Search results for: Hybrid genetic algorithm

Authors: Abdolsalam Ghaderi

Abstract:

In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: Location-routing problem, robust optimization, Stochastic Programming, variable neighborhood search.

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Authors: Anju Shri, Parvinder S. Sandhu, Vikas Gupta, Sanyam Anand

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In literature, there are metrics for identifying the quality of reusable components but the framework that makes use of these metrics to precisely predict reusability of software components is still need to be worked out. These reusability metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the software component and hence improve the productivity due to probabilistic increase in the reuse level. As CK metric suit is most widely used metrics for extraction of structural features of an object oriented (OO) software; So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO and LCOM, is used to obtain the structural analysis of OO-based software components. An algorithm has been proposed in which the inputs can be given to K-Means Clustering system in form of tuned values of the OO software component and decision tree is formed for the 10-fold cross validation of data to evaluate the in terms of linguistic reusability value of the component. The developed reusability model has produced high precision results as desired.

Keywords: CK-Metric, Desicion Tree, Kmeans, Reusability.

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Authors: Srinivasan Chandrasekaran, Manda Hari Venkata Ramachandra Rao

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Increase in the oil and gas exploration in ultra deep-water demands an adaptive structural form of the platform. Triceratops has superior motion characteristics compared to that of the Tension Leg Platform and Single Point Anchor Reservoir platforms, which is well established in the literature. Buoyant legs that support the deck are position-restrained to the sea bed using tethers with high axial pretension. Environmental forces that act on the platform induce dynamic tension variations in the tethers, causing the failure of tethers. The present study investigates the dynamic response behavior of the restraining system of the platform under the failure of a single tether of each buoyant leg in high sea states. Using the rain-flow counting algorithm and the Goodman diagram, fatigue damage caused to the tethers is estimated, and the fatigue life is predicted. Results shows that under failure conditions, the fatigue life of the remaining tethers is quite alarmingly low.

Keywords: Fatigue life, Failure analysis, PM spectrum, rain flow counting, triceratops.

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Piyangkun Kukutapan, Siridech Boonsang

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The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.

Keywords: Maximum power point tracking, multilayer perceptron neural network, optimal duty cycle.

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Authors: Y. T. Yang, H. S. Peng, H. T. Hsu

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This study presents the numerical simulation of optimum pin-fin heat sink with air impinging cooling by using Taguchi method. 9 L ( 4 3 ) orthogonal array is selected as a plan for the four design-parameters with three levels. The governing equations are discretized by using the control-volume-based-finite-difference method with a power-law scheme on the non-uniform staggered grid. We solved the coupling of the velocity and the pressure terms of momentum equations using SIMPLEC algorithm. We employ the k −ε two-equations turbulence model to describe the turbulent behavior. The parameters studied include fin height H (35mm-45mm), inter-fin spacing a , b , and c (2 mm-6.4 mm), and Reynolds number ( Re = 10000- 25000). The objective of this study is to examine the effects of the fin spacings and fin height on the thermal resistance and to find the optimum group by using the Taguchi method. We found that the fin spacings from the center to the edge of the heat sink gradually extended, and the longer the fin’s height the better the results. The optimum group is 3 1 2 3 H a b c . In addition, the effects of parameters are ranked by importance as a , H , c , and b .

Keywords: Heat sink, Optimum, Electronics cooling, CFD.

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Authors: S. Cherif, M. Turki-Hadj Alouane

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In this paper, we propose a modified version of the Constant Modulus Algorithm (CMA) tailored for blind Decision Feedback Equalizer (DFE) of first order Markovian time varying channels. The proposed NonStationary CMA (NSCMA) is designed so that it explicitly takes into account the Markovian structure of the channel nonstationarity. Hence, unlike the classical CMA, the NSCMA is not blind with respect to the channel time variations. This greatly helps the equalizer in the case of realistic channels, and avoids frequent transmissions of training sequences. This paper develops a theoretical analysis of the steady state performance of the CMA and the NSCMA for DFEs within a time varying context. Therefore, approximate expressions of the mean square errors are derived. We prove that in the steady state, the NSCMA exhibits better performance than the classical CMA. These new results are confirmed by simulation. Through an experimental study, we demonstrate that the Bit Error Rate (BER) is reduced by the NSCMA-DFE, and the improvement of the BER achieved by the NSCMA-DFE is as significant as the channel time variations are severe.

