Computational Intelligence and Neuroscience The latest articles from Hindawi © 2018 , Hindawi Limited . All rights reserved. On Synchronizing Coupled Retinogeniculocortical Pathways: A Toy Model Thu, 08 Mar 2018 00:00:00 +0000 A Newman-Watts graph is formed by including random links in a regular lattice. Here, the emergence of synchronization in coupled Newman-Watts graphs is studied. The whole neural network is considered as a toy model of mammalian visual pathways. It is composed by four coupled graphs, in which a coupled pair represents the lateral geniculate nucleus and the visual cortex of a cerebral hemisphere. The hemispheres communicate with each other through a coupling between the graphs representing the visual cortices. This coupling makes the role of the corpus callosum. The state transition of neurons, supposed to be the nodes of the graphs, occurs in discrete time and it follows a set of deterministic rules. From periodic stimuli coming from the retina, the neuronal activity of the whole network is numerically computed. The goal is to find out how the values of the parameters related to the network topology affect the synchronization among the four graphs. B. L. Mayer and L. H. A. Monteiro Copyright © 2018 B. L. Mayer and L. H. A. Monteiro. All rights reserved. TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment Mon, 05 Mar 2018 00:00:00 +0000 This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot. Awatef Aouf, Lotfi Boussaid, and Anis Sakly Copyright © 2018 Awatef Aouf et al. All rights reserved. Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems Thu, 22 Feb 2018 00:00:00 +0000 When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases. José Carlos Ortiz-Bayliss, Ivan Amaya, Santiago Enrique Conant-Pablos, and Hugo Terashima-Marín Copyright © 2018 José Carlos Ortiz-Bayliss et al. All rights reserved. Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface Thu, 22 Feb 2018 00:00:00 +0000 A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount. Ibrahim Hossain, Abbas Khosravi, Imali Hettiarachchi, and Saeid Nahavandi Copyright © 2018 Ibrahim Hossain et al. All rights reserved. PS-FW: A Hybrid Algorithm Based on Particle Swarm and Fireworks for Global Optimization Tue, 20 Feb 2018 00:00:00 +0000 Particle swarm optimization (PSO) and fireworks algorithm (FWA) are two recently developed optimization methods which have been applied in various areas due to their simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optima owing to the lack of powerful global exploration capability, and fireworks algorithm is difficult to converge in some cases because of its relatively low local exploitation efficiency for noncore fireworks. In this paper, a hybrid algorithm called PS-FW is presented, in which the modified operators of FWA are embedded into the solving process of PSO. In the iteration process, the abandonment and supplement mechanism is adopted to balance the exploration and exploitation ability of PS-FW, and the modified explosion operator and the novel mutation operator are proposed to speed up the global convergence and to avoid prematurity. To verify the performance of the proposed PS-FW algorithm, 22 high-dimensional benchmark functions have been employed, and it is compared with PSO, FWA, stdPSO, CPSO, CLPSO, FIPS, Frankenstein, and ALWPSO algorithms. Results show that the PS-FW algorithm is an efficient, robust, and fast converging optimization method for solving global optimization problems. Shuangqing Chen, Yang Liu, Lixin Wei, and Bing Guan Copyright © 2018 Shuangqing Chen et al. All rights reserved. Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model Mon, 19 Feb 2018 00:00:00 +0000 The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford’s law, fractal dimension, global contrast factor, and Shannon’s entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art. Tao Wu Copyright © 2018 Tao Wu. All rights reserved. Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials Thu, 15 Feb 2018 00:00:00 +0000 Recent studies indicate that the local field potential (LFP) carries information about an animal’s behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the -nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 8%) was significantly higher than that of the power features (61 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network. Yan Chen, Xinyu Liu, Shan Li, and Hong Wan Copyright © 2018 Yan Chen et al. All rights reserved. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems Tue, 13 Feb 2018 00:00:00 +0000 The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor () is modified based on the Metropolis criterion in simulated annealing. The redesigned could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive -constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. Hailong Wang, Zhongbo Hu, Yuqiu Sun, Qinghua Su, and Xuewen Xia Copyright © 2018 Hailong Wang et al. All rights reserved. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models Mon, 12 Feb 2018 09:37:41 +0000 Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU). Nouar AlDahoul, Aznul Qalid Md Sabri, and Ali Mohammed Mansoor Copyright © 2018 Nouar AlDahoul et al. All rights reserved. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression Mon, 12 Feb 2018 00:00:00 +0000 Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. Xu Yu, Jun-yu Lin, Feng Jiang, Jun-wei Du, and Ji-zhong Han Copyright © 2018 Xu Yu et al. All rights reserved. Liraglutide Activates the Nrf2/HO-1 Antioxidant Pathway and Protects Brain Nerve Cells against Cerebral Ischemia in Diabetic Rats Mon, 12 Feb 2018 00:00:00 +0000 This study aimed to determine the effect of liraglutide pretreatment and to elucidate the mechanism of nuclear factor erythroid 2-related factor (Nrf2)/heme oxygenase-1 (HO-1) signaling after focal cerebral ischemia injury in diabetic rats model. Adult male Sprague-Dawley rats were randomly divided into the sham-operated (S) group, diabetes mellitus ischemia (DM + MCAO) group, liraglutide pretreatment normal blood glucose ischemia (NDM+MCAO+L) group, and liraglutide pretreatment diabetes ischemia (DM + MCAO + L) group. At 48 h after middle cerebral artery occlusion (MCAO), neurological deficits and infarct volume of brain were measured. Oxidative stress brain tissue was determined by superoxide dismutase (SOD) and myeloperoxidase (MPO) activities. The expression levels of Nrf2 and HO-1 of brain tissue were analyzed by western blotting. In the DM + MCAO + L group, neurological deficits scores and cerebral infarct volume seemed to decrease at 48 h after MCAO cerebral ischemia compared with those in DM + MCAO group (). In addition, the expression of Nrf2 and HO-1 increased in 48 h at liraglutide pretreatment groups after MCAO cerebral ischemia if compared with those in the DM + MCAO group (). Furthermore, the DM + MCAO + L group has no significant difference compared with the NDM + MCAO + L group (). To sum up, alleviating effects of liraglutide on diabetes complicated with cerebral ischemia injury rats would be related to Nrf2/HO-1 signaling pathway. Caihong Deng, Jun Cao, Jiangquan Han, Jianguo Li, Zhaohun Li, Ninghua Shi, and Jing He Copyright © 2018 Caihong Deng et al. All rights reserved. A Pruning Neural Network Model in Credit Classification Analysis Sun, 11 Feb 2018 00:00:00 +0000 Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs) to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency. Yajiao Tang, Junkai Ji, Shangce Gao, Hongwei Dai, Yang Yu, and Yuki Todo Copyright © 2018 Yajiao Tang et al. All rights reserved. Deep Learning for Computer Vision: A Brief Review Thu, 01 Feb 2018 08:14:22 +0000 Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis Copyright © 2018 Athanasios Voulodimos et al. All rights reserved. An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing Wed, 31 Jan 2018 09:00:57 +0000 Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and -thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the -thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity. Haiqiang Liu, Gang Hua, Hongsheng Yin, and Yonggang Xu Copyright © 2018 Haiqiang Liu et al. All rights reserved. Dynamical Motor Control Learned with Deep Deterministic Policy Gradient Wed, 31 Jan 2018 00:00:00 +0000 Conventional models of motor control exploit the spatial representation of the controlled system to generate control commands. Typically, the control command is gained with the feedback state of a specific instant in time, which behaves like an optimal regulator or spatial filter to the feedback state. Yet, recent neuroscience studies found that the motor network may constitute an autonomous dynamical system and the temporal patterns of the control command can be contained in the dynamics of the motor network, that is, the dynamical system hypothesis (DSH). Inspired by these findings, here we propose a computational model that incorporates this neural mechanism, in which the control command could be unfolded from a dynamical controller whose initial state is specified with the task parameters. The model is trained in a trial-and-error manner in the framework of deep deterministic policy gradient (DDPG). The experimental results show that the dynamical controller successfully learns the control policy