Search results for: Unmanned Intervention Algorithm.

Authors: Ahyoung Jeon, Geunchul Park, Jung-Hoon Ro, Gye-rok Geon

Abstract:

Falls are the primary cause of accidents in people over the age of 65, and frequently lead to serious injuries. Since the early detection of falls is an important step to alert and protect the aging population, a variety of research on detecting falls was carried out including the use of accelerators, gyroscopes and tilt sensors. In exiting studies, falls were detected using an accelerometer with errors. In this study, the proposed method for detecting falls was to use two accelerometers to reject wrong falls detection. As falls are accompanied by the acceleration of gravity and rotational motion, the falls in this study were detected by using the z-axial acceleration differences between two sites. The falls were detected by calculating the difference between the analyses of accelerometers placed on two different positions on the chest of the subject. The parameters of the maximum difference of accelerations (diff_Z) and the integration of accelerations in a defined region (Sum_diff_Z) were used to form the fall detection algorithm. The falls and the activities of daily living (ADL) could be distinguished by using the proposed parameters without errors in spite of the impact and the change in the positions of the accelerometers. By comparing each of the axial accelerations, the directions of falls and the condition of the subject afterwards could be determined.In this study, by using two accelerometers without errors attached to two sites to detect falls, the usefulness of the proposed fall detection algorithm parameters, diff_Z and Sum_diff_Z, were confirmed.

Keywords: Tri-axial accelerometer, fall detection.

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Authors: T. Ahilan, V. Aswin Adityan, S. Kailash

Abstract:

UAV’s are small remote operated or automated aerial surveillance systems without a human pilot aboard. UAV’s generally finds its use in military and special operation application, a recent growing trend in UAV’s finds its application in several civil and nonmilitary works such as inspection of power or pipelines. The objective of this paper is the augmentation of a UAV in order to replace the existing expensive sonar (Sound Navigation And Ranging) based equipment amongst small scale fisherman, for whom access to sonar equipment are restricted due to limited economic resources. The surveillance equipment’s present in the UAV will relay data and GPS (Global Positioning System) location onto a receiver on the fishing boat using RF signals, using which the location of the schools of fishes can be found. In addition to this, an emergency beacon system is present for rescue operations and drone recovery.

Keywords: GPS, RF signals, School of fish, Sonar, Surveillance UAV, Video stream.

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Authors: Chouvalit Khancome, Veera Boonjing

Abstract:

This paper proposes a new solution to string matching problem. This solution constructs an inverted list representing a  string pattern to be searched for. It then uses a new algorithm to process an input string in a single pass. The preprocessing phase  takes 1) time complexity O(m) 2) space complexity O(1) where m is  the length of pattern. The searching phase time complexity takes 1)  O(m+α ) in average case 2) O(n/m) in the best case and 3) O(n) in  the worst case, where α is the number of comparing leading to  mismatch and n is the length of input text.

Keywords: String matching, inverted list, inverted index, pattern, algorithm.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

Abstract:

This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: Abdulmajid Mukhtar Afat

Abstract:

This paper aims at introducing nondeterministic finite automata with ε value which is used to perform some operations on languages. a program is created to implement the algorithm that converts nondeterministic finite automata with ε value (ε-NFA) to deterministic finite automata (DFA).The program is written in c++ programming language. The program inputs are FA 5-tuples from text file and then classifies it into either DFA/NFA or ε -NFA. For DFA, the program will get the string w and decide whether it is accepted or rejected. The tracking path for an accepted string is saved by the program. In case of NFA or ε-NFA automation, the program changes the automation to DFA to enable tracking and to decide if the string w exists in the regular language or not.

Keywords: Finite automata, DFA, NFA, ε-NFA, Eclose, operations on languages.

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Authors: Abhinav Kumar, Raja Sekhar Bandaru, B Madhusudan Rao, Saket Kulkarni, Nilesh Ghatpande

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This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts.

Keywords: Histogram Matching, Image Alignment, ImageStitching, Medical Imaging.

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Authors: Ahmad Rabanimotlagh

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In this paper an ant colony optimization algorithm is developed to solve the permutation flow shop scheduling problem. In the permutation flow shop scheduling problem which has been vastly studied in the literature, there are a set of m machines and a set of n jobs. All the jobs are processed on all the machines and the sequence of jobs being processed is the same on all the machines. Here this problem is optimized considering two criteria, makespan and total flow time. Then the results are compared with the ones obtained by previously developed algorithms. Finally it is visible that our proposed approach performs best among all other algorithms in the literature.

