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Purpose. The aim of this paper is to perform a simple and robust control method based on the well-known sliding control approach for a self-excited induction generator supplying an isolated DC load; this adopted technique does not require much computation and could be easily implemented in practice. In this context, the present work will begin with a mathematical development of this control technique and its application to the self-excited induction generator case. For this purpose, the machine provides the produced active power to the load through a static PWM converter equipped with a single capacitor on the DC side. In order to insure the output DC-bus voltage regulation with respect to the load-power demands and the rotor speed fluctuations, the required stator currents references are computed by considering the reactive power required for the machine core magnetization, the induced voltages through the stator windings and the active power set value obtained from the corresponding sliding mode DC-bus voltage controller. Regarding the nonlinearity of the DC-bus voltage mathematical model and the discontinuity characterizing the converter-machine behavior association, the sliding mode strategy will constitute a perfect tool to sizing the controller structure with high control performances. Results of simulation carried out to demonstrate the proposed control validity are presented., Целью данной статьи является разработка простого и надежного метода управления, основанного на хорошо известном подходе к управлению скольжением для асинхронного генератора с самовозбуждением, питающего изолированную нагрузку постоянного тока; данный принятый метод не требует больших объемов вычислений и может быть легко реализован на практике. В этом контексте данная работа начинается с развития математических основ этого метода управления и его применения в случае асинхронного генератора с самовозбуждением. Для этого машина подает произведенную активную мощность в нагрузку через статический ШИМ-преобразователь, оснащенный единственным конденсатором на стороне постоянного тока. Чтобы обеспечить регулирование выходного напряжения шины постоянного тока с учетом требований к нагрузке и колебаниям скорости вращения ротора, требуемые токи статора рассчитываются с учетом реактивной мощности, необходимой для намагничивания сердечника машины, наведенных напряжений в обмотках статора и заданного значения активной мощности, полученного из соответствующего контроллера напряжения шины постоянного тока в режиме скольжения. Что касается нелинейности математической модели напряжения шины постоянного тока и неоднородности, характеризующей поведение системы «преобразователь-машина», стратегия скользящего режима будет представлять собой идеальный инструмент для определения размеров конструкции контроллера с высокими характеристиками управления. Для демонстрации обоснованности предлагаемого метода контроля, приведены результаты выполненного моделирования.

Robot table tennis systems require a vision system that can track the ball position with low latency and high sampling rate. Altering the ball to simplify the tracking using, for instance, infrared coating changes the physics of the ball trajectory. As a result, table tennis systems use custom tracking systems to track the ball based on heuristic algorithms respecting the real-time constrains applied to RGB images captured with a set of cameras. However, these heuristic algorithms often report erroneous ball positions, and the table tennis policies typically need to incorporate additional heuristics to detect and possibly correct outliers. In this paper, we propose a vision system for object detection and tracking that focuses on reliability while providing real-time performance. Our assumption is that by using multiple cameras, we can find and discard the errors obtained in the object detection phase by checking for consistency with the positions reported by other cameras. We provide an open source implementation of the proposed tracking system to simplify future research in robot table tennis or related tracking applications with strong real-time requirements. We evaluate the proposed system thoroughly in simulation and in the real system, outperforming previous work. Furthermore, we show that the accuracy and robustness of the proposed system increases as more cameras are added. Finally, we evaluate the table tennis playing performance of an existing method in the real robot using the proposed vision system. We measure a slight increase in performance compared to a previous vision system even after removing all the heuristics previously present to filter out erroneous ball observations.

The increasing global energy demand, scarcity of fossil fuels, and climate change have encouraged the use of renewable energy sources to decrease the reliance on fossil fuels and reduce the environmental impacts. Liquid biofuels have received noticeable attention lately as renewable feedstock for hydrogen production. Since the ever-growing production of biodiesel leads to an accumulated surplus of glycerol in the world market with a low commercial value, research is focused on finding alternative methods to make use of glycerol with the aim of upgrading the biodiesel business and improving its economic feasibility. The main objective of the current study is to perform a parametric analysis and optimization of bio-hydrogen production via steam reforming of the glycerol and biodiesel mixtures. A simulation model for steam reforming of glycerol/biodiesel mixture was developed using Aspen Plus software. The simulation results were validated with experimental data. The comparison showed a good agreement between the simulation and experimental results. A parametric study was carried out to investigate the effects of the gasification temperature (500–800 °C), steam-to-fuel ratio (SFR) (6–12), and biodiesel-to-glycerol ratio (BGR) (0–20%) on the composition of the syngas fuel. The response surface methodology (RSM) with conjunction of central composite design (CCD) was used in this study to optimize the steam fuel reforming process. The simulation results showed that the reformer temperature was found to significantly affect hydrogen production compared to the SFR and BGR. A quadratic equation developed in this study for the hydrogen versus the three input factors (temperature, SFR, BGR) fits very well with the predicted value obtained from the simulation (R2 = 0.99). The optimized operating conditions of the reformer for maximum bio-hydrogen production (66.06 mol%) are as follows: reformer temperature of 689.27 °C, SFR of 9.87, and BGR of 5.63%. This research study brings clear evidence for the research community as well as industry that bio-hydrogen can be successfully produced via steam reforming of glycerol and biodiesel mixtures.

Various solutions have been proposed in literature for extracting positive and negative sequences related to three phase voltages and currents imbalanced systems. One category of these techniques uses the delay signal cancelation (DSC) according to the well-known Fortescue transformation, other class of these methods are based on the use of an appropriate low pass filters and other ones are based on an advanced computing algorithms such us the recursive least-squares approach (RLS filter). This paper presents a comparative study between the RLS and the delay signal cancelation (DSC) algorithms. In order to evaluate each algorithm performances in terms of rapidity and accuracy in extracting positive and negative sequences components, several tests have been carried out via simulink/Matlab software under balanced and unbalanced source voltages to show the effectiveness poper to each algorithm.

Developing robust signature verification systems is one of the most attracting topics in the handwriting recognition field. In fact, dealing with signature spoofing requires the use of robust features that help to distinguish authentic signatures from the forged ones. Presently, we propose a multiscale fusion of two histogram-based features to perform signatures description. Precisely, we introduce the Local Difference Feature as new descriptor that is fused with the Histogram Of Templates. These features are calculated on a multiscale neighborhood to highlight pixels distribution within the signature shape. The verification stage is achieved by using SVM classifier. Performance assessment is carried out on GPDS-300 and MCYT-75 datasets. Results in terms of average error rates evince the robustness of the proposed features, which outperform various state of the art methods.

The present work focuses on combining Convolutional Neural Network (CNN) features to strengthen the writer retrieval in historical document databases. Various CNN models that are LeNet, ResNet, and VGG are used to get writer-independent features of handwritten documents. These features are associated with dissimilarity measures to achieve the retrieval task. Then, developed writer retrieval systems are combined through SVM classifier. Experiments are conducted on ICDAR-2017 dataset which contains historical handwritten documents. The results obtained highlight the robustness of CNN features and the combination stage as well.

In the present work, we propose the multi-scale Local Difference Features (LDF) for improving handwritten signature verification. This descriptor is similar to local binary patterns, since it highlights intensity differences between a pixel and its neighbors. Presently, we propose a multi-scale implementation of LDF in which, neighboring pixels are taken on several radiuses. This helps to adapt LDF to variations of handwritten strokes within signatures. The verification stage is carried out by using Support Vector Machines classifier. The performance assessment is elaborated on two benchmark datasets. Obtained error rates highlight the effectiveness of the proposed features, since they outperform several state of the art results.

One-class classifier (OCC) is involved for solving different kinds of problems due to its ability to represent a class distribution regardless the remaining classes. Its main advantage for multi-class classification is offering an open system and therefore allows easily extending new classes without retraining OCCs. So far, hidden Markov models, support vector machines and neural networks are the most used classifiers for Arabic word recognition, which provides a system with closed lexicon. In this paper, the OCCs are explored in order to perform an Arabic word recognition system with an open lexicon. Generally, pattern recognition systems designed by a single system suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining multiple systems becomes an attractive research topic for performance and robustness enhancement. Fixed rules are commonly used us combiners for the hybrid OCC ensembles. The present paper aims to propose a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Furthermore, an alternative framework is proposed to design a parameter-independent and open-lexicon handwritten Arabic word recognition system as well as a new density measure function. Experimental results conducted on Arabic handwritten dataset using different types of OCCs with large number of classes highlight the superiority of FI for hybrid OCC ensembles.

