Search results for: detection algorithm.

Authors: Sergei Aleinik, Yuri Matveev

Abstract:

In this paper a novel method for the detection of  clipping in speech signals is described. It is shown that the new  method has better performance than known clipping detection  methods, is easy to implement, and is robust to changes in signal  amplitude, size of data, etc. Statistical simulation results are  presented.

 

Keywords: Clipping, clipped signal, speech signal processing.

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Authors: R. Srinivasa Rao, S.V.L. Narasimham, M. Ramalingaraju

Abstract:

Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.

Keywords: Distribution system, Network reconfiguration, Loss reduction, Artificial Bee Colony Algorithm.

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Authors: P. Suryakumari, P. Kantarao

Abstract:

Optimal Power Flow (OPF) problem in electrical power system is considered as a static, non-linear, multi-objective or a single objective optimization problem. This paper presents an algorithm for solving the voltage stability objective reactive power dispatch problem in a power system .The proposed approach employs cat swarm optimization algorithm for optimal settings of RPD control variables. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. CSO algorithm is tested on standard IEEE 30 bus system and the results are compared with other methods to prove the effectiveness of the new algorithm. As a result, the proposed method is the best for solving optimal reactive power dispatch problem.

Keywords: RPD problem, voltage stability enhancement, CSO algorithm.

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Authors: Huynh Quyet Thang, Le Hai Quan

Abstract:

Interaction Model plays an important role in Modelbased Intelligent Interface Agent Architecture for developing Intelligent User Interface. In this paper we are presenting some improvements in the algorithms for development interaction model of interface agent including: the action segmentation algorithm, the action pair selection algorithm, the final action pair selection algorithm, the interaction graph construction algorithm and the probability calculation algorithm. The analysis of the algorithms also presented. At the end of this paper, we introduce an experimental program called “Personal Transfer System".

Keywords: interface agent, interaction model, user model.

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

Abstract:

Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: Call center agents, fatigue, skin color detection, face recognition.

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Authors: Thuy Thi Nguyen, Nguyen Dang Binh, Horst Bischof

Abstract:

In this work, we present a novel active learning approach for learning a visual object detection system. Our system is composed of an active learning mechanism as wrapper around a sub-algorithm which implement an online boosting-based learning object detector. In the core is a combination of a bootstrap procedure and a semi automatic learning process based on the online boosting procedure. The idea is to exploit the availability of classifier during learning to automatically label training samples and increasingly improves the classifier. This addresses the issue of reducing labeling effort meanwhile obtain better performance. In addition, we propose a verification process for further improvement of the classifier. The idea is to allow re-update on seen data during learning for stabilizing the detector. The main contribution of this empirical study is a demonstration that active learning based on an online boosting approach trained in this manner can achieve results comparable or even outperform a framework trained in conventional manner using much more labeling effort. Empirical experiments on challenging data set for specific object deteciton problems show the effectiveness of our approach.

Keywords: Computer vision, object detection, online boosting, active learning, labeling complexity.

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Authors: Ahmad T. Al-Taani

Abstract:

Car failure detection is a complicated process and requires high level of expertise. Any attempt of developing an expert system dealing with car failure detection has to overcome various difficulties. This paper describes a proposed knowledge-based system for car failure detection. The paper explains the need for an expert system and the some issues on developing knowledge-based systems, the car failure detection process and the difficulties involved in developing the system. The system structure and its components and their functions are described. The system has about 150 rules for different types of failures and causes. It can detect over 100 types of failures. The system has been tested and gave promising results.

Keywords: Expert system, car failure diagnosis, knowledgebasedsystem, CLIPS.

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Authors: Zhengyu Yan, Yan Yu, Jianqiu Chen

Abstract:

A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect.

Keywords: CdS quantum dots, modification, detection, naphthalene.