Keywords: Time varying channel, Markov model, Blind DFE, CMA, NSCMA.

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Authors: Phannee Saengkaew, Weerasak Ussawawongaraya, Sasiphan Khaweerat, Supagorn Rugmai, Sirisart Ouajai, Jiraporn Luengviriya, Sakuntam Sanorpim, Manop Tirarattanasompot, Somboon Rhianphumikarakit

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We present a preliminary x-ray study on human-hair microstructures for a health-state indicator, in particular a cancer case. As an uncomplicated and low-cost method of x-ray technique, the human-hair microstructure was analyzed by wide-angle x-ray diffractions (XRD) and small-angle x-ray scattering (SAXS). The XRD measurements exhibited the simply reflections at the d-spacing of 28 Å, 9.4 Å and 4.4 Å representing to the periodic distance of the protein matrix of the human-hair macrofibrous and the diameter and the repeated spacing of the polypeptide alpha helixes of the photofibrils of the human-hair microfibrous, respectively. When compared to the normal cases, the unhealthy cases including to the breast- and ovarian-cancer cases obtained higher normalized ratios of the x-ray diffracting peaks of 9.4 Å and 4.4 Å. This likely resulted from the varied distributions of microstructures by a molecular alteration. As an elemental analysis by x-ray fluorescence (XRF), the normalized quantitative ratios of zinc(Zn)/calcium(Ca) and iron(Fe)/calcium(Ca) were determined. Analogously, both Zn/Ca and Fe/Ca ratios of the unhealthy cases were obtained higher than both of the normal cases were. Combining the structural analysis by XRD measurements and the elemental analysis by XRF measurements exhibited that the modified fibrous microstructures of hair samples were in relation to their altered elemental compositions. Therefore, these microstructural and elemental analyses of hair samples will be benefit to associate with a diagnosis of cancer and genetic diseases. This functional method would lower a risk of such diseases by the early diagnosis. However, the high-intensity x-ray source, the highresolution x-ray detector, and more hair samples are necessarily desired to develop this x-ray technique and the efficiency would be enhanced by including the skin and fingernail samples with the human-hair analysis.

Keywords: Human-hair analysis, XRD, SAXS, breast cancer, health-state indicator

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Authors: Michalis D. Galanis, Gregory Dimitroulakos, Costas E. Goutis

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Speedups from mapping four real-life DSP applications on an embedded system-on-chip that couples coarsegrained reconfigurable logic with an instruction-set processor are presented. The reconfigurable logic is realized by a 2-Dimensional Array of Processing Elements. A design flow for improving application-s performance is proposed. Critical software parts, called kernels, are accelerated on the Coarse-Grained Reconfigurable Array. The kernels are detected by profiling the source code. For mapping the detected kernels on the reconfigurable logic a prioritybased mapping algorithm has been developed. Two 4x4 array architectures, which differ in their interconnection structure among the Processing Elements, are considered. The experiments for eight different instances of a generic system show that important overall application speedups have been reported for the four applications. The performance improvements range from 1.86 to 3.67, with an average value of 2.53, compared with an all-software execution. These speedups are quite close to the maximum theoretical speedups imposed by Amdahl-s law.