Offline handwritten signature identification has received less attention in comparison with the offline signature verification, despite its crucial applications such as in law-enforcements, automatic bank check and historical documents processing. In this paper, an Open Handwritten Signature Identification System (OHSIS) is proposed by using conjointly the Curvelet Transform (CT) and the One-Class classifier based on Principal Component Analysis (OC-PCA). CT is explored for feature generation due to its efficient characterization of curves contained into the local orientations within the signature image. While, OC-PCA is used for its effectiveness to absorb the high feature size generated by the CT and allows achieving at the same time an open system. Then, in order to improve the robustness of the OHSIS when few reference signatures are available, a new combination approach based on Choquet fuzzy integral is proposed to combine multiple individual OHSISs. Furthermore, a designing protocol with limited number of writers and reference signatures is proposed to perform a parameter-independent OHSIS. Experimental results conducted on standard CEDAR and GPDS handwritten signature datasets report 97.99% and 94.96% correct identification rate, respectively, which highlights the effectiveness of the proposed OHSIS since it can comfortably outperform the state-of-the-art when using few reference signatures.

This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.

This contribution deals with a detail of an approach based on genetic algorithms (GAs) exclusively adapted for the implementation of on-line doubly fed induction machine parameter estimation on FPGA. Thus, knowing just the stator/rotor voltage ratio initially, the proposed solution offers a significantly reduced computational complexity without any simplifying assumptions. The resulting identification problem is linear in a similar way to the separated DC machine case. The considered identification problem algorithm is performed using the Matlab/Simulink software; this simulation model is used then to implement the algorithm using the Xilinx Tool Box. Finally, the System Generator (SysGen) tool in the Xilinx block set is used to produce the corresponding bit file, this last will be downloaded on the FPGA via hardware co-simulation. The proposed procedure illustrates that the obtained results are in perfect concordance with respect to the simulated ones with reasonable usage of the considered FPGA’s available resources.

In this work, we propose a new textural feature for solving offline handwritten signature verification. The proposed feature that is called Local Difference Feature (LDF) is a LBP-like texture descriptor. LDF calculates differences between a central pixel and eight neighbors taken on a specific neighborhood radius. Differences are coded into binary thresholded values before evaluating the histogram of codes. The verification step is achieved by SVM classifier trained on genuine signatures. Furthermore, the test stage is performed on both genuine signatures and skilled forgeries. Exepriments are conducted on GPDS and CEDAR datasets. Results obtained showed that LDF outperforms classical features such as Histogram of oriented gradients and local binary patterns. Also, the comparison with the state of the art highlights the LDF robustness for handwritten signature characterization

this paper describes a novel approach to control an isolated induction generator (IG) used for wind power converting applications. In the considered configuration, the machine provides the produced active power to the load through an inverter coupled to a single capacitor on the DC-side. For this purpose, a simplified vector control strategy is used to obtain a similar induction machine mathematical model to one of the separated DC machine. Thus, for a given load power value, the necessary stator voltages references to be performed by the inverter are calculated from the desired DC-bus voltage square value and the rotor speed ratio using a simplified form of the well-known rotor field oriented control laws established in steady-state conditions. Computer simulations confirmed by experimental results indicate that the proposed control requiring just one PI controller provides perfect tracking performances of the DC-bus voltage to its reference trajectory under the load-power demand variations.

The aim of this paper is to present a study of the efficiency of the electrical part of a wind generation system. Two back-to-back PWM voltage-fed inverters connected between the stator and the rotor are used to allow bidirectional power flow. The second inverter grid side, has a role of a power active filter, to eliminate the harmonic generated by the non linear load, in the same time gives an active and reactive power needed by the rotor of DFIG. The harmonics of switching frequency in the current stator, pose a major problem in the moment where commutations in the diode bridge, to solve this problem, we introduce a small-sized passive LC filter for the purpose of eliminating high-frequency shaft voltage and grid current from a DFIG driven by a voltage-source pulse width-modulation rotor inverter controlled with SVM. The control theory is discussed, and the controller implementation is described. Design criteria are also given. The results of simulation tests show excellent static and dynamic performances.

Graphical abstractDisplay Omitted Currently, writer's soft-biometrics prediction is gaining an important role in various domains related to forensics and anonymous writing identification. The purpose of this work is to develop a robust prediction of the writer's gender, age range and handedness. First, three prediction systems using SVM classifier and different features, that are pixel density, pixel distribution and gradient local binary patterns, are proposed. Since each system performs differently to the others, a combination method that aggregates a robust prediction from individual systems, is proposed. This combination uses Fuzzy MIN and MAX rules to combine membership degrees derived from predictor outputs according to their performances, which are modeled by Fuzzy measures. Experiments are conducted on two Arabic and English public handwriting datasets. The comparison of individual predictors with the state of the art highlights the relevance of proposed features. Besides, the proposed Fuzzy MIN-MAX combination comfortably outperforms individual systems and classical combination rules. Relatively to Sugeno's Fuzzy Integral, it has similar computational complexity while performing better in most cases.

A new system for Handwritten Signature Verification is proposed.The Artificial Immune Recognition System is proposed to achieve the verification task.Two new features that are Gradient Local Binary Patterns and Longest Run Features are proposed.Results obtained on CEDAR and GPDS-100 corpuses reveal the effectiveness of the proposed methods. Natural Immune System offers many interesting features that inspired the design of Artificial Immune Systems (AIS) used to solve various problems of engineering and artificial intelligence. AIS are particularly successful in fault detection and diagnosis applications where anomalies such as errors and failures are assimilated to viruses that should be detected. Thereby, AIS seem suitable to automatically detect forgeries in signature verification systems. This paper proposes a novel method for off-line signature verification that is based on the Artificial Immune Recognition System (AIRS). For feature generation, two different descriptors are proposed to generate signature traits. The first is the Gradient Local Binary Patterns that estimates gradient features based on the LBP neighborhood. The second descriptor is the Longest Run Feature, which describes the signature topology by considering longest suites of text pixels. Performance evaluation is carried out on CEDAR and GPDS-100 datasets. The results obtained showed that the proposed system has promising performance and often comfortably outperforms the state of the art.

Retrieval based on query images supports interesting applications in handwritten document analysis, such as checking manuscripts originality, and authorship. In this respect, writer retrieval systems aim to automatically find all manuscripts belonging to the same author. Presently, we propose a new combination scheme for multiple writer retrieval systems that employ different features and dissimilarities. The proposed combination is founded on writer-independent, SVM dissimilarity learning. For experimental evaluation, three individual systems are proposed each of which, has its specific features. To develop the first system, we propose the Multiscale Histogram Of Templates (M-HOT). For the second system, we introduce the so-called Multi-Gradient Elongated Quinary Pattern (MG-EQP) as new descriptor for handwriting characterization. The third system uses the well-known Run Length Features. Retrieval tests are performed on CVL, ICDAR-2011, ICDAR-2013 and ICDAR-2017 datasets. Furthermore, to highlight the language-independence aspect, experiments are performed on KHATT dataset that contains Arabic handwritten documents. Results obtained evince the effectiveness of the proposed features as well as the combination scheme, which outperforms both individual systems and the state of the art.

A new Histogram Of Templates (HOT) feature describing stroke orientations in handwritten signatures is proposed. Several predefined templates are used to model all possible orientations of handwritten strokes. A comparison between these templates and handwritten strokes is evaluated through a sliding window that considers a central pixel with two neighboring pixels. Presently, a quad-tree partitioning according to the center of gravity is applied to allow a local calculation of HOT. The verification task is achieved by SVM classifier. Experiments are conducted on the well-known CEDAR and MCYT-75 databases. The comparison with the state-of-the-art highlights the effectiveness of the proposed feature.

