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  • communication, networking and broadcast technologies  (15)
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  • 1
    Language: English
    In: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), August 2012, Vol.42(4), pp.1169-1186
    Description: Terrain classification over polarimetric synthetic aperture radar (SAR) images has been an active research field where several features and classifiers have been proposed up to date. However, some key questions, e.g., 1) how to select certain features so as to achieve highest discrimination over certain classes?, 2) how to combine them in the most effective way?, 3) which distance metric to apply?, 4) how to find the optimal classifier configuration for the classification problem in hand?, 5) how to scale/adapt the classifier if large number of classes/features are present?, and finally, 6) how to train the classifier efficiently to maximize the classification accuracy?, still remain unanswered. In this paper, we propose a collective network of (evolutionary) binary classifier (CNBC) framework to address all these problems and to achieve high classification performance. The CNBC framework adapts a "Divide and Conquer" type approach by allocating several NBCs to discriminate each class and performs evolutionary search to find the optimal BC in each NBC. In such an (incremental) evolution session, the CNBC body can further dynamically adapt itself with each new incoming class/feature set without a full-scale retraining or reconfiguration. Both visual and numerical performance evaluations of the proposed framework over two benchmark SAR images demonstrate its superiority and a significant performance gap against several major classifiers in this field.
    Keywords: Neurons ; Scattering ; Covariance Matrix ; Training ; Matrix Decomposition ; Indexes ; Measurement ; Evolutionary Classifiers ; Multidimensional Particle Swarm Optimization (MD-Pso) ; Polarimetric Synthetic Aperture Radar (SAR) ; Sciences (General) ; Engineering
    ISSN: 1083-4419
    E-ISSN: 1941-0492
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  • 2
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, December 2016, Vol.27(12), pp.2458-2471
    Description: In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
    Keywords: Training ; Neurons ; Clustering Algorithms ; Optimization ; Neural Networks ; Heuristic Algorithms ; Particle Swarm Optimization ; Clustering Methods ; Particle Swarm Optimization (Pso) ; Radial Basis Function Networks (Rbfnns) ; Supervised Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 3
    Language: English
    In: IEEE Transactions on Industrial Electronics, November 2019, Vol.66(11), pp.8760-8771
    Description: Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and promising topology for such applications. MMC is composed of many identical controlled voltage sources called modules or cells. Each cell may have one or more switches and a switch failure may occur in anyone of these cells. The steady-state normal and fault behavior of a cell voltage will also significantly vary according to the changes in the load current and the fault timing. This makes it a challenging problem to detect and identify such faults as soon as they occur. In this paper, we propose a real-time and highly accurate MMC circuit monitoring system for early fault detection and identification using adaptive one-dimensional convolutional neural networks. The proposed approach is directly applicable to the raw voltage and current data and thus eliminates the need for any feature extraction algorithm, resulting in a highly efficient and reliable system. Simulation results obtained using a four-cell, eight-switch MMC topology demonstrate that the proposed system has a high reliability to avoid any false alarm and achieves a detection probability of 0.989, and average identification probability of 0.997 in less than 100 ms.
    Keywords: Fault Diagnosis ; Circuit Faults ; Topology ; Fault Detection ; Switches ; Capacitors ; Convolutional Neural Network (CNN) ; Fault Detection ; Fault Identification ; Modular Multilevel Converter (Mmc) ; Engineering
    ISSN: 0278-0046
    E-ISSN: 1557-9948
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 4
    Language: English
    In: IEEE Transactions on Multimedia, January 2018, Vol.20(1), pp.82-95
    Description: We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.
    Keywords: Image Color Analysis ; Spatiotemporal Phenomena ; Object Detection ; Estimation ; Optimization ; Electronic Mail ; Computational Modeling ; Salient Object Detection ; Foreground Detection ; Spatiotemporal ; Saliency ; Spectral Graph Theory ; Engineering ; Computer Science
    ISSN: 1520-9210
    E-ISSN: 1941-0077
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 5
    Language: English
    In: IEEE Transactions on Industrial Electronics, November 2016, Vol.63(11), pp.7067-7075
    Description: Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
    Keywords: Induction Motors ; Feature Extraction ; Fault Detection ; Convolution ; Real-Time Systems ; Neural Networks ; Mathematical Model ; Convolutional Neural Networks (Cnns) ; Motor Current Signature Analysis (Mcsa) ; Engineering
    ISSN: 0278-0046
    E-ISSN: 1557-9948
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 6
    Language: English
    In: IEEE Transactions on Neural Networks and Learning Systems, 31 May 2019, pp.1-15
    Description: The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.