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Authors: R. Padilla, C. F. F. Costa Filho, M. G. F. Costa

Abstract:

In the past years a lot of effort has been made in the field of face detection. The human face contains important features that can be used by vision-based automated systems in order to identify and recognize individuals. Face location, the primary step of the vision-based automated systems, finds the face area in the input image. An accurate location of the face is still a challenging task. Viola-Jones framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. An evaluation of these classifiers will help researchers to choose the best classifier for their particular need. This work focuses of the evaluation of face detection classifiers minding facial landmarks.

Keywords: Face datasets, face detection, facial landmarking, haar wavelets, Viola-Jones detectors.

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

Abstract:

In this paper, we propose a face recognition algorithm using AAM and Gabor features. Gabor feature vectors which are well known to be robust with respect to small variations of shape, scaling, rotation, distortion, illumination and poses in images are popularly employed for feature vectors for many object detection and recognition algorithms. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization method employed in EBGM is based on Gabor jet similarity and is sensitive to initial values. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we devise a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based facial feature localization method with initial points set by the rough facial feature points obtained from AAM, and propose a face recognition algorithm using the devised localization method for facial feature localization and Gabor feature vectors. It is observed through experiments that such a cascaded localization method based on both AAM and Gabor jet similarity is more robust than the localization method based on only Gabor jet similarity. Also, it is shown that the proposed face recognition algorithm using this devised localization method and Gabor feature vectors performs better than the conventional face recognition algorithm using Gabor jet similarity-based localization method and Gabor feature vectors like EBGM.

Keywords: Face Recognition, AAM, Gabor features, EBGM.

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Authors: Rui Wu, Tapani Ristaniemi

Abstract:

In this paper, we propose a new algorithm for joint time-delay and direction-of-arrival (DOA) estimation, here called two-dimensional code acquisition, in an asynchronous directsequence code-division multiple-access (DS-CDMA) array system. This algorithm depends on eigenvector-eigenvalue decomposition of sample correlation matrix, and requires to know desired user-s training sequence. The performance of the algorithm is analyzed both analytically and numerically in uncorrelated and coherent multipath environment. Numerical examples show that the algorithm is robust with unknown number of coherent signals.

Keywords: Two-Dimensional Code Acquisition, EV-t, DSCDMA

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Authors: N. M. Nawi, R. S. Ransing, M. R. Ransing

Abstract:

The conjugate gradient optimization algorithm is combined with the modified back propagation algorithm to yield a computationally efficient algorithm for training multilayer perceptron (MLP) networks (CGFR/AG). The computational efficiency is enhanced by adaptively modifying initial search direction as described in the following steps: (1) Modification on standard back propagation algorithm by introducing a gain variation term in the activation function, (2) Calculation of the gradient descent of error with respect to the weights and gains values and (3) the determination of a new search direction by using information calculated in step (2). The performance of the proposed method is demonstrated by comparing accuracy and computation time with the conjugate gradient algorithm used in MATLAB neural network toolbox. The results show that the computational efficiency of the proposed method was better than the standard conjugate gradient algorithm.

Keywords: Adaptive gain variation, back-propagation, activation function, conjugate gradient, search direction.

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Authors: Noor Suhana Sulaiman, Rohani Abu Bakar, Norrozila Sulaiman

Abstract:

Intrusion Detection System is significant in network security. It detects and identifies intrusion behavior or intrusion attempts in a computer system by monitoring and analyzing the network packets in real time. In the recent year, intelligent algorithms applied in the intrusion detection system (IDS) have been an increasing concern with the rapid growth of the network security. IDS data deals with a huge amount of data which contains irrelevant and redundant features causing slow training and testing process, higher resource consumption as well as poor detection rate. Since the amount of audit data that an IDS needs to examine is very large even for a small network, classification by hand is impossible. Hence, the primary objective of this review is to review the techniques prior to classification process suit to IDS data.

Keywords: Intrusion Detection System, security, soft computing, classification.

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Authors: Shazia Javed, Noor Atinah Ahmad

Abstract:

The householder RLS (HRLS) algorithm is an O(N2) algorithm which recursively updates an arbitrary square-root of the input data correlation matrix and naturally provides the LS weight vector. A data dependent householder matrix is applied for such an update. In this paper a recursive estimate of the eigenvalue spread and misalignment of the algorithm is presented at a very low computational cost. Misalignment is found to be highly sensitive to the eigenvalue spread of input signals, output noise of the system and exponential window. Simulation results show noticeable degradation in the misalignment by increase in eigenvalue spread as well as system-s output noise, while exponential window was kept constant.