In this work, we present a new protocol for a novel biometric scenario that is called writer retrieval. Precisely, we propose to use the Histogram Of Templates (HOT) to generate features from handwritten text images. Then, the retrieval task is achieved by SVM classifier trained according to a writer independent strategy. Experiments are conducted on the CVL database which contains 310 writers (1604 documents written in English and German). The results obtained in terms of overall accuracy highlight the effectiveness of the proposed system.

In order to take advantage from collections of digitized handwritten documents, effective indexing and retrieval techniques are required. This work focuses on automatic writer retrieval, which is the task of finding in a dataset, all documents written by the same person. Contrary to conventional writer retrieval techniques that are based on dissimilarity measures, we propose to use the SVM classifier to perform the retrieval task. First, local gradient features are used to generate handwritten features. Then, dissimilarities calculated between intra-writer and inter-writer documents are used to train a SVM to allow an automatic retrieval of all the writers documents. Experiments are conducted on CVL and ICDAR 2011 datasets. The performance evaluation of the proposed system is carried out comparatively to the cosine similarity. Results obtained evince a significant improvement offered by SVM, which gives comparable and sometimes better scores than the state of the art.

Verifying the authenticity of handwritten signatures is required in various current life domains, notably with official contracts, banking or financial transactions. Therefore, in this paper a novel histogram-based descriptor and an improved classification of the bio-inspired Artificial Immune Recognition System (AIRS) are proposed for handwritten signature verification. Precisely, the Histogram Of Templates (HOT) is introduced to characterize the most widespread orientations of local strokes in handwritten signatures, while the combination of AIRS and SVM is proposed to achieve the verification task. Usually, using the k Nearest Neighbor rule, a questioned signature is classified by computing dissimilarities with respect to all AIRS outputs. In this work, using these dissimilarities, a second round of training is achieved by the SVM classifier to further improve the discrimination power. In comparison with existing methods, the experiments on two widely-used datasets show the potential and the effectiveness of the proposed system.

Soft-biometrics prediction from handwriting analysis is gaining a wide interest in writer identification since it gives additional knowledge about the writer like its gender (man or woman), its handedness (left-handed or right-handed) and its age range. All research works developed in this context were focused on predicting a single soft-biometric trait. Nevertheless, it could be more interesting to develop a system that predicts several traits from a handwritten text. Presently, we investigate the feasibility of such multiple trait prediction. To reach this end, we propose two prediction schemes. The first combines individual prediction scores to aggregate a global prediction. The second scheme is based on a multi-class prediction. For both schemes, the prediction is based on SVM classifier associated with Gradient features. Experimental corpus is collected from IAM handwritten database. Conclusively, the second scheme proved to be more promising and evinced that the age characteristic is stable over time for a certain category of writers.

Recently, the combination of classification systems with semi-supervised learning has attracted researchers in several fields. Usually, for tasks with high complexity such as handwriting based age prediction, individual systems, using one classifier associated with specific data features, cannot provide satisfactory performance. In this paper, we investigate the contribution of the Co-training approach, as an enhancement procedure, for age range prediction from handwriting analysis. By using samples extracted from IAM dataset, several descriptors for feature generation and an SVM predictor for classification are employed. The comparison with the state of the art indicates the effectiveness of this approach.

This paper introduces a new system to identify handwritten signatures. For feature generation, we propose the Histogram of templates, while the Artificial Immune recognition System (AIRS) is used to achieve the identification task. A writer-independent strategy is proposed to train the AIRS to get an open system that can identify any new writer. Experiments are conducted on a benchmark dataset composed of 55 writers. The results obtained are satisfactory and confirm the usefulness of the proposed system.

Offline signature identification and verification systems encounter several challenges such as the diversity of signatories and the limited number of references. To address these problems we propose a new writer-independent system for signature identification and verification. Besides, a new feature generation scheme is proposed by using the Histogram Of Templates (HOT). The identification and verification step is performed by SVM. Experiments are conducted on a standard dataset which contains off-line signatures of 55 persons. The results obtained are very promising.

