Search results for: genetic algorithm

Authors: Chia-Chen Wei, Pack Hsieh, Jeffrey Chen

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Surface Mount Technology (SMT) is widely used in the area of the electronic assembly in which the electronic components are mounted to the surface of the printed circuit board (PCB). Most of the defects in the SMT process are mainly related to the quality of solder paste printing. These defects lead to considerable manufacturing costs in the electronics assembly industry. Therefore, the solder paste inspection (SPI) machine for controlling and monitoring the amount of solder paste printing has become an important part of the production process. So far, the setting of the SPI threshold is based on statistical analysis and experts’ experiences to determine the appropriate threshold settings. Because the production data are not normal distribution and there are various variations in the production processes, defects related to solder paste printing still occur. In order to solve this problem, this paper proposes an online machine learning algorithm, called the automatic threshold adjustment (ATA) algorithm, and closed-loop architecture in the SMT process to determine the best threshold settings. Simulation experiments prove that our proposed threshold settings improve the accuracy from 99.85% to 100%.

Keywords: big data analytics, Industry 4.0, SPI threshold setting, surface mount technology

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Authors: Y. Landkocz, C. Lepers, P.J. Martin, B. Fougère, F. Roy Saint-Georges. A. Verdin, F. Cazier, F. Ledoux, D. Courcot, F. Sichel, P. Gosset, P. Shirali, S. Billet

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In 2013, the International Agency for Research on Cancer (IARC) classified air pollution and fine particles as carcinogenic to humans. Causal relationships exist between elevated ambient levels of airborne particles and increase of mortality and morbidity including pulmonary diseases, like lung cancer. However, due to a double complexity of both physicochemical Particulate Matter (PM) properties and tumor mechanistic processes, mechanisms of action remain not fully elucidated. Furthermore, because of several common properties between air pollution PM and tobacco smoke, like the same route of exposure and chemical composition, potential mechanisms of synergy could exist. Therefore, smoking could be an aggravating factor of the particles toxicity. In order to identify some mechanisms of action of particles according to their size, two samples of PM were collected: PM0.03 2.5 and PM0.33 2.5 in the urban-industrial area of Dunkerque. The overall cytotoxicity of the fine particles was determined on human bronchial cells (BEAS-2B). Toxicological study focused then on the metabolic activation of the organic compounds coated onto PM and some genetic and epigenetic changes induced on a co-culture model of BEAS-2B and alveolar macrophages isolated from bronchoalveolar lavages performed in smokers and non-smokers. The results showed (i) the contribution of the ultrafine fraction of atmospheric particles to genotoxic (eg. DNA double-strand breaks) and epigenetic mechanisms (eg. promoter methylation) involved in tumor processes, and (ii) the influence of smoking on the cellular response. Three main conclusions can be discussed. First, our results showed the ability of the particles to induce deleterious effects potentially involved in the stages of initiation and promotion of carcinogenesis. The second conclusion is that smoking affects the nature of the induced genotoxic effects. Finally, the in vitro developed cell model, using bronchial epithelial cells and alveolar macrophages can take into account quite realistically, some of the existing cell interactions existing in the lung.

Keywords: air pollution, fine and ultrafine particles, genotoxic and epigenetic alterations, smoking

Procedia PDF Downloads 321

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time

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Authors: Epaminondas G. Kyriakidis, Theodosis D. Dimitrakos, Constantinos C. Karamatsoukis