Keywords: HRLS algorithm, eigenvalue spread, misalignment.

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Authors: Sanae Attioui, Said Najah

Abstract:

The Earth's surface is constantly changing through forces of nature and human activities. Reliable, accurate, and timely change detection is critical to environmental monitoring, resource management, and planning activities. Recently, interest in deep learning algorithms, especially convolutional neural networks, has increased in the field of image change detection due to their powerful ability to extract multi-level image features automatically. However, these networks are prone to drawbacks that limit their applications, which reside in their inability to capture spatial relationships between image instances, as this necessitates a large amount of training data. As an alternative, Capsule Network has been proposed to overcome these shortcomings. Although its effectiveness in remote sensing image analysis has been experimentally verified, its application in change detection tasks remains very sparse. Motivated by its greater robustness towards improved hierarchical object representation, this study aims to apply a capsule network for PolSAR image Change Detection. The experimental results demonstrate that the proposed change detection method can yield a significantly higher detection rate compared to methods based on convolutional neural networks.

Keywords: Change detection, capsule network, deep network, Convolutional Neural Networks, polarimetric synthetic aperture radar images, PolSAR images.

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Authors: Yi Zhang, Kai Lu, Ying-hui Gao, Mo Wang

Abstract:

Histogram plays an important statistical role in digital image processing. However, the existing quantum image models are deficient to do this kind of image statistical processing because different gray scales are not distinguishable. In this paper, a novel quantum image representation model is proposed firstly in which the pixels with different gray scales can be distinguished and operated simultaneously. Based on the new model, a fast quantum algorithm of constructing histogram for quantum image is designed. Performance comparison reveals that the new quantum algorithm could achieve an approximately quadratic speedup than the classical counterpart. The proposed quantum model and algorithm have significant meanings for the future researches of quantum image processing.

Keywords: Quantum Image Representation, Quantum Algorithm, Image Histogram.

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Authors: Abhishek Swaroop, Awadhesh Kumar Singh

Abstract:

The group mutual exclusion (GME) problem is a variant of the mutual exclusion problem. In the present paper a token-based group mutual exclusion algorithm, capable of handling transient faults, is proposed. The algorithm uses the concept of dynamic request sets. A time out mechanism is used to detect the token loss; also, a distributed scheme is used to regenerate the token. The worst case message complexity of the algorithm is n+1. The maximum concurrency and forum switch complexity of the algorithm are n and min (n, m) respectively, where n is the number of processes and m is the number of groups. The algorithm also satisfies another desirable property called smooth admission. The scheme can also be adapted to handle the extended group mutual exclusion problem.

Keywords: Dynamic request sets, Fault tolerance, Smoothadmission, Transient faults.

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Authors: O. Hasançebi, S. Kazemzadeh Azad

Abstract:

In the present study the efficiency of Big Bang-Big Crunch (BB-BC) algorithm is investigated in discrete structural design optimization. It is shown that a standard version of the BB-BC algorithm is sometimes unable to produce reasonable solutions to problems from discrete structural design optimization. Two reformulations of the algorithm, which are referred to as modified BB-BC (MBB-BC) and exponential BB-BC (EBB-BC), are introduced to enhance the capability of the standard algorithm in locating good solutions for steel truss and frame type structures, respectively. The performances of the proposed algorithms are experimented and compared to its standard version as well as some other algorithms over several practical design examples. In these examples, steel structures are sized for minimum weight subject to stress, stability and displacement limitations according to the provisions of AISC-ASD.

Keywords: Structural optimization, discrete optimization, metaheuristics, big bang-big crunch (BB-BC) algorithm, design optimization of steel trusses and frames.

