Digit recognition using neural networks

This paper investigates the use of feed-forward multi-layer perceptrons trained by back-propagation in speech recognition. Besides this, the paper also proposes an automatic technique for both training and recognition. The use of neural networks for speaker independent isolated word recognition on small vocabularies is studied and an automated system from the training stage to the recognition stage without the need of manual cropping for speech signals is developed to evaluate the performance of the automatic speech recognition (ASR) system. Linear predictive coding (LPC) has been applied to represent speech signal in frames in early stage. Features from the selected frames are used to train multilayer perceptrons (MLP) using back-propagation. The same routine is applied to the speech signal during the recognition stage and unknown test patterns are classified to the nearest patterns. In short, the selected frames represent the local features of the speech signal and all of them contribute to the global similarity for the whole speech signal. The analysis, design and development of the automation system are done in MATLAB, in which an isolated word speaker independent digits recogniser is developed

Grid base classifier in comparison to nonparametric methods in multiclass classification

In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification

Querying ontology using keywords and quantitative restriction phrases

Many approaches for converting keyword queries to formal query languages are presented for natural language interfaces to ontologies. Some approaches present fixed formal query templates, so they lack in providing support with increasing number of words in the user query. Other approaches work on constructing and manipulating subgraphs from RDF graphs so their processing is complex with respect to time and space. Techniques are presented to perform operations by obtaining a reduced RDF graph but they limit the input to some type of resources so their complete complexity with all type of input resources is unknown. For formal query generation, we present a variable query template whose computation is facilitated by less complex and distributed RDF property and relation graphs. A prototype QuriOnto is developed to evaluate our design. The user can query QuriOnto with any number of words and resource types. Also, to the best of our knowledge, it is the first system that can handle quantitative restrictions with keyword queries. As QuriOnto has no support for semantic similarity at this time except for rdfs labels so its recall is low but high precision shows that the approach is promising for the generation of corresponding formal queries

Brain computer interface design and applications: challenges and future

For many decades, instrument control by just thinking, using brain waves, has been accepted in science fiction. However, it is only in the last ten years that these systems have been shown to be feasible in laboratories. Successful Brain Computer Interface (BCI) systems have many potential applications, especially for patients who are paralyzed. Although extensive research has been done in this area, to date, BCI systems have not been implemented successfully outside of laboratories. The problems that impede transferring the successful research results to the outside world are highlighted in this paper. The main problems can be classified into two distinct parts, first, the sensory interfacing problems and, second, the reliability of the different classification algorithms for the ElectroEncephaloGraphic patterns. Potential future applications for this technology have been addressed

EEG activity in Muslim prayer: a pilot study

Almost all religions incorporate some form of meditation. Muslim prayer is the meditation of Islam. It is an obligatory prayer for all Muslims that is performed five times a day. Although a large body of literature exists on EEG changes in meditation, to date there has been no research published in a peer-reviewed journal on EEG changes during Muslim prayer. The purpose of this pilot study is to encourage further investigation on this type of meditation. Results of EEG analysis in twenty-five trials of Muslim prayer are reported. Some of the findings are consistent with the majority of the previous meditation studies (alpha rhythm slowing, increased alpha rhythm coherence). However, Muslim prayer does not show an increase in alpha and/or theta power like most of the results of other meditation studies. The possible cause of this discrepancy in meditation-related studies is highlighted and a systematic and standardised roadmap for future Muslim prayer EEG research is proposed

Error concealment innovator based on the multi-directional interpolation by using the similarity segmentation

In this paper, an error concealment algorithm based on the multi-directional interpolation (MDI) was proposed. The algorithm has the capability to recover the damaged blocs, whether located in smooth or non-smooth areas. In the case of smooth regions, the missing coefficients were estimated by interpolating these coefficients with undamaged adjacent pixels. While, in the non-smooth areas (for example edge components), these blocks were portioned to at least four quarters, in the intention to exploit all border pixels. In the meantime, pixels of the border left and right were estimated with horizontal interpolation, pixels of the border top, and bottom were estimated with vertical interpolation, Whereas the remaining pixels of each quarter were simultaneously guessed with vertical and horizontal interpolation. Finally, another algorithm to convert pixels to feet proposed. The motivation behind the current implementation and the problem that we aim to solve lies on how to convert the size of the base and height of triangles from pixels-to-feet. In the intention to calculate the areas of these triangles, for the purpose of compensation. The experimental results showed that the number of pixels occurred and the error was relatively low

Experimental Study of Breast Cancer Detection Using UWB Imaging

Breast cancer detection using UWB imaging is presented in this paper. The study is performed experimentally. Homogeneous breast phantom is constructed using pure petroleum jelly. The tumor is modeled using mixture of water and wheat flour. The breast fatty tissue and tumor tissue are put in breast shaped glass which mimics the skin. The dielectric properties values are comparable to previous study. Neural Network (NN) was trained and tested using feature vector which is prepared by performing discrete cosine transform (DCT) of the received UWB signals. Very encouraging results were obtained. Up to 100 % tumor existence detection was achieved. Tumor size and location detection rate were 91.3% and 95.6% respectively

Experimental approximation of breast tissue permittivity and conductivity using NN-based UWB imaging

This paper presents experimental study to distinguish between malignant and benign tumors in early breast cancer detection using Ultra Wide Band (UWB) imaging. The contrast between dielectric properties of these two tumor types is the main key. Mainly water contents control the dielectric properties. Breast phantom and tumor are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. A complete system including Neural Network (NN) model is developed for experimental investigation. Received UWB signals through the tumor embedded breast phantom are fed into the NN model to train, test and determine the tumor type. The accuracy of the experimental data is about 98.6% and 99.5% for permittivity and conductivity respectively. This leads to determine tumor dielectric properties accurately followed by distinguish between malignant and benign tumors. As malignant tumors need immediate further medical action and removal, this findings could contribute to save precious file in near future

A UWB imaging system to detect early breast cancer in heterogeneous breast phantom

This paper presents an experimental early breast cancer detection system in terms of heterogeneous breast phantom. The system consists of commercial Ultrawide-Band (UWB) transceivers and our developed Neural Network (NN) based Pattern Recognition (PR) software for imaging. A simple way to construct cancer- tissue and heterogeneous breast phantom using available low cost materials and their mixtures is also proposed here. The materials are: (i) A mixture of petroleum jelly, soy oil, wheat flour and water as heterogeneous tissue; (ii) A particular glass as skin; and (iii) A specific mixture of water and wheat flour as cancer- tissue. All the materials and their mixtures are considered according to the ratio of the dielectric properties of the breast tissues. To experimentally detect cancer, the UWB signals are transmitted from one side of the breast phantom and received from opposite side diagonally. By using discrete cosine transform (DCT) of the received signals, a Neural Network (NN) is trained, tested and interfaced with the UWB transceiver to form the complete system. The achieved detection rate of cancer cell's existence, size and location are approximately 100%, 93.1% and 93.3% respectively