Search results for: searching algorithm

Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

Procedia PDF Downloads 119

Authors: Zaide Montes Ortiz, Jorge Alberto Molina, Alejandro Reyes

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Insects are extremely successful organisms. A sophisticated olfactory system is in part responsible for their survival and reproduction. The detection of volatile organic compounds can positively or negatively affect many behaviors in insects. Compounds such as carbon dioxide (CO2), ammonium, indol, and lactic acid are essential for many species of mosquitoes like Anopheles gambiae in order to locate vertebrate hosts. For instance, in A. gambiae, the olfactory receptor AgOR2 is strongly activated by indol, which accounts for almost 30% of human sweat. On the other hand, in some insects of agricultural importance, the detection and identification of pheromone receptors (PRs) in lepidopteran species has become a promising field for integrated pest management. For example, with the disruption of the pheromone receptor, BmOR1, mediated by transcription activator-like effector nucleases (TALENs), the sensitivity to bombykol was completely removed affecting the pheromone-source searching behavior in male moths. Then, the detection and identification of olfactory receptors in the genomes of insects is fundamental to improve our understanding of the ecological interactions, and to provide alternatives in the integrated pests and vectors management. Hence, the objective of this study is to propose a bioinformatic workflow to enhance the detection and identification of potential olfactory receptors in genomes of relevant insects. Applying Hidden Markov models (Hmms) and different computational tools, potential candidates for pheromone receptors in Tuta absoluta were obtained, as well as potential carbon dioxide receptors in Rhodnius prolixus, the main vector of Chagas disease. This study showed the validity of a bioinformatic workflow with a potential to improve the identification of certain olfactory receptors in different orders of insects.

Keywords: bioinformatic workflow, insects, olfactory receptors, protein prediction

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

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Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

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Authors: Lim Chen Kim, Tan Kian Lam, Chan Yi Chee

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This research presents a multi-modal simulation in the reconstruction of the past and the construction of present in digital cultural heritage on mobile platform. In bringing the present life, the virtual environment is generated through a presented scheme for rapid and efficient construction of 360° panoramic view. Then, acoustical heritage model and crowd model are presented and improvised into the 360° panoramic view. For the reconstruction of past life, the crowd is simulated and rendered in an old trading port. However, the keystone of this research is in a virtual walkthrough that shows the virtual present life in 2D and virtual past life in 3D, both in an environment of virtual heritage sites in George Town through mobile device. Firstly, the 2D crowd is modelled and simulated using OpenGL ES 1.1 on mobile platform. The 2D crowd is used to portray the present life in 360° panoramic view of a virtual heritage environment based on the extension of Newtonian Laws. Secondly, the 2D crowd is animated and rendered into 3D with improved variety and incorporated into the virtual past life using Unity3D Game Engine. The behaviours of the 3D models are then simulated based on the enhancement of the classical model of Boid algorithm. Finally, a demonstration system is developed and integrated with the models, techniques and algorithms of this research. The virtual walkthrough is demonstrated to a group of respondents and is evaluated through the user-centred evaluation by navigating around the demonstration system. The results of the evaluation based on the questionnaires have shown that the presented virtual walkthrough has been successfully deployed through a multi-modal simulation and such a virtual walkthrough would be particularly useful in a virtual tour and virtual museum applications.

Keywords: Boid Algorithm, Crowd Simulation, Mobile Platform, Newtonian Laws, Virtual Heritage

Procedia PDF Downloads 277

Authors: Ł. Morawiński, C. Jasiński, M. Jurkiewicz, S. Bou Habib, M. Bondyra

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With the development of optics, electronics, and computers, vision systems are increasingly used in various areas of life, science, and industry. Vision systems have a huge number of applications. They can be used in quality control, object detection, data reading, e.g., QR-code, etc. A large part of them is used for measurement purposes. Some of them make it possible to obtain a 3D reconstruction of the tested objects or measurement areas. 3D reconstruction algorithms are mostly based on creating depth maps from data that can be acquired from active or passive methods. Due to the specific appliance in airfield technology, only passive methods are applicable because of other existing systems working on the site, which can be blinded on most spectral levels. Furthermore, reconstruction is required to work long distances ranging from hundreds of meters to tens of kilometers with low loss of accuracy even with harsh conditions such as fog, rain, or snow. In response to those requirements, HRESS (Heliport REmote Safeguard System) was developed; which main part is a rotational head with a two-camera stereovision rig gathering images around the head in 360 degrees along with stereovision 3D reconstruction and point cloud combination. The sub-pixel analysis introduced in the HRESS system makes it possible to obtain an increased distance measurement resolution and accuracy of about 3% for distances over one kilometer. Ultimately, this leads to more accurate and reliable measurement data in the form of a point cloud. Moreover, the program algorithm introduces operations enabling the filtering of erroneously collected data in the point cloud. All activities from the programming, mechanical and optical side are aimed at obtaining the most accurate 3D reconstruction of the environment in the measurement area.

Keywords: airfield monitoring, artificial intelligence, stereovision, 3D reconstruction

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

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Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Niroshan K. Walgama Wellalage, Tieling Zhang, Richard Dwight

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State-based Markov deterioration models (SMDM) sometimes fail to find accurate transition probability matrix (TPM) values, and hence lead to invalid future condition prediction or incorrect average deterioration rates mainly due to drawbacks of existing nonlinear optimization-based algorithms and/or subjective function types used for regression analysis. Furthermore, a set of separate functions for each condition state with age cannot be directly derived by using Markov model for a given bridge element group, which however is of interest to industrial partners. This paper presents a new approach for generating Homogeneous SMDM model output, namely, the Modified Weibull approach, which consists of a set of appropriate functions to describe the percentage condition prediction of bridge elements in each state. These functions are combined with Bayesian approach and Metropolis Hasting Algorithm (MHA) based Markov Chain Monte Carlo (MCMC) simulation technique for quantifying the uncertainty in model parameter estimates. In this study, factors contributing to rail bridge deterioration were identified. The inspection data for 1,000 Australian railway bridges over 15 years were reviewed and filtered accordingly based on the real operational experience. Network level deterioration model for a typical bridge element group was developed using the proposed Modified Weibull approach. The condition state predictions obtained from this method were validated using statistical hypothesis tests with a test data set. Results show that the proposed model is able to not only predict the conditions in network-level accurately but also capture the model uncertainties with given confidence interval.

Keywords: bridge deterioration modelling, modified weibull approach, MCMC, metropolis-hasting algorithm, bayesian approach, Markov deterioration models

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Authors: Min Wang, Sergey Utev

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The lessons from the 2007-09 global financial crisis have driven scientific research, which considers the design of new methodologies and financial models in the global market. The quantum mechanics approach was introduced in the unpredictable stock market modeling. One famous quantum tool is Feynman path integral method, which was used to model insurance risk by Tamturk and Utev and adapted to formalize the path-dependent option pricing by Hao and Utev. The research is based on the path-dependent calculation method, which is motivated by the Feynman path integral method. The path calculation can be studied in two ways, one way is to label, and the other is computational. Labeling is a part of the representation of objects, and generating functions can provide many different ways of representing share price paths. In this paper, the recent works on graphical theoretical construction of individual share price path via matroid is presented. Firstly, a study is done on the knowledge of matroid, relationship between lattice path matroid and Tutte polynomials and ways to connect points in the lattice path matroid and Tutte polynomials is suggested. Secondly, It is found that a general binary tree can be validly constructed from a connected lattice path matroid rather than general lattice path matroid. Lastly, it is suggested that there is a way to represent share price paths via a general binary tree, and an algorithm is developed to construct share price paths from general binary trees. A relationship is also provided between lattice integer points and Tutte polynomials of a transversal matroid. Use this way of connection together with the algorithm, a share price path can be constructed from a given connected lattice path matroid.

Keywords: combinatorial construction, graphical representation, matroid, path calculation, share price, Tutte polynomial

Procedia PDF Downloads 138

Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

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In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

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Authors: Anne M. Denton, Rahul Gomes, David W. Franzen

