Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

When the Whole Is Less Than the Sum of Its Parts: Maximum Object Category Information and Behavioral Prediction in Multiscale Activation Patterns.

Neural codes are reflected in complex neural activation patterns. Conventional electroencephalography (EEG) decoding analyses summarize activations by averaging/down-sampling signals within the analysis window. This diminishes informative fine-grained patterns. While previous studies have proposed distinct statistical features capable of capturing variability-dependent neural codes, it has been suggested that the brain could use a combination of encoding protocols not reflected in any one mathematical feature alone. To check, we combined 30 features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across three datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed across most of the time points by the multiscale feature of Wavelet coefficients. Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the EEG neural codes better than any combination of protocols. Our findings put new constraints on the models of neural information encoding in EEG

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Toward an Imagined Speech-Based Brain Computer Interface Using EEG Signals

Individuals with physical disabilities face difficulties in communication. A number of neuromuscular impairments could limit people from using available communication aids, because such aids require some degree of muscle movement. This makes brain–computer interfaces (BCIs) a potentially promising alternative communication technology for these people. Electroencephalographic (EEG) signals are commonly used in BCI systems to capture non-invasively the neural representations of intended, internal and imagined activities that are not physically or verbally evident. Examples include motor and speech imagery activities. Since 2006, researchers have become increasingly interested in classifying different types of imagined speech from EEG signals. However, the field still has a limited understanding of several issues, including experiment design, stimulus type, training, calibration and the examined features. The main aim of the research in this thesis is to advance automatic recognition of imagined speech using EEG signals by addressing a variety of issues that have not been solved in previous studies. These include (1)improving the discrimination between imagined speech versus non-speech tasks, (2) examining temporal parameters to optimise the recognition of imagined words and (3) providing a new feature extraction framework for improving EEG-based imagined speech recognition by considering temporal information after reducing within-session temporal non-stationarities. For the discrimination of speech versus non-speech, EEG data was collected during the imagination of randomly presented and semantically varying words. The non-speech tasks involved attention to visual stimuli and resting. Time-domain and spatio-spectral features were examined in different time intervals. Above-chance-level classification accuracies were achieved for each word and for groups of words compared to the non-speech tasks. To classify imagined words, EEG data related to the imagination of five words was collected. In addition to words classification, the impacts of experimental parameters on classification accuracy were examined. The optimization of these parameters is important to improve the rate and speed of recognizing unspoken speech in on-line applications. These parameters included using different training sizes, classification algorithms, feature extraction in different time intervals and the use of imagination time length as classification feature. Our extensive results showed that Random Forest classifier with features extracted using Discrete Wavelet Transform from 4 seconds fixed time frame EEG yielded that highest average classification of 87.93% in classification of five imagined words. To minimise within class temporal variations, a novel feature extraction framework based on dynamic time warping (DTW) was developed. Using linear discriminant analysis as the classifier, the proposed framework yielded an average 72.02% accuracy in the classification of imagined speech versus silence and 52.5% accuracy in the classification of five words. These results significantly outperformed a baseline configuration of state-of-the art time-domain features

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

Non-Invasive Prospective Approach for the Detection of Alzheimer’s disease

We proposed an automated novel method to detect Alzheimer disease (AD). The methodology involves the analysis of normal and AD MRI (Magnetic Resonance Imaging) brain scans, we extracted some specific portions of brain which changes in case of diseased subjects such as Hippocampus, Septum Pellucidum, fornix and some portion of thalamus. We measured the area of brain parts lost due to AD and compared these measurements with the same aged normal subjects. In this research work various pattern recognition techniques were used that separates the AD brain scans form the brain scans of healthy controlled subjects. These pattern recognition techniques includes segmentation of brain images, wavelet based texture features extraction for the classification of brain scans. We used two different classifiers ANN (Artificial Neural Network) and SVM (Support Vector Machine) and which showed the comparable accuracy, execution time than other classifiers reported so far