biometric is used to identify the unique gait of the person by extracting moving features. The main advantage of gait biometric to identify the gait of a person at a distance, without any physical contact. In this work, the gait biometric is used for person re-identification. The person walking naturally compared with the same person walking with bag, coat, and case recorded using longwave infrared, short wave infrared, medium wave infrared, and visible cameras. The videos are recorded in rural and in urban environments. The pre-processing technique includes human identified using YOLO, background subtraction, silhouettes extraction, and synthesis Gait Entropy Image by averaging the silhouettes. The moving features are extracted from the Gait Entropy Energy Image. The extracted features are dimensionality reduced by the principal component analysis and recognised using different classifiers. The comparative results with the different classifier show that linear discriminant analysis outperforms other classifiers with 95.8% for visible in the rural dataset and 94.8% for longwave infrared in the urban dataset.

Keywords: biometric, gait, silhouettes, YOLO

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Authors: Ping Yang

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Dating interaction between males and females remains an interesting and mysterious event, particularly in different cultural contexts. This paper focuses on a comparative study of different communication styles males and females use while engaged in dating interaction in the Australian and Chinese contexts. Using communication accommodation theory (CAT) as an analytical framework, the researcher studies how the Australian males and females used a generally different communication style in an Australian dating show (Married at First Sight) than that used by their Chinese counterparts in a Chinese one (非诚勿扰, You Are the One). Based on the qualitative data analysis through NVivo 12 as a research tool, the researcher finds that Australian males and females generally use a divergent communication style characterized by self-orientation, directness, and confrontation, while Chinese counterparts use a convergent communication style characterized by other-orientation, indirectness, and non-confrontation. The researcher concludes with two possible reasons behind the similar TV dating event but with different dramas. One is due to different cultures with varying styles of communication, and the other is because of different drama effect designs suitable for different audience expectations in different cultural contexts.

Keywords: communication styles, cultural contexts, face-to-face interaction, TV dating.

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Authors: Ana Sofia Saldanha

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The globalization is changing the translation world day after day, year after year. The need to know more about new technologies, clients, companies and social networks is becoming more and more demanding and competitive. The recently graduated translators usually do not know where to go, what to do or even who to contact to start their careers in translation. It is well known that there are innumerous webinars, books, blogs, webpages and even Facebook pages indicating what to do, what not to do, rates, how your CV should look like, etc. but are these pieces of advice of real translators? Translators, who work daily with clients, who understand their demands, requests, questions? As far as today`s trends, the answer is NO. Most of these pieces of advice are just theoretical and far away from the real translation world. Therefore, mentoring is becoming a very important tool to help and guide new translators starting their career. An effective and well-oriented mentoring is a powerful way to orient these translators on how to create their CVs, where to send CVs, how to approach clients, how to answer emails and how to negotiate rates in an efficient way. Mentoring is crucial when properly delivered by professional and experienced translators, to help developing careers. The advice and orientation sessions are almost a 'weapon' to destroy the barriers created by opinions, by influences or even by universities. This new trend is the future path of new translators and is the future of the translation industry and professionals, however minds and spirits need to be opened and engaged in this new way of developing skills.

Keywords: mentoring, translation, translators, orientation, professional path

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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: Laurel Chikwado Madumere, Kevin O. Ugorji

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A speech is said to be full of hate when it appropriates disparaging and vituperative locutions and/or appellations, which are riddled with prejudices and misconceptions about an antagonizing party on the grounds of gender, race, political orientation, religious affiliations, tribe, etc. Due largely to the dichotomies and polarities that exist in Nigeria across political ideological spectrum, tribal affiliations, and gender contradistinctions, there are possibilities for the existence of socioeconomic, religious and political conditions that would induce, provoke and catalyze hate speeches in Nigeria’s mainstream media. Therefore the aim of this paper is to investigate, using select daily newspapers in Nigeria, the extent and complexity of those likely hate speeches that emanate from the pluralism in Nigeria and to set in to relief, the discrepancies and contrariety in the interpretation of those hate words. To achieve the above, the paper shall be qualitative in orientation as it shall be using the Speech Act Theory of J. L. Austin and J. R. Searle to interpret and evaluate the hate speeches in the select Nigerian daily newspapers. Also this paper shall help to elucidate the conditions that generate hate, and inform the government and NGOs how best to approach those conditions and put an end to the possible violence and extremism that emanate from extreme cases of hate.

