Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • robotics and control systems  (9)
  • computing and processing
Type of Medium
Language
Year
  • 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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: English
    In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, March 2016, Vol.24(3), pp.386-398
    Description: In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincaré section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
    Keywords: Electroencephalography ; Feature Extraction ; Trajectory ; Nonlinear Dynamical Systems ; Epilepsy ; Geometry ; Benchmark Testing ; Dynamics ; Electroencephalography (EEG) ; Phase Space ; Poincaré Section ; Seizure Detection ; Two-Layer Classifier Topology ; Occupational Therapy & Rehabilitation
    ISSN: 1534-4320
    E-ISSN: 1558-0210
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3769-3774
    Description: In this paper, we propose a graph affinity learning method for a recently proposed graph-based salient object detection method, namely Extended Quantum Cuts (EQCut). We exploit the fact that the output of EQCut is differentiable with respect to graph affinities, in order to optimize linear combination coefficients and parameters of several differentiable affinity functions by applying error backpropagation. We show that the learnt linear combination of affinities improves the performance over the baseline method and achieves comparable (or even better) performance when compared to the state-of-the-art salient object segmentation methods.
    Keywords: Object Detection ; Object Segmentation ; Symmetric Matrices ; Image Segmentation ; Quantum Mechanics ; Graph Theory ; Computer Vision ; Graph Affinity Learning ; Salient Object Segmentation ; Spectral Graph Theory
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.3645-3649
    Description: Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces a joint multi-layer K-Means clustering solution for determination of the codebooks. The performance of the proposed method is improved further by a joint encoding scheme. Experimental results verify the success of the proposed algorithm as it outperforms the state-of-the-art methods.
    Keywords: Encoding ; Training ; Hamming Distance ; Optimization ; Vector Quantization ; Search Problems
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010, Vol.2010, pp.4695-8
    Description: In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.
    Keywords: Algorithms ; Cluster Analysis ; Expert Systems ; Arrhythmias, Cardiac -- Diagnosis ; Diagnosis, Computer-Assisted -- Methods ; Electrocardiography, Ambulatory -- Methods ; Pattern Recognition, Automated -- Methods
    ISBN: 9781424441235
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Language: English
    In: 2016 23rd International Conference on Pattern Recognition (ICPR), December 2016, pp.2276-2281
    Description: Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap between human taxa identification accuracy (typically 90-95% on 30-40 classes of macroinvertebrates) and results of automatic fine-grained classification by introducing a novel technique based on Convolutional Neural Networks (CNN). CNN learns optimal features for macroinvertebrate classification and achieves near human accuracy when tested on 29 macroinvertebrate classes. Moreover, we perform comparative evaluation of the learned features against the hand-crafted features, which have been commonly used in classical approaches, and confirm superiority of the learned deep features over the engineered ones.
    Keywords: Feature Extraction ; Ecosystems ; Water Resources ; Machine Vision ; Microscopy ; Databases
    ISSN: 10514651
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Language: English
    In: IEEE Access, 2019, Vol.7, pp.67305-67318
    Description: The use of unmanned aerial vehicles (UAVs) in future wireless networks is gaining attention due to their quick deployment without requiring the existing infrastructure. Earlier studies on UAV-aided communication consider generic scenarios, and very few studies exist on the evaluation of UAV-aided communication in practical networks. The existing studies also have several limitations, and hence, an extensive evaluation of the benefits of UAV communication in practical networks is needed. In this paper, we proposed a UAV-aided Wi-Fi Direct network architecture. In the proposed architecture, a UAV equipped with a Wi-Fi Direct group owner (GO) device, the so-called Soft-AP, is deployed in the network to serve a set of Wi-Fi stations. We propose to use a simpler yet efficient algorithm for the optimal placement of the UAV. The proposed algorithm dynamically places the UAV in the network to reduce the distance between the GO and client devices. The expected benefits of the proposed scheme are to maintain the connectivity of client devices to increase the overall network throughput and to improve energy efficiency. As a proof of concept, realistic simulations are performed in the NS-3 network simulator to validate the claimed benefits of the proposed scheme. The simulation results report major improvements of 23% in client association, 54% in network throughput, and 33% in energy consumption using single UAV relative to the case of stationary or randomly moving GO. Further improvements are achieved by increasing the number of UAVs in the network. To the best of our knowledge, no prior work exists on the evaluation of the UAV-aided Wi-Fi Direct networks.
    Keywords: Wireless Fidelity ; Throughput ; Network Architecture ; Unmanned Aerial Vehicles ; Communication Networks ; Base Stations ; Spread Spectrum Communication ; Access Point ; Group Owner ; Ns-3 ; Unmanned Aerial Vehicle ; Wi-Fi Direct ; Engineering
    E-ISSN: 2169-3536
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages