Search results for: extraction techniques

Authors: H. El Fadili, K. Zenkouar, H. Qjidaa

Abstract:

This paper proposes a neural network weights and topology optimization using genetic evolution and the backpropagation training algorithm. The proposed crossover and mutation operators aims to adapt the networks architectures and weights during the evolution process. Through a specific inheritance procedure, the weights are transmitted from the parents to their offsprings, which allows re-exploitation of the already trained networks and hence the acceleration of the global convergence of the algorithm. In the preprocessing phase, a new feature extraction method is proposed based on Legendre moments with the Maximum entropy principle MEP as a selection criterion. This allows a global search space reduction in the design of the networks. The proposed method has been applied and tested on the well known MNIST database of handwritten digits.

Keywords: Genetic algorithm, Legendre Moments, MEP, Neural Network.

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

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Authors: Y. Boutiche, N. Ramou, M. Ben Gharsallah

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This paper is devoted to present and discuss a model that allows a local segmentation by using statistical information of a given image. It is based on Chan-Vese model, curve evolution, partial differential equations and binary level sets method. The proposed model uses the piecewise constant approximation of Chan-Vese model to compute Signed Pressure Force (SPF) function, this one attracts the curve to the true object(s)-s boundaries. The implemented model is used to extract weld defects from weld radiographic images in the aim to calculate the perimeter and surfaces of those weld defects; encouraged resultants are obtained on synthetic and real radiographic images.

Keywords: Active contour, Chan-Vese Model, local segmentation, weld radiographic images.

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

Abstract:

With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: credit card fraud detection, user authentication, behavioral biometrics, machine learning, literature survey

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Authors: Abhijit Mitra

Abstract:

An effective approach for extracting document images from a noisy background is introduced. The entire scheme is divided into three sub- stechniques – the initial preprocessing operations for noise cluster tightening, introduction of a new thresholding method by maximizing the ratio of stan- dard deviations of the combined effect on the image to the sum of weighted classes and finally the image restoration phase by image binarization utiliz- ing the proposed optimum threshold level. The proposed method is found to be efficient compared to the existing schemes in terms of computational complexity as well as speed with better noise rejection.

Keywords: Document image extraction, Preprocessing, Ratio of stan-dard deviations, Bi-level adaptive thresholding.

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Authors: J. I. Asnor, S. Rosnah, Z. W. H. Wan, H. A. B. Badrul

Abstract:

Pineapples can be classified using an index with seven levels of maturity based on the green and yellow color of the skin. As the pineapple ripens, the skin will change from pale green to a golden or yellowish color. The issues that occur in agriculture nowadays are to do with farmers being unable to distinguish between the indexes of pineapple maturity correctly and effectively. There are several reasons for why farmers cannot properly follow the guideline provide by Federal Agriculture Marketing Authority (FAMA) and one of reason is that due to manual inspection done by experts, there are no specific and universal guidelines to be adopted by farmers due to the different points of view of the experts when sorting the pineapples based on their knowledge and experience. Therefore, an automatic system will help farmers to identify pineapple maturity effectively and will become a universal indicator to farmers.

Keywords: Artificial Neural Network, Image Processing, Index of Maturity, Pineapple

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Authors: S.C. Swain, S. Panda, A.K. Mohanty, C. Ardil

Abstract:

This paper presents the applications of computational intelligence techniques to economic load dispatch problems. The fuel cost equation of a thermal plant is generally expressed as continuous quadratic equation. In real situations the fuel cost equations can be discontinuous. In view of the above, both continuous and discontinuous fuel cost equations are considered in the present paper. First, genetic algorithm optimization technique is applied to a 6- generator 26-bus test system having continuous fuel cost equations. Results are compared to conventional quadratic programming method to show the superiority of the proposed computational intelligence technique. Further, a 10-generator system each with three fuel options distributed in three areas is considered and particle swarm optimization algorithm is employed to minimize the cost of generation. To show the superiority of the proposed approach, the results are compared with other published methods.