Keywords: Scheduling, Flow shop, Ant colony optimization, Makespan, Flow time

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Authors: A. Tellaeche, R. Arana

Abstract:

Injection molding is a very complicated process to monitor and control. With its high complexity and many process parameters, the optimization of these systems is a very challenging problem. To meet the requirements and costs demanded by the market, there has been an intense development and research with the aim to maintain the process under control. This paper outlines the latest advances in necessary algorithms for plastic injection process and monitoring, and also a flexible data acquisition system that allows rapid implementation of complex algorithms to assess their correct performance and can be integrated in the quality control process. This is the main topic of this paper. Finally, to demonstrate the performance achieved by this combination, a real case of use is presented.

Keywords: Plastic injection, machine learning, rapid complex algorithm prototyping.

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Authors: J.-C. Cexus, A.O. Boudraa

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In this paper, a new approach for target recognition based on the Empirical mode decomposition (EMD) algorithm of Huang etal. [11] and the energy tracking operator of Teager [13]-[14] is introduced. The conjunction of these two methods is called Teager-Huang analysis. This approach is well suited for nonstationary signals analysis. The impulse response (IR) of target is first band pass filtered into subsignals (components) called Intrinsic mode functions (IMFs) with well defined Instantaneous frequency (IF) and Instantaneous amplitude (IA). Each IMF is a zero-mean AM-FM component. In second step, the energy of each IMF is tracked using the Teager energy operator (TEO). IF and IA, useful to describe the time-varying characteristics of the signal, are estimated using the Energy separation algorithm (ESA) algorithm of Maragos et al .[16]-[17]. In third step, a set of features such as skewness and kurtosis are extracted from the IF, IA and IMF energy functions. The Teager-Huang analysis is tested on set of synthetic IRs of Sonar targets with different physical characteristics (density, velocity, shape,? ). PCA is first applied to features to discriminate between manufactured and natural targets. The manufactured patterns are classified into spheres and cylinders. One hundred percent of correct recognition is achieved with twenty three echoes where sixteen IRs, used for training, are free noise and seven IRs, used for testing phase, are corrupted with white Gaussian noise.

Keywords: Target recognition, Empirical mode decomposition, Teager-Kaiser energy operator, Features extraction.

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Authors: Jehad Al Dallal

Abstract:

Program slicing is the task of finding all statements in a program that directly or indirectly influence the value of a variable occurrence. The set of statements that can affect the value of a variable at some point in a program is called a program slice. In several software engineering applications, such as program debugging and measuring program cohesion and parallelism, several slices are computed at different program points. In this paper, algorithms are introduced to compute all backward and forward static slices of a computer program by traversing the program representation graph once. The program representation graph used in this paper is called Program Dependence Graph (PDG). We have conducted an experimental comparison study using 25 software modules to show the effectiveness of the introduced algorithm for computing all backward static slices over single-point slicing approaches in computing the parallelism and functional cohesion of program modules. The effectiveness of the algorithm is measured in terms of time execution and number of traversed PDG edges. The comparison study results indicate that using the introduced algorithm considerably saves the slicing time and effort required to measure module parallelism and functional cohesion.

Keywords: Backward slicing, cohesion measure, forward slicing, parallelism measure, program dependence graph, program slicing, static slicing.

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Authors: I. L. Ahmad, M. Mohamed, N. A. Ab. Ghani

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This paper presents the work of signal discrimination specifically for Electrocardiogram (ECG) waveform. ECG signal is comprised of P, QRS, and T waves in each normal heart beat to describe the pattern of heart rhythms corresponds to a specific individual. Further medical diagnosis could be done to determine any heart related disease using ECG information. The emphasis on QRS Complex classification is further discussed to illustrate the importance of it. Pan-Tompkins Algorithm, a widely known technique has been adapted to realize the QRS Complex classification process. There are eight steps involved namely sampling, normalization, low pass filter, high pass filter (build a band pass filter), derivation, squaring, averaging and lastly is the QRS detection. The simulation results obtained is represented in a Graphical User Interface (GUI) developed using MATLAB.