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The vehicle routing problem (VRP) is a well-known problem in Operations Research and has been widely studied during the last fifty-five years. The context of the VRP is that of delivering or collecting products to or from customers who are scattered in a geographical area and have placed orders for these products. A vehicle or a fleet of vehicles start their routes from a depot and visit the customers in order to satisfy their demands. Special attention has been given to the capacitated VRP in which the vehicles have limited carrying capacity for the goods that are delivered or collected. In the present work, we present a specific capacitated stochastic vehicle routing problem which has many realistic applications. We develop and analyze a mathematical model for a specific vehicle routing problem in which a vehicle starts its route from a depot and visits N customers according to a particular sequence in order to collect from them two similar but not identical products. We name these products, product 1 and product 2. Each customer possesses items either of product 1 or product 2 with known probabilities. The number of the items of product 1 or product 2 that each customer possesses is a discrete random variable with known distribution. The actual quantity and the actual type of product that each customer possesses are revealed only when the vehicle arrives at the customer’s site. It is assumed that the vehicle has two compartments. We name these compartments, compartment 1 and compartment 2. It is assumed that compartment 1 is suitable for loading product 1 and compartment 2 is suitable for loading product 2. However, it is permitted to load items of product 1 into compartment 2 and items of product 2 into compartment 1. These actions cause costs that are due to extra labor. The vehicle is allowed during its route to return to the depot to unload the items of both products. The travel costs between consecutive customers and the travel costs between the customers and the depot are known. The objective is to find the optimal routing strategy, i.e. the routing strategy that minimizes the total expected cost among all possible strategies for servicing all customers. It is possible to develop a suitable dynamic programming algorithm for the determination of the optimal routing strategy. It is also possible to prove that the optimal routing strategy has a specific threshold-type strategy. Specifically, it is shown that for each customer the optimal actions are characterized by some critical integers. This structural result enables us to design a special-purpose dynamic programming algorithm that operates only over these strategies having this structural property. Extensive numerical results provide strong evidence that the special-purpose dynamic programming algorithm is considerably more efficient than the initial dynamic programming algorithm. Furthermore, if we consider the same problem without the assumption that the customers are ordered, numerical experiments indicate that the optimal routing strategy can be computed if N is smaller or equal to eight.

Keywords: dynamic programming, similar products, stochastic demands, stochastic preferences, vehicle routing problem

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Authors: Hadi Ardiny, Amir Mohammad Beigzadeh

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Through the utilization of a combination of various sensors and data fusion methods, the detection of potential nuclear threats can be significantly enhanced by extracting more information from different data. In this research, an experimental and modeling approach was employed to track a radioactive source by combining a surveillance camera and a radiation detector (NaI). To run this experiment, three mobile robots were utilized, with one of them equipped with a radioactive source. An algorithm was developed in identifying the contaminated robot through correlation between camera images and camera data. The computer vision method extracts the movements of all robots in the XY plane coordinate system, and the detector system records the gamma-ray count. The position of the robots and the corresponding count of the moving source were modeled using the MCNPX simulation code while considering the experimental geometry. The results demonstrated a high level of accuracy in finding and locating the target in both the simulation model and experimental measurement. The modeling techniques prove to be valuable in designing different scenarios and intelligent systems before initiating any experiments.

Keywords: nuclear threats, radiation detector, MCNPX simulation, modeling techniques, intelligent systems

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Piotr Kacejko, Mariusz Duk, Miroslaw Wendeker

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This paper presents an adaptive controller to track the maximum power point of a photovoltaic modules (PV) under fast irradiation change on the city-bus roof. Photovoltaic systems have been a prominent option as an additional energy source for vehicles. The Municipal Transport Company (MPK) in Lublin has installed photovoltaic panels on its buses roofs. The solar panels turn solar energy into electric energy and are used to load the buses electric equipment. This decreases the buses alternators load, leading to lower fuel consumption and bringing both economic and ecological profits. A DC–DC boost converter is selected as the power conditioning unit to coordinate the operating point of the system. In addition to the conversion efficiency of a photovoltaic panel, the maximum power point tracking (MPPT) method also plays a main role to harvest most energy out of the sun. The MPPT unit on a moving vehicle must keep tracking accuracy high in order to compensate rapid change of irradiation change due to dynamic motion of the vehicle. Maximum power point track controllers should be used to increase efficiency and power output of solar panels under changing environmental factors. There are several different control algorithms in the literature developed for maximum power point tracking. However, energy performances of MPPT algorithms are not clarified for vehicle applications that cause rapid changes of environmental factors. In this study, an adaptive MPPT algorithm is examined at real ambient conditions. PV modules are mounted on a moving city bus designed to test the solar systems on a moving vehicle. Some problems of a PV system associated with a moving vehicle are addressed. The proposed algorithm uses a scanning technique to determine the maximum power delivering capacity of the panel at a given operating condition and controls the PV panel. The aim of control algorithm was matching the impedance of the PV modules by controlling the duty cycle of the internal switch, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PV system is a general solution and can be used for other types of PV systems of both high and low power. Experimental results obtained from comparison of algorithms by a motion loop are presented and discussed. Experimental results are presented for fast change in irradiation and partial shading conditions. The results obtained clearly show that the proposed method is simple to implement with minimum tracking time and high tracking efficiency proving superior to the proposed method. This work has been financed by the Polish National Centre for Research and Development, PBS, under Grant Agreement No. PBS 2/A6/16/2013.

