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A robot autonomous tracking system is developed in this paper through integrating vision and distance sensory information. A neural network is utilized to fuse the image processing results and sonar measurements, and select a correct motion command for the follower robot. The network architecture, data pre-processing, sample design and training results are presented. The simulation and experimental...
In this paper, we present a Bayesian approach for perception of touch and control of robot emotion. Touch is an important sensing modality for the development of social robots, and it is used in this work as stimulus through a human-robot interaction. A Bayesian framework is proposed for perception of various types of touch. This method together with a sequential analysis approach allow the robot...
Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection...
The order in which measurements are carried out, determines the accuracy of models in early stages of the measurement process, i.e. while measurements are still in progress. Reliable models in early stages of the data acquisition phase allow for model-based investigations like optimization runs or an earlier switching to an active learning phase. This paper compares different methods to determine...
The maneuver control of hypersonic vehicles (HSVs) during re-entry is a challenging work due to the object features of serious nonlinearity, strong uncertainty and fast time variation. In this paper, we design the maneuver control architecture of lateral turning for the HSV first, and then a new self-organizing recurrent functional link network (SORFLN) is proposed to estimate the dynamical uncertainties/disturbances...
This paper presents a syntactic approach for the authorship attribution to literary texts. To this end, syntactic features were used in verification and identification approaches. We used also, a classification method based on dissimilarity that has been successfully applied in cases of authorship attribution. In addition, to evaluating two models, the writer-dependent model and writer-independent,...
Recently, personal identification, which is based on the palmprint texture features analysis, has widely attracted the attention of several researchers and has gained a great popularity in the pattern recognition field. In this paper, we present a novel methodology based on texture information extracted from palmprint. Firstly, we propose an algorithm to robustly locate the Region Of Interest (ROI)...
Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties and disturbances associated with point forecasts. Despite of its numerous applications in prediction problems, the use of PIs in control application is still limited. In this paper, a PI-based ANFIS controller is proposed and designed for nonlinear systems. In the proposed algorithm, a PI-based neural network...
Statistical and machine learning methods have been proposed to predict hard drive failure based on SMART attributes, and many achieve good performance. However, these models do not give a good indication as to when a drive will fail, only predicting that it will fail. To this end, we propose a new notion of a drive's health degree based on the remaining working time of hard drive before actual failure...
Deep networks like the convolutional neural network and its variants usually learn hierarchical features from labeled images, which is very expensive to obtain. How can we find an unsupervised way to effectively extract deep and abstract features from images without annotations? Even from large qualities of images with noise? In this paper, we propose a robust deep neural network, named as stacked...
In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological neighbourhoods (i.e., structural information)...
Exploratory search is cumbersome with today's search engines, where a user aims to better understand complex concepts. Query expansions techniques have been widely used in exploratory search. However, query expansions often recommend queries that differ from the user's search intentions due to different contexts. Yet, many of users' needs could be addressed by asking people via popular Community Question...
Unmanned aerial vehicles (UAVs) are versatile in maneuverability for both civilian and military applications. To facilitate the long-term tasks of UAVs, autonomous rendezvous and docking (ARaD) will be a need in the emerging field of UAV research. In this paper, we proposed a vision-based auxiliary system (VAS) for multirotor UAVs to implement autonomous rendezvous and docking. The VAS consists of...
Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS...
Understanding the mechanisms of restoration of activity in biological neural systems following exposure to damage is key for design of future neuro-prosthetic devices and restorative treatments. The pyloric rhythm network within the crustacean stomatogastric ganglion is a biological neural system that shows spontaneous restoration of activity following the stopping of inputs from higher control ganglia...
A novel fMRI classification method designed for rapid event related fMRI experiments is described and applied to the classification of loud reading of isolated words in Hebrew. Three comparisons of different grammatical complexity were performed: (i) words versus asterisks (ii) “with diacritics versus without diacritics” and (iii) “with root versus no root”. We discuss the most difficult task and,...
Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work...
The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the...
The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration, but it works badly when there are many outliers and noises in the point sets. This paper instead proposes a novel method based on the ICP algorithm to deal with this problem. Firstly, correntropy is introduced into the rigid registration problem and then a new energy function based on maximum correntropy...
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