In this work, we propose a new descriptor that is called Gradient Local Binary Patterns (GLBP) for automatic keyword spotting in handwritten documents. GLBP is a gradient feature that improves the Histogram of Oriented Gradients (HOG) by calculating the gradient information at transitions of the Local Binary Pattern code. For the matching step, we use the Euclidian Distance and the Cosine Similarity. To show GLBP's performance, we used a Benchmark dataset which contains 100 documents written if 4 languages, from those documents 300 query were extracted to be spotted. The results obtained highlight the effectiveness of the proposed descriptor.

The gender identification in handwritten documents becomes to gain importance for various writer authentication purposes. It provides information for anonymous documents for which we need to know if they were written by a Man or a Woman. In this work, we propose a system for writer’s gender classification that is based on local textural and gradient features. Especially our proposed features are Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), which are successful in various pattern recognition applications. The classification step is achieved by SVM classifier. The results obtained on samples extracted from IAM dataset showed that the proposed features provide quite promising results.

Tularaemia is a rare infectious disease endemic in most European countries caused by the bacteriumFrancisella tularensis.1Patients often show acute non-specific symptoms, which causes a delay in diagnosis and proper treatment, potentially resulting in significant morbidities such as deep neck abscess, meningitis, endocarditis and septic shock. The authors present a case of a 5-year old boy with a 4-day history of fever, sore throat and painful cervical lymphadenopathy, whose clinical progression worsened despite being treated with recommended antibiotics as per WHO guidelines once the diagnosis of Tularaemia was confirmed by serologic tests. He developed a parapharyngeal abscess and a persistent left necrotic cervical lymph node, which both were surgically drained and excised, respectively, and an extended course of antibiotic was given. Subsequently, the patient fully recovered from the illness and the follow-up was negative for relapse.

This work addresses soft-biometrics prediction from handwriting analysis, which aims to predict the writer's gender, age range and handedness. Three SVM predictors associated each to a specific data feature are developed and subsequently combined to aggregate a robust prediction. For the combination step, Sugeno's Fuzzy Integral is proposed. Experiments are conducted on public Arabic and English handwriting datasets. The performance assessment is carried out comparatively to individual systems as well as to max and average rules, using independent and blended corpuses. The results obtained demon-strated the usefulness of the Fuzzy Integral, which provides a gain of more than 4% over individual systems as well as other combination rules. Moreover, with respect to the state of the art methods, the proposed approach seems to be much more relevant.

In this work, writer’s gender, handedness and age range prediction is addressed through automatic analysis of handwritten sentences. Three SVM-based predictors associated to different data features are developed. Then, a Fuzzy MIN-MAX combination rule is proposed to aggregate robust prediction from individual systems. Experiments are carried on two public Arabic and English datasets. Results in terms of prediction accuracy demonstrate the usefulness of the proposed algorithm, which provides a gain between 1 % and 10 % over both individual systems and classical combination rules. Moreover, it is much more relevant than various state of the art methods.

This paper proposes a new handwritten signature verification method based on a combination of an artificial immune algorithm with SVM. In a first step, the Artificial Immune Recognition System (AIRS) is trained to develop a set of representative data (memory cells) of both genuine and forged signature classes. Usually, to classify a questioned signature, dissimilarities are calculated with respect to all memory cells and handled according to the k Nearest Neighbor rule. Presently, we propose the training of these dissimilarities by a Support Vector Machine (SVM) classifier to get a more discriminating decision. Histogram of oriented gradients is used for feature generation. Experiments conducted on two standard datasets reveal that the proposed system provides a significant accuracy improvement compared to the conventional AIRS.

This paper proposes a fast and robust system for handwritten alphanumeric character recognition. Specifical- ly, a neural SVM (N-SVM) combination is adopted for the classification stage in order to accelerate the running time of SVM classifiers. In addition, we investigate the use of tangent similarities to deal with data variability. Experimental analy- sis is conducted on a database obtained by combining the well known USPS database with C-Cube uppercase letters where the N-SVM combination is evaluated in comparison with the One-Against-All implementation. The results indicate that the N-SVM system gives the best performance in terms of training time and error rate.