for arm reaching movements, while the analysis of the internal activities of the dynamical controller provides the computational evidence to the DSH of the neural coding in motor cortices. Haibo Shi, Yaoru Sun, and Jie Li Copyright © 2018 Haibo Shi et al. All rights reserved. The EEG Activity during Binocular Depth Perception of 2D Images Tue, 30 Jan 2018 00:00:00 +0000 The central brain functions underlying a stereoscopic vision were a subject of numerous studies investigating the cortical activity during binocular perception of depth. However, the stereo vision is less explored as a function promoting the cognitive processes of the brain. In this work, we investigated a cortical activity during the cognitive task consisting of binocular viewing of a false image which is observed when the eyes are refocused out of the random-dot stereogram plane (3D phenomenon). The power of cortical activity before and after the onset of the false image perception was assessed using the scull EEG recording. We found that during stereo perception of the false image the power of alpha-band activity decreased in the left parietal area and bilaterally in frontal areas of the cortex, while activity in beta-1, beta-2, and delta frequency bands remained to be unchanged. We assume that this suppression of alpha rhythm is presumably associated with increased attention necessary for refocusing the eyes at the plane of the false image. Marsel Fazlyyyakhmatov, Nataly Zwezdochkina, and Vladimir Antipov Copyright © 2018 Marsel Fazlyyyakhmatov et al. All rights reserved. Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process Sun, 28 Jan 2018 07:40:41 +0000 A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compared with other algorithms, firstly, with a classical method which is the Nelder-Mead algorithm and, secondly, with intelligent methods such as Genetic Algorithm and Particle Swarm Optimization approaches. Intissar Khoja, Taoufik Ladhari, Faouzi M’sahli, and Anis Sakly Copyright © 2018 Intissar Khoja et al. All rights reserved. Permutation Entropy and Signal Energy Increase the Accuracy of Neuropathic Change Detection in Needle EMG Wed, 24 Jan 2018 00:00:00 +0000 Background and Objective. Needle electromyography can be used to detect the number of changes and morphological changes in motor unit potentials of patients with axonal neuropathy. General mathematical methods of pattern recognition and signal analysis were applied to recognize neuropathic changes. This study validates the possibility of extending and refining turns-amplitude analysis using permutation entropy and signal energy. Methods. In this study, we examined needle electromyography in 40 neuropathic individuals and 40 controls. The number of turns, amplitude between turns, signal energy, and “permutation entropy” were used as features for support vector machine classification. Results. The obtained results proved the superior classification performance of the combinations of all of the above-mentioned features compared to the combinations of fewer features. The lowest accuracy from the tested combinations of features had peak-ratio analysis. Conclusion. Using the combination of permutation entropy with signal energy, number of turns and mean amplitude in SVM classification can be used to refine the diagnosis of polyneuropathies examined by needle electromyography. O. Dostál, O. Vysata, L. Pazdera, A. Procházka, J. Kopal, J. Kuchyňka, and M. Vališ Copyright © 2018 O. Dostál et al. All rights reserved. A Multiple Kernel Learning Model Based on -Norm Tue, 23 Jan 2018 00:00:00 +0000 By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ -norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the -norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of -norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (- and -norms based GMKL) method, in which    established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the -norm, by joining - and -norms. Consequently, the - and -norms based GMKL is a special case in our method when . Experiments demonstrated that our - and -norms based MKL offers a higher accuracy than the - and -norms based GMKL in the classification, while keeping the properties of the - and -norms based on GMKL. Jinshan Qi, Xun Liang, and Rui Xu Copyright © 2018 Jinshan Qi et al. All rights reserved. Automated Text Analysis Based on Skip-Gram Model for Food Evaluation in Predicting Consumer Acceptance Mon, 22 Jan 2018 00:00:00 +0000 The purpose of this paper is to evaluate food taste, smell, and characteristics from consumers’ online reviews. Several studies in food sensory evaluation have been