Keywords: Reconfigurable computing, Coarse-grained reconfigurable array, Embedded systems, DSP, Performance

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Authors: Cathy SW. Chow, Kristina Shin, Faming Wang, B. C. L. So

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During some dynamic giant puppet performances, an ergonomically designed load carrier system is necessary for the puppeteers to carry a giant puppet body’s heavy load with minimum muscle stress. A load carrier (i.e. prototype) was designed with two small wheels on the foot; and a hybrid spring device on the knee in order to assist the sliding and knee bending movements respectively. Thus, the purpose of this study was to evaluate the effect of three load carriers including two other commercially available load mounting systems, Tepex and SuitX, and the prototype. Ten male participants were recruited for the experiment. Surface electromyography (sEMG) was used to collect the participants’ muscle activities during forward moving and bouncing and with and without load of 11.1 kg that was 60 cm above the shoulder. Five bilateral muscles including the lumbar erector spinae (LES), rectus femoris (RF), bicep femoris (BF), tibialis anterior (TA), and gastrocnemius (GM) were selected for data collection. During forward moving task, the sEMG data showed smallest muscle activities by Tepex harness which exhibited consistently the lowest, compared with the prototype and SuitX which were significantly higher on left LES 68.99% and 64.99%, right LES 26.57% and 82.45%; left RF 87.71% and 47.61%, right RF 143.57% and 24.28%; left BF 80.21% and 22.23%, right BF 96.02% and 21.83%; right TA 6.32% and 4.47%; left GM 5.89% and 12.35% respectively. The result above reflected mobility was highly restricted by tested exoskeleton devices. On the other hand, the sEMG data from bouncing task showed the smallest muscle activities by prototype which exhibited consistently the lowest, compared with the Tepex harness and SuitX which were significantly lower on lLES 6.65% and 104.93, rLES 23.56% and 92.19%; lBF 33.21% and 93.26% and rBF 24.70% and 81.16%; lTA 46.51% and 191.02%; rTA 12.75% and 125.76%; IGM 31.54% and 68.36%; rGM 95.95% and 96.43% respectively.

Keywords: Exoskeleton, load carriage aid, giant puppet performers, electromyography.

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Authors: Lihui Ma, Kunlun Xin, Suiqing Liu

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Modern managements of water distribution system (WDS) need water quality models that are able to accurately predict the dynamics of water quality variations within the distribution system environment. Before water quality models can be applied to solve system problems, they should be calibrated. Although former researchers use GA solver to calibrate relative parameters, it is difficult to apply on the large-scale or medium-scale real system for long computational time. In this paper a new method is designed which combines both macro and detailed model to optimize the water quality parameters. This new combinational algorithm uses radial basis function (RBF) metamodeling as a surrogate to be optimized for the purpose of decreasing the times of time-consuming water quality simulation and can realize rapidly the calibration of pipe wall reaction coefficients of chlorine model of large-scaled WDS. After two cases study this method is testified to be more efficient and promising, and deserve to generalize in the future.

Keywords: Metamodeling, model calibration, radial basisfunction, water distribution system, water quality model.

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Authors: Yuan-Lin Chen

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This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway.

Keywords: Approaching index, forward collision probability, time to collision, time headway.

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Authors: Shih-Wen Hsiao, Chu-Hsuan Lee, Rong-Qi Chen

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When it comes to last, it is regarded as the critical foundation of shoe design and development. Not only the last relates to the comfort of shoes wearing but also it aids the production of shoe styling and manufacturing. In order to enhance the efficiency and application of last development, a computer aided methodology for customized last form designs is proposed in this study. The reverse engineering is mainly applied to the process of scanning for the last form. Then the minimum energy is used for the revision of surface continuity, the surface of the last is reconstructed with the feature curves of the scanned last. When the surface of a last is reconstructed, based on the foundation of the proposed last form reconstruction module, the weighted arithmetic mean method is applied to the calculation on the shape morphing which differs from the grading for the control mesh of last, and the algorithm of subdivision is used to create the surface of last mesh, thus the feet-fitting 3D last form of different sizes is generated from its original form feature with functions remained. Finally, the practicability of the proposed methodology is verified through later case studies.

Keywords: 3D last design, Customization, Reverse engineering, Weighted morphing, Shape blending.