Keywords: adaptive control, photovoltaic energy, city bus electric load, DC-DC converter

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Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Seong-Jin Cho, Tae-Hyeon Oh, Dae-Kyu Park

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Sound absorption coefficient is considered important when designing because noise affects emotion quality of car. It is designed with lots of experiment tunings in the field because it is unreliable to predict it for multi-layer material. In this paper, we present the design of sound absorption for automotive interior material with multiple layers using estimation software of sound absorption coefficient for reverberation chamber. Additionally, we introduce the method for estimation of physical properties required to predict sound absorption coefficient of car interior materials with multiple layers too. It is calculated by inverse algorithm. It is very economical to get information about physical properties without expensive equipment. Correlation test is carried out to ensure reliability for accuracy. The data to be used for the correlation is sound absorption coefficient measured in the reverberation chamber. In this way, it is considered economical and efficient to design automotive interior materials. And design optimization for sound absorption coefficient is also easy to implement when it is designed.

Keywords: sound absorption coefficient, optimization design, inverse algorithm, automotive interior material, multiple layers nonwoven, scaled reverberation chamber, sound impedance tubes

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Authors: Hung Chih Hsieh, Cheng Hao Chang, Yun Hsiang Chang, Yu Lin Chang

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In the applications of the spectrometer, the stray light that comes from the environment affects the measurement results a lot. Hence, environment and instrument quality control for the stray reduction is critical for the spectral reflectance measurement. In this paper, a simple and practical method has been developed to correct a spectrometer's response for measurement errors arising from the environment's and instrument's stray light. A sinusoidal modulated light intensity signal was incident on a tested sample, and then the reflected light was collected by the spectrometer. Since a sinusoidal signal modulated the incident light, the reflected light also had a modulated frequency which was the same as the incident signal. Using the three-parameter sine curve fitting algorithm, we can extract the primary reflectance signal from the total measured signal, which contained the primary reflectance signal and the stray light from the environment. The spectra similarity between the extracted spectra by this proposed method with extreme environment stray light is 99.98% similar to the spectra without the environment's stray light. This result shows that we can measure the reflectance spectra without the affection of the environment's stray light.

Keywords: spectrometer, stray light, three-parameter sine curve fitting, spectra extraction

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Authors: Mahdi Motahari, Mojtaba Farzaneh, Ebrahim Sepidbar

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Tracking of moving and flying targets is one of the most important issues in image processing topic. Estimating of trajectory of desired object in short-term and long-term scale is more important than tracking of moving and flying targets. In this paper, a new way of identifying and estimating of future trajectory of a moving ball in long-term scale is estimated by using synthesis and interaction of image processing algorithms including noise removal and image segmentation, Kalman filter algorithm in order to estimating of trajectory of ball in football game in short-term scale and intelligent adaptive neuro-fuzzy algorithm based on time series of traverse distance. The proposed system attain more than 96% identify accuracy by using aforesaid methods and relaying on aforesaid algorithms and data base video in format of synthesis and interaction. Although the present method has high precision, it is time consuming. By comparing this method with other methods we realize the accuracy and efficiency of that.