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High-resolution elevation data are becoming increasingly available, but typical approaches for computing topographic features, like slope and curvature, still assume small sliding windows, for example, of size 3x3. That means that the digital elevation model (DEM) has to be resampled to the scale of the landform features that are of interest. Any higher resolution is lost in this resampling. When the topographic features are computed through regression that is performed at the resolution of the original data, the accuracy can be much higher, and the reported result can be adjusted to the length scale that is relevant locally. Slope and variance are calculated for overlapping windows, meaning that one regression result is computed per raster point. The number of window centers per area is the same for the output as for the original DEM. Slope and variance are computed by performing regression on the points in the surrounding window. Such an approach is computationally feasible because of the additive nature of regression parameters and variance. Any doubling of window size in each direction only takes a single pass over the data, corresponding to a logarithmic scaling of the resulting algorithm as a function of the window size. Slope and variance are stored for each aggregation step, allowing the reported slope to be selected to minimize variance. The approach thereby adjusts the effective window size to the landform features that are characteristic to the area within the DEM. Starting with a window size of 2x2, each iteration aggregates 2x2 non-overlapping windows from the previous iteration. Regression results are stored for each iteration, and the slope at minimal variance is reported in the final result. As such, the reported slope is adjusted to the length scale that is characteristic of the landform locally. The length scale itself and the variance at that length scale are also visualized to aid in interpreting the results for slope. The relevant length scale is taken to be half of the window size of the window over which the minimum variance was achieved. The resulting process was evaluated for 1-meter DEM data and for artificial data that was constructed to have defined length scales and added noise. A comparison with ESRI ArcMap was performed and showed the potential of the proposed algorithm. The resolution of the resulting output is much higher and the slope and aspect much less affected by noise. Additionally, the algorithm adjusts to the scale of interest within the region of the image. These benefits are gained without additional computational cost in comparison with resampling the DEM and computing the slope over 3x3 images in ESRI ArcMap for each resolution. In summary, the proposed approach extracts slope and aspect of DEMs at the lengths scales that are characteristic locally. The result is of higher resolution and less affected by noise than existing techniques.

Keywords: high resolution digital elevation models, multi-scale analysis, slope calculation, window-based regression

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Authors: Hannah K. M. Tang, Muhammad Ateeq, Mark J. Lake, Badr Abdullah, Frederic A. Bezombes

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There is little research applying virtual reality (VR) to the treatment of musculoskeletal injury in athletes. This is despite their prevalence, and the implications for physical and psychological health. Nevertheless, developments of wireless VR headsets better facilitate dynamic movement in VR environments (VREs), and more research is expected in this emerging field. This systematic review identified publications that used VR interventions for the analysis or treatment of lower-limb musculoskeletal injury of athletes. It established a search protocol, and through narrative discussion, identified existing trends. Database searches encompassed four term sets: 1) VR systems; 2) musculoskeletal injuries; 3) sporting population; 4) movement outcome analysis. Overall, a total of 126 publications were identified through database searching, and twelve were included in the final analysis and discussion. Many of the studies were pilot and proof of concept work. Seven of the twelve publications were observational studies. However, this may provide preliminary data from which clinical trials will branch. If specified, the focus of the literature was very narrow, with very similar population demographics and injuries. The trends in the literature findings emphasised the role of VR and attentional focus, the strategic manipulation of movement outcomes, and the transfer of skill to the real-world. Causal inferences may have been undermined by flaws, as most studies were limited by the practicality of conducting a two-factor clinical-VR-based study. In conclusion, by assessing the exploratory studies, and combining this with the use of numerous developments, techniques, and tools, a novel application could be established to utilise VR with dynamic movement, for the effective treatment of specific musculoskeletal injuries of athletes.

Keywords: athletes, lower-limb musculoskeletal injury, rehabilitation, return-to-sport, virtual reality

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Authors: Alina Murtishcheva

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The tendency of globalization, challenges to democracy and peace caused by the Russian invasion of Ukraine, and other global conflicts require searching general orientations of governmental development, including local government. The formation of a common theoretical framework for local government guarantees not only of harmonisation of European legislation but also creates prerequisites for the integration of new members into the European Union. One of the most important milestones of such a theoretical framework is the concept of decentralization. Decentralization as a phenomenon is characteristic of most European Union countries at different historical stages. For Ukraine, as a country that has clearly defined a European integration vector of development, understanding not only the legal but also the theoretical basis of decentralisation processes in European countries is an important prerequisite for further reforms. Decentralisation takes different forms, which leads to a variety of understandings in doctrine and, consequently, different interpretations in national legislation. Despite of this, decentralisation is based on common ideas and values such as democracy, participation, the rule of law, and proximity government that are shared by all EU member states. Nevertheless, not all EU countries are currently implementing broad decentralization in their political and legal practices. Some countries are gradually moving in this direction, while others remain quite centralised. There is also a new, insufficiently studied trend today – recentralisation, which can be broadly defined as the strengthening of centralization tendencies in countries that were considered to be decentralized. Consequently, an exploratory theoretical study is needed to identify how the concept of decentralization is combined with the recentralization tendency in EU member states. The purpose of this study is to empirically analyse scientific approaches to the concept of “decentralisation”, to highlight the tendency of recentralisation and its consequences, to analyse Ukraine's experience in the field of decentralisation of public power, and to outline the prospects for further development of Ukrainian legislation in this area.