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Authors: Tomislav Stambolic, Anton Causevski

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The deficit of power for electricity demand reaches almost 30% for consumers in the last few years. This reflects with continually increasing the price of electricity, and today the price for small industry is almost 110Euro/MWh. The high price is additional problem for the owners in the economy crisis which is reflected with higher price of the goods. The paper gives analyses of the energy needs for real agro complex in Macedonia, private vinery with capacity of over 2 million liters in a year and with self grapes and fruits fields. The existing power supply is from grid with 10/04 kV transformer. The geographical and meteorological condition of the vinery location gives opportunity for including renewable as a power supply option for the vinery complex. After observation of the monthly energy needs for the vinery, the base scenario is the existing power supply from the distribution grid. The electricity bill in small industry has three factors: electricity in high and low tariffs in kWh and the power engaged for the technological process of production in kW. These three factors make the total electricity bill and it is over 110 Euro/MWh which is the price near competitive for renewable option. On the other side investments in renewable (especially photovoltaic (PV)) has tendency of decreasing with price of near 1,5 Euro/W. This means that renewable with PV can be real option for power supply for small industry capacities (under 500kW installed power). Therefore, the other scenarios give the option with PV and the last one includes wind option. The paper presents some scenarios for power supply of the vinery as the followings: • Base scenario of existing conventional power supply from the grid • Scenario with implementation of renewable of Photovoltaic • Scenario with implementation of renewable of Photovoltaic and Wind power The total power installed in a vinery is near 570 kW, but the maximum needs are around 250kW. At the end of the full paper some of the results from scenarios will be presented. The paper also includes the environmental impacts of the renewable scenarios, as well as financial needs for investments and revenues from renewable.

Keywords: Energy, Power Supply, Renewable, Efficiency.

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Authors: F. Caliskan

Abstract:

This paper presents a Neural Network (NN) identification of icing parameters in an A340 aircraft and a reconfiguration technique to keep the A/C performance close to the performance prior to icing. Five aircraft parameters are assumed to be considerably affected by icing. The off-line training for identifying the clear and iced dynamics is based on the Levenberg-Marquard Backpropagation algorithm. The icing parameters are located in the system matrix. The physical locations of the icing are assumed at the right and left wings. The reconfiguration is based on the technique known as the control mixer approach or pseudo inverse technique. This technique generates the new control input vector such that the A/C dynamics is not much affected by icing. In the simulations, the longitudinal and lateral dynamics of an Airbus A340 aircraft model are considered, and the stability derivatives affected by icing are identified. The simulation results show the successful NN identification of the icing parameters and the reconfigured flight dynamics having the similar performance before the icing. In other words, the destabilizing icing affect is compensated.

Keywords: Aircraft Icing, Stability Derivatives, Neural NetworkIdentification, Reconfiguration.

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Authors: Amarunnishad T.M., Govindan V.K., Abraham T. Mathew

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An image compression method has been developed using fuzzy edge image utilizing the basic Block Truncation Coding (BTC) algorithm. The fuzzy edge image has been validated with classical edge detectors on the basis of the results of the well-known Canny edge detector prior to applying to the proposed method. The bit plane generated by the conventional BTC method is replaced with the fuzzy bit plane generated by the logical OR operation between the fuzzy edge image and the corresponding conventional BTC bit plane. The input image is encoded with the block mean and standard deviation and the fuzzy bit plane. The proposed method has been tested with test images of 8 bits/pixel and size 512×512 and found to be superior with better Peak Signal to Noise Ratio (PSNR) when compared to the conventional BTC, and adaptive bit plane selection BTC (ABTC) methods. The raggedness and jagged appearance, and the ringing artifacts at sharp edges are greatly reduced in reconstructed images by the proposed method with the fuzzy bit plane.