Keywords: tracking, signal processing, moving targets and flying, artificial intelligent systems, estimating of trajectory, Kalman filter

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Sai Sudarshan, Harsh Vyas, Riddhiman Sherlekar

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The oil and gas industry has been unwilling to adopt stochastic definition of reserves. Nevertheless, Monte Carlo simulation methods have gained acceptance by engineers, geoscientists and other professionals who want to evaluate prospects or otherwise analyze problems that involve uncertainty. One of the common applications of Monte Carlo simulation is the estimation of recoverable hydrocarbon from a reservoir.Monte Carlo Simulation makes use of random samples of parameters or inputs to explore the behavior of a complex system or process. It finds application whenever one needs to make an estimate, forecast or decision where there is significant uncertainty. First, the project focuses on performing Monte-Carlo Simulation on a given data set using U. S Department of Energy’s MonteCarlo Software, which is a freeware e&p tool. Further, an algorithm for simulation has been developed for MATLAB and program performs simulation by prompting user for input distributions and parameters associated with each distribution (i.e. mean, st.dev, min., max., most likely, etc.). It also prompts user for desired probability for which reserves are to be calculated. The algorithm so developed and tested in MATLAB further finds implementation in Python where existing libraries on statistics and graph plotting have been imported to generate better outcome. With PyQt designer, codes for a simple graphical user interface have also been written. The graph so plotted is then validated with already available results from U.S DOE MonteCarlo Software.

Keywords: simulation, probability, confidence interval, sensitivity analysis

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Authors: Arjumand Warsy

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The Holy Quran was revealed to Prophet Mohammad (May Peace and Blessings of Allah be upon Him) over fourteen hundred years ago, at a time when majority of the people in Arabia were illiterate and very few could read or write. Any knowledge about medicine, anatomy, biology, astronomy, physics, geology, geophysics or other sciences were almost non-existent. Many superstitious and groundless believes were prevalent and these believes were passed down through past generations. At that time, the Holy Quran was revealed and it presented several phenomenon that have been only currently unveiled, as scientists have worked endlessly to provide explanation for these physical and biological phenomenon applying scientific technologies. Many important discoveries were made during the 20th century and it is interesting to note that many of these discoveries were already present in the Holy Quran fourteen hundred years ago. The Scientific phenomenon, mentioned in the Holy Quran, cover many different fields in biological and physical sciences and have been the source of guidance for a number of scientists. A perfect description of the creation of the universe, the orbits in space, the development process, development of hearing process prior to sight, importance of the skin in sensing pain, uniqueness of fingerprints, role of males in selection of the sex of the baby, are just a few of the many facts present in the Quran that have astonished many scientists. The Quran in Chapter 20, verse 50 states: قَالَ رَبُّنَا الَّذِيۤ اَعْطٰى كُلَّ شَيْءٍ خَلْقَهٗ ثُمَّ هَدٰى ۰۰ (He said "Our Lord is He, Who has given a distinctive form to everything and then guided it aright”). Explaining this brief statement in the light of the modern day Molecular Genetics unveils the entire genetic basis of life and how guidance is stored in the genetic material (DNA) present in the nucleus. This thread like structure, made of only six molecules (sugar, phosphate, adenine, thymine, cytosine and guanine), is so brilliantly structured by the Creator that it holds all the information about each and every living thing, whether it is viruses, bacteria, fungi, plants, animals or humans or any other living being. This paper will present an update on some of the physical and biological phenomena’ presented in the Holy Quran, unveiled using advanced technologies during the last century and will discuss how the need to incorporate this information in the curricula.

Keywords: The Holy Quran, scientific facts, curriculum, Muslims

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Authors: Chalakorn Chitsaart, Suchada Rianmora, Mann Rattana-Areeyagon, Wutichai Namjaiprasert

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G-code is the main factor in computer numerical control (CNC) machine for controlling the tool-paths and generating the profile of the object’s features. For obtaining high surface accuracy of the surface finish, non-stop operation is required for CNC machine. Recently, to design a new product, the strategy that concerns about a change that has low impact on business and does not consume lot of resources has been introduced. Cost and time for designing minor changes can be reduced since the traditional geometric details of the existing models are applied. In order to support this strategy as the alternative channel for machining operation, this research proposes the automatic generating codes for CNC milling operation. Using this technique can assist the manufacturer to easily change the size and the geometric shape of the product during the operation where the time spent for setting up or processing the machine are reduced. The algorithm implemented on MATLAB platform is developed by analyzing and evaluating the geometric information of the part. Codes are created rapidly to control the operations of the machine. Comparing to the codes obtained from CAM, this developed algorithm can shortly generate and simulate the cutting profile of the part.