Keywords: Economic Load Dispatch, Continuous Fuel Cost, Quadratic Programming, Real-Coded Genetic Algorithm, Discontinuous Fuel Cost, Particle Swarm Optimization.

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Authors: Angelo Corallo, Maria Elena Latino, Marta Menegoli

Abstract:

Agri-food value chain involves various stakeholders with different roles. All of them abide by national and international rules and leverage marketing strategies to advance their products. Food products and related processing phases carry with it a big mole of data that are often not used to inform final customer. Some data, if fittingly identified and used, can enhance the single company, and/or the all supply chain creates a math between marketing techniques and voluntary traceability strategies. Moreover, as of late, the world has seen buying-models’ modification: customer is careful on wellbeing and food quality. Food citizenship and food democracy was born, leveraging on transparency, sustainability and food information needs. Internet of Things (IoT) and Analytics, some of the innovative technologies of Industry 4.0, have a significant impact on market and will act as a main thrust towards a genuine ‘4.0 change’ for agriculture. But, realizing a traceability system is not simple because of the complexity of agri-food supply chain, a lot of actors involved, different business models, environmental variations impacting products and/or processes, and extraordinary climate changes. In order to give support to the company involved in a traceability path, starting from business model analysis and related business process a Framework to Manage Product Data in Agri-Food Supply Chain for Voluntary Traceability was conceived. Studying each process task and leveraging on modeling techniques lead to individuate information held by different actors during agri-food supply chain. IoT technologies for data collection and Analytics techniques for data processing supply information useful to increase the efficiency intra-company and competitiveness in the market. The whole information recovered can be shown through IT solutions and mobile application to made accessible to the company, the entire supply chain and the consumer with the view to guaranteeing transparency and quality.

Keywords: Agriculture 4.0, agri-food supply chain, Industry 4.0, voluntary traceability.

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Authors: Habibollah Najafi, Amir Abbas Yazdani, Hosseinali Nahavandi

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The review performed on the condition of energy consumption & rate in Iran, shows that unfortunately the subject of optimization and conservation of energy in active industries of country lacks a practical & effective method and in most factories, the energy consumption and rate is more than in similar industries of industrial countries. The increasing demand of electrical energy and the overheads which it imposes on the organization, forces companies to search for suitable approaches to optimize energy consumption and demand management. Application of value engineering techniques is among these approaches. Value engineering is considered a powerful tool for improving profitability. These tools are used for reduction of expenses, increasing profits, quality improvement, increasing market share, performing works in shorter durations, more efficient utilization of sources & etc. In this article, we shall review the subject of value engineering and its capabilities for creating effective transformations in industrial organizations, in order to reduce energy costs & the results have been investigated and described during a case study in Mazandaran wood and paper industries, the biggest consumer of energy in north of Iran, for the purpose of presenting the effects of performed tasks in optimization of energy consumption by utilizing value engineering techniques in one case study.

Keywords: Value Engineering (VE), Expense, Energy, Industrial

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Authors: K. Parandhama Gowd

Abstract:

The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation.

Keywords: Flight data recorder (FDR), black box, diagnostic tool, global networking, cockpit voice and data recorder (CVDR), air traffic control (ATC), air traffic, telemetry, tracking and control centers ATTTCC).

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Authors: Algirdas Jasinskas, Egidijus Šarauskis, Raimondas Šarauskis, Antanas Sakalauskas

Abstract:

This paper presents the new results of energy plant – rye and triticale at yellow ripeness and ripe, pre-treatment in high pressure steam reactor and monosaccharide extraction. There were investigated the influence of steam pressure (20 to 22 bar), retention duration (180 to 240 s) and catalytic sulphuric acid concentration strength (0 to 0.5 %) on the pre-treatment process, contents of monosaccharides (glucose, arabinose, xylose, mannose) and undesirable by-compounds (furfural and HMF) in the reactor. The study has determined that the largest amount of monosaccharides (37.2 % of glucose, 2.7 % of arabinose, 8.4 % of xylose, and 1.3 % of mannose) was received in the rye at ripe, the samples of which were mixed with 0.5 % concentration of catalytic sulphuric acid, and hydrolysed in the reactor, where the pressure was 20 bar, whereas the reaction time – 240 s.

