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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
Yongkai Ye, Xinwang Liu, Qiang Liu, Jianping Yin
Multiview clustering aims to improve clustering performance through optimal integration of information from multiple views. Though demonstrating promising performance in various applications, existing multiview clustering algorithms cannot effectively handle the view's incompleteness. Recently, one pioneering work was proposed that handled this issue by integrating multiview clustering and imputation into a unified learning framework. While its framework is elegant, we observe that it overlooks the consistency between views, which leads to a reduction in the clustering performance...
2017: Computational Intelligence and Neuroscience
Wenfen Liu, Mao Ye, Jianghong Wei, Xuexian Hu
Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets...
2017: Computational Intelligence and Neuroscience
Tao Zhang, Zhong Chen, June Liu, Xiong Li
A two-stage artificial neural network (ANN) based on scalarization method is proposed for bilevel biobjective programming problem (BLBOP). The induced set of the BLBOP is firstly expressed as the set of minimal solutions of a biobjective optimization problem by using scalar approach, and then the whole efficient set of the BLBOP is derived by the proposed two-stage ANN for exploring the induced set. In order to illustrate the proposed method, seven numerical examples are tested and compared with results in the classical literature...
2017: Computational Intelligence and Neuroscience
Hongchao Song, Zhuqing Jiang, Aidong Men, Bo Yang
Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE) and an ensemble k-nearest neighbor graphs- (K-NNG-) based anomaly detector...
2017: Computational Intelligence and Neuroscience
Zhibin Yu, Yubo Wang, Bing Zheng, Haiyong Zheng, Nan Wang, Zhaorui Gu
Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology...
2017: Computational Intelligence and Neuroscience
Qing-Yan Yin, Jun-Li Li, Chun-Xia Zhang
As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models...
2017: Computational Intelligence and Neuroscience
Jianli Liu, Edwin Lughofer, Xianyi Zeng
Several models of visual aesthetic perception have been proposed in recent years. Such models have drawn on investigations into the neural underpinnings of visual aesthetics, utilizing neurophysiological techniques and brain imaging techniques including functional magnetic resonance imaging, magnetoencephalography, and electroencephalography. The neural mechanisms underlying the aesthetic perception of the visual arts have been explained from the perspectives of neuropsychology, brain and cognitive science, informatics, and statistics...
2017: Computational Intelligence and Neuroscience
Jun-He Yang, Ching-Hsue Cheng, Chia-Pan Chan
Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci...
2017: Computational Intelligence and Neuroscience
Qiang Lan, Zelong Wang, Mei Wen, Chunyuan Zhang, Yijie Wang
Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory...
2017: Computational Intelligence and Neuroscience
Eduardo Carabez, Miho Sugi, Isao Nambu, Yasuhiro Wada
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on these people as potential users of BCI, we obtained electroencephalogram (EEG) readings from nine healthy subjects who were presented with auditory stimuli via earphones from six different virtual directions. We presented the stimuli following the oddball paradigm to elicit P300 waves within the subject's brain activity for later identification and classification using convolutional neural networks (CNN)...
2017: Computational Intelligence and Neuroscience
Yu Zhang, Bei Wang, Jin Jing, Jian Zhang, Junzhong Zou, Masatoshi Nakamura
Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain...
2017: Computational Intelligence and Neuroscience
Long Binh Tran, Thai Hoang Le
In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification...
2017: Computational Intelligence and Neuroscience
Turky N Alotaiby, Saleh A Alshebeili, Faisal M Alotaibi, Saud R Alrshoud
This paper presents a patient-specific epileptic seizure predication method relying on the common spatial pattern- (CSP-) based feature extraction of scalp electroencephalogram (sEEG) signals. Multichannel EEG signals are traced and segmented into overlapping segments for both preictal and interictal intervals. The features extracted using CSP are used for training a linear discriminant analysis classifier, which is then employed in the testing phase. A leave-one-out cross-validation strategy is adopted in the experiments...
2017: Computational Intelligence and Neuroscience
Abid Hussain, Yousaf Shad Muhammad, M Nauman Sajid, Ijaz Hussain, Alaa Mohamd Shoukry, Showkat Gani
Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations...
2017: Computational Intelligence and Neuroscience
Jie-Hao Chen, Zi-Qian Zhao, Ji-Yun Shi, Chong Zhao
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms...
2017: Computational Intelligence and Neuroscience
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