Keywords: geometric shapes, milling operation, minor changes, CNC Machine, G-code, cutting parameters

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Authors: Olaiya Folorunsho, Comfort Y. Daramola, Joel N. Ugwu, Lawrence B. Adewole, Olufisayo S. Ekundayo

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In today world, there is a tremendous rise in the usage of internet due to the fact that almost all the communication and information sharing is done over the web. Conversely, there is a continuous growth of unauthorized access to confidential data. This has posed a challenge to information security expertise whose major goal is to curtail the menace. One of the approaches to secure the safety delivery of data/information to the rightful destination without any modification is steganography. Steganography is the art of hiding information inside an embedded information. This research paper aimed at designing a secured algorithm with the use of image steganographic technique that makes use of Least Significant Bit (LSB) algorithm for embedding the data into the bit map image (bmp) in order to enhance security and reliability. In the LSB approach, the basic idea is to replace the LSB of the pixels of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The system was implemented using C# programming language of Microsoft.NET framework. The performance evaluation of the proposed system was experimented by conducting a benchmarking test for analyzing the parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result showed that image steganography performed considerably in securing data hiding and information transmission over the networks.

Keywords: steganography, image steganography, least significant bits, bit map image

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Authors: Justin Fu, Thomas Mazzuchi, Shahram Sarkani

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A key factor in technological immaturity in defense weapons acquisition is lack of understanding critical integrations at the subsystem and component level. To address this shortfall, recent research in integration readiness level (IRL) combines with technology readiness level (TRL) to form a system readiness level (SRL). SRL can be enriched with more robust quantitative methods to provide the program manager a useful tool prior to committing to major weapons acquisition programs. This research harnesses previous mathematical models based on graph theory, Petri nets, and tropical algebra and proposes a modification of the desirable SRL mathematical properties such that a tightly integrated (multitude of interfaces) subsystem can display a lower SRL than an inherently less coupled subsystem. The synthesis of these methods informs an improved decision tool for the program manager to commit to expensive technology development. This research ties the separately developed manufacturing readiness level (MRL) into the network representation of the system and addresses shortfalls in previous frameworks, including the lack of integration weighting and the over-importance of a single extremely immature component. Tropical algebra (based on the minimum of a set of TRLs or IRLs) allows one low IRL or TRL value to diminish the SRL of the entire system, which may not be reflective of actuality if that component is not critical or tightly coupled. Integration connections can be weighted according to importance and readiness levels are modified to be a cardinal scale (based on an analytic hierarchy process). Integration arcs’ importance are dependent on the connected nodes and the additional integrations arcs connected to those nodes. Lack of integration is not represented by zero, but by a perfect integration maturity value. Naturally, the importance (or weight) of such an arc would be zero. To further explore the impact of grouping subsystems, a multi-objective genetic algorithm is then used to find various clusters or communities that can be optimized for the most representative subsystem SRL. This novel calculation is then benchmarked through simulation and using past defense acquisition program data, focusing on the newly introduced Middle Tier of Acquisition (rapidly field prototypes). The model remains a relatively simple, accessible tool, but at higher fidelity and validated with past data for the program manager to decide major defense acquisition program milestones.

Keywords: readiness, maturity, system, integration

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Authors: Ajish Sreedharan

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Visual cryptography encodes a secret image into shares of random binary patterns. If the shares are exerted onto transparencies, the secret image can be visually decoded by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. The binary patterns of the shares, however, have no visual meaning and hinder the objectives of visual cryptography. In the Secret Message Transmission through Meaningful Shares a secret message to be transmitted is converted to grey scale image. Then (2,2) visual cryptographic shares are generated from this converted gray scale image. The shares are encrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. Two separate color images which are of the same size of the shares, taken as cover image of the respective shares to hide the shares into them. The encrypted shares which are covered by meaningful images so that a potential eavesdropper wont know there is a message to be read. The meaningful shares are transmitted through two different transmission medium. During decoding shares are fetched from received meaningful images and decrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. The shares are combined to regenerate the grey scale image from where the secret message is obtained.