Keywords: Bioethanol, Pre-treatment, Rye, Triticale.

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Authors: Anil Kumar

Abstract:

Supersymmetric Quantum Mechanics is an interesting framework to analyze nonrelativistic quantal problems. Using these techniques, we construct a family of strictly isospectral Hulth´en potentials. Isospectral wave functions are generated and plotted for different values of the deformation parameter.

Keywords: Hulth´en potential, Isospectral Hamiltonian.

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Authors: Abdelghafour Galadi

Abstract:

In this paper, accurate power MOSFET models including quasi-saturation effect are presented. These models have no internal node voltages determined by the circuit simulator and use one JFET or one depletion mode MOSFET transistors controlled by an “effective” gate voltage taking into account the quasi-saturation effect. The proposed models achieve accurate simulation results with an average error percentage less than 9%, which is an improvement of 21 percentage points compared to the commonly used standard power MOSFET model. In addition, the models can be integrated in any available commercial circuit simulators by using their analytical equations. A description of the models will be provided along with the parameter extraction procedure.

Keywords: Power MOSFET, drift layer, quasi-saturation effect, SPICE model, circuit simulation.

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Authors: Azah Kamilah Muda, Siti Mariyam Shamsuddin, Maslina Darus

Abstract:

Writer identification is one of the areas in pattern recognition that attract many researchers to work in, particularly in forensic and biometric application, where the writing style can be used as biometric features for authenticating an identity. The challenging task in writer identification is the extraction of unique features, in which the individualistic of such handwriting styles can be adopted into bio-inspired generalized global shape for writer identification. In this paper, the feasibility of generalized global shape concept of complimentary binding in Artificial Immune System (AIS) for writer identification is explored. An experiment based on the proposed framework has been conducted to proof the validity and feasibility of the proposed approach for off-line writer identification.

Keywords: Writer identification, generalized global shape, individualistic, pattern recognition.

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Authors: Hiroshi Yamakawa, Michiko Yoshida, Motohiro Tsuchiya

Abstract:

The recent development of Information and Communication Technology (ICT) enables new ways of "democratic" decision-making such as a page-ranking system, which estimates the importance of a web page based on indirect trust on that page shared by diverse group of unorganized individuals. These kinds of "democracy" have not been acclaimed yet in the world of real politics. On the other hand, a large amount of data about personal relations including trust, norms of reciprocity, and networks of civic engagement has been accumulated in a computer-readable form by computer systems (e.g., social networking systems). We can use these relations as a new type of social capital to construct a new democratic decision-making system based on a delegation network. In this paper, we propose an effective decision-making support system, which is based on empowering someone's vote whom you trust. For this purpose, we propose two new techniques: the first is for estimating entire vote distribution from a small number of votes, and the second is for estimating active voter choice to promote voting using a delegation network. We show that these techniques could increase the voting ratio and credibility of the whole decision by agent-based simulations.

Keywords: Delegation, network centrality, social network, voting ratio.

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Authors: Mousmita Sarma, Krishna Dutta, Kandarpa Kumar Sarma

Abstract:

Speech corpus is one of the major components in a Speech Processing System where one of the primary requirements is to recognize an input sample. The quality and details captured in speech corpus directly affects the precision of recognition. The current work proposes a platform for speech corpus generation using an adaptive LMS filter and LPC cepstrum, as a part of an ANN based Speech Recognition System which is exclusively designed to recognize isolated numerals of Assamese language- a major language in the North Eastern part of India. The work focuses on designing an optimal feature extraction block and a few ANN based cooperative architectures so that the performance of the Speech Recognition System can be improved.