presented for consumer acceptance. However, these studies need taste descriptive word lexicon, and they are not suitable for analyzing large number of evaluators to predict consumer acceptance. In this paper, an automated text analysis method for food evaluation is presented to analyze and compare recently introduced two jjampong ramen types (mixed seafood noodles). To avoid building a sensory word lexicon, consumers’ reviews are collected from SNS. Then, by training word embedding model with acquired reviews, words in the large amount of review text are converted into vectors. Based on these words represented as vectors, inference is performed to evaluate taste and smell of two jjampong ramen types. Finally, the reliability and merits of the proposed food evaluation method are confirmed by a comparison with the results from an actual consumer preference taste evaluation. Augustine Yongwhi Kim, Jin Gwan Ha, Hoduk Choi, and Hyeonjoon Moon Copyright © 2018 Augustine Yongwhi Kim et al. All rights reserved. A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification Thu, 18 Jan 2018 00:00:00 +0000 One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. Yunlong Yu and Fuxian Liu Copyright © 2018 Yunlong Yu and Fuxian Liu. All rights reserved. Feature Selection for Object-Based Classification of High-Resolution Remote Sensing Images Based on the Combination of a Genetic Algorithm and Tabu Search Thu, 18 Jan 2018 00:00:00 +0000 In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. Lei Shi, Youchuan Wan, Xianjun Gao, and Mingwei Wang Copyright © 2018 Lei Shi et al. All rights reserved. -Iterative Exponential Forgetting Factor for EEG Signals Parameter Estimation Mon, 15 Jan 2018 08:18:49 +0000 Electroencephalograms (EEG) signals are of interest because of their relationship with physiological activities, allowing a description of motion, speaking, or thinking. Important research has been developed to take advantage of EEG using classification or predictor algorithms based on parameters that help to describe the signal behavior. Thus, great importance should be taken to feature extraction which is complicated for the Parameter Estimation (PE)–System Identification (SI) process. When based on an average approximation, nonstationary characteristics are presented. For PE the comparison of three forms of iterative-recursive uses of the Exponential Forgetting Factor (EFF) combined with a linear function to identify a synthetic stochastic signal is presented. The one with best results seen through the functional error is applied to approximate an EEG signal for a simple classification example, showing the effectiveness of our proposal. Karen Alicia Aguilar Cruz, María Teresa Zagaceta Álvarez, Rosaura Palma Orozco, and José de Jesús Medel Juárez Copyright © 2018 Karen Alicia Aguilar Cruz et al. All rights reserved. Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System Mon, 15 Jan 2018 07:40:33 +0000 This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations. Chi-Chung Chen and Yi-Ting Liu Copyright © 2018 Chi-Chung Chen and Yi-Ting Liu. All rights reserved. Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition Sun, 14 Jan 2018 10:43:23 +0000 Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature and temporal information of human motions. Consequently, they suffer from data dependencies and encounter the curse of dimension and the overfitting issue. Their models are hard to be intuitively understood. Given a specific motion set, if structured domain knowledge could be manually obtained, it could be used for better recognizing certain motions. In this study, we start from a deep analysis on natural physical properties and temporal recurrent transformation possibilities of human motions and then propose a useful Recurrent Transformation Prior Knowledge-based Decision Tree (RT-PKDT) model for recognition of specific human motions. RT-PKDT utilizes temporal information and hierarchical classification method, making the most of sensor streaming data and human knowledge to compensate the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as SVM, BP neural networks, and Bayesian Network, obtaining an accuracy of 96.68%. Cheng Xu, Jie He, Xiaotong Zhang, Haipiao Cai, Shihong Duan, Po-Hsuan Tseng, and Chong Li Copyright © 2018 Cheng Xu et al. All rights reserved. Development of a System Architecture for Evaluation and Training of Proprioceptive Deficits of the Upper Limb Wed, 10 Jan 2018 00:00:00 +0000 Proprioception plays a fundamental role in maintaining posture and executing movement, and the quantitative evaluation of proprioceptive deficits in poststroke patients