Keywords: visual cryptography, wavelet transform, meaningful shares, grey scale image

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Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Swati Gautam, Salman Akhtar, S. P. Jaiswar, Amita Jain

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Background: FGTB is now a major global health problem mostly in developing countries including India. In humans, Mycobacterium Tuberculosis (M.tb) is a causating agent of infection. High index of suspicion is required for early diagnosis due to asymptomatic presentation of FGTB disease. In macrophages Toll Like Receptor-2 (TLR-2) is one which mediated host’s immune response to M.tb. The expression of TLR-2 on macrophages is important to determine the fate of innate immune responses to M.tb. TLR-2 have two work. First its high expression on macrophages worsen the outer of infection and another side, it maintains M.tb to its dormant stage avoids activation of M.tb from latent phase. Single Nucleotide Polymorphism (SNP) of TLR-2 gene plays an important role in susceptibility to TB among different populations and subsequently, in the development of infertility. Methodology: This Case-Control study was done in the Department of Obs and Gynae and Department of Microbiology at King George’s Medical University, U.P, Lucknow, India. Total 300 subjects (150 Cases and 150 Controls) were enrolled in the study. All subjects were enrolled only after fulfilling the given inclusion and exclusion criteria. Inclusion criteria: Age 20-35 years, menstrual-irregularities, positive on Acid-Fast Bacilli (AFB), TB-PCR, (LJ/MGIT) culture in Endometrial Aspiration (EA). Exclusion criteria: Koch’s active, on ATT, PCOS, and Endometriosis fibroid women, positive on Gonococal and Chlamydia. Blood samples were collected in EDTA tubes from cases and healthy control women (HCW) and genomic DNA extraction was carried out by salting-out method. Genotyping of TLR2 genetic variants (Arg753Gln and Arg677Trp) were performed by using single amplification refractory mutation system (ARMS) PCR technique. PCR products were analyzed by electrophoresis on 1.2% agarose gel and visualized by gel-doc. Statistical analysis of the data was performed using the SPSS 16.3 software and computing odds ratio (OR) with 95% CI. Linkage Disequiliribium (LD) analysis was done by SNP stats online software. Results: In TLR-2 (Arg753Gln) polymorphism significant risk of FGTB observed with GG homozygous mutant genotype (OR=13, CI=0.71-237.7, p=0.05), AG heterozygous mutant genotype (OR=13.7, CI=0.76-248.06, p=0.03) however, G allele (OR=1.09, CI=0.78-1.52, p=0.67) individually was not associated with FGTB. In TLR-2 (Arg677Trp) polymorphism a significant risk of FGTB observed with TT homozygous mutant genotype (OR= 0.020, CI=0.001-0.341, p < 0.001), CT heterozygous mutant genotype (OR=0.53, CI=0.33-0.86, p=0.014) and T allele (OR=0.463, CI=0.32-0.66, p < 0.001). TT mutant genotype was only found in FGTB cases and frequency of CT heterozygous more in control group as compared to FGTB group. So, CT genotype worked as protective mutation for FGTB susceptibility group. In haplotype analysis of TLR-2 genetic variants, four possible combinations, i.e. (G-T, A-C, G-C, and A-T) were obtained. The frequency of haplotype A-C was significantly higher in FGTB cases (0.32). Control group did not show A-C haplotype and only found in FGTB cases. Conclusion: In conclusion, study showed a significant association with both genetic variants of TLR-2 of FGTB disease. Moreover, the presence of specific associated genotype/alleles suggest the possibility of disease severity and clinical approach aimed to prevent extensive damage by disease and also helpful for early detection of disease.

Keywords: ARMS, EDTA, FGTB, TLR

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of weights of elements. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research and designing of optimal structure systems are carried out.