    Keywords: Neurons ; Biological Neural Networks ; Network Topology ; Learning Systems ; Topology ; Computational Modeling ; Nonhomogeneous Media ; Architecture Learning ; Feedforward Network ; Generalized Operational Perceptron (GOP) ; Progressive Learning ; Computer Science
    ISSN: 2162-237X
    E-ISSN: 2162-2388
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  • 7
    Language: English
    In: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, April 2010, Vol.40(2), pp.298-319
    Description: In this paper, we propose two novel techniques, which successfully address several major problems in the field of particle swarm optimization (PSO) and promise a significant breakthrough over complex multimodal optimization problems at high dimensions. The first one, which is the so-called multidimensional (MD) PSO, re-forms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in an MD search space, where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. Nevertheless, MD PSO is still susceptible to premature convergences due to lack of divergence. Among many PSO variants in the literature, none yields a robust solution, particularly over multimodal complex problems at high dimensions. To address this problem, we propose the fractional global best formation (FGBF) technique, which basically collects all the best dimensional components and fractionally creates an artificial global best (aGB) particle that has the potential to be a better "guide" than the PSO's native gbest particle. This way, the potential diversity that is present among the dimensions of swarm particles can be efficiently used within the aGB particle. We investigated both individual and mutual applications of the proposed techniques over the following two well-known domains: 1) nonlinear function minimization and 2) data clustering. An extensive set of experiments shows that in both application domains, MD PSO with FGBF exhibits an impressive speed gain and converges to the global optima at the true dimension regardless of the search space dimension, swarm size, and the complexity of the problem.
    Keywords: Particle Swarm Optimization ; Multidimensional Systems ; Organisms ; Genetic Programming ; Particle Tracking ; Robustness ; Stochastic Processes ; Multidimensional Signal Processing ; Computer Simulation ; Fractional Global Best Formation (Fgbf) ; Multidimensional (MD) Search ; Particle Swarm Optimization (Pso) ; Sciences (General) ; Engineering;
    ISSN: 10834419
    E-ISSN: 1941-0492
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  • 8
    In: IEEE Transactions on Multimedia, 2007, Vol.9(1), pp.102-119
    Description: One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. The developers and users who are accustomed to making queries to retrieve a particular multimedia item from a large scale database can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the requirements of a multimedia database, such as dynamic indexing or the presence of high-dimensional audiovisual features. Such structures do not scale well with the ever increasing size of multimedia databases whilst inducing corruption and resulting in an over-crowded indexing structure. This paper addresses such problems and presents a novel indexing technique, Hierarchical Cellular Tree (HCT), which is designed to bring an effective solution especially for indexing large multimedia databases. Furthermore it provides an enhanced browsing capability, which enables user to make a guided tour within the database. A pre-emptive cell-search mechanism is introduced in order to prevent corruption, which may occur due to erroneous item insertions. Among the hierarchical levels that are built in a bottom-up fashion, similar items are collected into appropriate cellular structures at some level. Cells are subject to mitosis operations when the dissimilarity exceeds a required level. By mitosis operations, cells are kept focused and compact and yet, they can grow into any dimension as long as the compactness is maintained. The proposed indexing scheme is then used along with a recently introduced query method, the progressive query, in order to achieve the ultimate goal, from the user point of view that is retrieval of the most relevant items in the earliest possible time regardless of the database size. Experimental results show that the speed of retrievals is significantly improved and the indexing structure shows no sign of degradations when the database size is increased. Furthermore, HCT indexing body can conveniently be used for efficient browsing and navigation operations among the multimedia database items.
    Keywords: Engineering ; Computer Science;
    ISSN: 1520-9210
    E-ISSN: 19410077
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  • 9
    Language: English
    In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, October 2015, pp.2130-2133
    Description: Devices equipped with accelerometer sensors such as today's mobile devices can make use of motion to exchange information. A typical example for shared motion is shaking of two devices which are held together in one hand. Deriving a shared secret (key) from shared motion, e.g. for device pairing, is an obvious application for this. Only the keys need to be exchanged between the peers and neither the motion data nor the features extracted from it. This makes the pairing fast and easy. For this, each device generates an information signal (key) independently of each other and, in order to pair, they should be identical. The key is essentially derived by quantizing certain well discriminative features extracted from the accelerometer data after an implicit synchronization. In this paper, we aim at finding a small set of effective features which enable a significantly simpler quantization procedure than the prior art. Our tentative results with authentic accelerometer data show that this is possible with a competent accuracy (76%) and key strength (entropy approximately 15 bits).
    Keywords: Acceleration ; Feature Extraction ; Accelerometers ; Sensors ; Quantization (Signal) ; Protocols ; Kernel ; Accelerometer ; Feature Extraction ; Quantization ; Information Signal
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  • 10
    Language: English
    In: 2015 International Conference on Information and Communication Technology Research (ICTRC), May 2015, pp.116-119
    Description: In this paper, we propose an improved spectrum access algorithm for cognitive radio applications using a Hidden Markov Model (HMM) for learning the primary user channel usage pattern. The proposed scheme maximizes the channel utilization without causing significant interference to the primary user. Simulation results show that the proposed algorithm provides about 3 times improvement in channel utilization compared to the system proposed in [1], with a slight degradation in collision probability. It is also observed that the proposed scheme performance is robust to variations in the primary user behavior.
    Keywords: Hidden Markov Models ; Cognitive Radio ; Prediction Algorithms ; Sensors ; Interference ; Signal to Noise Ratio ; Cognitive Radio ; Hidden Markov Models ; Spectrum Access
    Source: IEEE Conference Publications
    Source: IEEE Xplore
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