Keywords: centralization, decentralization, local government, recentralization, reforms

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Authors: Hosam Alzahrani, Martin Mackey, Emmanuel Stamatakis, Marina B. Pinheiro, Manuela Wicks, Debra Shirley

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Objective: To investigate the effectiveness of incidental (non-structured) physical activity interventions compared with other commonly prescribed interventions for the management of people with low back pain (LBP). Methods: We performed a systematic review with meta-analyses of eligible randomized controlled trials obtained by searching Medline, Scopus, CINAHL, EMBASE, and CENTRAL. This review considered trials investigating the effect of incidental physical activity interventions compared to other interventions in people aged 18 years or over, diagnosed with non-specific LBP. Analyses were conducted separately for short-term (≤3 months), intermediate-term (> 3 and < 12 months), and long-term (≥ 12 months), for each outcome. The analyses were conducted using the weighted mean difference (WMD). The overall quality of evidence was assessed using the GRADE system. Meta-analyses were only performed for pain and disability outcomes as there was insufficient data on the other outcomes. Results: For pain, the pooled results did not show any significant effects between the incidental physical activity intervention and other interventions at any time point. For disability, incidental physical activity was not statistically more effective than other interventions at short-term; however, the pooled results favored incidental physical activity at intermediate-term (WMD= -6.05, 95% CI: -10.39 to -1.71, p=0.006) and long-term (WMD= -6.40 95% CI: -11.68 to -1.12, p=0.02) follow-ups among participants with chronic LBP. The overall quality of evidence was rated “moderate quality” based on the GRADE system. Conclusion: The incidental physical activity intervention provided intermediate and long disability relief for people with chronic LBP, although this improvement was small and not likely to be clinically important.

Keywords: physical activity, incidental, low back pain, systematic review, meta-analysis

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Authors: Animish Jain

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This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 79

Authors: Sheila Shahidi, Hootan Rezaee, Abosaeed Rashidi, Mahmood Ghoranneviss

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Plasma treatment has an explosive increase in interest and use in industrial applications as for example in medical, biomedical, automobile, electronics, semiconductor and textile industry. A lot of intensive basic research has been performed in the last decade in the field of textiles along with technical textiles. Textile manufacturers and end-users alike have been searching for ways to improve the surface properties of natural and man-made fibers. Specifically, there is a need to improve adhesion and wettability. Functional groups may be introduced onto the fiber surface by using gas plasma treatments, improving fiber surface properties without affecting the fiber’s bulk properties. In this research work, ZnO nanoparticles (ZnO-NPs) were insitue synthesized by sonochemical method at room temperature on both untreated and plasma pretreated cotton woven fabric. Oxygen and nitrogen plasmas were used for pre-functionalization of cotton fabric. And the effect of oxygen and nitrogen pre-functionalization on adhesion properties between ZnO nanoparticles and cotton surface were studied. The results show that nanoparticles with average sizes of 20-100 nm with different morphologies have been created on the surface of samples. Synthesis of ZnO-NPs was varied in the morphological transformation by changes in zinc acetate dehydrate concentration. Characterizations were carried out using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Inductive coupled plasma (ICP) and Spectrophotometery. The antibacterial activities of the fabrics were assessed semi-quantitatively by the colonies count method. The results show that the finished fabric demonstrated significant antibacterial activity against S. aureus in antibacterial test. The wash fastness of both untreated and plasma pretreated samples after 30 times of washing was investigated. The results showed that the parameters of plasma reactor plays very important role for improving the antibacterial durability.