Keywords: Complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability, weight of element

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: Amir Ahmad Dehghani, Morteza Nabizadeh

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This paper presents the application of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to flood hydrograph modeling of Shahid Rajaee reservoir dam located in Iran. This was carried out using 11 flood hydrographs recorded in Tajan river gauging station. From this dataset, 9 flood hydrographs were chosen to train the model and 2 flood hydrographs to test the model. The different architectures of neuro-fuzzy model according to the membership function and learning algorithm were designed and trained with different epochs. The results were evaluated in comparison with the observed hydrographs and the best structure of model was chosen according the least RMSE in each performance. To evaluate the efficiency of neuro-fuzzy model, various statistical indices such as Nash-Sutcliff and flood peak discharge error criteria were calculated. In this simulation, the coordinates of a flood hydrograph including peak discharge were estimated using the discharge values occurred in the earlier time steps as input values to the neuro-fuzzy model. These results indicate the satisfactory efficiency of neuro-fuzzy model for flood simulating. This performance of the model demonstrates the suitability of the implemented approach to flood management projects.

Keywords: adaptive neuro-fuzzy inference system, flood hydrograph, hybrid learning algorithm, Shahid Rajaee reservoir dam

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Authors: Krzysztof Malczewski

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PET is widely applied scanning procedure in medical imaging based research. It delivers measurements of functioning in distinct areas of the human brain while the patient is comfortable, conscious and alert. This article presents the new compression sensing based super-resolution algorithm for improving the image resolution in clinical Positron Emission Tomography (PET) scanners. The issue of motion artifacts is well known in Positron Emission Tomography (PET) studies as its side effect. The PET images are being acquired over a limited period of time. As the patients cannot hold breath during the PET data gathering, spatial blurring and motion artefacts are the usual result. These may lead to wrong diagnosis. It is shown that the presented approach improves PET spatial resolution in cases when Compressed Sensing (CS) sequences are used. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were traditionally thought necessary. The application of CS to PET has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the goal is to combine super-resolution image enhancement algorithm with CS framework to achieve high resolution PET output. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity.

Keywords: PET, super-resolution, image reconstruction, pattern recognition

Procedia PDF Downloads 344

Authors: G. Blanco-Lizana, C. García-Fernández, J. A. Sánchez

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Growth is a productive trait regulated by a large and complex gene network with very different effect. Some of they (candidate genes) have a higher effect and are excellent resources to search in them polymorphisms correlated with differences in growth rates. This study was focused on the identification of single nucleotide polymorphism (SNP) in MyoD-1 and MyoD-2 genes, members of the family of myogenic regulatory genes with a key role in the differentiation and development of muscular tissue.(MFRs), and its evaluation as potential markers in genetic selection programs for growth in gilthead sea bream (Sparus aurata). Through a sequencing in 30 seabream (classified as unrelated by microsatellite markers) of 1.968bp in MyoD-1 gene [AF478568 .1] and 1.963bp in MyoD-2 gene [AF478569.1], three SNPs were identified in each gene (SaMyoD-1 D2100A (D indicate a deletion) SaMyoD-1 A2143G and SaMyoD-1 A2404G and SaMyoD-2_A785C, SaMyoD-2_C1982T and SaMyoD-2_A2031T). The relationships between SNPs and body weight were evaluated by SNP genotyping of 53 breeders from two broodstocks (A:18♀-9♂; B:16♀-10♂) and 389 offspring divided into two groups (slow- and fast-growth) with significant differences in growth at 18 months of development (A18Slow: N=107, A18Fast: N=103, B18Slow: N=92 and B18Fast: N=87) (Borrell et al., 2011). Haplotype and diplotype were reconstructed from genotype data by Phase 2.1 software. Differences among means of different diplotypes were calculated by one-way ANOVA followed by post-hoc Tukey test. Association analysis indicated that single SNP did not show significant effect on body weight. However, when the analysis is carried out considering haplotype data it was observed that the DGG haplotipe of MyoD-1 gen and CCA haplotipe of MyoD- 2gen were associated to with lower body weight. This haplotype combination always showed the lowest mean body weight (P<0.05) in three (A18Slow, A18Fast & B18Slow) of the four groups tested. Individuals with DGG haplotipe of MyoD-1 gen have a 25,5% and those with CCA haplotipe of MyoD- 2gen showed 14-18% less on mean body weight. Although further studies are need to validate the role of these 3 SNPs as marker for body weight, the polymorphism-trait association established in this work create promising expectations on the use of these variants as genetic tool for future giltead seabream breeding programs.

