Search results for: algorithm optimisation

Authors: Loyd R. Hook, Maryam Moharek

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With the envisioned future growth of low altitude urban aircraft operations for airborne delivery service and advanced air mobility, strategies to coordinate and deconflict aircraft flight paths must be prioritized. Autonomous coordination and planning of flight trajectories is the preferred approach to the future vision in order to increase safety, density, and efficiency over manual methods employed today. Difficulties arise because any conflict resolution must be constrained by all other aircraft, all airspace restrictions, and all ground-based obstacles in the vicinity. These considerations make pair-wise tactical deconfliction difficult at best and unlikely to find a suitable solution for the entire system of vehicles. In addition, more traditional methods which rely on long time scales and large protected zones will artificially limit vehicle density and drastically decrease efficiency. Instead, strategic planning, which is able to respond to highly dynamic conditions and still account for high density operations, will be required to coordinate multiple vehicles in the highly constrained low altitude urban environment. This paper develops and evaluates such a planning algorithm which can be implemented autonomously across multiple aircraft and situations. Data from this evaluation provide promising results with simulations showing up to 10 aircraft deconflicted through a relatively narrow low-altitude urban canyon without any vehicle to vehicle or obstacle conflict. The algorithm achieves this level of coordination beginning with the assumption that each vehicle is controlled to follow an independently constructed flight path, which is itself free of obstacle conflict and restricted airspace. Then, by preferencing speed change deconfliction maneuvers constrained by the vehicles flight envelope, vehicles can remain as close to the original planned path and prevent cascading vehicle to vehicle conflicts. Performing the search for a set of commands which can simultaneously ensure separation for each pair-wise aircraft interaction and optimize the total velocities of all the aircraft is further complicated by the fact that each aircraft's flight plan could contain multiple segments. This means that relative velocities will change when any aircraft achieves a waypoint and changes course. Additionally, the timing of when that aircraft will achieve a waypoint (or, more directly, the order upon which all of the aircraft will achieve their respective waypoints) will change with the commanded speed. Put all together, the continuous relative velocity of each vehicle pair and the discretized change in relative velocity at waypoints resembles a hybrid reachability problem - a form of control reachability. This paper proposes two methods for finding solutions to these multi-body problems. First, an analytical formulation of the continuous problem is developed with an exhaustive search of the combined state space. However, because of computational complexity, this technique is only computable for pairwise interactions. For more complicated scenarios, including the proposed 10 vehicle example, a discretized search space is used, and a depth-first search with early stopping is employed to find the first solution that solves the constraints.

Keywords: strategic planning, autonomous, aircraft, deconfliction

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Authors: Raed Alnajjar, Mohd Zakree, Ahmad Nazri

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In this study, we apply Discrete Group Search Optimizer (DGSO) for solving Traveling Salesman Problem (TSP). The DGSO is a nature inspired optimization algorithm that imitates the animal behavior, especially animal searching behavior. The proposed DGSO uses a vector representation and some discrete operators, such as destruction, construction, differential evolution, swap and insert. The TSP is a well-known hard combinatorial optimization problem, which seeks to find the shortest path among numbers of cities. The performance of the proposed DGSO is evaluated and tested on benchmark instances which listed in LIBTSP dataset. The experimental results show that the performance of the proposed DGSO is comparable with the other methods in the state of the art for some instances. The results show that DGSO outperform Ant Colony System (ACS) in some instances whilst outperform other metaheuristic in most instances. In addition to that, the new results obtained a number of optimal solutions and some best known results. DGSO was able to obtain feasible and good quality solution across all dataset.

Keywords: discrete group search optimizer (DGSO); Travelling salesman problem (TSP); Variable neighborhood search(VNS)

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Authors: Yuanhao Gao, Ping Lin, Kees Weijer

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An optimal control system model is proposed for the cell flow in the process of chick embryo gastrulation in this paper. The target is to determine the cellular interaction forces which are hard to measure. This paper will take an approach to investigate the forces with the idea of the inverse problem. By choosing the forces as the control variable and regarding the cell flow as Stokes fluid, an objective functional will be established to match the numerical result of cell velocity with the experimental data. So that the forces could be determined by minimizing the objective functional. The Lagrange multiplier method is utilized to derive the state and adjoint equations consisting the optimal control system, which specifies the first-order necessary conditions. Finite element method is used to discretize and approximate equations. A conjugate gradient algorithm is given for solving the minimum solution of the system and determine the forces.

