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This paper proposes a neural network (NN) based noise prediction model for electric machines, applied to the case of synchronous reluctance motors (SynRMs). The natural frequencies of various vibration modes for a SynRM stator with generalized tooth geometry and slot numbers have been obtained using structural FEA based computations and then used to build a NN based surrogate model. The accuracy of...
In this paper we consider an important practical aspect of texture region detection in remote sensing images. One specific feature of our study is that we assume a processed image noisy with a priori known type and parameters of the noise. Another specific feature is that we try to detect textural regions for a wide variety of textures without having a priori knowledge of their properties. The considered...
A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN)...
Use of modern technological advances in real-time biomedical analysis is very crucial. Current work focuses on glottal pathology discrimination based on non-invasive speech analysis techniques. Primary set back in developing such method is irregular performance depreciation of several state of the art acoustic features. To excuse such problems, we have used glottal to noise excitation ratio, which...
Active Noise Control (ANC) has been gaining an increasing interest in recent years. Much attention has been devoted to design of efficient control algorithms, enabling noise reduction at a high level, with computational load acceptable by currently available electronics. Among different approaches to noise control, employment of vibrating plates as secondary sources or as active barriers is particularly...
Trilateration is an effective way to localize a sensor network based on relative distance measures, but the conditions that guarantee the existence of a solution are quite restrictive. If the network topology is a unit disk graph, however, the localization of the network can be achieved also when the standard trilateration fails, using a priori information about “not being connected”. Such an information...
We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the...
Heart rate variability (HRV) measures the instantaneous change in heart rate and is an important marker for checking physical condition as well as mental stress of a person. In this paper, we propose a methodology to calculate HRV of a person using smart phone audio. Heart sound is captured in the inbuilt microphone of a smart phone, by placing the device on the chest of the person. We propose a process...
Improvement of classification accuracy is importance in data analysis problems. Enhancement of techniques have been proposed previously to address the problems as regard to classification performance, however, the issues of misclassification and noise elimination in the early stage of processing have been ignored by many researchers. If these problems were addressed, the performance of the classification...
Conventional learning theory's failure in training Neural Network to provide acceptable levels of generalization on the occurrences of fault in network has lead to the advent of Fault Tolerant Learning. Radial Basis Function networks are assumed to have in built Fault Tolerance capabilities. With this paper our attempt is to bring forth a detailed and time ordered survey of the literature available...
The paper describes the use of associative models for integrating different sensors. Integrated associative structures are outlined and related to previous approaches; the enhanced robustness resulting from the integration of Associative Memories (AMs) and Neural Networks (NNs) is shown. Discussion then focuses on how different information sources can cooperate on associative visual recognition. Experimental...
This paper studies a Quantized Gossip-based Interactive Kalman Filtering (QGIKF) algorithm implemented in a wireless sensor network, where the sensors exchange their quantized states with neighbors via inter-sensor communications. We show that with the information loss due to quantization, the network can still achieve weak consensus, i.e., the estimation error variance sequence at a randomly selected...
In this paper, an amplify-and-forward two-way relay system with unknown carrier frequency offsets (CFOs) is considered. A double differential transmission scheme is proposed to achieve successful two-way relaying transmission without any CFOs information. The average symbol error rate (SER) performance of the proposed scheme is analyzed and a closed-form upper bound of the average SER is derived....
The artificial neural network (ANN) has been shown that, is an effect technique used to gain insight into channel equalizer design, to combat nonlinear distortion in wireless communication systems. Also, the joint design of channel equalizer and decoder can provides great advantages for system performance. However, research on the soft output of an ANN-based equalizer still remains largely open. Towards...
Model-based FDI systems are considered here. The problem of constructing the diagnosed system model as well as the automatic search for the best rule base of the residual analyzer is reduced to a set of global optimization tasks. Various optimization problems are considered depending on the chosen technology of the non-analytical model construction as well as that of the residual evaluation. Most...
The estimation of the power spectrum of discrete-time signals is one of the most fundamental and useful tools in signal processing. However, there are practical situations where one needs to look beyond the power spectrum, especially to extract information regarding the phase relations and deviations from Gaussianity. This has created considerable interest in the use of higher order spectra such as...
When faced with the struggle to extract insights from complex and noisy data, often the end user may assume that there exist no significant relation between the features and target in the dataset and is forced to either quit the study or resort to alternate means. Artificial Neural Networks (ANNs) might be of help to predict some of the most complex data used in the industry. But it is neither easy...
This paper discusses one of the route security problems called the black hole attack. In the network, we can capture some AODV route tables to gain a rank sequences by using the FP-Growth, which is a data association rule mining. We choose the rank sequences for detecting the malicious node because the rank sequences are not sensitive to the noise interfered. A suspicious set consists of nodes which...
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could...
This paper presents a signal processing technique for segmenting short speech utterances into unvoiced and voiced sections and identifying points where the spectrum becomes steady. The segmentation process is part of a system for deriving musculoskeletal articulation data from disordered utterances, in order to provide training feedback for people with speech articulation problem. The approach implement...
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