Keywords: ECG, Pan Tompkins Algorithm, QRS Complex, Simulation

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Authors: Farha Sattar, Laurence Tamatea, Muhammad Nawaz

Abstract:

Drones, the Unmanned Aerial Vehicles are playing an important role in real-world problem-solving. With the new advancements in technology, drones are becoming available, affordable and user- friendly. Use of drones in education is opening new trends in teaching and learning practices in an innovative and engaging way. Drones vary in types and sizes and possess various characteristics and capabilities which enhance their potential to be used in education from basic to advanced and challenging learning activities which are suitable for primary, middle and high school level. This research aims to provide an insight to explore different types of drones and their compatibility to be used in teaching different subjects at various levels. Research focuses on integrating the drone technology along with Australian curriculum content knowledge to reinforce the understanding of the fundamental concepts and helps to develop the critical thinking and reasoning in the learning process.

Keywords: Critical thinking, drone technology, drone types, innovative learning.

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Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp

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Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, we present three feature selection methods: Information Gain, Support Vector Machine feature selection called (SVM_FS) and Genetic Algorithm with SVM (called GA_SVM). We show that the best results were obtained with GA_SVM method for a relatively small dimension of the feature vector.

Keywords: Feature Selection, Learning with Kernels, Support Vector Machine, Genetic Algorithm, and Classification.

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Authors: Z. Zainuddin, W. K. Lye

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In this paper, a clustering algorithm named KHarmonic means (KHM) was employed in the training of Radial Basis Function Networks (RBFNs). KHM organized the data in clusters and determined the centres of the basis function. The popular clustering algorithms, namely K-means (KM) and Fuzzy c-means (FCM), are highly dependent on the initial identification of elements that represent the cluster well. In KHM, the problem can be avoided. This leads to improvement in the classification performance when compared to other clustering algorithms. A comparison of the classification accuracy was performed between KM, FCM and KHM. The classification performance is based on the benchmark data sets: Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM algorithm shows better accuracy in classification problem.

Keywords: Neural networks, Radial basis functions, Clusteringmethod, K-harmonic means.

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Authors: C. Porkodi, R. Arumuganathan

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In this paper a Public Key Cryptosystem is proposed using the number theoretic transforms (NTT) over a ring of integer modulo a composite number. The key agreement is similar to ElGamal public key algorithm. The security of the system is based on solution of multivariate linear congruence equations and discrete logarithm problem. In the proposed cryptosystem only fixed numbers of multiplications are carried out (constant complexity) and hence the encryption and decryption can be done easily. At the same time, it is very difficult to attack the cryptosystem, since the cipher text is a sequence of integers which are interrelated. The system provides authentication also. Using Mathematica version 5.0 the proposed algorithm is justified with a numerical example.

Keywords: Cryptography, decryption, discrete logarithm problem encryption, Integer Factorization problem, Key agreement, Number Theoretic Transform.

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Authors: N. S. Pradeep, M. Balasingh Moses, V. Aarthi

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Any signal transmitted over a channel is corrupted by noise and interference. A host of channel coding techniques has been proposed to alleviate the effect of such noise and interference. Among these Turbo codes are recommended, because of increased capacity at higher transmission rates and superior performance over convolutional codes. The multimedia elements which are associated with ample amount of data are best protected by Turbo codes. Turbo decoder employs Maximum A-posteriori Probability (MAP) and Soft Output Viterbi Decoding (SOVA) algorithms. Conventional Turbo coded systems employ Equal Error Protection (EEP) in which the protection of all the data in an information message is uniform. Some applications involve Unequal Error Protection (UEP) in which the level of protection is higher for important information bits than that of other bits. In this work, enhancement to the traditional Log MAP decoding algorithm is being done by using optimized scaling factors for both the decoders. The error correcting performance in presence of UEP in Additive White Gaussian Noise channel (AWGN) and Rayleigh fading are analyzed for the transmission of image with Discrete Cosine Transform (DCT) as source coding technique. This paper compares the performance of log MAP, Modified log MAP (MlogMAP) and Enhanced log MAP (ElogMAP) algorithms used for image transmission. The MlogMAP algorithm is found to be best for lower Eb/N0 values but for higher Eb/N0 ElogMAP performs better with optimized scaling factors. The performance comparison of AWGN with fading channel indicates the robustness of the proposed algorithm. According to the performance of three different message classes, class3 would be more protected than other two classes. From the performance analysis, it is observed that ElogMAP algorithm with UEP is best for transmission of an image compared to Log MAP and MlogMAP decoding algorithms.

Keywords: AWGN, BER, DCT, Fading, MAP, UEP.

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Authors: Limouzade E., Joorabian M.