Keywords: antibacterial activity, cotton, fabric, nanoparticles, plasma

Procedia PDF Downloads 537

Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

Procedia PDF Downloads 103

Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

Procedia PDF Downloads 67

Authors: David Whitehouse

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Some of the strengths of online teaching include flexibility, creativity, and comprehensiveness. A challenge can be motivation. How can an instructor repeating the same lessons over and over, day in and day out, year after year, maintain motivation? Enthusiasm? Does motivating the student and creating enthusiasm in class build the same things inside the instructor? The answers lie in the adoption of what I label EUQ—The Empathy and Understanding Quotient. In the online environment, students who are adults have many demands on their time: civilian careers, families (spouse, children, older parents), and sometimes even military service. Empathetic responses on the part of the instructor will lead to open and honest communication on the part of the student, which will lead to understanding on the part of the instructor and a rise in motivation in both parties. Understanding the demands can inform an instructor’s relationship with the student throughout the temporal parameters of classwork. In practicing EUQ, instructors can build motivation in their students and find internal motivation in an enhanced classroom dynamic. The presentation will look at what motivates a student to accomplish more than the minimum required and how that can lead to excellent results for an instructor’s own motivation. Through direct experience of having students give high marks on post-class surveys and via direct messaging, the presentation will focus on how applying EUQ in granting extra time, searching for intent while grading, communicating with students via Quick Notes, responses in Forums, comments in Assignments, and comments in grading areas - - - how applying these things infuses enthusiasm and energy in the instructor which drive creativity in teaching. Three primary ways of communicating with students will be given as examples. The positive response and negative response each for a Forum, an Assignment, and a Message will be explored. If there is time, participants will be invited to craft their own EUQ responses in a role playing exercise involving two common classroom scenarios—late work and plagiarism.

Keywords: education, instruction, motivation, online, teaching

Procedia PDF Downloads 171

Authors: Riaz A. Agha, Alexander J. Fowler, Seon-Young Lee, Buket Gundogan, Katharine Whitehurst, Harkiran K. Sagoo, Kyung Jin Lee Jeong, Douglas G. Altman, Dennis P. Orgill

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Introduction: Case Series are an important and common study type. Currently, no guideline exists for reporting case series and there is evidence of key data being missed from such reports. We propose to develop a reporting guideline for case series using a methodologically robust technique. The first step in this process is a systematic review of literature relevant to the reporting deficiencies of case series. Methods: A systematic review of methodological and reporting quality in surgical case series was performed. The electronic search strategy was developed by an information specialist and included MEDLINE, EMBASE, Cochrane Methods Register, Science Citation index and Conference Proceedings Citation index, from the start of indexing until 5th November 2014. Independent screening, eligibility assessments and data extraction was performed. Included articles were analyzed for five areas of deficiency: failure to use standardized definitions missing or selective data transparency or incomplete reporting whether alternate study designs were considered. Results: The database searching identified 2,205 records. Through the process of screening and eligibility assessments, 92 articles met inclusion criteria. Frequency of methodological and reporting issues identified was a failure to use standardized definitions (57%), missing or selective data (66%), transparency, or incomplete reporting (70%), whether alternate study designs were considered (11%) and other issues (52%). Conclusion: The methodological and reporting quality of surgical case series needs improvement. Our data shows that clear evidence-based guidelines for the conduct and reporting of a case series may be useful to those planning or conducting them.