Recently, Support Vector Machines (SVMs) have shown a practical relevance in various image processing applications. This paper investigates their applicability for land cover and land use change detection using multi-sensor images of remote sensing. Then, the most widely used approaches for multi-class SVMs, which are the One-Against-All and the One-Against-One with both Max-Win and DDAG decision rules are implemented to perform multi-class change detection. SVMs are evaluated in comparison with artificial neural networks using different accuracy indicators. The results obtained showed that SVMs are much more efficient than artificial neural networks and highlighted their suitability for land cover change detection.

The major drawback of Support Vector Machines (SVMs) consists of the training time, which is at least quadratic to the number of data. Among the multitude of approaches developed to alleviate this limitation, several research works showed that mixtures of experts can drastically reduce the runtime of SVMs. The mixture employs a set of SVMs each of which is trained on a sub-set of the original dataset while the final decision is evaluated throughout a gater. The present work proposes a new support vector mixture in which Sugeno's fuzzy integral is used as a gater to remove the time complexity induced by conventional gaters such as artificial neural networks. Experiments conducted on standard datasets of optical character and face recognition reveal that the proposed approach gives a significant reduction of the runtime while keeping at least the same accuracy as the SVM trained over the whole dataset.

This paper presents a new non-linear control Algorithm based on the Backstepping approach for an isolated induction generator (IG) driven by a wind turbine. For this purpose and in order to reduce the complexity of the real induction machine mathematical model, the multi-scalar machine model is exploited. The machine delivers an active power to the load via a converter connected to a single capacitor on the dc-side. So, during the voltage build-up process, the necessary stator currents references to be injected by the converter are calculated from the desired active power to be sent to the load and the rotor flux magnitude. Simulation results show that the proposed control provides perfect tracking performances of the DC-bus voltage and the rotor flux magnitude to their reference trajectories.

This paper investigates a number of verification rules to validate the segmentation of connected handwritten digits. The verification technique based on statistical reasoning and fuzzy integrals is employed to verify the segmentation through decision functions produced by multiclass SVM based recognizers. The segmentation relies on an oriented sliding window which identifies potential cut points. The resulting segmented digits are fed to recognizers and the best segmentation is identified by the verification module that combines the recognizer outputs using fuzzy integrals. The proposed methodology is evaluated on a database of handwritten digits with single as well as multiple connections. Comparative analysis shows that the use of the fuzzy integral allows providing high recognition rates comparatively to the state of the art.

This work focuses on automatic prediction of the writer's biometrics including gender, handedness and age information. The proposed prediction system is based on the use of Histogram of Oriented Gradients (HOG), which aims to extract gradient directions from the handwritten text. The prediction task is achieved using SVM classifier. Experiments performed on IAM and KHATT datasets, reveal promising results. Precisely, the comparison with the state of the art shows that, the proposed prediction system provides quite encouraging performance.

This work focuses on automatic off-line handwritten signature verification where a new gradient feature is proposed for signature characterization. This feature namely, Gradient Local Binary Patterns (GLBP) takes advantage from textural information, to improve the gradient description within images. The verification step is performed in a writer-dependent framework using SVM classifier. Experimental analysis is conducted on CEDAR and GPDS-300 datasets. The results obtained in terms of average error rate highlight the high performance of the proposed feature, which significantly overcomes several state of the art results.

This paper introduces an improved implementation of Artificial Immune Recognition System (AIRS) to solve the automatic off-line handwritten signature verification. Conventionally, the AIRS training provide a set of memory cells that are used with a k-Nearest Neighbors decision to classify test patterns. In order to improve the verification ability, we propose to substitute the k-NN classification by a trainable decision function using SVM classifier. In addition, for signature characterization, new gradient local binary pattern features are introduced. Experiments are conducted on CEDAR and GPDS-300 corpuses. The results show that the proposed algorithm overcomes the conventional AIRS-kNN by more than 9% in the average error rate. Also, it gives similar and sometimes better performance than the state of the art.