Keywords: extremism, gender, hate speech, pluralism, prejudice, speech act theory

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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: 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: 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: Dharni Vasudhevan Venkatesan, K. E. Shanjay, H. Sujith Kumar, N. A. Abhilash, D. Aswin Ram, V. R. Sanal Kumar

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Numerical studies on race car aerodynamics at wing in ground effect have been carried out using a steady 3d, double precision, pressure-based, and standard k-epsilon turbulence model. Through various parametric analytical studies we have observed that at a particular speed and ground clearance of the wings a favorable negative lift was found high at a particular angle of attack for all the physical models considered in this paper. The fact is that if the ground clearance height to chord length (h/c) is too small, the developing boundary layers from either side (the ground and the lower surface of the wing) can interact, leading to an altered variation of the aerodynamic characteristics at wing in ground effect. Therefore a suitable ground clearance must be predicted throughout the racing for a better performance of the race car, which obviously depends upon the coupled effects of the topography, wing orientation with respect to the ground, the incoming flow features and/or the race car speed. We have concluded that for the design of high performance and high speed race cars the adjustable wings capable to alter the ground clearance and the angles of attack is the best design option for any race car for racing safely with variable speeds.

Keywords: external aerodynamics, external flow choking, race car aerodynamics, wing in ground effect

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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: Ram Moorat Singh, Arun Kumar Sharma, Ravi Chaurey

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Assessment of characteristics of Himalayan glaciers with or without glacier lakes was carried out for 1937glaciers of Kosi sub-basin, Ganga basin by using remote sensing and GIS techniques. Analysis of IRS-P6 AWiFS Data of 2004-07 periods, SRTM DEM and MODIS Land Surface Temperature (LST) data (15year mean) using image processing and GIS tools has provided significant information on various glacier parameters. The glacier area, length, width, ice exposed area, debris cover area, glacier slope, orientation, elevation and temperature data was analysed. The 119 supra glacier lakes and 62 moraine dam/peri-glacier lakes (area > 0.02 km2) in the study were studied to discern the suitable glacier conditions for glacier lake formation. On analysis it is observed that the glacial lakes are preferably formed in association with large dimension glaciers (area, length and width), glaciers with higher percent ice exposed area, lower percent debris cover area and in general mean elevation value greater than 5300 m amsl. On analysis of lake type shows that the moraine dam lakes are formed associated with glaciers located at relatively higher altitude as compared to altitude of glaciers with supra glacier lakes. Analysis of frequency of occurrence of lakes vis a vis glacier orientation shows that more number of glacier lakes are formed associated with glaciers having orientation south, south east, south west, east and west directions. The supra glacial lakes are formed in association with glaciers having higher mean temperature as compared to moraine dam lakes as verified using LST data of 15 years (2000-2014).

Keywords: remote sensing, supra glacial lake, Himalaya, Kosi sub-basin, glaciers, moraine-dammed lake

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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: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

Abstract:

Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Srishti Bhatt, Vaibhav Bhavsar, Adil Hussain, Aashay Mhaske, S. C. Bali, T. S. Srikanth

Abstract:

Shape Memory Alloys (SMA) are widely known for their unique shape recovery properties. SMA based actuation systems have high-force to weight ratio, light weight and also bio-compatible material. Which is why they are being used in different fields of aerospace, robotics, automotive and biomedical industries. However, in the automotive industry plenty of patents are available but commercially viable products are very few in market. This could be due to SMA material limitations like small stroke, direct dependability of lifecycle on stroke, pull load of the wire and high cycle time. In automotive sector, SMA being considered as an actuator which is required to have high stroke and constraint arises to accommodate a long length of wire (to compensate maximum 4 % strain as per better fatigue life cycle) not only increases complexity but also adds on the cost. More than 200 different types of actuators are used in an automobile, few of them whose efficiency can highly increase by replacing them with SMA based actuators which include latch lock mechanism, glove box, Head lamp leveling, side mirror and rear mirror leveling, tailgate opener and fuel lid cap actuator. To overcome the limitation of available space for required stroke of an actuator which leads to study the effect of different loading positions on SMA wires, different orientations of SMA wire by using pulleys and lever based systems to achieve maximum stroke. This investigation summarizes the loading under the V shape orientation the required stroke and carrying load capacity in more compact in comparison with straight orientation of wire. Similarly, the U shape orientation its showing higher load carrying capacity but reduced stroke which is aligned with concept of bundled wire method. Life-cycle of these orientations were also evaluated.

Keywords: actuators, automotive, nitinol, shape memory alloy, SMA wire orientations

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Authors: Yong Zhao, Jian He, Cheng Zhang

Abstract:

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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