Abstract:

Most of the losses in a power system relate to the distribution sector which always has been considered. From the important factors which contribute to increase losses in the distribution system is the existence of radioactive flows. The most common way to compensate the radioactive power in the system is the power to use parallel capacitors. In addition to reducing the losses, the advantages of capacitor placement are the reduction of the losses in the release peak of network capacity and improving the voltage profile. The point which should be considered in capacitor placement is the optimal placement and specification of the amount of the capacitor in order to maximize the advantages of capacitor placement. In this paper, a new technique has been offered for the placement and the specification of the amount of the constant capacitors in the radius distribution network on the basis of Genetic Algorithm (GA). The existing optimal methods for capacitor placement are mostly including those which reduce the losses and voltage profile simultaneously. But the retaliation cost and load changes have not been considered as influential UN the target function .In this article, a holistic approach has been considered for the optimal response to this problem which includes all the parameters in the distribution network: The price of the phase voltage and load changes. So, a vast inquiry is required for all the possible responses. So, in this article, we use Genetic Algorithm (GA) as the most powerful method for optimal inquiry.

Keywords: Genetic Algorithm (GA), capacitor placement, voltage profile, network losses, Simulating Annealing (SA), distribution network.

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Authors: Menglong Su, Shaoyun Shi, Qing Xu

Abstract:

In this paper, by introducing twice continuously differentiable mappings, we develop an interior path following following method, which enables us to give a constructive proof of the general Brouwer fixed point theorem and thus to solve fixed point problems in a class of non-convex sets. Under suitable conditions, a smooth path can be proven to exist. This can lead to an implementable globally convergent algorithm. Several numerical examples are given to illustrate the results of this paper.

Keywords: interior path following method, general Brouwer fixed point theorem, non-convex sets, globally convergent algorithm

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

Abstract:

The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: Optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization.

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Authors: Sandeep Singh Gill, Rajeevan Chandel, Ashwani Chandel

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This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.

Keywords: Partitioning, genetic algorithm, ant colony optimization, non-polynomial hard, netlist, mutation.

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Authors: J. Arneric, A. Rozga

Abstract:

In this paper usefulness of quasi-Newton iteration procedure in parameters estimation of the conditional variance equation within BHHH algorithm is presented. Analytical solution of maximization of the likelihood function using first and second derivatives is too complex when the variance is time-varying. The advantage of BHHH algorithm in comparison to the other optimization algorithms is that requires no third derivatives with assured convergence. To simplify optimization procedure BHHH algorithm uses the approximation of the matrix of second derivatives according to information identity. However, parameters estimation in a/symmetric GARCH(1,1) model assuming normal distribution of returns is not that simple, i.e. it is difficult to solve it analytically. Maximum of the likelihood function can be founded by iteration procedure until no further increase can be found. Because the solutions of the numerical optimization are very sensitive to the initial values, GARCH(1,1) model starting parameters are defined. The number of iterations can be reduced using starting values close to the global maximum. Optimization procedure will be illustrated in framework of modeling volatility on daily basis of the most liquid stocks on Croatian capital market: Podravka stocks (food industry), Petrokemija stocks (fertilizer industry) and Ericsson Nikola Tesla stocks (information-s-communications industry).

Keywords: Heteroscedasticity, Log-likelihood Maximization, Quasi-Newton iteration procedure, Volatility.

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Authors: Hazem M. El-Bakry

Abstract:

In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.

Keywords: Hopfield Neural Networks, Cross Correlation, Nuclear Magnetic Resonance, Magnetic Resonance Spectroscopy, Fast Fourier Transform.

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Authors: G. Stillo, L. Bonzano, M. Chiappalone, A. Vato, F. Davide, S. Martinoia

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Electrophysiological signals were recorded from primary cultures of dissociated rat cortical neurons coupled to Micro-Electrode Arrays (MEAs). The neuronal discharge patterns may change under varying physiological and pathological conditions. For this reason, we developed a new burst detection method able to identify bursts with peculiar features in different experimental conditions (i.e. spontaneous activity and under the effect of specific drugs). The main feature of our algorithm (i.e. Burst On Hurst), based on the auto-similarity or fractal property of the recorded signal, is the independence from the chosen spike detection method since it works directly on the raw data.

Keywords: Burst detection, cortical neuronal networks, Micro-Electrode Array (MEA), wavelets.