Keywords: Filter, Feature, LMS, LPC, Cepstrum, ANN.

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Authors: Susmita Das, Kala Praveen Bagadi

Abstract:

SDMA (Space-Division Multiple Access) is a MIMO (Multiple-Input and Multiple-Output) based wireless communication network architecture which has the potential to significantly increase the spectral efficiency and the system performance. The maximum likelihood (ML) detection provides the optimal performance, but its complexity increases exponentially with the constellation size of modulation and number of users. The QR decomposition (QRD) MUD can be a substitute to ML detection due its low complexity and near optimal performance. The minimum mean-squared-error (MMSE) multiuser detection (MUD) minimises the mean square error (MSE), which may not give guarantee that the BER of the system is also minimum. But the minimum bit error rate (MBER) MUD performs better than the classic MMSE MUD in term of minimum probability of error by directly minimising the BER cost function. Also the MBER MUD is able to support more users than the number of receiving antennas, whereas the rest of MUDs fail in this scenario. In this paper the performance of various MUD techniques is verified for the correlated MIMO channel models based on IEEE 802.16n standard.

Keywords: Multiple input multiple output, multiuser detection, orthogonal frequency division multiplexing, space division multiple access, Bit error rate

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Authors: Muhammad Suryanegara, Dendi Wijayatullah, Dadang Gunawan

Abstract:

We review a knowledge extractor model in constructing 3G Killer Applications. The success of 3G is essential for Government as it became part of Telecommunications National Strategy. The 3G wireless technologies may reach larger area and increase country-s ICT penetration. In order to understand future customers needs, the operators require proper information (knowledge) lying inside. Our work approached future customers as complex system where the complex knowledge may expose regular behavior. The hidden information from 3G future customers is revealed by using fractal-based questionnaires. Afterward, further statistical analysis is used to match the results with operator-s strategic plan. The developments of 3G applications also consider its saturation time and further improvement of the application.

Keywords: 3G Killer Applications, Knowledge, Telecommunications Strategy.

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Authors: Jang-Hee Yoo, Doosung Hwang, Jong-Wook Han, Ki-Young Moon

Abstract:

The ability to recognize humans and their activities by computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.

Keywords: Background Subtraction, Human Silhouette Extraction, HLS Color Space, and Object Segmentation

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Authors: A. K. Aswathy, Asha Manoharan, Arya Manoharan

Abstract:

The purpose of this study are to define the nature of oral sensory issues in children with autism spectrum disorder (ASD), identify important components of the assessment and treatment of this issues specific to this population, and delineate specific therapeutic techniques designed to improve assessment and treatment within therapeutic settings. Literature review and case example is used to define the predominant nature of the oral sensory issues that are experienced by some children on the autism spectrum. Characteristics of this complex disorder that can have an impact on feeding skill and behavior are also identified. These factors are then integrated to create assessment and intervention techniques that can be used in conjunction with traditional feeding approaches to facilitate improvements in eating as well as reducing oral apraxic component in this unique population. The complex nature of ASD and its many influences on feeding skills and behavior create the need for modification to both assessment and treatment approaches. Additional research is needed to create therapeutic protocols that can be used by speech-language pathologists to effectively assess and treat feeding and oro motor apraxic difficulties that are commonly encountered in children with ASD.

Keywords: Autism, feeding, intervention, oral sensory issues, oral apraxia.

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

Abstract:

Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: Document analysis, sentimental analysis, emotion detection, WEKA tool, NRC Lexicon.

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Authors: E. Moroydor Derun, Z. Eslek, S. Piskin

Abstract:

Hypericum perforatum L. is a member of the Hypericaceae (Guttiferae) family and commonly known as St. John’s wort. There is a growing interest in this medicinal plant because of the constituents of this genus. A number of species have been shown to possess various biological activities such as antiviral, wound healing, analgesic, hepatoprotective, antimicrobial and antioxidant activities and also have therapeutic effects on burns, bruises, swelling, anxiety and mild to moderate depression. In this study, the aerial parts of Hypericum perforatum L. are extracted and the main and effective constituents are determined. The analysis of the extracts was performed by GC-MS and LC-MS. As a next step, it is aimed to investigate the usage of the main constituents of the medicinal plant.

