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The precise reconstruction of continuous hand movements is a complex problem that remains unsolved to a large extent. In particular, this problem needs to be addressed to build reliable arm prosthesis for trans-humeral amputees. Current attempts using noninvasive techniques either had specifications that made them inapplicable to the real world or had low correlation values. In this study we use simultaneous...
The aim of automatic multi-document abstractive summarization is to create a compressed version of the source text and preserves the salient information. Existing graph based summarization methods treat sentence as bag of words, rely on content similarity measure and did not consider semantic relationships between sentences. These methods may fail in determining redundant sentences that are semantically...
In the field of High Speed SerDes (HSS) channel analysis and design, the most widely accepted metrics for gauging signal integrity are Time Domain (TD) metrics: Bit Error Rate (BER), Eye-Height (EH) and Eye-Width (EW). With increasing bit-rates, TD simulations are getting compute-time intensive especially as the BER criterion is getting lower. Learning based mapping of Frequency Domain (FD) S-Parameter...
Given the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its uncertainty by wavelet transform. To avoid...
Interrupted reporting has recently been introduced as an effective method to increase the energy efficiency of cooperative spectrum sensing schemes in cognitive radio networks. In this paper, joint optimization of the reporting and fusion phases in a cooperative sensing with interrupted reporting is considered. This optimization aims at finding the best weights used at the fusion center to construct...
In developing the human-machine technology, it is essentially important to infer human mind state. A machine learning approach is promising to this need. However, the machine-learning approach essentially requires training data, ideally supervised training data, which may not be readily available. An idea is to overcome this shortcoming is to take the so-called subjective rate measure. Take the problem...
By influencing the demand side by means of price signals (Demand Response) additional flexibility potential in electric supply systems can be provided. However, by influencing the demand side typical consumption patterns of previously unaffected consumers are changed. This will lead to increasing uncertainty in load forecasting. This paper deals with the forecast of load time series in consideration...
Risk assessment plays crucial role in the software project management. The critical examination of different risk assessment methods help researchers and practitioners to evaluate the impact of various project related risks. The existing Fuzzy Ex-COM (Fuzzy Expert COCOMO) model is a combination of fuzzy technique and Expert COCOMO. It takes help of expertise and information from earlier activities...
The electricity grid is currently transforming and becoming more and more decentralised. Green energy generation has many incentives throughout the world thus small renewable generation units become popular. Intermittent generation units pose threat to system stability so new balancing techniques like Demand Side Management must be researched. Residential hot water heaters are perfect candidates to...
In coastal waters, accurate remote sensing retrieval of Chlorophyll-a (Chl-a) is challenging. In a spatially complex urban coastal region like Hong Kong, the development of a single Chl-a estimation algorithm over whole region is unrealistic. In such case the best strategy will be to develop an individual algorithm for each water type to precisely estimate Chl-a concentration. Therefore, to define...
Transformation of the initial feature in NSL-KDD dataset based on principal component analysis (PCA), generates the new features in smaller dimension. In that dimension, network scanning (Ra-Probe) has a characteristic sign of the average value that is different from the normal activity. The selection used the characteristics of these factors result in two-dimensional subset of the 75% rate reduction...
This study was aimed at estimating subjects' 3-back working memory task error rate using electroencephalogram (EEG) signals. Firstly, spatio-temporal band power features were selected based on statistical significance of across-subject correlation with the task error rate. Method-wise, ensemble network model was adopted where multiple artificial neural networks were trained independently and produced...
Here we propose an artificial neural network (ANN) model for spectrum sensing in TV band specifically for identifying presence of audio signals. The ANN model is trained with parameters which are a combination of cyclostationary and SNR based features like channel capacity, bandwidth efficiency, autocorrelation. The ANN model is trained based on a new decision making factor termed as utilization factor...
Recognition of objects using an industrial image sensor is an important tool that has been motivated by the necessity of automatic recognition systems in the industrial automation. In this context, an interesting problem is the automatic image acquiring and a high reliability in objects classification. To this end, this paper presents a comparison between k-Nearest Neighbors Classifier using Euclidean,...
In this paper, a sub-band correlation-based method is proposed for the automatic detection of epilepsy and seizure. The analysis is carried out by decomposing the electroencephalogram (EEG) signals, collected from a publicly available EEG database, into the dual tree complex wavelet transform(DT-CWT) domain. An Artificial Neural Network(ANN) is employed as a classifier where the maximum cross-correlation...
ECG refers to non-invasive bioelectrical recording of the heart. Under the clinical settings, the ECG is interpreted by cardiologists via conventional inspection techniques. The methods however are exposed to visual error which leads to inaccurate diagnosis of the heart condition. Hence, as an attempt towards an automated diagnostic system, the paper elaborates on arrhythmia modelling based on ECG...
In this paper, we employed both traditional and chaotic approaches for time series forecasting. It concerns the forecasting of cash withdrawal amounts at automated teller machines (ATMs) for which the NN5 forecasting competitions data was used. The data consists of 111 time series representing daily withdrawal amounts. In the first method (traditional, non-chaotic) missing values of the time series...
A novel method for correlated short-term fading simulation is presented and analyzed in this paper. Proposed solution is based on artificial neural networks. In order to obtain an adequate training data set, extensive measurements of the electric field strength were carried out in an indoor environment. Due to its modular architecture, the proposed simulator could be used to generate potentially any...
Because of the complex dynamic behavior of supercapacitor, its modeling must be based on parallel, distributed structures (each component has to represent a model of activity, distributed on many processing units), with learning capacity. For this purpose, the paper proposes a new feed forward artificial neural network structure with two hidden layers and with backpropagation training. The neural...
In this paper, we propose a new framework to optimize the utilization of the image quality estimation without reference. This framework is based on two principal steps. Features are first extracted from the image to characterize each considered degradation type. From this modeling step, a No Reference Image Quality Metric (NR-IQM) per degradation type is obtained. In the second stage, outputs of the...
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