Keywords: optimal control model, Stokes equation, conjugate gradient method, finite element method, chick embryo gastrulation

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Authors: Asra Yousuf, Ajaykumar Tannirkulum

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Inferring rural-urban migration trends can help design effective policies that promote better urban planning and rural development. In this paper, we describe how machine learning algorithms can be applied to predict internal migration decisions of people. We consider data collected from household surveys in Tamil Nadu to train our model. To measure the performance of the model, we use data on past migration from National Sample Survey Organisation of India. The factors for training the model include socioeconomic characteristic of each individual like age, gender, place of residence, outstanding loans, strength of the household, etc. and his past migration history. We perform a comparative analysis of the performance of a number of machine learning algorithm to determine their prediction accuracy. Our results show that machine learning algorithms provide a stronger prediction accuracy as compared to statistical models. Our goal through this research is to propose the use of data science techniques in understanding human decisions and behaviour in developing countries.

Keywords: development, migration, internal migration, machine learning, prediction

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Authors: Rajesh Bhatt, Dilip Kumar Maiti

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The incompressible shear flow past a square cylinder placed parallel to a plane wall of side length A in presence of upstream rectangular cylinder of height 0.5A and width 0.25A in an inline tandem arrangement are numerically investigated using finite volume method. The discretized equations are solved by an implicit, time-marching, pressure correction based SIMPLE algorithm. This study provides the qualitative insight in to the dependency of basic structure (i.e. vortex shedding or suppression) of flow over the downstream square cylinder and the upstream rectangular cylinder (and hence the aerodynamic characteristics) on inter-cylinder spacing (S) and Reynolds number (Re). The spacing between the cylinders is varied systematically from S = 0.5A to S = 7.0A so the sensitivity of the flow structure between the cylinders can be inspected. A sudden jump in strouhal number is observed, which shows the transition of flow pattern in the wake of the cylinders. The results are presented at Re = 100 and 200 in term of Strouhal number, RMS and mean of lift and drag coefficients and contour plots for different spacing.

Keywords: square cylinder, vortex shedding, isolated, tandem arrangement, spacing distance

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Authors: Gon Park

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Seoul in South Korea established the 2030 Seoul City Master Plan that contains green-link projects to connect critical green areas within the city. However, the plan does not have detailed analyses for green infrastructure to incorporate land-cover information to many structural classes. This study maps green infrastructure networks of Seoul for complementing their green plans with identifying and raking green areas. Hubs and links of main elements of green infrastructure have been identified from incorporating cadastral data of 967,502 parcels to 135 of land use maps using geographic information system. Network analyses were used to rank hubs and links of a green infrastructure map with applying a force-directed algorithm, weighted values, and binary relationships that has metrics of density, distance, and centrality. The results indicate that network analyses using cadastral parcel data can be used as the framework to identify and rank hubs, links, and networks for the green infrastructure planning under a variable scenarios of green areas in cities.

Keywords: cadastral data, green Infrastructure, network analysis, parcel data

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Authors: Jainendra Singh, Zaheeruddin

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A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

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Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

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Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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Authors: Seyed Sadegh Naseralavi, Sohrab Nemati, Ehsan Khojastehfar, Sadegh Balaghi

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In the present research, various formulations of wavelet transform are applied on acceleration time history of earthquake. The mentioned transforms decompose the strong ground motion into low and high frequency parts. Since the high frequency portion of strong ground motion has a minor effect on dynamic response of structures, the structure is excited by low frequency part. Consequently, the seismic response of structure is predicted consuming one half of computational time, comparing with conventional time history analysis. Towards reducing the computational effort needed in seismic optimization of structure, seismic optimization of a shear frame structure is conducted by applying various forms of mentioned transformation through genetic algorithm.