is important. But currently it is not widely performed due to the complexity of the evaluation tools required for a reliable assessment. The aims of this pilot study were to (a) develop a system architecture for upper limb evaluation and training of proximal and distal sense of position in the horizontal plane and (b) test the system in healthy and pathological subjects. Two robotic devices for evaluation and training of, respectively, wrist flexion/extension and shoulder-elbow manipulation were employed. The system we developed was applied in a group of 12 healthy subjects and 10 patients after stroke. It was able to quantitatively evaluate upper limb sense of position in the horizontal plane thanks to a set of quantitative parameters assessing position estimation errors, variability, and gain. In addition, it was able to distinguish healthy from pathological conditions. The system could thus be a reliable method to detect changes in the sense of position of patients with sensory deficits after stroke and could enable the implementation of novel training approaches for the recovery of normal proprioception. Roberto Colombo, Alessandra Mazzone, Carmen Delconte, and Fabrizio Pisano Copyright © 2018 Roberto Colombo et al. All rights reserved. Discriminant WSRC for Large-Scale Plant Species Recognition Mon, 25 Dec 2017 07:14:04 +0000 In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted. Shanwen Zhang, Chuanlei Zhang, Yihai Zhu, and Zhuhong You Copyright © 2017 Shanwen Zhang et al. All rights reserved. Selection of the Optimal Algorithm for Real-Time Estimation of Beta Band Power during DBS Surgeries in Patients with Parkinson’s Disease Sun, 24 Dec 2017 00:00:00 +0000 Deep Brain Stimulation (DBS) is a surgical procedure for the treatment of motor disorders in patients with Parkinson’s Disease (PD). DBS involves the application of controlled electrical stimuli to a given brain structure. The implantation of the electrodes for DBS is performed by a minimally invasive stereotactic surgery where neuroimaging and microelectrode recordings (MER) are used to locate the target brain structure. The Subthalamic Nucleus (STN) is often chosen for the implantation of stimulation electrodes in DBS therapy. During the surgery, an intraoperative validation is performed to locate the dorsolateral region of STN. Patients with PD reveal a high power in the band (frequencies between 13 Hz and 35 Hz) in MER signal, mainly in the dorsolateral region of STN. In this work, different power spectrum density methods were analyzed with the aim of selecting one that minimizes the calculation time to be used in real time during DBS surgery. In particular, the results of three nonparametric and one parametric methods were compared, each with different sets of parameters. It was concluded that the optimum method to perform the real-time spectral estimation of beta band from MER signal is Welch with Hamming windows of 1.5 seconds and 50% overlap. Ángeles Tepper, Mauricio Carlos Henrich, Luciano Schiaffino, Alfredo Rosado Muñoz, Antonio Gutiérrez, and Juan Guerrero Martínez Copyright © 2017 Ángeles Tepper et al. All rights reserved. Clustering Categorical Data Using Community Detection Techniques Thu, 21 Dec 2017 00:00:00 +0000 With the advent of the -modes algorithm, the toolbox for clustering categorical data has an efficient tool that scales linearly in the number of data items. However, random initialization of cluster centers in -modes makes it hard to reach a good clustering without resorting to many trials. Recently proposed methods for better initialization are deterministic and reduce the clustering cost considerably. A variety of initialization methods differ in how the heuristics chooses the set of initial centers. In this paper, we address the clustering problem for categorical data from the perspective of community detection. Instead of initializing modes and running several iterations, our scheme, CD-Clustering, builds an unweighted graph and detects highly cohesive groups of nodes using a fast community detection technique. The top- detected communities by size will define the modes. Evaluation on ten real categorical datasets shows that our method outperforms the existing initialization methods for -modes in terms of accuracy, precision, and recall in most of the cases. Huu Hiep Nguyen Copyright © 2017 Huu Hiep Nguyen. All rights reserved. Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control Tue, 19 Dec 2017 08:07:53 +0000 The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller. YanBin Liu, YuHui Li, and FeiTeng Jin Copyright © 2017 YanBin Liu et al. All rights reserved.