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Artificial neural networks are one of the most popular and promising areas of artificial intelligence research. Training data containing outliers are often a problem for supervised neural networks learning algorithms that may not always come up with acceptable performance. Many robust learning algorithms have been proposed so far to improve the performance of neural networks in the presence of outliers...
Underwater swarm robots will enable various applications such as underwater environment monitoring, gathering of data and search and rescue mission. These robots serves as sensor networks comprises of large number of wireless sensor nodes communicating to each other within a coverage range of distance. Thus, maintaining these coverage positions of each robot plays important roles to prolong the network...
The size of fraudulent activity is increasing rapidly, with individuals and organisations being at great risk. This paper inspects and determines the various components required to deliver a successful fraud detection system. It is hoped that in reading this report, the reader will comprehend what is required and see the true benefit of implementing such a solution. Following the structure of a robust...
The Traffic matrix Estimation of IP networks has become a research topic in this later 10 years, where several methods have been used to resolve this ill posed problem. This paper deals with the later and presents a comparison study on training algorithms in Artificial Neural Networks (ANN) method, namely the BFGS Quasi-Newton; the Levenberg-Marquardt and Bayesian Regularization algorithms, which...
The bag-of-words (BoW) model has been widely used for acoustic event classification (AEC). The performance of the BoW based AEC model is much influenced by "codebook construction" and "histogram generation". The common approaches for constructing the codebook and generating the histogram are the K-means and vector quantization encoding (VQE) respectively. However, they have some...
In today's age of automation, face recognition is a vital component for authorization and security. It has received substantial attention from researchers in various fields of science such as biometrics and computer vision. In this paper, a face recognition system using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is analysed. A neural based algorithm is presented...
In this paper, a new controller has been proposed for pulse width modulation (PWM) based rectifiers. Control scheme of proposed PWM rectifier is used two Neuro-Fuzzy Controllers (NFC) which has robust and nonlinear structure in order to control the reactive power and DC voltage of PWM rectifier. Thus, reactive power and DC voltage are controlled effectively. Moreover, simulation studies are carried...
Digital fundus photographs are often used to provide clinical diagnostic information about several pathologies such as diabetes, glaucoma, macular degeneration and vascular and neurologic disorders. To allow a precise analysis, digital fundus image quality should be assessed to evaluate if minimum requirements are present. Focus is one of the causes of low image quality. This paper describes a method...
The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult to achieve the desired tracking performance. In this paper, a new network controller (multi-dimensional Taylor network) is proposed, which only relies on the output of system. Firstly...
In this paper, we propose an efficient image stitching using structure deformation. We use image stitching based on common stitching algorithms such as speeded up robust features (SURF) feature detection, approximated nearest neighbor (ANN) matching and random sample consensus (RANSAC) parameter estimation. And we use homography similarity to identify if input images have enough correlation. To reduce...
The time series analysis and forecasting is an essential tool which can be widely applied for identifying the meaningful characteristics for making future ad-judgements; especially making decisions in finance under the numerous type of economic policies and reforms have been regarding as the one of the biggest challenge in the modern economy today.
Human hand functions range from precise-minute handling to heavy and robust movements. Remarkably, 50 percent of all hand functions are made possible by the thumb. Therefore, developing an artificial thumb which can mimic the actions of a real thumb precisely is a major achievement. Despite many efforts dedicated to this area of research, control of artificial thumb movements in resemblance to our...
First, the basic structure and principle of quadrotor UAV and its practical applications are introduced. Then, some control algorithms are also presented, such as, PID, LQR/LQG, H∞, sliding mode, feedback linearization, back stepping, model predictive, robust, adaptive, nested saturation, fuzzy logic, neural network, reinforcement learning, iterative learning, memory and brain emotional learning-based...
Considering the demand on energy savings in future wireless networks, consumptions on cooperative links between different base stations becomes more and more critical with the rapid development of wireless communication systems. Coordinated Multi-Point Transmission/Reception (CoMP) gradually becomes popular due to the demand of higher transmission rate and more reliable Quality of Service (QoS), but...
The purpose of this paper is to obtain on-line trained Artificial Neural Network Controller for PMSM multi-mass high dynamic drive. Structure of the controller with training algorithm and idea of Kalman Filter as observer are shortly described. The Resilient Back Propagation algorithm (RPROP) was chosen for ANN training process. There is assumed rotor position can be sufficient to the possibility...
In a time when personal data circulates constantly between devices and within the cloud, biometric security systems represents one of the most viable security solutions. A relatively new biometric modality based on the individual's Electroencephalogram (EEG) is starting now to gain popularity among researchers. Its relevance relay mainly on its prospects of high security and robustness against intruders...
In this paper, we study the problem of resilient consensus of sampled-data multi-agent networks with doubleintegrator dynamics. The term resilient points to the presence of faulty agents in the network. Each normal agent updates its state based on a predetermined control law using its neighbors' information while misbehaving agents make updates arbitrarily and might threaten the consensus within the...
Traffic experts try to optimise the signalisation of traffic light controllers during design-time based on historic traffic flow data. Traffic exhibits dynamic behaviour. Due to changing traffic demands, new and flexible traffic management systems are needed that optimise themselves during runtime. Organic Traffic Control is such a decentralised, self-organising system that adapts the green times...
Intention understanding is a basic requirement for human-machine interaction. Action classification and object affordance recognition are two possible ways to understand human intention. In this study, Multiple Timescale Recurrent Neural Network (MTRNN) is adapted to analyze human action. Supervised MTRNN, which is an extension of Continuous Timescale Recurrent Neural Network (CTRNN), is used for...
Network diagnosis is a vital aspect in ensuring an efficient and robust functioning of any kind of mesh network. In this paper we present a network diagnosis method which determines the delay map of a mesh network using only end-to-end delay measurements without having the knowledge of the path taken. We model the problem of network diagnosis as an inverse problem and using a concept of ray tracing,...
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