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Authors: Khairil Iskandar Othman, Zarina Bibi Ibrahim, Mohamed Suleiman

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A parallel block method based on Backward Differentiation Formulas (BDF) is developed for the parallel solution of stiff Ordinary Differential Equations (ODEs). Most common methods for solving stiff systems of ODEs are based on implicit formulae and solved using Newton iteration which requires repeated solution of systems of linear equations with coefficient matrix, I - hβJ . Here, J is the Jacobian matrix of the problem. In this paper, the matrix operations is paralleled in order to reduce the cost of the iterations. Numerical results are given to compare the speedup and efficiency of parallel algorithm and that of sequential algorithm.

Keywords: Backward Differentiation Formula, block, ordinarydifferential equations.

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Authors: Choon Ki Ahn, Jung Hun Park, Wook Hyun Kwon

Abstract:

CEMTool is a command style design and analyzing package for scientific and technological algorithm and a matrix based computation language. In this paper, we present new 2D & 3D finite element method (FEM) packages for CEMTool. We discuss the detailed structures and the important features of pre-processor, solver, and post-processor of CEMTool 2D & 3D FEM packages. In contrast to the existing MATLAB PDE Toolbox, our proposed FEM packages can deal with the combination of the reserved words. Also, we can control the mesh in a very effective way. With the introduction of new mesh generation algorithm and fast solving technique, our FEM packages can guarantee the shorter computational time than MATLAB PDE Toolbox. Consequently, with our new FEM packages, we can overcome some disadvantages or limitations of the existing MATLAB PDE Toolbox.

Keywords: CEMTool, Finite element method (FEM), Numericalanalysis, Partial differential equation (PDE)

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Authors: Adil AL-Rammahi

Abstract:

When we prefer to make the data secure from various attacks and fore integrity of data, we must encrypt the data before it is transmitted or stored. This paper introduces a new effective and lossless image encryption algorithm using a natural logarithmic function. The new algorithm encrypts an image through a three stage process. In the first stage, a reference natural logarithmic function is generated as the foundation for the encryption image. The image numeral matrix is then analyzed to five integer numbers, and then the numbers’ positions are transformed to matrices. The advantages of this method is useful for efficiently encrypting a variety of digital images, such as binary images, gray images, and RGB images without any quality loss. The principles of the presented scheme could be applied to provide complexity and then security for a variety of data systems such as image and others.

Keywords: Linear Systems, Image Encryption, Calculus.

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Authors: Jan Busch, Peter Nyhuis

Abstract:

Considering the challenges of short product life cycles and growing variant diversity, cost minimization and manufacturing flexibility increasingly gain importance to maintain a competitive edge in today’s global and dynamic markets. In this context, an aerodynamic part feeding system for high-speed industrial assembly applications has been developed at the Institute of Production Systems and Logistics (IFA), Leibniz Universitaet Hannover. The aerodynamic part feeding system outperforms conventional systems with respect to its process safety, reliability, and operating speed. In this paper, a multi-objective optimisation of the aerodynamic feeding system regarding the orientation rate, the feeding velocity, and the required nozzle pressure is presented.

Keywords: Aerodynamic feeding system, genetic algorithm, multi-objective optimization.

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Authors: Hyeon-Cheol Lee

Abstract:

The adaptive power control of Code Division Multiple Access (CDMA) communications using Remote Radio Head (RRH) between multiple Unmanned Aerial Vehicles (UAVs) with a link-budget based Signal-to-Interference Ratio (SIR) estimate is applied to four inner loop power control algorithms. It is concluded that Base Station (BS) can calculate not only UAV distance using linearity between speed and Consecutive Transmit-Power-Control Ratio (CTR) of Adaptive Step-size Closed Loop Power Control (ASCLPC), Consecutive TPC Ratio Step-size Closed Loop Power Control (CS-CLPC), Fixed Step-size Power Control (FSPC), but also UAV position with Received Signal Strength Indicator (RSSI) ratio of RRHs.

Keywords: speed estimation, adaptive power control, link-budget, SIR, multi-bit quantizer, RRH

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Authors: John N. Ellinas

Abstract:

In this paper, a robust watermarking algorithm using the wavelet transform and edge detection is presented. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The watermark embedding process is carried over the subband coefficients that lie on edges, where distortions are less noticeable, with a subband level dependent strength. Also, the watermark is embedded to selected coefficients around edges, using a different scale factor for watermark strength, that are captured by a morphological dilation operation. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency to various attacks such as median filtering, Gaussian noise, JPEG compression and geometrical transformations.

Keywords: Watermarking, wavelet transform, edge detection.

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

Abstract:

In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: Video tracking, particle filter, greedy snake, neural network.

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