Keywords: time history analysis, wavelet transform, optimization, earthquake

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Authors: Jonas P. Reges, Jessyca A. Bessa, Auzuir R. Alexandria

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The research came up with the need of monitoring them of temperature and relative moisture in past work that enveloped the study of a greenhouse located in the Research and Extension Unit(UEPE). This research brought several unknowns that were resolved from bibliographical research. Based on the studies performed were found some monitoring methods, including the serial communication between the arduino and matlab which showed a great option due to the low cost. The project was conducted in two stages, the first, an algorithm was developed to the Arduino and Matlab, and second, the circuits were assembled and performed the monitoring tests the following variables: moisture, temperature, and distance. During testing it was possible to momentarily observe the change in the levels of monitored variables. The project showed satisfactory results, such as: real-time verification of the change of state variables, the low cost of acquisition of the prototype, possibility of easy change of programming for the execution of monitoring of other variables. Therefore, the project showed the possibility of monitoring through software and hardware that have easy programming and can be used in several areas. However, it is observed also the possibility of improving the project from a remote monitoring via Bluetooth or web server and through the control of monitored variables.

Keywords: automation, monitoring, programming, arduino, matlab

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Authors: Andre Peres Ramos, Franklin Cesar Flores

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Colorization is the process of add colors to a monochromatic image or video. Usually, the process involves to segment the image in regions of interest and then apply colors to each one, for videos, this process is repeated for each frame, which makes it a tedious and time-consuming job. We propose a new assisted method for video colorization; the user only has to colorize one frame, and then the colors are propagated to following frames. The user can intervene at any time to correct eventual errors in color assignment. The method consists of to extract intensity and texture descriptors from the frames and then perform a feature matching to determine the best color for each segment. To reduce computation time and give a better spatial coherence we narrow the area of search and give weights for each feature to emphasize texture descriptors. To give a more natural result, we use an optimization algorithm to make the color propagation. Experimental results in several image sequences, compared to others existing methods, demonstrates that the proposed method perform a better colorization with less time and user interference.

Keywords: colorization, feature matching, texture descriptors, video segmentation

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Authors: Po-Jen Chen, Jian-Jiun Ding, Hung-Wei Hsu, Chien-Yao Wang, Jia-Ching Wang

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A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved.

Keywords: convolutional neural network, label relation, hierarchy neural network, scene classification

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Authors: Manar Amayri, Hussain Kazimi, Quoc-Dung Ngo, Stephane Ploix

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A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates.

Keywords: energy, management, control, optimization, Bayesian methods, learning theory, sensor networks, knowledge modelling and knowledge based systems, artificial intelligence, buildings

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Authors: Kang-Mo Jung

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The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data.

Keywords: adaptive penalty function, robust penalized regression, variable selection, weighted rank regression

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Authors: Lindelwa Mngomezulu, Tonderai Muchenje

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Over the decades, E-mails provide easy, fast and timely communication enabling businesses and state owned agencies to communicate with their stakeholders and with their own employees in real-time. Moreover, since the launch of Microsoft office 365 and many other clouds based E-mail services, many businesses have been migrating from the on premises E-mail services to the cloud and more precisely since the beginning of the Covid-19 pandemic, there has been a significant increase of E-mails utilization, which then leads to the increase of cyber-attacks. In that regard, E-mail security has become very important in the E-mail transportation to ensure that the E-mail gets to the recipient without the data integrity being compromised. The classification of the features to enhance E-mail security for further from the enhanced cyber-attacks as we are aware that since the technology is advancing so at the cyber-attacks. Therefore, in order to maximize the data integrity we need to also maximize security of the E-mails such as enhanced E-mail authentication. The successful enhancement of E-mail security in the future may lessen the frequency of information thefts via E-mails, resulting in the data of South African State-owned agencies not being compromised.