Keywords: Hypericaceae, Hypericum perforatum L., GC-MS, Guttiferae, LC-MS, Medicinal plant, St. John’s wort.

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Authors: Prakash G L, Chaitra K Meti, Poojitha K, Divya R.K.

Abstract:

Wireless Sensor Network is Multi hop Self-configuring Wireless Network consisting of sensor nodes. The deployment of wireless sensor networks in many application areas, e.g., aggregation services, requires self-organization of the network nodes into clusters. Efficient way to enhance the lifetime of the system is to partition the network into distinct clusters with a high energy node as cluster head. The different methods of node clustering techniques have appeared in the literature, and roughly fall into two families; those based on the construction of a dominating set and those which are based solely on energy considerations. Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with cluster head only. The energy constraint and limited computing resources of the sensor nodes present the major challenges in gathering the data. In this paper we propose a framework to study how partially correlated data affect the performance of clustering algorithms. The total energy consumption and network lifetime can be analyzed by combining random geometry techniques and rate distortion theory. We also present the relation between compression distortion and data correlation.

Keywords: Clusters, multi hop, random geometry, rate distortion.

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Authors: Ming-Ta Yang, Jin-Lung Guan, Jhy-Cherng Gu

Abstract:

The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.

Keywords: Continuous wavelet transform, discrete wavelet transform, high impedance faults, statistical confidence.

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Authors: Maha Jazouli

Abstract:

Suicide is one of the leading causes of death among prisoners, both in Canada and internationally. In recent years, rates of attempts of suicide and self-harm suicide have increased, with hangings being the most frequently used method. The objective of this article is to propose a method to automatically detect suicidal behaviors in real time. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Tests show that the proposed system gives satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: Suicide detection, Kinect Azure, RGB-D camera, SVM, gesture recognition.

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Authors: L. A. Muhammad, M. S. Argungu

Abstract:

Educational data mining (EDM) has recorded substantial considerations. Techniques of data mining in one way or the other have been proposed to dig out out-of-sight knowledge in educational data. The result of the study got assists academic institutions in further enhancing their process of learning and methods of passing knowledge to students. Consequently, the performance of students boasts and the educational products are by no doubt enhanced. This study adopted a student performance prediction model premised on techniques of data mining with Students' Essential Features (SEF). SEF are linked to the learner's interactivity with the e-learning management system. The performance of the student's predictive model is assessed by a set of classifiers, viz. Bayes Network, Logistic Regression, and Reduce Error Pruning Tree (REP). Consequently, ensemble methods of Bagging, Boosting, and Random Forest (RF) are applied to improve the performance of these single classifiers. The study reveals that the result shows a robust affinity between learners' behaviors and their academic attainment. Result from the study shows that the REP Tree and its ensemble record the highest accuracy of 83.33% using SEF. Hence, in terms of the Receiver Operating Curve (ROC), boosting method of REP Tree records 0.903, which is the best. This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, bagging, Random Forest, boosting, data mining, classifiers, machine learning.

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Authors: Jürgen Dorn, Tabbasum Naz

Abstract:

In the area of Human Resource Management, the trend is towards online exchange of information about human resources. For example, online applications for employment become standard and job offerings are posted in many job portals. However, there are too many job portals to monitor all of them if someone is interested in a new job. We developed a prototype for integrating information of different job portals into one meta-search engine. First, existing job portals were investigated and XML schema documents were derived automated from these portals. Second, translation rules for transforming each schema to a central HR-XML-conform schema were determined. The HR-XML-schema is used to build a form for searching jobs. The data supplied by a user in this form is now translated into queries for the different job portals. Each result obtained by a job portal is sent to the meta-search engine that ranks the result of all received job offers according to user's preferences.

