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Computational Intelligence and Neuroscience

Roberto Colombo, Alessandra Mazzone, Carmen Delconte, Fabrizio Pisano
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...
2018: Computational Intelligence and Neuroscience
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
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...
2018: Computational Intelligence and Neuroscience
Shanwen Zhang, Chuanlei Zhang, Yihai Zhu, Zhuhong You
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...
2017: Computational Intelligence and Neuroscience
Ángeles Tepper, Mauricio Carlos Henrich, Luciano Schiaffino, Alfredo Rosado Muñoz, Antonio Gutiérrez, Juan Guerrero Martínez
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...
2017: Computational Intelligence and Neuroscience
Huu Hiep Nguyen
With the advent of the k-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 k-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...
2017: Computational Intelligence and Neuroscience
YanBin Liu, YuHui Li, FeiTeng Jin
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...
2017: Computational Intelligence and Neuroscience
Liyun Zhuang, Yepeng Guan
This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms...
2017: Computational Intelligence and Neuroscience
Xuebai Zhang, Xiaolong Liu, Shyan-Ming Yuan, Shu-Fan Lin
Eye movement can be regarded as a pivotal real-time input medium for human-computer communication, which is especially important for people with physical disability. In order to improve the reliability, mobility, and usability of eye tracking technique in user-computer dialogue, a novel eye control system with integrating both mouse and keyboard functions is proposed in this paper. The proposed system focuses on providing a simple and convenient interactive mode by only using user's eye. The usage flow of the proposed system is designed to perfectly follow human natural habits...
2017: Computational Intelligence and Neuroscience
Haimin Yang, Zhisong Pan, Qing Tao
Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers...
2017: Computational Intelligence and Neuroscience
Gülsüm Akdeniz
Objective: Complexity analysis of functional brain structure data represents a new multidisciplinary approach to examining complex, living structures. I aimed to construct a connectivity map of visual brain activities using resting-state functional magnetic resonance imaging (fMRI) data and to characterize the level of complexity of functional brain activity using these connectivity data. Methods: A total of 25 healthy controls and 20 patients with attention deficit hyperactivity disorder (ADHD) participated...
2017: Computational Intelligence and Neuroscience
Patricia Batres-Mendoza, Mario A Ibarra-Manzano, Erick I Guerra-Hernandez, Dora L Almanza-Ojeda, Carlos R Montoro-Sanjose, Rene J Romero-Troncoso, Horacio Rostro-Gonzalez
We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique...
2017: Computational Intelligence and Neuroscience
Addisson Salazar, Vicente Zarzoso, Manuel Rosa-Zurera, Luis Vergara
No abstract text is available yet for this article.
2017: Computational Intelligence and Neuroscience
George A Papakostas, Konstantinos I Diamantaras, Francesco A N Palmieri
No abstract text is available yet for this article.
2017: Computational Intelligence and Neuroscience
Chong Wu, Tao Wu, Kaiyuan Fu, Yuan Zhu, Yongbo Li, Wangyong He, Shengwen Tang
This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system...
2017: Computational Intelligence and Neuroscience
Yanhua Wang, Xiyu Liu, Laisheng Xiang
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome...
2017: Computational Intelligence and Neuroscience
Xiaohui Zhao, Yicheng Jiang, Tania Stathaki
A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set...
2017: Computational Intelligence and Neuroscience
Alonso-Valerdi Luz María, Mercado-García Víctor Rodrigo
Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment...
2017: Computational Intelligence and Neuroscience
Rafael D de C Silva, Saulo G C Albuquerque, Artur de V Muniz, Pedro P Rebouças Filho, Sidarta Ribeiro, Plácido Rogerio Pinheiro, Victor Hugo C Albuquerque
Schizophrenia is a chronic mental disease that usually manifests psychotic symptoms and affects an individual's functionality. The stigma related to this disease is a serious obstacle for an adequate approach to its treatment. Stigma can, for example, delay the start of treatment, and it creates difficulties in interpersonal and professional relationships. This work proposes a new tool based on augmented reality to reduce the stigma related to schizophrenia. The tool is capable of simulating the psychotic symptoms typical of schizophrenia and simulates sense perception changes in order to create an immersive experience capable of generating pathological experiences of a patient with schizophrenia...
2017: Computational Intelligence and Neuroscience
Piotr Borkowski
The article presents an original design of an expert system, whose function is to automatically stabilize ship's course. The focus is put on the inference engine, a mechanism that consists of two functional components. One is responsible for the construction of state space regions, implemented on the basis of properly processed signals recorded by sensors from the input and output of an object. The other component is responsible for generating a control decision based on the knowledge obtained in the first module...
2017: Computational Intelligence and Neuroscience
Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Dimitrios Dres, Matthaios Bimpas
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar...
2017: Computational Intelligence and Neuroscience
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