Keywords: e-mail security, cyber-attacks, data integrity, authentication

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Authors: Kenichiro Hida, Shin-Ichi Kuribayashi

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In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions which are one of critical network functions, and presents the virtual routing function allocation algorithm that minimizes the total power consumption. In addition, this study presents the useful allocation policy of virtual routing functions, based on an evaluation with a ladder-shaped network model. This policy takes the ratio of the power consumption of a routing function to that of a circuit and traffic distribution between areas into consideration. Furthermore, the present paper shows that there are cases where the use of NFV-based routing functions makes it possible to reduce the total power consumption dramatically, in comparison to a conventional network, in which it is not economically viable to distribute small-capacity routing functions.

Keywords: NFV, resource allocation, virtual routing function, minimum power consumption

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Ana Paula P. dos Santos, Claudia R. Andrade, Edson L. Zaparoli

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Pressure loss in ductworks is an important factor to be considered in design of engineering systems such as power-plants, refineries, HVAC systems to reduce energy costs. Ductwork can be composed by straight ducts and different types of fittings (elbows, transitions, converging and diverging tees and wyes). Duct fittings are significant sources of pressure loss in fluid distribution systems. Fitting losses can be even more significant than equipment components such as coils, filters, and dampers. At the present work, a conventional 90o round elbow under turbulent incompressible airflow is studied. Mass, momentum, and k-e turbulence model equations are solved employing the finite volume method. The SIMPLE algorithm is used for the pressure-velocity coupling. In order to validate the numerical tool, the elbow pressure loss coefficient is determined using the same conditions to compare with ASHRAE database. Furthermore, the effect of Reynolds number variation on the elbow pressure loss coefficient is investigated. These results can be useful to perform better preliminary design of air distribution ductworks in air conditioning systems.

Keywords: duct fitting, pressure loss, elbow, thermodynamics

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Authors: A. Gherbi, L. Dahmani, A. Boudjemia

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This paper presents the Finite Element Method (FEM) for analyzing the failure pattern of rectangular slab with various edge conditions. Non-Linear static analysis is carried out using ANSYS 15 Software. Using SOLID65 solid elements, the compressive crushing of concrete is facilitated using plasticity algorithm, while the concrete cracking in tension zone is accommodated by the nonlinear material model. Smeared reinforcement is used and introduced as a percentage of steel embedded in concrete slab. The behavior of the analyzed concrete slab has been observed in terms of the crack pattern and displacement for various loading and boundary conditions. The finite element results are also compared with the experimental data. One of the other objectives of the present study is to show how similar the crack path found by ANSYS program to those observed for the yield line analysis. The smeared reinforcement method is found to be more practical especially for the layered elements like concrete slabs. The value of this method is that it does not require explicit modeling of the rebar, and thus a much coarser mesh can be defined.

Keywords: ANSYS, cracking pattern, displacements, reinforced concrete slab, smeared reinforcements

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Authors: Prawal Sinha, Peeyush Singh, Pravir Dutt

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Problems in elastohydrodynamic lubrication have attracted a lot of attention in the last few decades. Solving a two-dimensional problem has always been a big challenge. In this paper, a new discontinuous finite volume method (DVM) for two-dimensional point contact Elastohydrodynamic Lubrication (EHL) problem has been developed and analyzed. A complete algorithm has been presented for solving such a problem. The method presented is robust and easily parallelized in MPI architecture. GMRES technique is implemented to solve the matrix obtained after the formulation. A new approach is followed in which discontinuous piecewise polynomials are used for the trail functions. It is natural to assume that the advantages of using discontinuous functions in finite element methods should also apply to finite volume methods. The nature of the discontinuity of the trail function is such that the elements in the corresponding dual partition have the smallest support as compared with the Classical finite volume methods. Film thickness calculation is done using singular quadrature approach. Results obtained have been presented graphically and discussed. This method is well suited for solving EHL point contact problem and can probably be used as commercial software.