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Authors: Y. Bentoutou

Abstract:

Memory Errors Detection and Correction aim to secure the transaction of data between the central processing unit of a satellite onboard computer and its local memory. In this paper, the application of a double-bit error detection and correction method is described and implemented in Field Programmable Gate Array (FPGA) technology. The performance of the proposed EDAC method is measured and compared with two different EDAC devices, using the same FPGA technology. Statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard the first Algerian microsatellite Alsat-1 is given.

Keywords: Error Detection and Correction, On-board computer, small satellite missions.

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Authors: G. Parmar, R. Prasad, S. Mukherjee

Abstract:

The authors present an algorithm for order reduction of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.

Keywords: Genetic algorithm, Integral square error, Orderreduction, Stability equation method.

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Authors: V. K. Banga, Y. Singh, R. Kumar

Abstract:

In this paper, we have proposed a low cost optimized solution for the movement of a three-arm manipulator using Genetic Algorithm (GA) and Analytical Hierarchy Process (AHP). A scheme is given for optimizing the movement of robotic arm with the help of Genetic Algorithm so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics evolved two solutions out of which the best-fit solution is selected with the help of Genetic Algorithm and is kept in search space for future use. The Inverse Kinematics, Fitness Value evaluation and Binary Encoding like tasks are simulated and tested. Although, three factors viz. Movement, Friction and Least Settling Time (or Min. Vibration) are used for finding the Fitness Function / Fitness Values, however some more factors can also be considered.

Keywords: Inverse Kinematics, Genetic Algorithm (GA), Analytical Hierarchy Process (AHP), Fitness Value, Fitness Function.

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Authors: Diogen Babuc

Abstract:

The question that is a motivation of writing is how many devote themselves to discovering something in the world of science where much is discerned and revealed, but at the same time, much is unknown. The insightful elements of this algorithm are the ciphering and deciphering algorithms of Playfair, Caesar, and Vigen`ere. Only a few of their main properties are taken and modified, with the aim of forming a specific functionality of the algorithm called Latin Djokovic. Specifically, a string is entered as input data. A key k is given, with a random value between the values a and b = a+3. The obtained value is stored in a variable with the aim of being constant during the run of the algorithm. In correlation to the given key, the string is divided into several groups of substrings, and each substring has a length of k characters. The next step involves encoding each substring from the list of existing substrings. Encoding is performed using the basis of Caesar algorithm, i.e. shifting with k characters. However, that k is incremented by 1 when moving to the next substring in that list. When the value of k becomes greater than b + 1, it will return to its initial value. The algorithm is executed, following the same procedure, until the last substring in the list is traversed. Using this polyalphabetic method, ciphering and deciphering of strings are achieved. The algorithm also works for a 100-character string. The x character is not used when the number of characters in a substring is incompatible with the expected length. The algorithm is simple to implement, but it is questionable if it works better than the other methods, from the point of view of execution time and storage space.

Keywords: Ciphering and deciphering, Authentic Algorithm, Polyalphabetic Cipher, Random Key, methods comparison.

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Authors: Sayani Sil, Sukanta Das

Abstract:

This paper reports a distributed mutual exclusion algorithm for mobile Ad-hoc networks. The network is clustered hierarchically. The proposed algorithm considers the clustered network as a logical tree and develops a token passing scheme to get the mutual exclusion. The performance analysis and simulation results show that its message requirement is optimal, and thus the algorithm is energy efficient.

Keywords: Critical section, Distributed mutual exclusion, MobileAd-hoc network, Token-based algorithms.

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Authors: Basit Shahzad, Muhammad Tanvir Afzal

Abstract:

Many algorithms are available for sorting the unordered elements. Most important of them are Bubble sort, Heap sort, Insertion sort and Shell sort. These algorithms have their own pros and cons. Shell Sort which is an enhanced version of insertion sort, reduces the number of swaps of the elements being sorted to minimize the complexity and time as compared to insertion sort. Shell sort improves the efficiency of insertion sort by quickly shifting values to their destination. Average sort time is O(n1.25), while worst-case time is O(n1.5). It performs certain iterations. In each iteration it swaps some elements of the array in such a way that in last iteration when the value of h is one, the number of swaps will be reduced. Donald L. Shell invented a formula to calculate the value of ?h?. this work focuses to identify some improvement in the conventional Shell sort algorithm. ''Enhanced Shell Sort algorithm'' is an improvement in the algorithm to calculate the value of 'h'. It has been observed that by applying this algorithm, number of swaps can be reduced up to 60 percent as compared to the existing algorithm. In some other cases this enhancement was found faster than the existing algorithms available.