This work introduces two gradient features for writer's gender, handedness, and age range prediction. The first feature is the Histogram of Oriented Gradients, which highlights the distribution of gradient orientations within images. The second feature is the so-called gradient local binary patterns, which is an improved gradient feature that incorporates the local binary pattern neighborhood in the gradient calculation. The prediction task is achieved by using SVM classifier. Experiments are performed on two corpuses of English and Arabic handwritten text. The results obtained in terms of classification accuracy highlight the effectiveness of the proposed features, which overcome the state of the art.

The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted.

This paper aims to propose a change‐detection system in remotely sensed imagery based on the combination of fuzzy sets and neural networks. Multitemporal images are directly classified into change and no‐change classes using a fuzzy membership model in order to provide complete information about the change. Presently, two fuzzy models derived from the Mahalanobis distance and a fuzzy neural network (FNN) combination are proposed and compared. In order to evaluate the performance of each model, extensive experiments using different performance indicators are carried out on two SPOT HRV images covering a region of Algeria. Results obtained showed that it has a great potential for land‐cover change detection since it allows the nature of change to be extracted automatically. Furthermore, the FNN‐based model gives the best performance. This model allows a reduced amount of false alarms with higher change detection accuracy.

This paper presents a new method for the voltage build-up process of an isolated-loaded dual star induction generator (DSIG) driven by a wind turbine. The machine delivers the active power to the load via two converters connected to a single capacitor on the DC side. During the voltage build-up process, the necessary stator currents references are injected by the converters; they could be calculated from the desired active power which will be sent to the load. This power is determined by the DC-bus voltage regulation, the reactive power which is necessary for the machine magnetization and finally from the six instantaneous voltages induced through the stator DSIG windings. Since the converter/capacitor model is non-linear, a fast and simple control strategy based on the input-output feedback linearization technique is applied to ensure the DC-bus voltage regulation. Computer simulations are provided to verify the validity of the proposed control algorithm.

In this paper, we propose new data features to improve the off-line handwritten signature verification. The proposed features combine advantages of LBP and topological characteristics. Specifically, the Orthogonal Combination of LBP, which provides an LBP histogram with a reduced size, is combined with a topological descriptor that is called longest run features. The verification task is achieved by SVM classifiers and the performance assessment is conducted comparatively to the basic LBP descriptors. Results obtained on both GPDS 300 and CEDAR datasets show that the proposed features improve the verification accuracy while reducing the data size.

This work presents a new system for off-line handwritten signature verification. Specifically, Artificial Immune Recognition System (AIRS) is employed to achieve the verification task. Also, to provide a robust signature character-ization, two new features are used. The first data feature is the Orthogonal Combination of Local Binary Patterns (OC-LBP), which aims to reduce the size of LBP histogram while keeping the same efficiency. In addition, we propose a topological feature that is based on the image Longest-Run-Features (LRF). The proposed features are evaluated comparatively to the state of the art methods. The results obtained for CEDAR dataset, highlight the efficiency of the proposed system.

The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often results in overfitting or singularity, which limits the generalizability of the results. In this paper, we propose a ”Doubly Regularized LOgistic Regression Algorithm” (DR LORA) which penalizes the voxels of the brain that are of no importance for the classification using the Alternating Direction Method of Multipliers (ADMM) and therefore alleviate this overfitting problem. Our algorithm was compared to other classification based algorithms such as Naive Bayes, Random forest and support vector machine. The results show clear performances for our algorithm.

One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. Generally, pattern recognition systems designed by a single source of information suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining information from multiple sources becomes a mode for designing pattern recognition systems. Usually, fixed rules such as average, product, minimum and maximum are the standard used combiners for OCC ensembles. However, fixed combiners cannot be useful to treat some difficult cases. Hence, we propose in this paper a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Experimental results conducted on different types of OCC and two different handwritten datasets prove the superiority of FI against fixed combiners for an open multi-class classification based on OCC ensemble.

In various pattern recognition applications, artificial immune systems achieve comparable and commonly higher performance than other classification schemes such as SVM. In this paper, we investigate their applicability for handwritten signature verification. Specifically, Ridgelet transform and grid features are used to extract pertinent characteristics. Performance assessment is conducted on the CEDAR dataset comparatively to SVM classifiers. The results in terms of average error rate highlight the high performance of artificial immune recognition algorithm.