Keywords: case series, reporting quality, surgery, systematic review

Procedia PDF Downloads 359

Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

Procedia PDF Downloads 186

Authors: Luc de Brabandere

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Searchers in various disciplines have long tried to understand how a human being thinks. Most of them seem to agree that the brain works in two very different modes. For us, the first phase of thought imagines, diverges, and unlocks the field of possibilities. The second phase, judges converge and choose. But if we were to stop there, that would give the impression that thought is essentially an individual effort that seldom depends on context. This is, however, not the case. Whether we be a champion in creativity, so primarily in induction, or a master in logic where we are confronted with reality, the ideas we layout are indeed destined to be presented to third parties. They should therefore be exposed, defended, communicated, negotiated, or even sold. Regardless of the quality of the concepts we craft (creative thinking) and the interferences we build (logical thinking) we will take one day, or another, be confronted by people whose beliefs, opinions and ideas differ from ours (critical thinking). Logic and critique: The shared characteristics of logical and critical thoughts include a three-level structure of reasoning invented by the Greeks. For the first time in history, Aristotle tried to model thought deployable in three stages: the concept, the statement, and the reasoning. The three levels can be assessed according to different criteria. A concept is more or less useful, a statement is true or false, and reasoning is right or wrong. This three-level structure allows us to differentiate logic and critique, where the intention and words used are not the same. Logic only deals with the structure of reasoning and exhausts the problem. It regards premises as acquired and excludes the debate. Logic is in all certainty and pursues the truth. Critique is most probably searching for the plausible. Logic and creativity: Many known models present the brain as a two-stroke engine (divergence vs convergence, fast vs. slow, left-brain vs right-brain, Yin vs Yang, etc.). But that’s not the only thing. “Why didn’t we think of that before?” How often have we heard that sentence? A creative idea is the outcome of logic, but you can only understand it afterward! Through the use of exercises, we will witness how logic and creativity work together. A third theme is hidden behind the two main themes of the conference: logical thought, which the author can shed some light on.

Keywords: creativity, logic, critique, digital

Procedia PDF Downloads 90

Authors: S. Ahmad, C. Yuan, Q. Zhang

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The assorted architectural plasticity of a plant is majorly specified by stooling, a phenomenon tackled by a combination of developmental, environmental and hormonal accelerators of lateral buds. Chrysanthemums (Chrysanthemum morifolium) are one of the most economically important ornamental plants worldwide on the account of having plentiful architectural patterns, diverse shapes and attractive colors. Side branching is the major determinant guaranteeing the consistent demand of cut chrysanthemum in flower industry. Presence of immense number of axillary branches devalues the economic importance of this imperative plant and is a major challenge for mum growers to hold a stake in the cut flower market. Restricting branches to a minimum level, or no branches at all, is the dire need of the day in order to introducing novelty in cut chrysanthemums. Temperature is a potent factor which affects largely the escalation, development of chrysanthemum, and also the genetic expression of various vegetative traits like branching. It affects differently the developmental characteristics and phenotypic expressions of inherent qualities, thereby playing a significant role in differentiating the developmental responses in different cultivars of chrysanthemum. A detailed study pertaining to the affect of temperature on branching in chrysanthemum is a clear lacking throughout the literature on mums. Therefore, searching with temperature as an effective means of reducing side branching to a desired level could be an influencing extension of struggles about how to nullify stooling. This requires plenty of research in order to reveal the extended penetration of temperature in manipulating the genetic control of various important traits like branching, which is a burning issue now a days in producing cut flowers in chrysanthemum. The present review will highlight the impact of temperature on branching control mechanism in chrysanthemum at morpho-physiological, hormonal and molecular levels.

Keywords: branching, chrysanthemum, genetic control, hormonal, morpho-physiological, temperature

Procedia PDF Downloads 283

Authors: Ika Jorum

Abstract:

The aim of this collaborative project was to develop library support that contributes in a long-term way to a technical university’s work on increased equality in education. The background was an assessment made by the Higher Education Authority that showed the need for improvement regarding equality in several programs at the university. The university’s Vice President for equality and Vice President for sustainability announced funds for projects that supported the improvement of equality in education. The library was granted funding for a one-year project that aimed both to support teachers in order to embed equality in education and to support the library staff and improve the organization’s own work. The part of the project that was directed to teachers was performed as activities in different areas and forms, such as acquisition and collections, teaching, exhibitions and book discussions. Besides the activities and support that was offered to teachers, the education team had journal clubs in order to develop and embed equality in their own teaching. The part that was directed to library staff and management was performed as workshops in collaboration with Equality Office in order to identify areas where the library could make improvements on work with equality and inclusion. The expectation was that the activities would be well attended since the project team had got indications that the content would be relevant. The outcome of this project was that some activities turned out to be more attended than others and what is expected to be found relevant, for example, a workshop on information searching from a gender and equality perspective for teachers, might still not attract participants. On the other hand, Ph.D. students and students participated in the book discussions and wanted them to continue after the project had ended. Results will be shared both on what was successful and what was challenging. Some reflections will be given on what can be done to attract participants to activities in the area of gender equality that is most likely relevant for the expected attendants and how results from a project on gender equality can be integrated into an organization’s daily work.

Keywords: equality, higher education, critical information literacy, collaboration

Procedia PDF Downloads 73