Keywords: elastohydrodynamic, lubrication, discontinuous finite volume method, GMRES technique

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Authors: Alexis John de Britto

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Studies were performed to select the superior genotypes based on intra-specific variations, caused by phytogeographical, climatic and edaphic parameters of three anti cancer drug yielding mangrove plants such as Acanthus ilicifolius L., Calophyllum inophyllum L. and Excoecaria agallocha L. using ISSR (Inter Simple Sequence Repeats) markers and phytochemical analysis such as preliminary phytochemical tests, TLC, HPTLC, HPLC and antioxidant tests. The plants were collected from five different geographical locations of the East Coast of south India. Genetic heterozygosity, Nei’s gene diversity, Shannon’s information index and Percentage of polymorphism between the populations were calculated using POPGENE software. Cluster analysis was performed using UPGMA algorithm. AMOVA and correlations between genetic diversity and soil factors were analyzed. Combining the molecular and phytochemical variations superior genotypes were selected. Conservation constraints and methods of efficient exploitation of the species are discussed.

Keywords: anti-cancer drug yielding plants, DNA fingerprinting, phytochemical analysis, selection of superior genotypes

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Qianhua He, Weili Zhou, Aiwu Chen

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A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: speech denoising, sparse representation, k-singular value decomposition, orthogonal matching pursuit

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: Yousif Mohamed Y. Abdallah, Razan Manofely, Rajab M. Ben Yousef

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X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.

Keywords: enhancement, x-rays, pixel intensity values, MatLab

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Authors: William Huang

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Retinal prostheses are currently limited to low resolution grayscale images that lack color and spatial information. This study develops a novel real-time image optimization framework and tools to encode maximum information to the prostheses which are constrained by the number of electrodes. One key idea is to localize main objects in images while reducing unnecessary background noise through region-contrast saliency maps. A novel color depth mapping technique was developed through MiniBatchKmeans clustering and color space selection. The resulting image was downsampled using bicubic interpolation to reduce image size while preserving color quality. In comparison to current schemes, the proposed framework demonstrated better visual quality in tested images. The use of the region-contrast saliency map showed improvements in efficacy up to 30%. Finally, the computational speed of this algorithm is less than 380 ms on tested cases, making real-time retinal prostheses feasible.

Keywords: retinal implants, virtual processing unit, computer vision, saliency maps, color quantization

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Authors: Elnaz Lashgari, Emel Demircan

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Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

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Authors: R. Vigneswaran, S. Thilakanathan

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Variable time-stepping algorithms for solving dynamical systems performed poorly for long time computations which pass close to a fixed point. To overcome this difficulty, several authors considered phase space error controls for numerical simulation of dynamical systems. In one generalized phase space error control, a step-size selection scheme was proposed, which allows this error control to be incorporated into the standard adaptive algorithm as an extra constraint at negligible extra computational cost. For this generalized error control, it was already analyzed the forward Euler method applied to the linear system whose coefficient matrix has real and negative eigenvalues. In this paper, this result was extended to the linear system whose coefficient matrix has complex eigenvalues with negative real parts. Some theoretical results were obtained and numerical experiments were carried out to support the theoretical results.

Keywords: adaptivity, fixed point, long time simulations, stability, linear system

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Authors: Hussaini Doko Ibrahim, Hamilton Cyprian Chinwenyi, Henrietta Nkem Ude

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In this paper, efforts were made to examine and compare the algorithmic iterative solutions of the conjugate gradient method as against other methods such as Gauss-Seidel and Jacobi approaches for solving systems of linear equations of the form Ax=b, where A is a real n×n symmetric and positive definite matrix. We performed algorithmic iterative steps and obtained analytical solutions of a typical 3×3 symmetric and positive definite matrix using the three methods described in this paper (Gauss-Seidel, Jacobi, and conjugate gradient methods), respectively. From the results obtained, we discovered that the conjugate gradient method converges faster to exact solutions in fewer iterative steps than the two other methods, which took many iterations, much time, and kept tending to the exact solutions.

Keywords: conjugate gradient, linear equations, symmetric and positive definite matrix, gauss-seidel, Jacobi, algorithm

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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