Keywords: growth, MyoD-1 and MyoD-2 genes, selective breeding, SNP-haplotype

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-doppler features

Procedia PDF Downloads 90

Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

Procedia PDF Downloads 275

Authors: Ching-Shoei Chiang

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The Malfatti’s Problem solves the problem of fitting 3 circles into a right triangle such that these 3 circles are tangent to each other, and each circle is also tangent to a pair of the triangle’s sides. This problem has been extended to any triangle (called general Malfatti’s Problem). Furthermore, the problem has been extended to have 1+2+…+n circles inside the triangle with special tangency properties among circles and triangle sides; we call it extended general Malfatti’s problem. In the extended general Malfatti’s problem, call it Tri(Tn), where Tn is the triangle number, there are closed-form solutions for Tri(T₁) (inscribed circle) problem and Tri(T₂) (3 Malfatti’s circles) problem. These problems become more complex when n is greater than 2. In solving Tri(Tn) problem, n>2, algorithms have been proposed to solve these problems numerically. With a similar idea, this paper proposed an algorithm to find the radii of circles with the same tangency properties. Instead of the boundary of the triangle being a straight line, we use a convex circular arc as the boundary and try to find Tn circles inside this convex circular triangle with the same tangency properties among circles and boundary Carc. We call these problems the Carc(Tn) problems. The CPU time it takes for Carc(T16) problem, which finds 136 circles inside a convex circular triangle with specified tangency properties, is less than one second.

Keywords: circle packing, computer-aided geometric design, geometric constraint solver, Malfatti’s problem

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Authors: Mark Davey

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Communications on a Network on Chip (NoC) produce timing information, i.e., network injection delays, packet traversal times, throughput metrics, and other attributes relating to the traffic being sent across the chip. The security requirements of a platform encompass each node to operate with confidentiality, integrity, and availability (ISO 27001). Inherently, a shared NoC interconnect is exposed to analysis of timing patterns created by contention for the network components, i.e., links and switches/routers. This phenomenon is defined as information leakage, which represents a ‘side channel’ of sensitive information that can be correlated to platform activity. The key algorithm presented in this paper evaluates how an adversary can control two platform neighbouring nodes of a target node to obtain sensitive information about communication with the target node. The actual information obtained is the period value of a periodic task communication. This enacts a breach of the expected confidentiality of a node operating in a multiprocessor platform. An experimental investigation of the side channel is undertaken to judge the level and significance of inferred information produced by access times to the NoC. Results are presented with a series of expanding task set scenarios to evaluate the efficacy of the side channel detection algorithm as the network load increases.

Keywords: embedded systems, multiprocessor, network on chip, side channel

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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WiMAX is a high speed broadband wireless access technology that adopted OFDM/OFDMA techniques to supply higher data rates with high spectral efficiency. However, OFDM suffers in view of high Peak to Average Power Ratio (PAPR) and high affect to synchronization errors. In this paper, the high PAPR problem is solved by using phase modulation to get Constant Envelop Orthogonal Frequency Division Multiplexing (CE-OFDM). The synchronization failures are brought down by employing a frequency lock loop, Poly phase clock synchronizer, Costas loop and blind equalizers such as Constant Modulus Algorithm (CMA) equalizer and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) equalizers. The WiMAX physical layer is executed on Software Defined Radio (SDR) prototype by utilizing USRP N210 as hardware and GNU Radio as software plat-forms. A SNR estimation is performed on the signal received through USRP N210. To empathize wireless propagation in specific environments, a sliding correlator wireless channel sounding system is designed by using SDR testbed.

Keywords: BER, CMA equalizer, Kurtosis equalizer, GNU Radio, OFDM/OFDMA, USRP N210

Procedia PDF Downloads 323