Keywords: Image compression, Edge detection, Ground truth image, Peak signal to noise ratio

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Authors: Awadhesh Kumar Singh

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Checkpointing is one of the commonly used techniques to provide fault-tolerance in distributed systems so that the system can operate even if one or more components have failed. However, mobile computing systems are constrained by low bandwidth, mobility, lack of stable storage, frequent disconnections and limited battery life. Hence, checkpointing protocols having lesser number of synchronization messages and fewer checkpoints are preferred in mobile environment. There are two different approaches, although not orthogonal, to checkpoint mobile computing systems namely, time-based and index-based. Our protocol is a fusion of these two approaches, though not first of its kind. In the present exposition, an index-based checkpointing protocol has been developed, which uses time to indirectly coordinate the creation of consistent global checkpoints for mobile computing systems. The proposed algorithm is non-blocking, adaptive, and does not use any control message. Compared to other contemporary checkpointing algorithms, it is computationally more efficient because it takes lesser number of checkpoints and does not need to compute dependency relationships. A brief account of important and relevant works in both the fields, time-based and index-based, has also been included in the presentation.

Keywords: Checkpointing, forced checkpoint, mobile computing, recovery, time-coordinated.

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Authors: Jean-Pierre Dubois, Rania Minkara, Rafic Ayoubi

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Maximal Ratio Combining (MRC) is considered the most complex combining technique as it requires channel coefficients estimation. It results in the lowest bit error rate (BER) compared to all other combining techniques. However the BER starts to deteriorate as errors are introduced in the channel coefficients estimation. A novel combining technique, termed Generalized Maximal Ratio Combining (GMRC) with a polynomial kernel, yields an identical BER as MRC with perfect channel estimation and a lower BER in the presence of channel estimation errors. We show that GMRC outperforms the optimal MRC scheme in general and we hereinafter introduce it to the scientific community as a new “supraoptimal" algorithm. Since diversity combining is especially effective in small femto- and pico-cells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to IP-based 4th generation networks.

Keywords: Bit error rate, femto-internet cells, generalized maximal ratio combining, signal-to-scattering noise ratio.

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Authors: A. Allirani, M. Suganthi

Abstract:

Data gathering is an essential operation in wireless sensor network applications. So it requires energy efficiency techniques to increase the lifetime of the network. Similarly, clustering is also an effective technique to improve the energy efficiency and network lifetime of wireless sensor networks. In this paper, an energy efficient cluster formation protocol is proposed with the objective of achieving low energy dissipation and latency without sacrificing application specific quality. The objective is achieved by applying randomized, adaptive, self-configuring cluster formation and localized control for data transfers. It involves application - specific data processing, such as data aggregation or compression. The cluster formation algorithm allows each node to make independent decisions, so as to generate good clusters as the end. Simulation results show that the proposed protocol utilizes minimum energy and latency for cluster formation, there by reducing the overhead of the protocol.

Keywords: Sensor networks, Low latency, Energy sorting protocol, data processing, Cluster formation.

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Authors: Sanghoon Kim, Sun-Tae Chung, Souhwan Jung, Seongwon Cho

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Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an improved illumination normalization method for face recognition. Illumination normalization algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing-based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing the effect of illumination variations. Due to the result of these improvements, face images preprocessed by the proposed illumination normalization method becomes to have more distinctive feature vectors (Gabor feature vectors) for face recognition. Through experiments of face recognition based on Gabor feature vector similarity, the effectiveness of the proposed illumination normalization method is verified.

Keywords: Illumination Normalization, Face Recognition, Anisotropic smoothing, Gabor feature vector.

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Authors: Kefaya Qaddoum

Abstract:

Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: Tomato yields prediction, naive Bayes, redundancy

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Authors: Wahyu Widowati, Hana Ratnawati, Tjandrawati Mozefis, Dwiyati Pujimulyani, Yelliantty Yelliantty