Keywords: Algorithm, Computation, Shell, Sorting.

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Authors: Doaa Hegazy, Joachim Denzler

Abstract:

SoftBoost is a recently presented boosting algorithm, which trades off the size of achieved classification margin and generalization performance. This paper presents a performance evaluation of SoftBoost algorithm on the generic object recognition problem. An appearance-based generic object recognition model is used. The evaluation experiments are performed using a difficult object recognition benchmark. An assessment with respect to different degrees of label noise as well as a comparison to the well known AdaBoost algorithm is performed. The obtained results reveal that SoftBoost is encouraged to be used in cases when the training data is known to have a high degree of noise. Otherwise, using Adaboost can achieve better performance.

Keywords: SoftBoost algorithm, AdaBoost algorithm, Generic object recognition.

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Authors: A. Mirrashid, M. Khoshbin, A. Atghaei, H. Shahbazi

Abstract:

In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.

Keywords: Attention, fire detection, smoke detection, spatiotemporal.

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Authors: M. Khouil, N. Saber, M. Mestari

Abstract:

In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the collision between a point and a polyhedron and then the collision between two convex polyhedra. The aim of this research is to determine through the AMAXNET network a mini maximum point in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: Collision identification, fixed time, convex polyhedra, neural network, AMAXNET.

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Authors: Wan-Ting Lin, Chuen-Horng Lin, Tsung-Ho Wu, Yung-Kuan Chan

Abstract:

This study aims to segment objects using the K-means algorithm for texture features. Firstly, the algorithm transforms color images into gray images. This paper describes a novel technique for the extraction of texture features in an image. Then, in a group of similar features, objects and backgrounds are differentiated by using the K-means algorithm. Finally, this paper proposes a new object segmentation algorithm using the morphological technique. The experiments described include the segmentation of single and multiple objects featured in this paper. The region of an object can be accurately segmented out. The results can help to perform image retrieval and analyze features of an object, as are shown in this paper.

Keywords: k-mean, multiple objects, segmentation, texturefeatures.

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Authors: Viet Dzung Nguyen, Duc Thuan Nguyen, Tien Dzung Nguyen, Van Thanh Pham

Abstract:

Mammography is the most effective procedure for an early diagnosis of the breast cancer. Nowadays, people are trying to find a way or method to support as much as possible to the radiologists in diagnosis process. The most popular way is now being developed is using Computer-Aided Detection (CAD) system to process the digital mammograms and prompt the suspicious region to radiologist. In this paper, an automated CAD system for detection and classification of massive lesions in mammographic images is presented. The system consists of three processing steps: Regions-Of- Interest detection, feature extraction and classification. Our CAD system was evaluated on Mini-MIAS database consisting 322 digitalized mammograms. The CAD system-s performance is evaluated using Receiver Operating Characteristics (ROC) and Freeresponse ROC (FROC) curves. The archived results are 3.47 false positives per image (FPpI) and sensitivity of 85%.

Keywords: classification, computer-aided detection, featureextraction, mass detection.

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Authors: Gökhan Silahtaroğlu

Abstract:

Decision tree algorithms have very important place at classification model of data mining. In literature, algorithms use entropy concept or gini index to form the tree. The shape of the classes and their closeness to each other some of the factors that affect the performance of the algorithm. In this paper we introduce a new decision tree algorithm which employs data (attribute) folding method and variation of the class variables over the branches to be created. A comparative performance analysis has been held between the proposed algorithm and C4.5.

Keywords: Classification, decision tree, split, pruning, entropy, gini.

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