Keywords: Meta-search, Information extraction and integration, human resource management, job search.

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Authors: Mansoor Ahsan, Khalid Rafique, Farrukh Mazhar

Abstract:

Unmanned Aerial Vehicles (UAVs) have gained tremendous importance, in both Military and Civil, during first decade of this century. In a UAV, onboard computer (autopilot) autonomously controls the flight and navigation of the aircraft. Based on the aircraft role and flight envelope, basic to complex and sophisticated controllers are used to stabilize the aircraft flight parameters. These controllers constitute the autopilot system for UAVs. The autopilot systems, most commonly, provide lateral and longitudinal control through Proportional-Integral-Derivative (PID) controllers or Phase-lead or Lag Compensators. Various techniques are commonly used to ‘tune’ gains of these controllers. Some techniques used are, in-flight step-by-step tuning, software-in-loop or hardware-in-loop tuning methods. Subsequently, numerous in-flight tests are required to actually ‘fine-tune’ these gains. However, an optimization-based tuning of these PID controllers or compensators, as presented in this paper, can greatly minimize the requirement of in-flight ‘tuning’ and substantially reduce the risks and cost involved in flight-testing.

Keywords: Unmanned aerial vehicle (UAV), autopilot, autonomous controls, PID controler gains tuning, optimization.

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Authors: Raja Asad Zaheer, Aisha Tanveer, Hafza Mehreen Fatima

Abstract:

Global Software Development (GSD) is becoming a common norm in software industry, despite of the fact that global distribution of the teams presents special issues for effective communication and coordination of the teams. Now trends are changing and project management for distributed teams is no longer in a limbo. GSD can be effectively established using agile and project managers can use different agile techniques/tools for solving the problems associated with distributed teams. Agile methodologies like scrum and XP have been successfully used with distributed teams. We have employed exploratory research method to analyze different recent studies related to challenges of GSD and their proposed solutions. In our study, we had deep insight in six commonly faced challenges: communication and coordination, temporal differences, cultural differences, knowledge sharing/group awareness, speed and communication tools. We have established that each of these challenges cannot be neglected for distributed teams of any kind. They are interlinked and as an aggregated whole can cause the failure of projects. In this paper we have focused on creating a scalable framework for detecting and overcoming these commonly faced challenges. In the proposed solution, our objective is to suggest agile techniques/tools relevant to a particular problem faced by the organizations related to the management of distributed teams. We focused mainly on scrum and XP techniques/tools because they are widely accepted and used in the industry. Our solution identifies the problem and suggests an appropriate technique/tool to help solve the problem based on globally shared knowledgebase. We can establish a cause and effect relationship using a fishbone diagram based on the inputs provided for issues commonly faced by organizations. Based on the identified cause, suitable tool is suggested, our framework suggests a suitable tool. Hence, a scalable, extensible, self-learning, intelligent framework proposed will help implement and assess GSD to achieve maximum out of it. Globally shared knowledgebase will help new organizations to easily adapt best practices set forth by the practicing organizations.

Keywords: Agile project management, agile framework, distributed teams, global software development.

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Authors: Siraa Ben Ftima, Mourad Talbi, Tahar Ezzedine

Abstract:

In this paper, we present a technique of secure watermarking of grayscale and color images. This technique consists in applying the Singular Value Decomposition (SVD) in LWT (Lifting Wavelet Transform) domain in order to insert the watermark image (grayscale) in the host image (grayscale or color image). It also uses signature in the embedding and extraction steps. The technique is applied on a number of grayscale and color images. The performance of this technique is proved by the PSNR (Pick Signal to Noise Ratio), the MSE (Mean Square Error) and the SSIM (structural similarity) computations.

Keywords: Color image, grayscale image, singular values decomposition, lifting wavelet transform, image watermarking, watermark, secure.

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