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Background: Atherosclerosis is the main cause of cardiovascular disease (CVD) with complex and multifactorial process including atherogenic lipoprotein, oxidized low density lipoprotein (LDL), endothelial dysfunction, plaque stability, vascular inflammation, thrombotic and fibrinolytic disorder, exercises and genetic factor Epidemiological studies have shown tea consumption inversely associated with the development and progression of atherosclerosis. The research objectives: to elucidate hypolipidemic, antioxidant effects, as well as ability to improve coronary artery’s histopathologyof black tea extract (BTE) and quercetin in atherosclerotic rats. Methods: The antioxidant activity was determined by using Superoxide Dismutase activity (SOD) of serum and lipid peroxidation product (Malondialdehyde) of plasma and lipid profile including cholesterol total, LDL, triglyceride (TG), High Density Lipoprotein (HDL) of atherosclerotic rats. Inducing atherosclerotic, rats were given cholesterol and cholic acid in feed during ten weeks until rats indicated atherosclerotic symptom with narrowed artery and foamy cells in the artery’s wall. After rats suffered atherosclerotic, the high cholesterol feed and cholic acid were stopped and rats were given BTE 450; 300; 150 mg/kg body weight (BW) daily, quercetin 15; 10; 5 mg/kg BW daily, compared to rats were given vitamin E 60 mg/kg/BW; simvastatin 2.7 mg/kg BW, probucol 30 mg/kg BW daily for 21 days (first treatment) and 42 days (second treatment), negative control (normal feed), positive control (atherosclerotic rats). Results: BTE and quercetin could lower cholesterol total, triglyceride, LDL MDA and increase HDL, SOD were comparable with simvastatin, probucol both for 21 days and 42 days treatment, as well to improve coronary arteries histopathology. Conclusions: BTE andquercetin have hypolipidemic and antioxidant effects, as well as improve coronary arteries histopathology in atherosclerotic rats.

Keywords: Black tea, quercetin, atherosclerosis, antioxidant, hypolipidemic, cardiovascular disease.

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Authors: Harshit Mehrotra, Gaurav Agrawal, M.C. Srivastava

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Computerized lip reading has been one of the most actively researched areas of computer vision in recent past because of its crime fighting potential and invariance to acoustic environment. However, several factors like fast speech, bad pronunciation, poor illumination, movement of face, moustaches and beards make lip reading difficult. In present work, we propose a solution for automatic lip contour tracking and recognizing letters of English language spoken by speakers using the information available from lip movements. Level set method is used for tracking lip contour using a contour velocity model and a feature vector of lip movements is then obtained. Character recognition is performed using modified k nearest neighbor algorithm which assigns more weight to nearer neighbors. The proposed system has been found to have accuracy of 73.3% for character recognition with speaker lip movements as the only input and without using any speech recognition system in parallel. The approach used in this work is found to significantly solve the purpose of lip reading when size of database is small.

Keywords: Contour Velocity Model, Lip Contour Tracking, LipReading, Visual Character Recognition.

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: Classification, classifier fusion, CNN, Deep Learning, prediction, SNR.

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Authors: Banashree N. P., R. Vasanta

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Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.

Keywords: OCR, Global Feature, End-Points, Neuro-Memetic model.

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Authors: Saule Mussabekova

Abstract:

Recent advances in genetics have allowed increasing acutely the capacities of the formation of reliable evidence in conducting forensic examinations. Thus, traces of biological origin are important sources of information about a crime. Currently, around the world, sexual offenses have increased, and among them are those in which the criminals use various detergents to remove traces of their crime. A feature of modern synthetic detergents is the presence of biological additives - enzymes. Enzymes purposefully destroy stains of biological origin. To study the nature and extent of the impact of modern washing powders on saliva stains on the physical evidence, specially prepared test specimens of different types of tissues to which saliva was applied have been examined. Materials and Methods: Washing machines of famous manufacturers of household appliances have been used with different production characteristics and advertised brands of washing powder for test washing. Over 3,500 experimental samples were tested. After washing, the traces of saliva were identified using modern research methods of forensic medicine. Results: The influence was tested and the dependence of the use of different washing programs, types of washing machines and washing powders in the process of establishing saliva trace and identify of the stains on the physical evidence while washing was revealed. The results of experimental and practical expert studies have shown that in most cases it is not possible to draw the conclusions in the identification of saliva traces on physical evidence after washing. This is a consequence of the effect of biological additives and other additional factors on traces of saliva during washing. Conclusions: On the basis of the results of the study, the feasibility of saliva traces of the stains on physical evidence after washing is established. The use of modern molecular genetic methods makes it possible to partially solve the problems arising in the study of unlaundered evidence. Additional study of physical evidence after washing facilitates detection and investigation of sexual offenses against women and children.

Keywords: Saliva research, modern synthetic detergents, laundry detergents, forensic medicine.

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Authors: Jeffrey L. Duffany

Abstract:

Graph coloring is an important problem in computer science and many algorithms are known for obtaining reasonably good solutions in polynomial time. One method of comparing different algorithms is to test them on a set of standard graphs where the optimal solution is already known. This investigation analyzes a set of 50 well known graph coloring instances according to a set of complexity measures. These instances come from a variety of sources some representing actual applications of graph coloring (register allocation) and others (mycieleski and leighton graphs) that are theoretically designed to be difficult to solve. The size of the graphs ranged from ranged from a low of 11 variables to a high of 864 variables. The method used to solve the coloring problem was the square of the adjacency (i.e., correlation) matrix. The results show that the most difficult graphs to solve were the leighton and the queen graphs. Complexity measures such as density, mobility, deviation from uniform color class size and number of block diagonal zeros are calculated for each graph. The results showed that the most difficult problems have low mobility (in the range of .2-.5) and relatively little deviation from uniform color class size.

Keywords: graph coloring, complexity, algorithm.

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Authors: Junjie Gao, Guishi Deng

Abstract:

In order to integrate knowledge in heterogeneous case-based reasoning (CBR) systems, ontology-based CBR system has become a hot topic. To solve the facing problems of ontology-based CBR system, for example, its architecture is nonstandard, reusing knowledge in legacy CBR is deficient, ontology construction is difficult, etc, we propose a novel approach for semi-automatically construct ontology-based CBR system whose architecture is based on two-layer ontology. Domain knowledge implied in legacy case bases can be mapped from relational database schema and knowledge items to relevant OWL local ontology automatically by a mapping algorithm with low time-complexity. By concept clustering based on formal concept analysis, computing concept equation measure and concept inclusion measure, some suggestions about enriching or amending concept hierarchy of OWL local ontologies are made automatically that can aid designers to achieve semi-automatic construction of OWL domain ontology. Validation of the approach is done by an application example.

Keywords: OWL ontology, Case-based Reasoning, FormalConcept Analysis, Knowledge Integration

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Authors: Romisaa Ali

Abstract:

The Policy Gradient approach is a subset of the Deep Reinforcement Learning (DRL) combines Deep Neural Networks (DNN) with Reinforcement Learning (RL). This approach finds the optimal policy of robot movement, based on the experience it gains from interaction with its environment. Unlike previous policy gradient algorithms, which were unable to handle the two types of error variance and bias introduced by the DNN model due to over- or underestimation, this algorithm is capable of handling both types of error variance and bias. This article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: Deep neural networks, deep reinforcement learning, Proximal Policy Optimization, state-of-the-art, trust region policy optimization.

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Authors: L. Hamsaveni, Navya Prakash, Suresha

Abstract:

Document Image Analysis recognizes text and graphics in documents acquired as images. An approach without Optical Character Recognition (OCR) for degraded document image analysis has been adopted in this paper. The technique involves document imaging methods such as Image Fusing and Speeded Up Robust Features (SURF) Detection to identify and extract the degraded regions from a set of document images to obtain an original document with complete information. In case, degraded document image captured is skewed, it has to be straightened (deskew) to perform further process. A special format of image storing known as YCbCr is used as a tool to convert the Grayscale image to RGB image format. The presented algorithm is tested on various types of degraded documents such as printed documents, handwritten documents, old script documents and handwritten image sketches in documents. The purpose of this research is to obtain an original document for a given set of degraded documents of the same source.

Keywords: Grayscale image format, image fusing, SURF detection, YCbCr image format.

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